Viral diseases are one of the leading causes of morbidity and mortality in the world. Virus-specific vaccines and antiviral drugs are the most powerful tools to combat viral diseases. However, broad-spectrum antiviral agents (BSAAs, i.e. compounds targeting viruses belonging to two or more viral families) could provide additional protection of general population from emerging and re-emerging viral diseases reinforcing the arsenal of available antiviral options. Here, we reviewed discovery and development of BSAAs and summarized the information on 120 safe-in-man agents in freely accessible database (https://drugvirus.info/). Future and ongoing pre-clinical and clinical studies will increase the number of BSAAs, expand spectrum of their indications, and identify drug combinations for treatment of emerging and re-emerging viral infections as well as co-infections.

The drug discovery process is a rigorous and time-consuming endeavor, typically requiring several years of extensive research and development. Although classical machine learning (ML) has proven successful in this field, its computational demands in terms of speed and resources are significant. In recent years, researchers have sought to explore the potential benefits of quantum computing (QC) in the context of ML, leading to the emergence of Quantum Machine Learning (QML) as a distinct research field. The objective of the current study is twofold: first, to present a review of the proposed QML algorithms for application in the drug discovery pipeline, and second, to compare QML algorithms with their classical and hybrid counterparts in terms of their efficiency. A query-based search of various databases took place, and five different categories of algorithms were identified in which QML was implemented. The majority of QML applications in drug discovery are primarily focused on the initial stages of the drug discovery pipeline, particularly with regard to the identification of novel drug-like molecules. Comparison results revealed that QML algorithms are strong rivals against the classical ones and a hybrid solution is the recommended approach at present.

Mosquitoes conveying Plasmodium store parasites into the skin of the mammalian host. Parasites make a trip through the circulation system to the liver, where they cross a few hepatocytes prior to building up a disease. Inside the last hepatocyte the parasite goes through morphogenesis and afterward abiogenetically partitions to become more than 20,000 blood-infective parasites, called merozoites. On account of P. vivax, P. ovale, and P. cynomolgi, the parasites can stay lethargic in the liver in structures called hypnozoites. The merozoites are delivered once again into the circulation system, where they start the repetitive blood stage. Inside erythrocytes, a little division of parasites separate into male or female gametocytes. These gametocytes are ingested by the mosquito during blood taking care of, where they will duplicate explicitly, in the long run prompting the arrangement of sporozoites

The worldwide outbreak of SARS-CoV-2 in early 2020 caused numer- ous deaths and unprecedented measures to control its spread. We employed our Computational Analysis of Novel Drug Opportunities (CANDO) multiscale therapeutic discovery, repurposing, and design platform to identify small molecule inhibitors of the virus to treat its resulting indication, COVID-19. Initially, few experimental studies existed on SARS-CoV-2, so we optimized our drug candidate prediction pipelines using results from two independent high-throughput screens against prevalent human coronaviruses. Ranked lists of candidate drugs were generated using our open source cando.py software based on viral protein inhibition and proteomic interaction similarity. For the former viral protein inhibition pipeline, we computed interaction scores between all compounds in the corresponding candidate library and eighteen SARS-CoV proteins using an interaction scoring protocol with extensive parameter optimization which was then applied to the SARS-CoV-2 proteome for prediction. For the latter similarity based pipeline, we computed interaction scores between all compounds and human protein structures in our libraries then used a consensus scoring approach to identify candidates with highly similar proteomic interaction signatures to multiple known anti-coronavirus actives. We published our ranked candidate lists at the very beginning of the COVID-19 pandemic. Since then, 51 of our 276 predictions have demonstrated anti-SARS-CoV-2 activity in published clinical and experimental studies. These results illustrate the ability our platform to rapidly respond to emergent pathogens and provide greater evidence that treating compounds in a multitarget context more accurately describes their behavior in biological systems.

Cone snails (genus Conus) are venomous marine snails that inject prey with a lethal cocktail of conotoxins, small, secreted, cysteine-rich peptides. Given the diversity and often high affinity for their molecular targets, consisting of ion channels, receptors or transporters, many conotoxins have become invaluable pharmacological probes, drug leads and therapeutics. Transcriptome sequencing of Conus venom glands followed by de novo assembly and homology-based toxin identification and annotation is currently the state-of-the-art for discovery of new conotoxins. However, homology-based search techniques, by definition, can only detect novel toxins that are homologous to previously reported conotoxins. To overcome these obstacles for discovery we have created ConusPipe, a machine learning tool that utilizes prominent chemical characters of conotoxins to predict whether a certain transcript in a Conus transcriptome, which has no otherwise detectable homologs in current reference databases, is a putative conotoxin. By using ConusPipe on RNASeq data of 10 species, we report 5,230 new putative conotoxin transcripts that have no homologues in current reference databases. 893 of these were identified by at least 3 out of 4 models used. These data significantly expand current publicly available conotoxin datasets and our approach provides a new computational avenue for the discovery of novel toxin families.

Covid-19 is one of humanity’s biggest threat in the 21st century with WHO figures reporting 636 million cases and up to 6.6 million deaths globally. SARS-CoV-2, the virus that causes the Covid-19 disease, is characterized by high mutation which contributes to its rapid spread. While several vaccines have been produced to minimize the severity of the coronavirus and diverse treatment regimens have been approved by the US FDA under Emergency Use Authorization (EUA), SARS-CoV-2 viral mutations continue to derail the efforts of scientists as the emerging variants evade the recommended therapies. Nonetheless, diverse computational models exist that offer an opportunity to overcome the barriers involved in developing new drugs. In this review, the focus is on the use of various virtual screening techniques like molecular docking, molecular dynamics simulations, QSAR, pharmacophore modeling, and homology modeling in repurposing SARS-CoV-2 therapeutics. The results have been promising from the computer-aided drug design (CADD) studies in suggesting potential compounds for treatment of Covid-19 and helping in bringing the pandemic under control.

Non-systemic oral drugs, which do not get absorbed through the gastrointestinal membrane, are an important group of compounds for targeting diseases residing within the gastrointestinal lumen. However, they require a set of design principles not sufficiently covered by traditional druglikeness metrics, such as Lipinski’s Rule of 5. These druglikeness metrics fail to accurately identify true negative outcomes; such is the requirement for novel cheminformatic based approaches for the discovery of non-systemic small molecules. One such example of these newer metrics is the fraction of SP3-hybridised carbon atoms (fSP3) which has shown promising application as an identifier of poor oral-bioavailability. Herein, we discuss the application of fSP3 in the drug discovery process in a number of case studies, including our own work discovering non-systemic fructose scavengers for the treatment of fructose malabsorption.

Artificial intelligence AI or machine learning has proven to be a potential activity in the health and biomedical sciences. Previous research it has found that AI can learn new data and transform it into the useful knowledge. In the field of pharmacology, the aim is to design more efficient and novel vaccines using this method which are also cost effective. The underlying fact is to predict the molecular mechanism and structure for increased likelihood of developing new drugs. Clinical, electronic and high resolution imaging datasets can be used as inputs to aid the drug development niche. Moreover, the use of comprehensive target activity has been performed for repurposing a drug molecule by extending target profiles of drugs which also include off targets with therapeutic potential providing a new indication.

The morbidity and mortality caused by invasive fungal infections is increasing across the globe due to developments in transplant surgery, the use of immunosuppressive agents, and the emergence of drug-resistant fungal strains, which has led to a challenge in terms of treatment due to the limitations of three classes of drugs. Hence, it is imperative to establish effective strategies to identify and design new antifungal drugs. Drug repurposing is an effective way of expanding the application of existing drugs. In the last years, various existing drugs have been shown to be useful in the prevention and treatment of the invasive fungi. In this review, we summarize the currently used antifungal agents. In addition, the most up to date information on the effectiveness of existing drugs with antifungal activity is discussed. Moreover, the antifungal mechanisms of existing drugs are highlighted. These data will provide valuable knowledge to stimulate further investigation and clinical application in this field.

G protein-coupled receptors (GPCRs) are critical drug targets. GPCRs convey signals from the extracellular to the intracellular environment through G proteins. There is evidence that some ligands that bind to the GPCRs activate different downstream signaling pathways. G protein activation or -arrestin biased signaling involves ligands binding to receptors and stabilizing conformations that trigger a specific pathway. Molecular dynamics (MD) simulations are especially valuable for obtaining detailed mechanistic information, including identification of allosteric sites and understanding modulators' interactions between receptors and ligands. Here, we highlight recent simulation studies and methods used to study biased G protein-coupled receptor signaling and their conformational dynamics. We also highlight applications of MD simulations to drug discovery.

Introduction: The combination of Virtual Screening (VS) techniques with in vivo screening in the zebrafish model is currently being used in tandem for drug development in a faster and more efficient way. Areas covered: We review the different virtual screening techniques, the use of zebrafish as a vertebrate model for drug discovery and the synergy that exists between them. Expert opinion: We highlight the advantages of combining virtual and zebrafish larvae screening for drug discovery. On the one hand, VS is a faster and cheaper tool for searching active compounds and possible candidates for therapy than in vivo screening when processing large compound libraries. On the other hand, zebrafish larvae form a vertebrate model which allows in vivo screening of large amounts of the compounds. Importantly physiology and chemical response are mostly conserved between zebrafish and mammals. The availability of the transgenic and mutant zebrafish lines allows an analysis of a specific phenotype upon treatment along with toxicity, off-target effect, side effects, and dosage. Advantages of VS, in vivo whole animal approach screening, and the screening combinations are also reviewed.

Mental health issues can have significant impacts on individuals and communities and hence on social sustainability. There are several challenges facing mental health treatment, however, more important is to remove the root causes of mental illnesses because doing so can help prevent mental health problems from occurring or recurring. This requires a holistic approach to understanding mental health issues that are missing from the existing research. Mental health should be understood in the context of social and environmental factors. More research and awareness are needed, as well as interventions to address root causes. The effectiveness and risks of medications should also be studied. This paper proposes a big data and machine learning-based approach for the automatic discovery of parameters related to mental health from Twitter data. The parameters are discovered from three different perspectives, Drugs & Treatments, Causes & Effects, and Drug Abuse. We used Twitter to gather 1,048,575 tweets in Arabic about psychological health in Saudi Arabia. We built a big data machine learning software tool for this work. A total of 52 parameters were discovered for all three perspectives. We defined 6 macro-parameters (Diseases & Disorders, Individual Factors, Social & Economic Factors, Treatment Options, Treatment Limitations, and Drug Abuse) to aggregate related parameters. We provide a comprehensive account of mental health, causes, medicines and treatments, mental health and drug effects, and drug abuse, as seen on Twitter, discussed by the public and health professionals. Moreover, we identify their associations with different drugs. The work will open new directions for social media-based identification of drug use and abuse for mental health, as well as other micro and macro factors related to mental health. The methodology can be extended to other diseases and provides a potential for discovering evidence for forensics toxicology from social and digital media.

Ubiquitin-specific protease 14 (USP14) is a member of the Deubiquitinating enzymes (DUBs) involved in disrupting the regulation of the ubiquitin-proteasome system, responsible for the degradation of impaired and misfolded proteins which is an essential mechanism in eukaryotic cells. The involvement of USP14 in cancer progression and neurodegenerative disorders has been reported. Thereof USP14 is a prime therapeutic target; hence, designing efficacious inhibitors of USP14 is central in curbing these conditions. Herein, we relied on structural bioinformatics methods incorporating molecular docking, Molecular Mechanics Generalized Born Surface Area (MM-GBSA), Molecular dynamics simulation (MD simulation) and ADME to identify potential allosteric USP14 inhibitors. A library of over 733 compounds from the PubChem repository with >90% match to the IU1 chemical structure was screened in a multi-step framework to attain prospective drug-like inhibitors. Two potential lead compounds (CID 43013232 and CID 112370349) were shown to record better binding affinity compared to IU1, but with subtle difference to IU1-47, a 10-fold potent compound when compared to IU1. The stability of the lead molecules complexed with USP14 was studied via MD simulations. The molecules were found to be stable within the binding site throughout the 50ns simulation time. Moreover, the protein-ligand interactions across the simulation run time suggest Phe331, Tyr476, and Gln197 as crucial residues for USP14 inhibition. Furthermore, in silico pharmacological evaluation revealed the lead compounds as pharmacological sound molecules. Overall, the methods deployed in this study revealed two novel candidates that may show selective inhibitory activity against USP14, which could be exploited to produce potent and harmless USP14 inhibitors.

Since the first published genome sequence of Trypanosoma cruzi in 2005, there has been tremendous technological advance in genomics, reverse genetics, and assay development for this elusive pathogen. However, there is still an unmet need for new and better drugs to treat Chagas disease. Here we introduce a T. cruzi assay strain that is useful for drug research as well as basic studies in host-pathogen interaction. Trypanosoma cruzi STIB980 is a strain of discrete typing unit TcI that grows well in culture as axenic epimastigotes or intracellular amastigotes. We have evaluated the optimal parameters for genetic transfection and constructed derivatives of T. cruzi STIB980 that express reporter genes for fluorescence- or bioluminescence-based drug efficacy testing, as well as a Cas9-expressing line for CRISPR/Cas9-mediated gene editing. The genome of T. cruzi STIB980 was sequenced by combining short-read Illumina with long-read Oxford Nanopore technologies. The latter served for the primary assembly, the former to correct mistakes and fill the gaps. This resulted in a high-quality nuclear haplotype assembly of 28 Mb in 400 contigs, containing 10,043 open-reading frames with a median length of 1077 bp. We believe that T. cruzi STIB980 is a useful addition to the antichagasic toolbox, and propose that it can serve as a DTU TcI reference strain for drug efficacy testing.

In an era of unparalleled technical advancement, the pharmaceutical industry is struggling to transform data into increased research and development efficiency, and as a corollary, new drugs for patients. Here, we briefly review some of the commonly discussed issues around this counterintuitive innovation crisis. Looking at both industry and science related factors, we posit that traditional preclinical research is front-loading the development pipeline with data and drug candidates that are unlikely to succeed in patients. Applying a first principles analysis, we highlight the critical culprits and provide suggestions as to how these issues can be rectified through pursuit of a Human Data-driven Discovery (HD3) paradigm. Consistent with other examples of disruptive innovation, we propose that new levels of success are not dependent on new inventions, but rather the strategic integration of existing data and technology assets. In support of these suggestions, we highlight the power of HD3, through recently published proof-of-concept applications in the areas of drug safety analysis and prediction, drug repositioning, rational design of combination therapies and the global response to the Covid19 pandemic. We conclude that innovators must play a key role in expediting the path to a largely human focused, systems-based approach to drug discovery and research.

Stellatolides are natural compounds that have shown promising biological activities, including antitumor, antimicrobial, and anti-inflammatory properties, making them potential candidates for drug development. Chemical Reactivity Theory (CRT) is a branch of chemistry that explains and predicts the behavior of chemical reactions based on the electronic structure of molecules. Conceptual Density Functional Theory (CDFT) and Computational Peptidology (CP) are computational approaches used to study the behavior of atoms, molecules, and peptides. In this study, we present the results of our investigation of the chemical reactivity and ADMET properties of Stellatolides A-H using a novel computational approach called Conceptual DFT-based Computational Peptidology (CDFT-CP). Our study uses CDFT and CP to predict the reactivity and stability of molecules and to understand the behavior of peptides at the molecular level. We also predict the ADMET properties of the Stellatolides A-H to provide insight into their effectiveness, potential side effects, and optimal dosage and route of administration. This study sheds light on the potential of Stellatolides A-H as promising candidates for drug development and highlights the potential of CDFT-CP for the study of other natural compounds and peptides.

Tuberculosis (TB) continues to pose a global health challenge, exacerbated by the rise of drug-resistant strains. The development of new TB therapies is an arduous and time-consuming process. To expedite the discovery of effective treatments, computational structure-based drug re-purposing has emerged as a promising strategy. From this perspective, conditionally essential targets present a valuable opportunity, and the mycobactin biosynthesis pathway stands out as a prime example highlighting the intricate response of Mycobacterium tuberculosis (Mtb) to changes in iron availability. This study focuses on the re-purposing and revival of FDA-approved drugs (library) as potential inhibitors of MbtA, a crucial enzyme in mycobactin biosynthesis in Mtb conserved among all species of mycobacteria. Literature suggests this pathway to be associated with drug efflux pumps, which potentially contribute to drug resistance. This makes it a potential target for antitubercular drug discovery. Herein we utilized cheminformatics and structure-based drug repurposing approaches, viz., molecular docking, dynamics, and PCA analysis, to decode the intermolecular interactions and binding affinity of the FDA-reported molecules against MbtA. The virtual screening revealed ten molecules with significant binding affinities and interactions with MbtA. These drugs, originally designed for different therapeutic indications (4: antiviral, 3: anticancer, 1: CYP450 inhibitor, 1: ACE inhibitor, and 1: leukotriene antagonist), are repurposed as potential MbtA inhibitors. Furthermore, our study explores the binding modes and interactions between these drugs and MbtA, shedding light on the structural basis of their inhibitory potential. Principal component analysis highlighted significant motions in MbtA-bound ligands, emphasizing the stability of the top Protein-Ligand Complexes (PLCs). This computational approach provides a swift and cost-effective method for identifying new MbtA inhibitors, which can subsequently undergo validation through experimental assays. This streamlined process is facilitated by the fact that these compounds are already FDA-approved and have established safety and efficacy profiles. This study has the potential to lay the groundwork for addressing the urgent global health challenge at hand, specifically in the context of combating Antimicrobial Resistance (AMR) and Tuberculosis (TB).

Calcium signaling and lipid metabolism are crucial in the infection processes of Apicomplexans parasites. Thus, enzymes involved in these processes can be drug targets against Apicomplexans. For example, in malaria infection, in-depth research into lipid metabolic pathways is crucial in understanding the parasite's infection cycle, particularly during its erythrocytic infection cycle, which has been demonstrated to be a critical stage during the disease progression. Most enzymes that play critical roles in lipid synthesis and calcium signaling have been extensively studied; nonetheless, a vast knowledge gap still exists, especially on specific enzymes and their roles in the transmission and progression of the Apicomplexan parasites. Many types of infections caused by Apicomplexans are life-threatening and hard to treat. These intracellular parasites proliferate within parasitophorous vacuoles in their host cells. As the parasites multiply, they need to meet their high demand for nutrients such as amino acids and lipids. They can acquire nutrients through scavenging and biosynthesis. This review summarizes a few interesting, unique pathways in selected Apicomplexa and how such unique pathways can be targets for drugs.

Despite being an abundant marine organism in Indonesia, black sea cucumber is still underutilised due to its slightly bitter taste. Previous studies have hinted at the potential of black sea cucumber as an anti-cancer agent. However, specific identification of bioactive compounds that can interact with cancer proteins is still lacking. In the same place, cancer ranks third as Indonesia's leading cause of death. Therefore, this study aims to identify potential anti-cancer compounds from black sea cucumbers using a comprehensive in silico drug discovery approach. This research uses machine learning, molecular docking, and ADMET analysis to identify bioactive compounds that specifically interact with cancer proteins. A combination of the Cascade Deep Forest algorithm and ECFP-AAIndex1 feature combination proved to be the most effective in predicting these interactions. Through molecular docking validation, four bioactive compounds with strong binding affinity were identified: Afimoxifene, Danazol, Taxifolin, and Terfenadine. ADMET analysis highlighted Taxifolin as the most promising candidate, as it passed most ADMET parameters. Further wet laboratory studies are required to confirm the effects and potential of these compounds as anti-cancer agents. This study builds a foundation for future investigations into alternative cancer treatments using abundant natural resources.

Tuberculosis (TB) remains a global healthcare crisis with an estimated 10 million new cases and 1.4 million deaths per year TB is caused by infection with the major human pathogen Mycobacte-rium tuberculosis, which is difficult to rapidly diagnose and treat. There is an urgent need for new methods of diagnosis, sufficient in vitro models which capably mimic all physiological condi-tions of the infection, and high-throughput drug screening platforms. Microfluidic-based tech-niques provide single-cell analysis which reduces experimental time, the cost of reagents, and have been extremely useful for gaining insight into monitoring microorganisms. This review out-lines the field of microfluidics and discusses the use of this novel technique so far in M. tuberculo-sis diagnostics, research methods, and drug discovery platforms. The practices of microfluidics have promising future applications for diagnosing and treating TB.

Small molecule inhibitors of adipocyte fatty-acid binding protein 4 (FABP4) have got interest following the recent publication of their pharmacologically beneficial effects. Recently it comes out that FABP4 is an attractive molecular target for the treatment of type 2 diabetes, other metabolic diseases, and some type of cancers. In the past years, hundreds of effective FABP4 inhibitors have been synthesized and discovered but, unfortunately, none of them is in the clinical research phase. The field of computer-aided drug design seems to be promising and useful for the identification of FABP4 inhibitors; hence, different structure- and ligand-based computational approaches were already performed for their identification. In this paper, we searched for new potentially active FABP4 ligands in the Marine Natural Products (MNP) database. 14,492 compounds were retrieved from this database and filtered through a statistical and computational filter. Seven compounds were suggested by our methodology to possess a potential inhibitory activity upon FABP4 in the range of 79–245 nM. ADMET properties prediction were performed to validate the hypothesis of the interaction with the intended target and to assess the drug-likeness of these derivatives; from these analyses, three molecules resulted as excellent candidates for becoming new drugs.

Biological systems are highly regulated. They are also highly resistant to sudden perturbations enabling them to maintain the dynamic equilibrium essential for sustenance of life. This robustness is conferred by regulatory mechanisms that influence the activity of enzymes/proteins within their cellular context, to adapt to changing environmental conditions. However, the initial rules governing the study of enzyme kinetics were tested and implemented for mostly cytosolic enzyme systems that were easy to isolate and/or recombinantly express. Moreover, these enzymes lacked complex regulatory modalities. Now, with academic labs and pharmaceutical companies turning their attention to more complex systems (for instance, multi-protein complexes, oligomeric assemblies, membrane proteins and post-translationally modified proteins), the initial axioms defined by Michaelis-Menten (MM) kinetics are rendered inadequate and the development of a new kind of kinetic analysis to study these systems is required. The current review strives to present an overview of enzyme kinetic mechanisms that are atypical and, oftentimes, do not conform to the classical MM kinetics. Further, it presents initial ideas on the design and analysis of experiments in early drug-discovery for such systems, to enable effective screening and characterisation of small-molecule inhibitors with desirable physiological outcomes.

Pseudomonas aeruginosa is an emerging opportunistic pathogen responsible for cystic fibrosis and nosocomial infections. In addition, empirical treatments are become inefficient due to their multiple-antibiotic resistance and extensive colonizing ability. Quorum sensing (QS) plays a vital role in the regulation of virulence factors in P. aeruginosa. Attenuation of virulence by QS inhibition could be an alternative and effective approach to control infections. Therefore, we sought to discover new QS inhibitors (QSIs) against LasR receptor in P. aeruginosa using chemoinformatics. Initially, a structure-based high-throughput virtual screening was performed using the LasR active site that identified 61404 relevant molecules. E-pharmacophore (ADAHH) screening of these molecules rendered 72 QSI candidates. In standard-precision docking, only 7 compounds were found as potential QSIs due to their higher binding affinity to LasR receptor (-7.53 to -10.32 kcal/mol compared to -7.43 kcal/mol of native ligands). The ADMET properties of these compounds were suitable to be QSIs. Later, extra-precision docking and binding energy calculation suggested ZINC19765885 and ZINC72387263 as the most promising QSIs. The dynamic simulation of the docked complexes showed good binding stability and molecular interactions. The current study suggested that these two compounds could be used in P. aeruginosa QS inhibition to combat bacterial infections.

Background: COVID-19 is a critical pandemic that has affected human communities worldwide, and there is an urgent need to develop effective drugs. Although there are a large number of candidate drug compounds that may be useful for treating COVID-19, the evaluation of these drugs is time-consuming and costly. Thus, screening to identify potentially effective drugs prior to experimental validation is necessary. Method: In this study, we applied the recently proposed method tensor decomposition (TD)-based unsupervised feature extraction (FE) to gene expression profiles of multiple lung cancer cell lines infected with severe acute respiratory syndrome coronavirus 2. We identified drug candidate compounds that significantly altered the expression of the 163 genes selected by TD-based unsupervised FE. Results: Numerous drugs were successfully screened, including many known antiviral drug compounds such as C646, chelerythrine chloride, canertinib, BX-795, sorafenib, sorafenib, QL-X-138, radicicol, A-443654, CGP-60474, alvocidib, mitoxantrone, QL-XII-47, geldanamycin, fluticasone, atorvastatin, quercetin, motexafin gadolinium, trovafloxacin, doxycycline, meloxicam, gentamicin, and dibromochloromethane. The screen also identified ivermectin, which was first identified as an anti-parasite drug and recently the drug was included in clinical trials for SARS-CoV-2. Conclusions: The drugs screened using our strategy may be effective candidates for treating patients with COVID-19.

Nucleic acid therapeutics are witnessing an impressive acceleration in recent years. They work through multiple mechanisms of action, including downregulation of gene expression and modulation of RNA splicing. While several drugs based on the former mechanism have been approved, few target the latter, despite the promise of RNA splicing modulation. To improve our ability to discover novel RNA splicing-modulating therapies, we developed HCS-Splice, a robust cell-based High-Content Screening (HCS) assay. By implementing the use of a two-colour (GFP/RFP) fluorescent splicing reporter plasmid, we developed a versatile, effective, rapid, and robust high-throughput strategy for the identification of potent splicing-modulating molecules. The HCS-Splice strategy can also be used to functionally confirm splicing mutations in human genetic disorders or to screen drug candidates. As a proof-of-concept, we introduced a dementia-related splice-switching mutation in Microtubule-Associated Protein Tau (MAPT) exon 10 splicing reporter. We applied HCS-Splice to the wild-type and mutant reporters and measured the functional change in exon 10 inclusion. To demonstrate the applicability of the method to cell-based drug discovery, HCS-Splice was used to evaluate the efficacy of an exon 10-targeting siRNA, which was able to restore the correct alternative splicing balance.

Neonatal drug information (DI) is essential for safe and effective pharmacotherapy in (pre)term neonates. Such information is usually absent from drug labels, making formularies a crucial part of the neonatal clinician’s toolbox. Several formularies exist worldwide, but they have never been fully mapped nor compared for content, structure and workflow. The objective of this review was to identify neonatal formularies, explore (dis)similarities, and raise awareness of their existence.Neonatal formularies were identified through self-acquaintance, experts and structured search. A questionnaire was sent to all identified formularies to provide details on formulary function. An original extraction tool was employed to collect DI from the formularies on the 10 most commonly used drugs in pre(term) neonates.Eight different neonatal formularies were identified worldwide (Europe, USA, Australia-New Zealand, Middle East). Six responded to the questionnaire and were compared for structure and content. Each formulary has its own workflow, monograph template and style, and update routine. Focus on certain aspects of DI also varies, as well as the type of initiative and funding.Clinicians should be aware of the various formularies available and their differences in characteristics and content to use them properly for the benefit of their patients.

Overactive bladder (OAB) is characterized by urinary urgency and increased urinary frequency, and can impact quality of life significantly. Tamsulosin and mirabegron combination therapy has been studied as a safe and effective treatment option for patients with OAB. This study evaluated the effects of combining these two drugs on their pharmacokinetics and safety profiles in healthy Korean males. In this open-label, fixed-sequence, 3-period, drug-drug interaction phase 1 study, a total of 36 male participants were administered multiple doses of tamsulosin alone (0.2 mg once daily), mirabegron alone (50 mg once daily), and a combination of both drugs. The results showed that the combination of tamsulosin and mirabegron increased tamsulosin exposure in the plasma by approximately 40%. In contrast, the maximum plasma concentration of mirabegron reduced by approximately 17%, when administered along with tamsulosin. No clinically significant changes in safety profiles, vital signs, or clinical laboratory test results were observed in this study. In conclusion, there were no clinically relevant drug-drug interactions between tamsulosin and mirabegron in terms of pharmacokinetics, safety, and tolerability, suggesting that their combination therapy could be a promising treatment option for patients with OAB.

Maximizing the indications potential and revenue from drugs that are already marketed offers a new take on the famous mantra of the Nobel Prize-winning pharmacologist, Sir James Black, “The most fruitful basis for the discovery of a new drug is to start with an old drug”. However, rational design of drug mixtures poses formidable challenges because of the lack of or limited information about in vivo cell regulation, mechanisms of genetic pathway activation, and in vivo pathway interactions. Most of the repositioned drugs therefore are the result of “serendipity” - based on late phase clinical studies of unexpected findings. One of the reasons that the connection between drug candidates and their potential adverse drug reactions or new applications could not be identified earlier is that the underlying mechanism associating them is either very intricate and unknown or dispersed and buried in a sea of information. Discovery of such multi-domain pharmacomodules - pharmacologically relevant sub-networks of biomolecules and/or pathways - from collection of databases by independent/simultaneous mining of multiple datasets is an active area of research. Here, while presenting some of the promising bioinformatics approaches and pipelines, we summarize and discuss the current and evolving landscape of computational drug repositioning.

Coronavirus disease (COVID-19) is the current global public health threat with no specific, effective, and approved treatment available till date. The outbreak of COVID-19 has led the world into an unimagined and uncertain situation by disrupting the economies, claiming human lives, and leaving many into secondary mental health problems. As per the latest WHO report, approximately 8.2 million people are infected, and nearly 0.44 million lives are lost to COVID. The infection has spread to over 200 countries and territories around the world. The world is in search of efficient diagnostics and therapeutics, including vaccines, biologics and drugs. With the rapid increase in rates of infection and time constraints, drug repurposing seems to be a potential and viable option to find the promising anti-COVID therapeutics. In the wake of a rapid increase in the number of clinical trials involving drugs for repurposing, we aim to provide information on the safety concerns related to the drugs currently investigated in trials. This review also highlights the possible mechanisms of actions, adverse drug reactions, and contraindications of the drugs under repurposing evaluation.

Of the manifold concepts in drug discovery and design, covalent drugs have re-emerged as one of the most promising over the past 20-or so years. All such drugs harness the ability of a covalent bond to drive an interaction between a target biomolecule, typically a protein, and a small molecule. Formation of a covalent bond necessarily prolongs target engagement, opening avenues to targeting shallower binding sites, protein complexes, and other difficult to drug manifolds, amongst other virtues. This opinion piece discusses frameworks around which to develop covalent drugs. Our argument, based on results from our research program on natural electrophile signaling, is that targeting specific residues innately involved in native signaling programs are ideally poised to be targeted by covalent drugs. We outline ways to identify electrophile-sensing residues, and discuss how studying ramifications of innate signaling by endogenous molecules can provide a means to predict drug mechanism and function and assess on- versus off-target behaviors.

We discuss further details on the concepts of “drug-likeness”, “lead-likeness”, and “natural product-likeness”. The discussion will first focus on natural products as drugs, then a discussion of previous studies in which the complexities of the scaffolds and chemical space of naturally occurring compounds have been compared with synthetic, semi-synthetic compounds and FDA-approved drugs. This is followed by guiding principles for designing “drug-like” natural product libraries for lead compound discovery purposes. We end up by presenting a tool for measuring “natural product-likeness” of compounds and a brief presentation of machine learning approaches and a binary quantitative structure-activity relationship (QSAR) for classifying drugs from non-drugs and natural compounds from non-natural ones, respectively.

Background: no reports have examined the profile of drug-induced dysphagia in detail. The goal of this study was to investigate if there are any differences in the profiles of drug-induced dysphagia. Methods: This study used the Japanese Adverse Drug Event Report (JADER) database. Further, reported odds ratios (RORs) were used to analyze data on adverse drug events from 2004 to 2021. The top ten drugs with the most drug-induced dysphagia occurrences were used as target drugs. The association between RORs and drug-induced dysphagia caused by the target drugs was evaluated, and the age distribution and time of onset of drug-induced dysphagia for each drug were compared. RORs were the primary endpoint. Moreover, age and time of onset of drug-induced dysphagia were secondary outcomes. Results: In total, 756,965 reports were analyzed. All the target drugs were associated with drug-induced dysphagia. Among them, cevimeline was a novel finding, as no dysphagia was observed during the clinical trial. For most drugs, the onset of drug-induced dysphagia was occurring within approximately 25 days of administration. However, even after long-term use, paroxetine and milnacipran were associated with drug-induced dysphagia. Conclusion: The onset profile of drug-induced dysphagia may differ from one drug to the next.

Drug repurposing is a valuable tool for combating the slowing rates of novel therapeutic discovery. The Computational Analysis of Novel Drug Opportunities (CANDO) platform performs shotgun repurposing of 2030 indications/diseases using 3733 drugs/compounds to predict interactions with 46,784 proteins and relating them via proteomic interaction signatures. An accuracy is calculated by comparing interaction similarities of drugs approved for the same indications. We performed a unique subset analysis by breaking down the full protein library into smaller subsets and then recombining the best performing subsets into larger supersets. Up to 14% improvement in accuracy is seen upon benchmarking the supersets, representing a 100–1000 fold reduction in the number of proteins considered relative to the full library. Further analysis revealed that libraries comprised of proteins with more equitably diverse ligand interactions are important for describing compound behavior. Using one of these libraries to generate putative drug candidates against malaria results in more drugs that could be validated in the biomedical literature than the list suggested by the full protein library. Our work elucidates the role of particular protein subsets and corresponding ligand interactions that play a role in drug repurposing, with implications for drug design and machine learning approaches to improve the CANDO platform.

Our bodies produce a host of electrophilic species that can label specific endogenous proteins in cells. The signaling roles of these molecules are underactive debate. However, in our opinion it is becoming increasingly likely that electrophiles can rewire cellular signaling processes at endogenous levels. Attention is turning more to understanding how nuanced electrophile signaling in cells is. In this perspective, we describe recent work from our laboratory that has started to inform on different levels of context-specific regulation of proteins by electrophiles. We discuss the relevance of these data to the field, and to the broader application of electrophile signaling to precision medicine development, beyond the traditional views of their pleiotropic cytotoxic roles.

Lung cancer (LC) is one of the leading causes of cancer occurrence and mortality worldwide. Treatment of patients with advanced and metastatic LC presents a significant challenge as malignant cells use different mechanisms to resist chemotherapy. Drug resistance (DR) is a complex process that occurs due to a variety of genetic and acquired factors. Identifying the mechanisms underlying DR in LC patients and possible therapeutic alternatives for more efficient therapy is a central goal of LC research. Advances in nanotechnology resulted in the development of targeted and multifunctional nanoscale drug constructs. The possible modulation of the components of nanomedicine, their surface functionalization, and encapsulation of various active therapeutics provide promising tools to bypass crucial biological barriers. These attributes enhance the delivery of multiple therapeutic agents directly to the tumor microenvironment (TME), resulting in reversal of LC resistance to anticancer treatment. This review provides a broad framework for understanding the different molecular mechanisms of DR in lung cancer; presents novel nanomedicine therapeutics aimed to improve the efficacy of treatment of various forms of resistant LC; outlines current challenges in using nanotechnology for reversing DR; and discusses the future directions for clinical application of nanomedicine in management of LC resistance.

The up-and-coming microfluidic technology is the most promising platform for designing anti-cancer drugs and new point-of-care diagnostics. Compared to conventional drug screening methods based on Petri dishes and animal studies, drug delivery in microfluidic systems has many advantages. For instance, these platforms offer high throughput drug screening, require a small amount of samples, provide an in vivo-like microenvironment for cells, and eliminate ethical issues associated with animal studies. Multiple cell cultures in microfluidic chips could better mimic the 3D tumor environment using low reagents consumption. The clinical experiments have shown that combinatorial drug treatments have a better therapeutic effect than monodrug therapy. So many attempts were performed in this field in the last decade. This review highlights the applications of microfluidic chips in anti-cancer drug screening and systematically categorizes these systems as a function of sample size and combination of drug screening. Finally, it provides a perspective on the future of the clinical applications of microfluidic systems for anti-cancer drug development.

Overexpression of BRF2, a selective subunit of RNA polymerase III, has been shown to be crucial in the development of several types of cancers, including breast cancer and lung squamous cell carcinoma . Predominately, BRF2 acts as a central redox-sensing transcription factor (TF) and is involved in rescuing oxidative stress (OS) -induced apoptosis. Here, we showed a novel link between BRF2 and DNA damage response. Due to the lack of BRF2 specific inhibitors, through virtual and molecular dynamics screening, we identified potential drug candidates that interfere with BRF2-TATA-binding Protein (TBP)-DNA complex interactions based on binding energy, intermolecular, and torsional energy parameters. We experimentally tested Bexarotene as a potential BRF2 inhibitor. We found that Bexarotene (Bex) treatment resulted in a dramatic decline in oxidative stress (Tert-butylhydroquinone (tBHQ))-induced levels of BRF2 and consequently, lead to a decrease in cellular proliferation of cancer cells which may in part be due to drug pretreatment induced reduction of ROS generated by the oxidizing agent. Our data thus, provide the first experimental evidence that BRF2 is a novel player in DNA damage response pathway and Bexarotene can be used as a potential inhibitor to treat cancers with the specific elevation of oxidative stress.

Background: drugs provide a significant benefit; however, their use implies an intrinsic potential danger, with the possibility to cause unwanted effects. These effects are known as adverse drug reactions (ADRs). Post-marketing drug safety surveillance detects unknown risks that have not been identified in clinical trials and it is necessary to monitor marketed medications under real-life practice. Due to the scarce information about fixed combination of ciprofloxacin 0.3% / dexamethasone 0.1% (SDO), we performed a drug safety surveillance study. (2) Methods: A prospective non-controlled drug safety surveillance study was conducted in Peruvian population. A total of 236 patients prescribed SDO were included derivates from 12 sites. Patients' standardized information was collected through two phone calls, including demographics, medical history, prescribing patterns of SDO, concomitant medication, and ADRs in detail. The ADRs were classified by causality and severity, followed by outcome measures to identify new risk. (3) Results: 236 patients prescribed with SDO participated in the study and 220 were included. A total of 82 ADRs/220 patients were reported after the use of SDO, presenting a ratio 0.37 ADR/patient. The most frequent ADR with SDO administration was eye irritation (30%). The totality of the ADR was classified as non-serious, and the 97.5% (n=80) was classified as mild and 2.5% as moderate (n=2). No cases under the severe category were identified. (4) Conclusion: No new risks were found in the population where this study was conducted.

The recent covid crisis has proven important lessons for academia and industry regarding digital reorganization. Among fascinating lessons from these times is the huge potential of data analytics and artificial intelligence. The crisis exponentially accelerated the adoption of analytics and artificial intelligence, and this momentum is predicted to continue into the 2020s and over. Moreover, drug development is a costly and time-consuming business, and only a minority of approved drugs return the revenue that exceeds the research and development costs. As a result, there is a huge drive to make drug discovery cheaper and faster. With modern algorithms and hardware, it is not too surprising that the new technologies of artificial intelligence and other computational simulation tools can help drug developers. In only two years of covid research, many novel molecules have been designed/identified using artificial intelligence methods with astonishing results in terms of time and effectiveness. This paper will review the most significant research on artificial intelligence in the de novo drug design for COVID-19 pharmaceutical research.

The emergence of resistance to first-line antimalarial drugs calls for development of new therapies for drug-resistant malaria. The efficacy of quinoline-based antimalarial drugs has prompted the development of novel quinolines. A panel of 4-aminoquinoline hydrazone analogues were tested on Plasmodium falciparum strains: IC50 values after a 48-hour cycle ranged from 0.60 - 49 µM, while the 72-hour cycle ranged from 26-219 nM on the multi-drug resistant K1 strain. Time-course assays were carried out to define the activity of the lead compounds which inhibited over 50 % growth in 24 hours and 90% growth in 72 hours. Cytotoxicity assays with HepG2 cells showed IC50 values of 0.87-11.1 M, whereas in MDBK cells IC50 values ranged from 1.66-11.7 M. High selectivity indices were observed for the lead compounds screened at 72 hours on P. falciparum. Analyses of stage-specificity revealed that the ring stage of the parasite life cycle were most affected. Based on antimalarial efficacy and in vitro safety profiles, lead compound 4-(2-benzylidenehydrazinyl)-6-methoxy-2-methylquinoline 2 was progressed to drug combination studies for the detection of synergism, with a combinatory index of 0.599 at IC90 for the combination of with artemether, indicating a synergistic antimalarial activity. Compound 2 was screened on different strains of P. falciparum (3D7, Dd2) which maintained similar activity to K1, suggesting no cross-resistance between multi-drug resistance and sensitive parasite strains. In vivo analysis with 2 showed suppression of parasitaemia with P. yoelii NL treated mice (20 mg/kg and 5 mg/kg).

Background & Aim: Drug sustainability (DS) is a surrogate marker for treatment efficacy. We aimed to compare the DS of two main biologics used to treat moderate-to-severe ulcerative colitis (UC), infliximab (IFX) and vedolizumab (VDZ), in a real-world setting. Methods: We conducted a retrospective cohort study at a tertiary medical center in Israel. We included patients treated between Dec 1st, 2017, and May 1st, 2021, who were followed for up to 300 weeks. DS was defined as corticosteroid, surgical, and hospitalization-free treatment. Results: 217 patients with UC were included. VDZ had a significantly longer median DS of 265.6 weeks compared to IFX's 106.5 weeks (p=0.001) in treatment-naïve patients, even when adjusting for disease severity (HR 0.55 95 CI 0.3-0.98, p=0.042). In treatment-experienced patients, DS was comparable between IFX and VDZ (p=0.593). Conclusion: VDZ showed significantly longer DS in treatment-naïve patients with UC compared to IFX, also when adjusted for disease severity. There was no difference in DS between VDZ and IFX in treatment-experienced patients and patients switching from one drug to another. VDZ may be a suitable first-line treatment for biologic-naïve patients with moderate-to-severe UC.

Although marketing authorization holders (MAHs) are involved in monitoring medication safety, it was unclear how they experience their role and current monitoring activities in pregnancy. Therefore, a qualitative study using online focus groups with MAHs and the Belgian umbrella organisation of MAHs was conducted in June-July 2021. In total, 38 representatives of nine organisations participated. Overall, participants reported multiple difficulties with data collection, including underreporting, collection of incomplete information and loss to follow-up. The limited number of high-quality data collected, the unknown denominator and the lack of comparator data complicate MAHs’ data processing activities, preventing them to timely provide evidence in the pregnancy label. Three ‘conflicts’ inherent to the specific position of MAHs were identified explaining the difficulties they experience, i.e., 1) mistrust from patients and healthcare professionals (HCPs); 2) MAHs’ legal obligations and regulatory framework; 3) MAHs’ position outside the healthcare context. To overcome these barriers, MAHs suggested that data registration should occur in close collaboration with patients and HCPs, organized within the healthcare context and performed by using a user-friendly system. In conclusion, the reported difficulties and underlying conflicts of MAHs highlight the need for more effective, collaborative data collection strategies to generate new evidence on this topic.

The lungs of tuberculosis (TB) patients contain a spectrum of granulomatous lesions ranging from solid and well vascularized cellular granulomas, to avascular caseous granulomas. In solid granulomas, current therapy kills actively replicating (AR) intracellular bacilli, while in low vascularized caseous granulomas the low oxygen tension stimulates aerobic and microaerophilic AR bacilli to transit into non-replicating (NR), drug-tolerant, extracellular stages. These stages, which do not have genetic mutations and are often referred to as persisters, are difficult to eradicate due to low drug penetration inside caseum and mycobacterial cell walls. The sputum of TB patients contains also viable bacilli called differentially detectable (DD) cells that, unlike persisters, grow in liquid, but not in solid media. This review provides a comprehensive update on drug combinations killing in vitro AR and drug-tolerant bacilli (persisters and DD cells), and sterilizing Mycobacterium tuberculosis-infected BALB/c and caseum-forming C3HeB/FeJ mice. These observations have been important for testing new drug combinations in noninferiority clinical trials, in order to shorten duration of current regimens against TB. In 2022, the World Health Organization, based on one of this trial, supported the use of a 4-month regimen for treatment of drug-susceptible TB as a possible alternative to the current 6-month regimen.

The multifactorial nature of some diseases, and in particular neurodegenerative diseases such as Alzheimer’s disease, frequently requires the use of several drugs. These drug cocktails are not without drawbacks in terms of increased adverse effects, drug-drug interactions or adherence to treatment. The concept of pleiotropic drugs, which combine, within a single molecule, several activities directed against distinct therapeutic targets, makes it possible to overcome some of these problems. In addition, these pleiotropic drugs, generally, lead to the expression of a synergy of effects, sometimes greater than that observed with a combination of drugs. This article will review, through recent examples, the different kinds of pleiotropic drugs being studied or already present on the market of medicines.

Esophageal cancer is a formidable challenge in the realm of cancer treatment. Conventional methods such as surgery, chemotherapy, and immunotherapy have demonstrated limited success rates in managing this disease. In response, targeted drug therapies have emerged as a promising strategy to improve outcomes for patients. These therapies aim to disrupt specific pathways involved in the growth and development of esophageal cancer cells. This review explores various drugs used to target specific pathways, including cetuximab and monoclonal antibodies (gefitinib) that target the epidermal growth factor receptor (EGFR), trastuzumab that targets human epidermal growth factor receptor 2 (HER-2), drugs targeting the vascular endothelial growth factor receptor (VEGFR), mTOR inhibitors, and cMET inhibitors. Additionally, the article discusses the impact of drug resistance on the effectiveness of these therapies, highlighting factors such as cancer stem cells, cancer-associated fibroblasts, immune-inflammatory cells, cytokines, hypoxia, and growth factors. While drug targeting approaches do not provide a complete cure for esophageal cancer due to drug resistance and associated side effects, they offer potential for improving patient survival rates.

Introduction: Valproic acid (VPA) is an antiepileptic drug extensively used for treating partial and generalised seizures, acute mania and as prophylaxis for bipolar disorder. Drug-induced liver injury (DILI) persists as a significant issue related to fatal outcomes by VPA. The aim of this study was to increase our knowledge about this condition and to better identify patients affected. Methods: We conducted an observational retrospective case-control study that identified cases of DILI by VPA from the Pharmacovigilance Programme from our Laboratory Signals at La Paz University Hospital from January 2007 to December 2019. From the Therapeutic VPA Monitoring Programme, two control groups were assigned, VPA-tolerant patients and the other with patients who developed mild VPA-related hepatitis but who did not meet the DILI criteria, matched for date, age and sex. Results: A total of 60 patients were included in the study: 15 cases of DILI, 30 VPA-tolerant controls and 15 controls with mild hepatitis. Mean age for the cases was 45.7 years, 4(26.7%) were women and 5(33.34%) were children under 18 years, of them 3(20%) were fatal. Polytherapy with other antiepileptic drugs (p=0.047) and alcohol consumption (p<0.001) were associated with a greater risk of developing DILI by VPA. A diagnosis of epileptic seizure was more frequently related to DILI when compared with the VPA-tolerant controls (p<0.001). The cases developed hepatocellular hepatitis (p<0.001), while the mild hepatitis controls had a higher rate of cholestatic hepatitis (p<0.001). The laboratory lactate dehydrogenase values were statistically higher (even at baseline) in patients with DILI than in both control groups (p= 0.033 and p=0.039). Conclusions: VPA hepatotoxicity remains a considerable problem. This study offers interesting findings for characterising VPA-induced liver injury and at-risk patients.

The Novel Coronavirus (COVID-19) is a positive-sense single-stranded RNA ((+)ssRNA) virus. The COVID-19 Main Proteases play very important role in the propagation of the Novel Coronavirus (COVID-19). It has already killed more than 8000 people around the world and thousands of people are getting infected every day. Therefore, it is very important to identify a potential inhibitor against COVID-19 Main Proteases to inhibit the propagation of the Novel Coronavirus (COVID-19). We have applied a drug repurposing approach of computational methodology, depending on the synergy of molecular docking and virtual screening techniques, aimed to identify possible potent inhibitors against Novel Coronavirus (COVID-19) from FDA approved antiviral compounds and from the library of active phytochemicals. On the basis of recently resolved COVID-19 Main Protease crystal structure (PDB:6LU7), the library of 100 FDA approved antiviral compounds and 1000 active components of Indian Medicinal Plants extracted for screening against COVID-19 Main Protease. The compounds were further screened using Pyrex virtual screening tool and then best inhibitors, top 19 compounds optimally docked to the COVID-19 Main Protease structure to understand the participation of specific amino acids with inhibitors at active sites. Total 19 best compounds were identified after screening based on their highest binding affinity with respect to the other screened compounds. Out of 19, 6 best compounds were further screened based on their binding affinity and best ADME properties. Nelfinavir exhibited highest binding energy -8.4 kcal/mol and strong stability with the TRP207, ILE281, LEU282, PHE3, PHE291, GLN127, ARG4, GLY283, GLU288, LYS5, LYS137, TYR126, GLY138, TYR126, SER139 and VAL135 amino acid residues of COVID-19 Main Protease participating in the interaction at the binding pocket. In addition to Nelfinavir (-8.4), Rhein (-8.1), Withanolide D (-7.8), Withaferin A (-7.7), Enoxacin (-7.4), and Aloe-emodin (-7.4) also showed good binding affinity and best ADME properties. Our findings suggest that these compounds can be used as potential inhibitors against COVID-19 Main Protease, which could be helpful in inhibiting the propagation of the Novel Coronavirus (COVID-19). Moreover, further in vitro and in vivo validation of these findings would be very helpful to bring these inhibitors to next level study.

The similarities between exosomes and liposomes, together with the high organotropism of several types of exosomes, have recently prompted the development of engineered-exosomes or exosome-mimetics, which may be artificial (liposomal) or cell-derived vesicles, as advanced platforms for targeted drug delivery. Here we provide the current state-of-the-art of using exosome or exosome-inspired systems for drug delivery. We review the various approaches investigated and the shortcomings of each approach. Finally the challenges identified up-to-date in this field are summarized.

Traditional methods for discovery and development of new drugs can be a very time-consuming and expensive process because it includes several stages such as compound identification, pre-clinical and clinical trials before the drug is approved by the US Food and Drug Administration (FDA). Therefore, drug repurposing, namely using currently FDA-approved drugs as therapeutics for other diseases than what they are originally prescribed for, is emerging to be a faster and more cost-effective alternative to current drug discovery methods. In this paper, we have described a three-step in silico protocol for analyzing transcriptomics data using online databases and bioinformatics tools for identifying potentially repurposable drugs. The efficacy of this protocol was evaluated by comparing its predictions with the findings of two case studies of recently reported repurposed drugs: HIV treating drug Zidovudine for the treatment of Dry Age-Related Macular Degeneration and the antidepressant Imipramine for Small-Cell Lung Carcinoma. The proposed protocol successfully identified the published findings, thus demonstrating the efficacy of this method. In addition, it also yielded several novel predictions that have not yet been published, including the finding that Imipramine could potentially treat Severe Acute Respiratory Syndrome (SARS), a disease that currently does not have any treatment or vaccine. Since this in-silico protocol is simple to use and does not require advanced computer skills, we believe any motivated participant with access to these databases and tools would be able to apply it to large datasets to identify other potentially repurposable drugs in the future.

Thanks to the continued development of experimental structural biology and the half-a-century old Protein Data Bank, 2021 saw a big step forward in the development of protein structure prediction with deep learning algorithms. Recently, DeepMinds AlphaFold has determined the structures of ∼ 200 million proteins from 1 million species. The speed of this progress raise the question of what becomes possible for computational drug discovery and design when we have a systems-wide account of the structures and motions of most proteins. Therefore, this article puts forward the concept of a general intermolecular binding affinity calculator (GIBAC): Kd = f(molA, molB, envPara), towards the acceleration of traditional computer-aided drug design (CADD) and artificial intelligence-integrated drug discovery (AIDD), for both small molecules and biologics such as therapeutic proteins.

The primary purpose of this study was to determine the prevalence of drug-drug interaction (DDI) and duplicate therapy in chronic patients in a completely random study population engaged in digital health apps. In this cross-sectional study, polypharmacy checks for 100 completely anonymous patients were analyzed for the occurrence of DDIs and duplicate therapy. Logistic regression models were used to identify factors associated with DDIs and duplicate therapy. DDIs and duplicate therapy prevalence were 34% and 33%, respectively. Chi-Square test discovered a significant association between the DDIs and duplicate therapy variables. Logistic regression models showed a strong association between the number of medications taken and higher odds of DDIs occurring in our population only. In conclusion, our study shows that polypharmacy is a determining factor for the occurrence of unwanted DDIs, and the prevalence of duplicate therapy and DDIs is around 33%, increasing an issue regarding patient safety and its burden to the healthcare system.

Overt hyperthyroidism during pregnancy is associated with risk of maternal-fetal complications. The antithyroid drugs (ATD) have a potential risk for teratogenic effects and fetal–neonatal hy-pothyroidism. This study evaluated ATD treatment and thyroid function control during preg-nancy, and pregnancy outcome in women with hyperthyroidism. Patients and methods: retro-spective analysis of 36 single fetus pregnancies in 29 consecutive women (median age 30.3 ± 4.7 years) with hyperthyroidism diagnosed before or during pregnancy; a control group of 39 healthy euthyroid pregnant women was used. Results: 26 women had Graves’ disease (GD, 33 pregnan-cies), 1 had a hyperfunctioning autonomous nodule, 2 had gestational transient thyrotoxicosis (GTT). Methimazole (MMI) was administered in 22 pregnancies (78.5%), Propylthiouracil (PTU) in 2 (7.1%), switch from MMI to PTU in 4 (14.2%), no treatment in 8 pregnancies (3 with subclinical hyperthyroidism, 5 euthyroid with previous GD remission before conception). One spontaneous abortion at 5 weeks (3.4% of pregnancies) and 1 premature delivery at 32 weeks with perinatal death in 24h (3.4%) were recorded in 2 of the 8 pregnancies of GD patients diagnosed shortly before (< 6 weeks) or during gestation. In women treated more than 6 months until conception (20 pregnancies): a) median ATD doses were lower than those in women diagnosed shortly before or during pregnancy; b) ATD was withdrawn in 40% of pregnancies in trimester (T) I, all on MMI < 10 mg/day (relapse in 14.2%), and in up to 55% in TIII; c) TSH level was below normal in 37%, 35% and 22% of pregnancies in T I, II and III respectively; FT4 was increased in 5.8% (T I) and sub-normal in 11.75% in TII and III; d) one fetal death due to a true umbilical cord knot was recorded. Hyperthyroidism relapsed postpartum in 83% of GD patients (at median 3 ± 2.6 months). One child had neonatal hyperthyroidism (3.3% of live children in GD women) and a small atrial sept defect (4% of live children in ATD treated women). Mean birth weight did not differ from that of the control group. Conclusion. In hyperthyroid women with long-term ATD control before con-ception, drugs could be withdrawn in TI in a third of them, and fetal complications were rare. Frequent serum TSH and FT4 monitoring is needed in order to maintain optimal thyroid function during pregnancy.

In this paper we propose the generation of synthetic small and more sophisticated molecule structures that optimize the binding affinity to a target (ASYNT-GAN). To achieve this we leverage on three important achievements in A.I.: Attention, Deep Learning on Graphs and Generative Adversarial Networks. Similar to text generation based on parts of text we are able to generate a molecule architecture based on an existing target. By adopting this approach, we propose a novel way of searching for existing compounds that are suitable candidates. Similar to question and answer Natural Language solutions we are able to find drugs with highest relevance to a target. We are able to identify substructures of the molecular structure that are the most suitable for binding. In addition, we are proposing a novel way of generating the molecule in 3D space in such a way that the binding is optimized. We show that we are able to generate compound structures and protein structures that are optimised for binding to a target.

HIV treatment strategies against viral enzymes are continuously hampered by viral drug resistance. Recent findings show that viral substrate Gag contributes to HIV-1 Protease Inhibitor (PI) resistance, leading to demands for new strategies in HIV treatment where Gag is recognized as a drug target. To successfully target Gag, there is a need of in-depth understanding of the Gag polyprotein and the effects of Gag mutations. Here, we propose new strategies in designing novel Gag inhibitors against existing and novel emerging Gag mutations via a structural understanding of the Gag-Protease relationship in PI resistance. In this review, we discuss the role of both novel and previously reported mutations, revealing insights to how they aid in PI resistance, and how new Gag inhibitors can be designed.

BRAF is one of the most frequently mutated oncogenes, with an overall frequency of about 50%. Targeting BRAF and its effector mitogen-activated protein kinase kinase 1/2 (MEK1/2) is now a key therapeutic strategy for BRAF-mutant tumors, and therapies based on dual BRAF/MEK inhibition showed significant efficacy in a broad spectrum of BRAF tumors. Nonetheless, BRAF/MEK inhibition therapy is not always effective for BRAF tumor suppression, and significant challenges remain to improve its clinical outcomes. First, certain BRAF tumors have an intrinsic ability to rapidly adapt to the presence of BRAF and MEK1/2 inhibitors by bypassing drug effects via rewired signaling, metabolic, and regulatory networks. Second, almost all tumors initially responsive to BRAF and MEK1/2 inhibitors eventually acquire therapy resistance via an additional genetic or epigenetic alteration(s). Overcoming these challenges requires identifying the molecular mechanism underlying tumor cell resistance to BRAF and MEK inhibitors and analyzing their specificity in different BRAF tumors. This review aims to update this information.

Infectious illnesses have always posed a threat to human health, with tuberculosis being a major concern. The use of various medications and antibiotics in the fight against such TB has led to the emergence of drug-resistant tuberculosis, which has become increasingly difficult to manage. While there have been a few studies and proposed conceptual models on how to manage and prevent various drug-resistant TB mutations and lineages, a model aimed at limiting and con-trolling such mutations in rural areas burdened with tuberculosis is lacking. This study seeks to map a model that is to be used to bridge the gap by facilitating the exchange of knowledge among healthcare professionals in healthcare facilities, diagnostic laboratories, and research institutes, particularly for underprivileged communities in the Eastern Cape. The model will also serve as a guide to monitor and evaluate epidemiological TB management plans.

The paradigm of molecular communications is applied to the concrete case for delivering electrically charged nanoparticles to tumor. Once them have been injecetd in blood it is expecetd an optimal outcome as to reduce toxicity and minimal dispersion of drugs in the blood stream. With a fraction of nanoparticles arrives to surface of tumor, the scattered part of injected ones can minimize the success of scheme of drug delivery. In this paper is presented a theory based at the sequence electrodynamics-diffusion-Bayes theorem. The resulting probability of Bayes at the end of sequence, might be telling us that dynamical processes based in the injection of electrically charged nanoparticles might be dictated by stochastic formalism more that biochemical approaches by which makes impossible to know the success or fail of drug delivery dynamics. Illustrations demonstrating the transition of a linear to nonlinear scenario are presented

Nanosystems-based antifungal agents have emerged as an effective strategy to address issues related to drug resistance, drug release, and toxicity. Among the different materials used for drug delivery, multifunctional polymers have proven to be ideal due to their versatility. This review provides an overview of the various types of nanoparticles used in antifungal drug delivery systems, with a particular emphasis on the types of polymers used. The review focuses on the application of drug delivery systems and the release behavior of these systems. Furthermore, the review summa-rizes the critical physical properties and relevant information utilized in antifungal polymer nanomedicine delivery systems, and briefly discusses the application prospects of these systems.

Background: Although Tuberculosis (TB) is one of the oldest diseases known to mankind, many practitioners are still unaware of various aspects of management of TB including drug resistant TB. To be able to make an impact on the disease burden we need to understand the knowledge, attitude and practices (KAP) of practitioners, both in government and private sector, towards TB case management. Methodology: A random cross-sectional survey of 99 respondents to assess the KAP towards TB case management using a digital semi-structured questionnaire which was pre-tested and administered to allopathic practitioners from across States in North and Central India. Results: 77% responders were government doctors and 23% were private practitioners. Chest physicians accounted for 61% of the responders. 26% of government and 78% of private doctors relied on sources which are difficult to validate for their knowledge on drug resistant TB management. There were large variations seen in knowledge on modes of transmission, standard precautions, extent of drug resistant TB, diagnostics as well as treatment regimen for drug resistant TB. Conclusion: The knowledge of both sector doctors especially amongst private practitioners with respect to the awareness of extent, diagnosis, and treatment and infection control measures for drug resistant TB is suboptimal which translates to poor diagnostic, therapeutic and infection control choices amongst private practitioners. If India has to achieve the targets for TB Elimination by 2025, serious work needs to be done to upgrade the knowledge of the private sector doctors on drug resistant tuberculosis. The Ministry of Health and Family Welfare, GoI needs to have regular educational programs for the private practitioners coupled with awareness campaigns and frequent surveys to assess the knowledge, attitude and practices being followed in the private sector for TB management including drug resistant TB.

Both Stevens Johnson Syndrome (SJS) and toxic epidermal necrolysis (TEN) are main injurious cutaneous medication reactions that mostly affect the epidermis and mucus membranes. TEN and SJS affecting nearly 1 or 2/1,000,000 people per year, and can recognized as medical crises since they may be deadly. Mucocutaneous discomfort, hemorrhagic erosions, erythema, and more or less severe epidermal separation that appear as ulcer and patches of dermic loss are their defining characteristics. The sole difference between TEN and SJS at this time is the degree of skin detachment, making them two extremes of a spectrum of severe cutaneous adverse drug reactions (cADRs). In the majority of cases, drugs are considered as the principal reason of SJS/TEN, but herpes simplex virus and Mycoplasma pneumoniae infections are also recognized causes, along with lesser number of cases in which the cause is still unknown. Among the drugs with a "high" likelihood of producing TEN/SJS are carbamazepine (CBZ), trimethoprim-sulfamethoxazole, phenytoin, aminopenicillins, allopurinol, cephalosporins, other sulfonamide antibiotics, quinolones, phenobarbital, and NSAIDs of the oxicam variety. There is strong genetic evidence for SJS and TEN in Han Chinese due to the substantial association between the human leukocyte antigen (HLA-B*1502) and SJS brought on by CBZ. The diagnosis is made mostly based on clinical symptoms and the histological study of a dermal biopsy. Pemphigus vulgaris, bullous pemphigoid, linear IgA dermatosis, paraneoplastic pemphigus, disseminated fixed bullous drug eruption, acute generalized exanthematous pustulosis (AGEP), and staphylococcal scalded skin syndrome (SSSS) are among the differential diagnoses. The management of patients with SJS/TEN is complicated by the high risk of mortality, necessitating early diagnosis, estimation of the SCORTEN prognosis, identification and discontinuation of the causative drug, specialized supportive care, and high-dose injectable Ig therapeutic interventions. The reported fatality rates for SJS are 1-5% on average and 25-35% for TEN; it can be even higher in patients who are elderly or who have a significant amount of epidermal detachment on their skin. More than 50% of TEN patients who survive the disease experience long-term consequences.

Nanotechnology is making significant transformation to our world, especially in healthcare and the treatment of diseases. It is widely used in different medical applications, such as in treatment and detection. Targeting diseased cell with nanomedicines is one of the numerous applications of nanotechnology. Targeted drug delivery systems for delivering various types of drugs to specific sites are such a dynamic area in pharmaceutical biotechnology and nanotechnology. Compared to conventional drugs, nanomedicines have a higher absorption and bioavailability rate, improving efficacy and minimizing side effects. There are several drug delivery systems including metallic nanoparticles, polymers, liposomes, and microspheres, but one of the most important is the niosomes, which are produced by nonionic surfactants. Because of the amphiphilic nature and structure, hydrophilic or hydrophobic drugs can be loaded into niosome structures. Other compounds, including cholesterol, can also be applied to the niosomes' backbone to rigidize the structure. Several variables such as the type of surfactant in niosome production, the preparation method, and the hydration temperature can affect the structure of the niosomes. Nevertheless, in-silico design of drug delivery formulations requires molecular dynamic simulation tools, molecular docking, and ADME (absorption; distribution; excretion; metabolism) properties, which evaluate physicochemical features of formulation and ADME attitudes before synthesis, investigating the interaction between nano-carriers and specific targets. Hence, experimenting in-vitro and in-vivo is essential. In this review, the basic aspects of niosomes are described including their structure, characterization, preparation methods, optimization with in-silico tools, factors affecting their formation, and limitations.

Drug discovery has been initially attributed to coincidence or trial and error where the traditional approach was complex, lengthy, and expensive. Conventional drug discovery methods require the costly random screening of synthesized compounds or natural products. Another downside for this approach is the wide dependency on the experimental use of animals for in vi-vo testing. Currently, in silico modeling has become a vital tool for drug discovery and repurposing, and molecular docking is being used to find the best matching between a ligand and a molecule. Practical application of in silico docking will predict the biomolecular interactions between the drug and the target host. Beauvericin (BEA) is an emerging mycotoxin produced by the entomopathogenic fungus Beauveria bassiana. Originally investigated for its pesticide capability, BEA is now considered as a molecule of interest for its potentially diverse biotechnological applications in the pharmacological industry and the field of medicine. In this manuscript, we will provide an overview of the repurposing of BEA into a potentially superior therapeutic molecule in a broad range of diseases. Furthermore, considerable attention has been given to the fundamental role of in silico techniques to i) further investigate the spectrum of this secondary metabolite and ii) elucidate the pathways of BEA for its promising therapeutic action

With increasing awareness amongst physicians and improved radiological imaging techniques, the peritoneal cavity is increasingly recognized as an important metastatic site in various malignancies. Prognosis of these patients is usually poor as traditional treatment including surgical resection or systemic treatment is relatively ineffective. Intraperitoneal delivery of chemotherapeutic agents is thought to be an attractive alternative as this results in high tumor tissue concentrations with limited systemic exposure. The addition of hyperthermia aims to potentiate the anti-tumor effects of chemotherapy, resulting in the concept of heated intraperitoneal chemotherapy (HIPEC) for the treatment of peritoneal metastases as it was developed about 3 decades ago. With increasing experience, HIPEC has become a safe and accepted treatment offered in many centers around the world. However, standardization of the technique has been poor and results from clinical trials have been equivocal. As a result, the true value of HIPEC in the treatment of peritoneal metastases remains a matter of debate. The current review aims to provide a critical overview of the theoretical concept and preclinical and clinical study results, to outline areas of persisting uncertainty, and to propose a framework to better define the role of HIPEC in the treatment of peritoneal malignancies.

Introduction Tuberculosis is common in Pakistan. Due to various factors including socioeconomic factors, compliance is poor to anti-tuberculosis drugs, leading to resistance. We aim to determine the prevalence of Multidrug resistance (MDR) tuberculosis in Pakistani population.Methods A prospective observational study was conducted from April 1, 2019, to December 31, 2019, in the Pulmonology department of a tertiary care hospital in Pakistan. Culture and sensitivity were assessed using a sputum sample or, in cases of an absent sputum sample, from Broncho alveolar lavage.ResultsApproximately 71.3% percent patients who had tuberculosis were found to be resistant to Isoniazid and around 48.6% did not respond to Rifampin. Multi-drug resistant was found in 29.4% participants.ConclusionMulti-drug resistance tuberculosis is very prevalent in Pakistan, which may increase burden on health care system and may lead to various complications of tuberculosis.

The influence of lignin modification on drug release and pH-dependent releasing behaviour of oral solid dosage form was investigated using three different formulations. The first formulation contains microcrystalline cellulose (MCC101) as excipient and paracetamol as active pharmaceutical ingredient (API). The second formulation includes Alcell lignin and MCC 101 as excipient and paracetamol, and the third formulation consists of carboxylated Alcell lignin, MCC 101 and paracetamol. Direct compaction was carried out in order to prepare the tablets. Lignin can be readily chemically modified due to the existence of different functional groups in its structure. The focus of this investigation is on lignin carboxylation and its influence on paracetamol control release behaviour at varying pH. Results suggest that carboxylated lignin tablets had the highest drug release, which is linked to their faster disintegration and lower tablet hardness.

Objective: To evaluate the status of receiving education on rational drug use, the criteria in medical drug selection, and level of knowledge of dentists working in a dentistry faculty in Turkey. Material and Methods: This was a descriptive study based on a questionnaire. One hundred seventeen (74%) dentists volunteered to participate in the study. The questionnaire consisted of 20 questions investigating sociodemographic features and rational drug use. Results: The mean age of the dentists was 30.8 ± 7.2 years, and 62.4% were men. The mean period of professional experience was 8.9±7.1 years. The most frequently used resources of references while prescribing medicine were Vademecum (medical drug guide) (61.5%), the internet (59.0%), and colleagues (49.6%). The most frequently reported condition described as ‘good’ was drug indications (43.6%). The dentists had a moderate level of information about posology, and administration route (48.7%), pharmacologic features (48.7%), and contraindications (46.2%). The number of dentists who stated that they considered cost while prescribing was low [always (6%), and frequently (15.4%)]. Rational drug use education had been received by 23.9% of the dentists. Conclusions: The dentists were found to have a lack of adequate and effective education on rational use of drugs. Regular and continuous education before and after graduation is a necessity for dentists and for their patients.

Urothelial cell carcinoma (UCC, bladder cancer) remains a difficult to treat malignancy with rising incidence worldwide. In the U.S., UCC is the sixth most incident neoplasm and ~90% of diagnoses are made in those &gt;55 years of age, ~four times more commonly observed in men than women. The most important risk factor for developing bladder cancer is tobacco smoking, which accounts for ∼50% of cases followed by occupational exposure to aromatic amines and ionizing radiation. The standard of care for advanced UCC includes platinum-based chemotherapy and programmed cell death (PD-1) or programmed cell death ligand 1 (PD-L1) inhibitors, administered as frontline, second-line, or maintenance therapy. UCC is highly aggressive and remains generally incurable since these cancers are associated with intrinsic and acquired drug resistance. UCC is highly lethal in the metastatic state and characterized by genomic instability, high PD-L1 expression, DNA damage-response mutations, and high tumor mutational burden. Although immune checkpoint inhibitors (ICIs) achieve long-term durable responses in other cancers, their ability to achieve similar results with metastatic UCC (mUCC) is not as well-defined. Here, we discuss the novel therapies to improve the management of mUCC.

Gastro-retentive floating microspheres were developed as a result of the recent advancements in floating delivery systems for drugs (FDDS), which included the uniform dispersion of multiparticulate dosage forms along the GIT. This could lead to more consistent drug absorption and a lower risk of local irritation. Microballoons (MB), a multi-unit extended release with a sphere-shaped cavity encased in a tough polymer shell, have been developed as a dosage form with exceptional buoyancy in the stomach. This preparation for constrained intestinal absorption is made to float on top of gastric acid, that has a relative density lower than 1.By using enteric acrylic polymers and the emulsion solvent diffusion method, microballoons are prepared and filled to drug in one‘s outer polymer casings. Enteric acrylic plastics are used to generate microballoons that are drug-loaded in one‘s external polymer casings and dissipate in a solution of dichloromethane and ethanol. Cavity development in microparticles seems to be particularly correlated with dichloromethane evaporation. Microballoons with a drug distributed or dispersed all through the particle-matrix have the potential for a controlled drug release and float continuously for more than 12 hours in vitro out over the surface of an acidified dissolution medium with surfactant. The drug is released slowly and at the desired rate as the microballoons glide over the components of the stomach, increasing gastro-retention time and lowering fluctuations in plasma concentration.

Sex differences are essential factors in disease etiology and manifestation in many diseases such as cardiovascular disease, cancer, and neurodegeneration (1). The biological influence of sex differences (including genomic, epigenetic, hormonal, immunological, and metabolic differences between males and females) and the lack of biomedical studies considering sex differences in their study design has led to several policies. For example, the National Institute of Health’s (NIH) sex as a biological variable (SABV) and Sex and Gender Equity in Research (SAGER)) policies to motivate researchers to consider sex differences (2). However, drug repurposing, a promising alternative to traditional drug discovery by identifying novel uses for FDA-approved drugs, lacks sex-aware methods that can improve the identification of drugs that have sex-specific responses (1,3–5). Sex-aware drug repurposing methods either select drug candidates that are more efficacious in one sex or deprioritize drug candidates based on if they are predicted to cause a sex-bias adverse event (SBAE), unintended therapeutic effects that are more likely to occur in one sex. Computational drug repurposing methods are encouraging approaches to develop for sex-aware drug repurposing because they can prioritize sex-specific drug candidates or SBAEs at lower cost and time than traditional drug discovery. Sex-aware methods currently exist for clinical, genomic, and transcriptomic information (3,6,7). They have not expanded to other data types, such as DNA variation, which has been beneficial in other drug repurposing methods that do not consider sex (8). Additionally, some sex-aware methods suffer from poorer performance because a disproportionate number of male and female samples are available to train computational methods (3). However, there is development potential for several different categories (i.e., data mining, ligand binding predictions, molecular associations, and networks). Low-dimensional representations of molecular association and network approaches are also especially promising candidates for future sex-aware drug repurposing methodologies because they reduce the multiple hypothesis testing burden and capture sex-specific variation better than the other methods (9,10). Here we review how sex influences drug response, the current state of drug repurposing including with respect to sex-bias drug response, and how model organism study design choices influence drug repurposing validation.

NF-κB is a central mediator of inflammation, response to DNA damage and oxidative stress. As a result of its central role in so many important cellular processes, NF-κB dysregulation has been implicated in the pathology of important human diseases. NF-κB activation causes inappropriate inflammatory responses in diseases including rheumatoid arthritis (RA) and multiple sclerosis (MS). Thus, modulation of NF-κB signaling is being widely investigated as an approach to treat chronic inflammatory diseases, autoimmunity and cancer. The emergence of COVID-19 in late 2019, the subsequent pandemic and the huge clinical burden of patients with life-threatening SARS-CoV-2 pneumonia led to a massive scramble to repurpose existing medicines to treat lung inflammation in a wide range of healthcare systems. These efforts continue and these efforts continue to be con-troversial. Drug repurposing strategies are a promising alternative to de-novo drug development, as they minimize drug development timelines and reduce the risk of failure due to unexpected side effects. Different experimental approaches have been applied to identify existing medicines which inhibit NF-κB that could be repurposed as anti-inflammatory drugs.

Elevated levels of oxidative stress in the corneal epithelium contribute to the progression of dry eye disease pathology. Previous studies have shown that antioxidant therapeutic intervention is a promising avenue to reduce disease burden and slow disease progression. In this study, we evaluated the pharmacological efficacy of Xanthohumol in preclinical models for dry eye disease. Xanthohumol is a naturally occurring prenylated chalconoid that promotes the transcription of phase II antioxidant enzymes. Xanthohumol exerted a dose-response in preventing tert-butylhydroxide-induced loss of cell viability in human corneal epithelial (HCE-T) cells and resulted in a significant increase in expression of nuclear factor erythroid 2-related factor 2 (Nrf2), the master regulator of the endogenous antioxidant system. Xanthohumol-encapsulating poly(lactic-co-glycolic acid) nanoparticles (PLGA NP) were cytoprotective against oxidative stress in vitro, and significantly reduced corneal fluorescein staining in the mouse desiccating stress/ scopolamine model for dry eye disease in vivo by reducing oxidative stress-associated DNA damage in corneal epithelial cells. PLGA NP represent a safe and efficacious drug delivery vehicle for hydrophobic small molecules to the ocular surface. Optimization of NP-based antioxidant formulations with the goal to minimize instillation frequency may represent future therapeutic options for dry eye disease and related ocular surface disease.

We use state-of-the-art computer-aided drug design (CADD) techniques to identify prospective inhibitors of the main protease enzyme, 3CLpro of the SARS-CoV-2 virus causing COVID-19. From our screening of over one million compounds including approved drugs, investigational drugs, natural products, and organic compounds, and a rescreening protocol incorporating enzyme dynamics via ensemble docking, we have been able to identify a range of prospective 3CLpro inhibitors. Importantly, some of the identified compounds had previously been reported to exhibit inhibitory activities against the 3CLpro enzyme of the closely related SARS-CoV virus. The top- ranking compounds are characterized by the presence of multiple bi- and monocyclic rings, many of them being heterocycles and aromatic, which are flexibly linked allowing the ligands to adapt to the geometry of the 3CLpro substrate site and involve a high amount of functional groups enabling hydrogen bond formation with surrounding amino acid residues, including the catalytic dyad residues H41 and C145. Among the top binding compounds we identified several tyrosine kinase inhibitors, which include a bioflavonoid, the group of natural products that binds best to 3CLpro. Another class of compounds that decently binds to the SARS-CoV-2 main protease are steroid hormones, which thus may be endogenous inhibitors and might provide an explanation for the age-dependent severity of COVID-19. Many of the compounds identified by our work show a considerably stronger binding than found for reference compounds with in vitro demonstrated 3CLpro inhibition and anticoronavirus activity. The compounds determined in this work thus represent a good starting point for the design of inhibitors of SARS-CoV-2 replication.

Endocannbinoids system (ECS) engrossed a considerable interest as potential therapeutic targets in various carcinomas and cancer related conditions alongside with neurodegenerative diseases. Cannabinoids are implemented in several physiological processes such as appetite stimulation, energy balance, pain modulation and the control of chemotherapy induced nausea and vomiting (CINV). However, pharmacokinetics and pharmacodynamics interactions could be perceived in drug combinations, so in this short review we tried to shed the light over the potential drug interactions of medicinal cannabis. Hitherto, few data have been provided to the healthcare practitioners about the drug-drug interactions of cannabinoids with other prescription medications. In general, cannabinoids are usually well tolerated, but the bidirectional effects may be expected with concomitant administered agents via affected membrane transporters (glycoprotein p, breast cancer resistance proteins) and metabolizing enzymes (Cytochrome P450 and UDP- glucuronosyltransferases). The caveats should be undertaken to closely monitor the responses of cannabis users with certain drugs to guard their safety, especially for the elderly and people with chronic diseases or kidney and liver conditions.

There are dozens of approved, investigational and experimental antiviral agents. Many of these agents cause serious side effects, which can be revealed only after drug administration. Identification of the side effects prior to drug administration is challenging. Here we describe an ex vivo approach for studying immuno- and neuro-modulatory properties of antiviral agents, which could be associated with potential side effects of these therapeutics. The approach combines drug toxicity/efficacy tests and transcriptomics, which is followed by cytokine and metabolite profiling. We demonstrated the utility of this approach with several examples of antiviral agents. We also showed that the approach can utilize different immune stimuli and cell types. It can also include other omics techniques, such as genomics and epigenomics, to allow identification of individual markers associated with adverse reactions to antivirals with immuno- and neuro-modulatory properties.

COVID-19 pandemic has spurred intense research efforts to identify effective treatments for SARS-CoV-2. In silico studies have emerged as a powerful tool in the drug discovery process, particularly in the search for drug candidates that interact with various SARS-CoV-2 receptors. These studies involve the use of computer simulations and computational algorithms to predict the potential interaction of drug candidates with target receptors. The primary receptors targeted by drug candidates include the RNA polymerase, main protease, spike protein, ACE2 receptor, TMPRSS2, and AP2-associated protein kinase 1. In silico studies have identified several promising drug candidates, including Remdesivir, Favipiravir, Ribavirin, Ivermectin, Lopinavir/Ritonavir, and Camostat mesylate, among others. The use of in silico studies offers several advantages, including the ability to screen a large number of drug candidates in a relatively short amount of time, thereby reducing the time and cost involved in traditional drug discovery methods. Additionally, in silico studies allow for the prediction of the binding affinity of drug candidates to target receptors, providing insight into their potential efficacy. However, it is crucial to consider both the advantages and limitations of these studies and to complement them with experimental validation to ensure the efficacy and safety of identified drug candidates.

Drugs-drugs interactions(DDI) are entities composed of different chemical substructures (functional groups). In existing methods that predict drug–drug interactions based on the usage of substructures, each node is considered the center of a substructure, and adjacent nodes eventually become centers of similar substructures, resulting in redundancy. Furthermore, the significant differ- ences in structure and properties among compounds can lead to unrelated pairings, making it difficult to integrate information. This heterogeneity negatively affects the prediction results. To address these issues, we propose a drug–drug interaction prediction method based on substructure signature learning (DDI-SSL). This method extracts useful information from local subgraphs surrounding drugs and effectively utilizes substructures to assist in predicting drug side effects. Additionally, a deep clustering algorithm is used to aggregate similar substructures, allowing any individual subgraph to be reconstructed using this set of global signatures. Furthermore, we developed a layer-independent collaborative attention mechanism to model the mutual influence between drugs, generating signal strength scores for each class of drugs to mitigate noise caused by heterogeneity. Finally, we evaluated DDI-SSL on a comprehensive dataset and demonstrated improved performance in DDI prediction compared to state-of-the-art methods.

The study aimed to identify clinical pharmacology patterns of prescribed and taken medications in older cardiovascular patients using electronic health records (EHRs) (n = 704) (2019–2022). Medscape Drug Interaction Checker was used to identify pairwise drug-drug interactions (DDIs). Prevalence rates of DDIs were 73.5% and 68.5% among taken and prescribed drugs, respectively. However, total number of DDIs was significantly higher among prescribed medications compared with the list of taken drugs (p < 0.05). Serious DDIs comprised 16% and 7% of all DDIs among prescribed and taken medications, correspondingly (p < 0.05). Median DDI numbers between prescribed versus taken medications were Me = 2, IQR 0-7 and Me = 3, IQR 0-7 per record, respectively. Prevalence of polypharmacy was significantly higher among prescribed medications compared with taken medications (p < 0.05). Women were taking significantly more drugs and had higher rates of polypharmacy and DDIs (p < 0.05). No sex-related differences were observed in the list of prescribed medications. ICD code U07.1 (COVID-19, virus identified) was associated with the highest median DDI number per record. Further research is warranted to improve EHR structure, patient engagement in reporting adverse drug reactions, and genetic profiling of patients to avoid potentially serious DDIs.

Background: This article evaluates the perception of drug use control and strategies in Valencia City (Spain) in a general and clinical population, in two independent studies. Material and Methods: 1071 people participated. In the Study 1 (n= 924) the entire sample came from general population (GP), and in the Study 2 (n=147), 68 were drug users being treated in an Addictive Behaviors Unit (ABU), and 79 people of the GP. The drug use control perception and strategies in both subgroups were compared. The participants filled in the Drug Use Strategies Scale and a Drug Use Survey. Results: A high level of perception of drug control in GP was obtained (72,7% in the Study 1 and 67,5% in the Study 2), and 32.5% in ABU subgroup. People in the PG and drug users in treatment differ in some control strategies. A predictive profile of the perception of control was obtained for the Study 2. Conclusion: The high degree of perception of controlling drug use in the GP, and partially in drug users being treated, and the specific control strategies reported suggests that moderate use and drug control strategies are a great value alternative to bear in mind compared to abstinence.

Flavonoids comprise a large group of structurally diverse polyphenolic compounds of plant origin and are abundantly found in human diet such as fruits, vegetables, grains, tea, dairy products, red wine and so on. Major classes of flavonoids include flavonols, flavones, flavanones, flavanols, anthocyanidins, isoflavones, and chalcones. Owing to their potential health benefits and medicinal significance, flavonoids are now considered as an indispensable component in a variety of medicinal, pharmaceutical, nutraceutical, and cosmetic preparations. However, flavonoids play a significant role in preventing cardiovascular diseases (CVDs), which could be mainly due to their antioxidant, antiatherogenic, and antithrombotic effects. Epidemiological and in vitro/in vivo evidences of antioxidant effects support the cardioprotective function of dietary flavonoids. Further, the inhibition of LDL oxidation and platelet aggregation following regular consumption of food containing flavonoids and moderate consumption of red wine might protect against atherosclerosis and thrombosis. A study suggests that daily intake of 100 mg of flavonoids through diet may reduce the risk of developing morbidity and mortality due to coronary heart disease (CHD) by approximately 10%. This review summarizes dietary flavonoids with their sources and potential health implications in CVDs including various redox-active cardioprotective (molecular) mechanisms with antioxidant effects. Pharmacokinetic (oral bioavailability, drug metabolism), toxicological and therapeutic aspects of dietary flavonoids are also addressed herein with future directions for the discovery and development of useful drug candidates/ therapeutic molecules.

In 2019, Chinese government implemented the first round of National Centralized Drug Procurement (NCDP) pilot (so-called "4+7" policy) in mainland China, achieved a prominent price reduction of 52% on average for 25 bidding winning products. Under cross-price elasticity theory, the price behavior of pharmaceutical enterprises for policy-related drugs might change. This study used drug purchasing data from the Centralized Drug Procurement Survey in Shenzhen 2019, and applied single-group Interruption Time Series (ITS) design to examine the impact of "4+7" policy on the drug price index (DPI) of policy-related drugs. The ITS analysis showed that the DPI of winning (-0.183 per month, p&lt;0.0001) and non-winning (-0.034 per month, p=0.046) products significantly decreased after the implementation of "4+7" policy. No significant difference was found for the immediate change of DPI for alternative drugs (p=0.537), while a significant decrease in change trend was detected in the post-"4+7" policy period (-0.003 per month, p=0.014). The DPI of the overall policy-related drugs significantly decreased (-0.261 per month, p&lt;0.0001) after "4+7" policy. These findings indicate that the price behavior of pharmaceutical enterprises changed under NCDP policy, while the price linkage effect is still limited. It is necessary to further expand the scope of centralized purchased drugs and strengthen the monitoring of related drugs regarding price change and consumption structure.

Nanoprecipitation by liquid anti-solvent precipitation is one of the most versatile methods to produce pure drug nanoparticles (PDNPs) owing to the ability to optimize the properties of the product. Nevertheless, nanoprecipitation shows broad particle size distribution and low physical stability, leading to high batch-to-batch variability and challenging the bench-to-bedside translation. Microfluidics has emerged as a powerful tool to produce PDNPs in a simple, reproducible, and cost-effective manner with excellent control over NP size. In this work, we designed and fabricated T- and Y-shaped Si-made microfluidics device and used it to produce pure NPs of three kinase inhibitors of different lipophilicity and water-solubility, namely imatinib, dasatinib and tofacitinib, without the use of colloidal stabilizers. PDNPs display sizes in the 90-350 nm range (dynamic light scattering) and a rounded shape (high-resolution scanning electron microscopy). Analysis by X-rays diffraction and differential scanning calorimetry confirmed that this method results in highly amorphous NPs. In addition, we show that the flow rate of solvent, the anti-solvent, and the channel geometry of the device play a key role in the size of the generated NPs.

The development of targeted therapeutics for cancer continues to receive intense research attention as laboratories and pharmaceutical companies seek to develop drugs and technologies that improve treatment efficacy and mitigate harmful side effects. In the aftermath of World War I, it was discovered that mustard gas destroys rapidly dividing cells and could be used to treat cancer. Since then, chemotherapy has remained a predominant treatment for cancer; however, the destruction of dividing cells throughout the body yields devastating side effects including off-target damage of the digestive tract, bone marrow, skin, and reproductive tract. Furthermore, the high mutation rate of cancerous cells often renders chemotherapy ineffective long-term. Therapies with improved specificity, localization, and efficacy are redefining cancer treatment. Herein, we define and summarize the principal advancements in targeted cancer treatment and briefly comment on the march towards personalized medicine in the treatment of human cancer.

There are guidelines on lactation following maternal analgo-sedative exposure, but these do not consider the effect of maternal fasting, nor fluid abstention on human milk macronutrient composition. We therefore performed a structured search (PubMed) on ‘human milk composition’ and screened title, abstract and full paper on ‘fasting’ or ‘abstention’ and ‘macronutrient composition’ (lactose, protein, fat, solids, triglycerides, cholesterol). This resulted in 6 papers and one abstract related to religious fasting (n=129 women) and observational studies in lactating women (n=23, healthy volunteers, fasting). These data reflect two different ‘fasting’ patterns: an acute (18-25h) model in 71 (healthy volunteers, Yom Kippur/Ninth of Av) women and a chronic fasting (Ramadan) model in 81 women. Changes were most related to electrolytes and were moderate, with almost no changes in macronutrients during acute fasting. We therefor conclude that neither short term fasting nor fluid abstention (18-25h) affect human milk macronutrient composition, so that women can be reassured when this topic were raised during consulting. Besides the nutritional relevance, this also matters as clinical research samples – especially to estimate analgo-sedative exposure by lactation - are commonly collected after maternal procedural sedation, associated with maternal fasting and physiology-based pharmacokinetic (PBPK) models assume stable human milk composition.

SARS-CoV-2 is the betacoronavirus responsible for the COVID-19 pandemic. It was listed as a potential global health threat by WHO due to high mortality, high basic reproduction number and lack of clinically approved drugs and vaccines for COVID-19. The genomic sequence of the virus responsible for COVID-19, as well as the experimentally determined three dimensional structure of the Main protease (Mpro) are available. The reported structure of the target Mpro was utilized in this study to identify potential drugs for COVID-19 using molecular docking based virtual screening of all approved drugs. The results of this study confirm preliminary reports that some of the drugs approved for treatment of other viral infections have the potential for treatment of COVID-19. Selected antiviral drugs, approved for human therapeutic applications, were ranked for potential effectiveness against COVID-19, based on predicted binding energy to the target Mpro of SARS-CoV-2, and novel candidates for drug repurposing were identified in this study. In addition, potential mechanisms for beneficial off target effects of some drugs in clinical trials were identified by using molecular docking.

(1) Background: Hemispherotomy is the generally accepted treatment for hemispheric drug resistant epilepsy (DRE). Lateral or vertical approaches are performed according to the surgeon's preference. Multiples technical variations have been proposed since Delalande first described his vertical technique. We propose a sub-insular variation of the vertical parasagittal hemispherotomy (VPH) and describe our case series of patients operated on with this procedure. (2) Methods: a continuous series of patients operated on by the senior author (CR) with the modified sub-insular VPH was retrospectively analyzed. We report the demographic pre-operative characteristics, epilepsy and functional outcome as well as the surgical complications.(3) Results: Twenty-five patients were operated on between August 2008 and August 2023, twenty-three of them have at least a 3-month follow-up. On this last group 86,3% of patients were seizure-free. Only two patients developed post-operative hydrocephalus (8.7%), all patients with autonomous preoperative walk and 86,3% of the total series were able to walk without assistance (86.3%) and perform adapted social activities at last follow-up.(4) Conclusion: The modified vertical parasagittal sub-insular hemispherotomy is a successful surgical technique for hemispheric DRE with seizure freedom rates similar to the biggest series reported in the literature. Compared to other series, patients operated with our modified technique had a lower rate of postoperative hydrocephalus and excellent long-term motor and cognitive outcome.

Skin cancer is a disease that reflects most malignant neoplasms worldwide, whose rate of involvement has increased significantly in recent decades. Treatments that offer efficient results with attenuation of adverse reactions and greater patient compliance have been the subject of many systematic investigations. This review aims to qualitatively present published works that showed new strategies to increase the penetration power of natural substances through the skin in the topical treatment against skin cancer. In this sense, nanoengineering mechanisms, the iontophoresis technique, and photodynamic therapy using natural products (NP) contribute to new knowledge about substance carrier strategies in topical treatments against skin cancer.

Increasing HIV drug resistance (DR) among children with HIV (CHIV) on antiretroviral treatment (ART) is concerning. CHIV ages 1-14 years enrolled March 2019 to December 2020 from five facilities in Kisumu County, Kenya were included. Children were randomized 1:1 to control (standard-of-care) or intervention (point-of-care viral load (POC VL) testing every three months with targeted genotypic drug resistance testing (DRT) for VF (&gt; 1000 copies/ml)). A multidisciplinary committee reviewed CHIV with DRT results and offered treatment recommendations. We describe DR mutations and present logistic regression models to identify factors associated with clinically significant DR. We enrolled 704 children in the study; median age was 9 years (interquartile range (IQR) 7,12), 344 (49%) were female and median time on ART was 5 years (IQR 3, 8). During the study period, 106 (15%) children had DRT results (84 intervention and 22 control). DRT detected mutations associate with DR in all participants tested, with 93 (88%) having major mutations, including 51 (54%) with dual class resistance. A history of VF in prior 2 years (adjusted odds ratio (aOR) 11.1; 95% confidence interval (CI) 6.3, 20.0) and less than 2 years on ART at enrollment (aOR 2.2; 95% CI 1.1, 4.4) were associated with increased odds of major DR. DR is highly prevalent among CHIV on ART with VF in Kenya. Factors associated with drug resistance may be used to determine which children should be prioritized for DRT.

The prevalence of antiviral drugs (AVTs) has seen a substantial increase in response to the COVID-19 pandemic, leading to heightened concentrations of these pharmaceuticals in wastewater systems. The hydrophilic nature of AVTs has been identified as a significant factor contributing to the low degradation efficiency observed in wastewater treatment plants. This characteristic often necessitates the implementation of additional treatment steps to achieve complete degradation of AVTs. Semiconductor-based photocatalysis has garnered considerable attention due to its promising potential in achieving efficient degradation rates and subsequent mineralization of pollutants, leveraging the inexhaustible energy of sunlight. However, in recent years, there have been few comprehensive reports that have thoroughly summarized and analyzed the application of photocatalysis for the removal of AVTs. This review commences by summarizing the types and occurrence of AVTs. Furthermore, it places a significant emphasis on delivering a comprehensive summary and analysis of the characteristics pertaining to the photocatalytic elimination of AVTs. Ultimately, the review sheds light on the identified research gaps and key concerns, offering invaluable insights to steer future investigations in this field.

Background: The clinical outcomes of antiretroviral drugs may be modified by drug interactions; thus, it is important to update the drug interactions in people living with HIV. Aim: To update clinically relevant drug interactions in people living with HIV on antiretroviral therapy. Methods: A systematic review in Medline/PubMed database from July 2017 to December 2022, using the Mesh terms: Anti-retroviral agents and drug interactions or herb-drug interactions or food-drug interactions. Publications with drug interactions in humans, in English or Spanish, and with full text were retrieved. The clinical relevance of drug interaction was grouped into 5 levels according to gravity and probability of occurrence. Results: 361 articles were identified and 148 were included, which allowed the identification of 894 drug interaction pairs. Among these 894 drug pairs, 355 have not been identified previously; and 89 (25.1%) and 72 (20.2%) were of levels 1 and 2, respectively. In addition, for 197 (55.5%) pairs the mechanism was pharmacokinetic. The non-nucleoside reverse transcriptase inhibitors (NNRTIs) and the protease inhibitors (PIs) with 91 (25.6%) and 76 (21.4%) pairs, respectively were more frequent. Conclusions: In people living with HIV on antiretroviral therapy, we identify 355 new drug interaction pairs, of them 161 (45.3%) are assessed as levels 1 and 2 and thus, clinically relevant; a figure that is lower compared to 2014-2107 update. The pharmacokinetic mechanism is the most frequently identified. The non-nucleoside reverse transcriptase inhibitors (NNRTIs) and the protease inhibitors (PIs) are the antiretroviral groups with the highest number of clinically relevant drug interactions.

Drug-resistant tuberculosis (DR-TB) is still a major public health concern in South Africa. Mutations in M. tuberculosis can cause varying levels of phenotypic resistance to anti-TB medications. There have been no prior studies on gene mutations and the genotyping of DR-TB in the rural Eastern Cape Province; hence we aimed to identify DR-TB mutations, genetic diversity and allocated lineages among patients in this area. Using Xpert® MTB/RIF, we assessed the rifampin-resistance of sputum samples collected from 1157 patients suspected of having tuberculosis. GenoType MTBDR plus VER 2.0 was used for the detection of mutations causing resistance to anti-TB medications. The next step was to spotlight type 441 isolates. The most prevalent rifampin resistance-conferring mutations were in rpoB codon S531L, in INH-resistant strains, katG gene at codon S315TB and the inhA gene at codon C-15TB had highest mutations; 54.5% and 24.7%, respectively. In addition, 24.6% of strains showed mutations in both the rpoB and inhA genes, while 69.9% of strains showed mutations in both the katG and rpoB genes. Heteroresistance was seen in 17.9% of all cases in the study. According to spoligotyping analysis, Beijing families predominated. Investigating the evolutionary lineages of M. tuberculosis isolates can be done using the information provided by the study's diversity of mutations. In locations where these mutations have been discovered, decision-making regarding standardization of treatment regimens or individualized treatment may be aided by the detection frequency of rpoB, katG, and inhA mutations in various study areas.

Antibody-drug conjugates (ADCs) have provided new therapeutic options and significant promise for patients with cancers, particularly where existing treatments are limited. Substantial effort in ADC development is underway globally, with 13 ADCs currently approved and many more in development [1]. Therapeutic benefits of ADCs leverage the ability to selectively target cancer cells through antibody binding, resultant relative sparing of non-malignant tissues, and the targeted delivery of a cytotoxic payload. Consequently, this drug class has demonstrated activity in multiple malignancies refractory to standard therapeutic options [1-4]. Despite this, limitations exist, including narrow therapeutic windows, unique toxicity profiles, development of therapeutic resistance, and appropriate biomarker selection [5-7]. This review will describe the development of ADCs, their mechanisms of action, pivotal trials, and approved indications and identify common themes. Current challenges and opportunities will be discussed for this drug class in cancer therapeutics at a time when significant developments in antibody therapies, immunotherapy and targeted agents are occurring.

Drug induced liver injury (DILI) is the most common cause of acute liver failure and 5-10% of patients hospitalized for jaundice are diagnosed with DILI. For a diagnosis of DILI to be made, there should be exclusion of other etiologies of liver injury and the use of a precipitator drug, latency of symptoms, and resolution of liver injury once the offending drug is identified and discontinued. In our case report, we present a patient with idiosyncratic hepatocellular pattern DILI after two doses of ocrelizumab for treatment of multiple sclerosis. Ocrelizumab was given 16 and 27 days prior to the onset of icterus, jaundice, and fatigue, in a patient without the evidence of prior exposure to hepatitis B virus. At presentation labs revealed severe acute hepatocellular liver injury with R factor of 30.42, marked hyperbilirubinemia, and transient hypoalbuminemia. No evidence of latent or active hepatitis B infection was detected. Drug dechallenge led to return of liver chemistries to near-normal levels 31 days after the onset of her symptoms. This case indicates DILI diagnosis associated with the use of ocrelizumab, and warrants careful monitoring of liver functions in patients even in the absence of hepatitis B.

Autoimmune disorders and some types of blood cancer originate when B lymphocytes malfunction. In particular, when B cells produce antibodies recognizing the body’s proteins, it leads to various autoimmune disorders. And when B cells of various developmental stages transform into cancer cells, it results in blood cancers, including multiple myeloma, lymphoma, and leukemia. Thus, new methods of targeting B cells are required for various patient groups. Here, we used protein kinase inhibitors alectinib, brigatinib, ceritinib, crizotinib, entrectinib, and lorlatinib previously approved as drugs treating anaplastic lymphoma kinase (ALK)-positive lung cancer cells. We hypothesized that the same inhibitors will efficiently target leukocyte tyrosine kinase (LTK)-positive, actively protein-secreting mature B lymphocytes, including plasma cells. We isolated CD19-positive human B cells from the blood of healthy donors and used two alternative methods to stimulate cell maturation toward plasma cells. Using cell proliferation and flow cytometry assays, we found that ceritinib and entrectinib eliminate plasma cells from B cell populations. Alectinib, brigatinib, and crizotinib also inhibited B cell proliferation, while lorlatinib had no or limited effect on B cells. More generally, we concluded that several drugs previously developed to treat ALK-positive malignant cells can be also used to treat LTK-positive B cells.

Increasing environmental distress is associated with a growing asthma incidence and, with no treatments available, montelukast (MTK) – an antagonist of the cysteinyl leukotrienes receptor 1 – is widely used in the management of symptoms among adults and children. Recently, new molecular targets have been identified and MTK has been proposed for repurposing in other therapeutic applications, with several ongoing clinical trials. The proposed applications include neuroinflammation control, which could be explored in some neurodegenerative disorders, such as Alzheimer’s and Parkinson’s diseases (AD and PD). However, this drug has been associated with an increasing number of reported neuropsychiatric adverse drug reactions. Besides, and despite being on the market since 1998, MTK metabolism is still poorly understood and the mechanisms underlying neuropsychiatric ADRs remain unknown. We review the role of MTK as modulator of leukotriene pathways and systematize the knowledge about MTK metabolism. Known toxic effects of MTK are discussed, and repurposing applications are presented comprehensively, with a focus on AD and PD.

Therapeutic Drug Monitoring (TDM) is potentially a useful tool that can be employed to increase the efficacy and decrease the toxicity of antifungal drugs. The aim of this narrative review is to provide an overview of the current use of TDM in clinical practice, and to present the evidence available regarding its use in proactive clinical settings for preventing and managing treatment failure. This review also presents the existing evidence regarding the association of various clinical outcomes with specific thresholds of drug concentrations in everyday practice. Articles concerning the use of TDM of triazoles in the treatment of fungal infections were retrieved through an electronic search using PubMed. In clinical practice, TDM has an increasingly important role in the management of antifungal drugs as a consequence of the improvement in the knowledge of the pharmacokinetics and pharmacodynamics of these drugs. The currently available evidence shows a direct exposure-response relationship for triazoles, though the PK/PD profile is unpredictable. Current guidelines and treatment consensus statements recommend the proactive TDM of voriconazole, posaconazole, and itraconazole to optimize dosage regimens and improve outcomes for adult and pediatric patients.

The treatment of peptides has played an important role in clinical practice since the discovery of insulin therapy in the 1920s. Over 60 peptide drugs are approved in the United States (US and other regional mar-kets, and peptides continue to undergo drug discovery steadily. Peptide research and development has lev-eraged a wider range of structures known from other plant sources, via pharmacology and medicinal molecular biology, beyond its conventional focus on individual endogenous peptides. We build a comprehensive database of peptides that have met scientific studies with more than 150 constantly evolving peptides. Here we provide a simple overview of the peptide-based drug therapy environment, comprising evolutionary points of view, structural properties, operational thresholds, and explanation of the therapeutic area.

Randomized clinical trials represent the gold standard in therapeutic research. Nevertheless, observational cohorts of patients treated for multidrug-resistant (MDR) or rifampin-resistant (RR) tuberculosis (TB) also play an important role in generating evidence to guide MDR/RR TB Generally, summary exposure classifications (e.g., ‘ever versus never’, ‘exposed at baseline’) have been used to characterize drug exposure, in the absence of detailed longitudinal data on MDR-TB regimen These summary classifications, along with an absence of data on covariates that change throughout the course of treatment, constrain researchers’ ability to answer the most relevant questions while accounting for known This paper highlights the importance of regimen changes in improving inference from observational studies of longer MDR-TB treatment regimens and offers an overview of the data and analytic strategies required to do

BACKGROUND: From March to April 2020, Spain was the center of the SARS-CoV-2 pandemic, particularly Madrid with approximately 30% of the cases in Spain. The aim of this study is to report the suspected serious adverse drug reactions (SADRs) in COVID-19 patients versus non-COVID-19 patients detected by the prospective pharmacovigilance program based on automatic laboratory signals (ALSs) in the hospital (PPLSH) during that period. We also compared the results with the suspected SADRs detected during the same period for 2019. METHODS: All ALSs that reflected potential SADRs (including neutropenia, pancytopenia, thrombocytopenia, anemia, eosinophilia, leukocytes in cerebrospinal fluid, hepatitis, pancreatitis, acute kidney injury, rhabdomyolysis and hyponatremia were prospectively monitored in hospitalized patients during the study periods. We analyzed the incidence and the distribution of causative drugs for the COVID-19 patients. RESULTS: The incidence rate of SADRs detected in the COVID-19 patients was 760.63 (95% CI 707.89–816.01) per 10,000 patients, 4.75-fold higher than the SADR rate for non-COVID-19 patients (160.15 per 10,000 patients,95% CI 137.09–186.80), and 5.84-fold higher than the SADR rate detected for the same period in 2019 (130.19 per 10,000 patients, 95% CI 109.53–154.36). The most frequently related drugs were tocilizumab (59.84%), dexketoprofen (13.93%), azithromycin (8.43%), lopinavir-ritonavir (7.35%), dexamethasone (7.62%), and chloroquine/hydroxychloroquine (6.91%). CONCLUSIONS: The incidence rate of SADRs detected by the PPSLH in patients with COVID-19 was 4.75-fold higher than that of the non-COVID-19 patients. Caution is recommended when using medications for COVID-19 patients, especially drugs that are hepatotoxic, myotoxic, and those that induce thromboembolic events.

Most recently, an outbreak of severe pneumonia caused by the infection of 2019-nCoV, a novel coronavirus first identified in Wuhan, China, imposes serious threats to public health. Many important aspects about 2019-nCoV remain largely unknown, among which, the limitation of antiviral therapies represents one of the most critical problems. More recently, it was confirmed that human ACE2 is the receptor for the entry of 2019-nCoV into lower respiratory tract epithelial cells. Give this observation, it is thus expected that the virus could be inhibited if we decrease the expression of ACE2. Here by screening two databases, Connectivity Map (CMap) and our JeaMoon Map (JMap), we identified a number of candidate agents that decrease ACE2 expression. CMap analysis identified 5 compounds, among which, Azathioprine is a possible therapeutic strategy for anti-2019-nCoV. Moreover, JMap analysis revealed a number of comounds, biologics, and traditional Chinese medicine, among which, Andrographis, Urtica, Sambucus, Astragalus, valproic acid, butyrate, and epoxomicin represent the most significant and possible strategies for anti-2019-nCoV therapies. This study provides a number of clues and possible therapeutic strategies for 2019-nCoV prevention and treatment.

Many approaches have been developed over time to counter the bioavailability limitations of poorly soluble drugs. With advances in nanotechnology in recent decades, science and industry have been approaching this issue through the formulation of drugs as nanocrystals, which consist of pure drugs and a minimum of surface active agents required for stabilization. They are carrier-free submicron colloidal drug delivery systems with a mean particle size in the nanometer range, typically between 10 and 800 nm. By reducing particle size to nanoscale, the particle surface area available for the molecule dissolution in the direction of dissolution medium is increased, and thus bioavailability is enhanced. This approach has proven successful, as demonstrated by the number of such drug products on the market. R&D and industry have offered many technological solutions to reduce the particle size to nanoscale, and also devised solutions for the handling of particle of nanodimensions, such as methods to accurately measure nanoparticle size and techniques to prevent physicochemical and stability related problems, such as aggregation. The present work provides an overview of the more recent achievements in improving the bioavailability of poorly soluble drugs according to their administration route, and describes the methods developed to overcome physicochemical and stability related problems.