The new class of mRNA vaccines is studied extensively, leading to many novel prospects based on the pivotal success of vaccines against the SARS-Cov-2 virus. Many novel mechanisms of immune response discovered have led to the possibility of mutation-resistant vaccines by combining multiple conserved epitopes as antigens to manipulate the T-cell and B-cell responses; these antigens can also come from different organisms, providing protection against numerous diseases and of most tremendous significance is the utility of mRNA vaccines in preventing and treating more than 100 autoimmune disorders. This is a significant humanitarian breakthrough given the ease and faster and low cost of mRNA vaccines, being a chemical entity. This paper provides a prospective analysis of mRNA vaccines with much broader applications than anticipated when these vaccines entered treating SARS-Cov-2 infections.

Gene or genome editing (GE) revises, removes, or replaces a mutated gene at the DNA level; it is a tool. Gene therapy (GT) offsets mutations by introducing a "normal" version of the gene into the body while the diseased gene remains in the genome; it is a medicine. So far, no in vivo GE product has been approved, as opposed to 22 GT products approved by the FDA. However, several are under development, and the FDA anticipates their fast entry, as evidenced by the FDA's actions of issuing GE-specific guidelines. The potential of GE in treating diseases far supersedes any other modality due to the recognized role of genes in most human diseases. While GT products add missing genes, GE tools can overcome many aberrations. However, GE presents many safety challenges, including off-target impact, delivery consistency, and long-term effects of gene-fixing. However, most of these concerns will be resolved once the FDA begins approving them, leading to hundreds of products capable of treating untreatable diseases.

Gene or genome editing (GE) revises, removes, or replaces a mutated gene at the DNA level; it is a tool. Gene therapy (GT) offsets mutations by introducing a "normal" version of the gene into the body while the diseased gene remains in the genome; it is a medicine. So far, no GE product has been approved, as opposed to 22 GT products that cost up to millions of dollars per dose. The FDA has recently added a guideline specific to gene editing that should be understood to enable faster development of GE products; at the same time, the FDA also needs to bring more clarification and make several amendments to this guideline to make it more rational.

The FDA has concluded that a biosimilar candidate capable of demonstrating pharmacodynamic biomarkers in a healthy subject need not be tested for clinical efficacy in patients, regardless of if the biomarker correlates with clinical response. Since monoclonal antibodies (mAbs) do not trig-ger pharmacodynamic response, they can be substituted with robust functional disqualifying them for this waiver that can be overcome by allowing comparison of functional properties that eventually result in clinical response. This suggestion is based on the FDA's preference for more sensitive testing methods. However, clinical efficacy testing in patients is the least sensitive method, as confirmed by statistical modeling, a fact that regulatory agencies need to admit. In this paper, I present a logical and rational argument to establish the biosimilarity of products that do not have pharmacodynamic biomarkers based on their orthogonally proven functional bio-similarity. This understanding will significantly lower the development cost of biosimilars, a goal that the FDA outlined in all its guidance. Keywords: biosimilarity 1; biosimilars 2; pharmacodynamic biomarkers 3; monoclonal antibodies 4; FDA 5; clinical efficacy testing in healthy subjects 6; clinical efficacy testing in patients 7; functional assays 8; receptor binding 9.

Scientific, technical and bioinformatics advances have made it possible to establish analytics-based molecular biosimilarity for the approval of biosimilars. If the molecular structure and other product- and process-related attributes are comparable within the limits of testing then a biosimilar candidate would have safe safety and efficacy as its reference products. The current model of animal and human testing becomes redundant since all of these studies have much lower sensitivity and reproducibility in confirming biosimilarity. The recent AI-based protein structure prediction model has confirmed that the 3D structure can be predicted from the amino acid sequence, reducing the need for structural analysis; however, the new test methods based on MS are millions of times more sensitive and accurate. While the regulatory agencies have begun waiving animal testing and, in some cases, clinical efficacy testing, removing clinical pharmacology profiling brings a dramatic paradigm shift, reducing development costs without compromising safety and efficacy. Also shared is a list of 160+ products ready to enter as biosimilars. Major actions from regulatory agencies and developers are required to make this paradigm shift.

Additionally, the League’s four observing states, with an additional population of 1.7 billion, if join the group, will form the world’s largest consortium to enhance the accessibility of biotechnology drugs. This effort will also remove the criticism of regulatory agencies that are well-equipped to regulate these products. This should also be a significant economic incentive to consortium states to develop these products, particularly biosimilars, to capture the most critical market anticipated. This plan will also reduce the cost burden on regulatory agencies. The program is divided into two classifications; for products sold in any of the Stringent Regulatory Authority (SRA) states, the registration is automatic but with several requirements that assure ongoing pharmacovigilance; this also applies to copies of reference products or biosimilars. For non-SRA sourcing, a stepwise plan requiring a rapporteur review, as practiced by the EMA and a third-party cGMP audit, is necessary to ensure data and product integrity. However, to reduce the cost burden, approval of these products is based on current scientific findings as presented in this paper.

In the dynamic landscape of drug discovery, Computer-rAided Drug Design (CADD) emerges as a transformative force, bridging the realms of biology and technology. This paper overviews CADD's historical evolution, categorization into structure-based and ligand-based approaches, and its crucial role in rationalizing and expediting drug discovery. As CADD advances, incorporating diverse biological data and ensuring data privacy become paramount. Challenges persist, demanding the optimization of algorithms and robust ethical frameworks. Integrating Machine Learning and Artificial Intelligence amplifies CADD's predictive capabilities, yet ethical considerations and scalability challenges linger. Collaborative efforts and global initiatives, exemplified by platforms like Open Source Malaria, underscore the democratization of drug discovery. The convergence of CADD with personalized medicine offers tailored therapeutic solutions, though ethical dilemmas and accessibility concerns must be navigated. Emerging technologies like quantum computing, immersive technologies, and green chemistry promise to redefine the future of CADD. The trajectory of CADD, marked by rapid advancements, anticipates challenges in ensuring accuracy, addressing biases in AI, and incorporating sustainability metrics. The paper concludes by highlighting the need for proactive measures in navigating the ethical, technological, and educational frontiers of CADD to shape a healthier, brighter future in drug discovery.

Device-mediated, non-invasive drug delivery across the blood-brain barrier (BBB) represents a significant advancement in treating neurological diseases. The BBB is a tightly packed layer of endothelial cells that shields the brain from harmful substances in the blood, allowing necessary nutrients to pass through. It is a highly selective barrier, which poses a challenge to delivering therapeutic agents into the brain. Several non-invasive techniques and devices have been proposed or investigated to enhance drug delivery across the BBB. This paper presents the current state of the art and case studies that address the pharmacology, technology, delivery systems, regulatory approval, ethical concerns, and future possibilities.

In the realm of modern drug discovery, the integration of chemoinformatics and quantitative structure-activity relationship (QSAR) modeling has emerged as a formidable alliance, enabling researchers to harness the vast potential of machine learning (ML) techniques for predictive molecular design and analysis. This review delves into the fundamental aspects of chemoinformatics, elucidating the intricate nature of chemical data and the crucial role of molecular descriptors in unveiling the underlying molecular properties. Molecular descriptors, including 2D fingerprints and topological indices, in conjunction with the structural-activity relationships (SARs), play a pivotal role in unlocking the pathway to small-molecule drug discovery. Technical intricacies of developing robust ML-QSAR models, including feature selection, model validation, and performance evaluation, are discussed herewith. Various ML algorithms, such as regression analysis and support vector machines, are showcased in the text for their ability to predict and comprehend the relationships between molecular structures and biological activities. This review serves as a comprehensive guide for researchers, providing an understanding of the synergy between chemoinformatics, QSAR, and ML. By embracing these cutting-edge technologies, predictive molecular analysis holds promise for expediting the discovery of novel therapeutic agents in the pharmaceutical sciences.

Reinventing approved therapeutic proteins for a new dose, a new formulation, a new route of administration, an improved safety profile, a new indication, or a new conjugate with a drug or a radioactive source is a creative approach to benefit from the billions spent on developing new therapeutic proteins. These new opportunities were created only recently with the arrival of AI/ML tools and high throughput screening technologies. Furthermore, the complex nature of proteins offers mining opportunities that are not possible with chemical drugs; bringing in newer therapies without spending billions makes this path highly lucrative financially while serving the dire needs of humanity. This paper analyses several practical reinventing approaches and suggests regulatory strategies to reduce development costs significantly. This should enable the entry of hundreds of new therapies at affordable costs.

Biological drugs are inaccessible to more than 80% of the world population due mainly to their high costs; this is a significant concern of the World Health Organization. Biosimilars are supposed to reduce the cost burden, but their approval process is complex, including expensive and irrelevant studies. While the Stringent Regulatory Authorities (SRAs) have adopted the guidance of the FDA or EMA, such adoptions are neither necessary nor practical for the rest of the world (ROW). We present a science-driven, rational approach to formulate regulatory guidelines that will enable faster biosimilars' entry into the ROW without compromising their safety and efficacy. The key recommendations include removing animal and safety efficacy testing, making analytical assessment more robust, and cGMP compliance assured through third-party audits. The ROW countries are also recommended to initiate a rapporteur system available in the EU, to overcome the biases and assure state-of-the-science evaluation as the common understanding of the critical quality attributes evolves. It is anticipated that stronger regional agencies like those in the MENA region, with the leadership of the Kingdom of Saudi Arabia, will help propel the idea faster across the globe.

Human Immunodeficiency Virus (HIV) infection and its latent recurrent infection make vaccine development a significant challenge for traditional vaccines; however, the mRNA delivery of multiepitope antigens capable of creating antibodies against the surface proteins of HIV-1 and HIV-2 along with their respective Nef proteins, responsible for the latent infection can bring a most effective, as well as affordable solution to the unresolved epidemics of HIV. In this paper, we present the complete design of a multiepitope vaccine to prevent acute infections and inactivate Nef proteins that block the MHC-1 and MHC-2 pathways to hide the virus for later reinfection.

The recent setbacks in the withdrawal and approval delays of antibody treatments of neurodegenerative disorders (NDs) can be attributed to their poor entry across the blood-brain barrier (BBB). While several invasive and noninvasive approaches can be used to make the BBB more permeable, their inconsistency makes them a poor choice; however, if an antibody is chemically modified to attach a transferrin molecule that enhances its permeation across the BBB, this could be the most practical and useful modification that can transform the science of ND treatment. Antibodies can be conjugated with transferrin in vitro, but preferably through mRNA, as we propose to convert current treatments to bring faster treatment options. The mRNA technology yields products at a meager cost, which is a consideration for the affordability of these drugs globally. This paper provides complete details with templates to convert the current medicines and those under development into mRNA delivery systems to ensure clinical success, avoiding the current failures in their efficacy, more specifically the products that have demonstrated efficacy but are being removed from the market.

Lead optimization in drug discovery is a crucial phase where initial hits are refined into compounds with improved pharmacological properties. While traditional methods rely on manual experimentation and modifications, AI-driven techniques have revolutionized this process by leveraging big data and predictive modeling. This review explores how AI-driven approaches accelerate lead optimization, showcasing examples like deep neural networks and reinforcement learning. Integration of multi-omic data and experimental validation further enhances AI-driven strategies. The future lies in refining these AI methods, democratizing tools, and interdisciplinary collaboration to streamline drug discovery and address medical needs efficiently.

This article delves into the intersection of generative AI and digital twins within drug discovery, exploring their synergistic potential to revolutionize pharmaceutical research and development. Through various instances and examples, we illuminate how generative AI algorithms, capable of simulating vast chemical spaces and predicting molecular properties, are increasingly integrated with digital twins of biological systems to expedite drug discovery. By harnessing the power of computational models and machine learning, researchers can design novel compounds tailored to specific targets, optimize drug candidates, and simulate their behavior within virtual biological environments. This paradigm shift offers unprecedented opportunities for accelerating drug development, reducing costs, and, ultimately, improving patient outcomes. As we navigate this rapidly evolving landscape, collaboration between interdisciplinary teams and continued innovation will be paramount in realizing the promise of generative AI and digital twins in advancing drug discovery.

Alzheimer's disease (AD) remains a significant challenge in the field of neurodegenerative disor-ders, even nearly a century after its discovery, due to the elusive nature of its causes. The develop-ment of drugs that target multiple aspects of the disease has emerged as a promising strategy to address the complexities of AD and related conditions. The immune system's role, particularly in AD, has gained considerable interest, with nanobodies representing a new frontier in biomedical research. Advances in targeting antibodies against amyloid-β (Aβ) and using messenger RNA for genetic translation have revolutionized the production of antibodies and drug development, open-ing new possibilities for treatment. Despite these advancements, conventional treatments for AD, such as Cognex, Exelon, Razadyne, and Aricept, often have limited long-term effectiveness, under-scoring the need for innovative solutions. This necessity has led to the incorporating of advanced technologies like artificial intelligence and machine learning into the drug discovery process for neurodegenerative diseases. These technologies help identify therapeutic targets and optimize lead compounds, offering a more effective approach to addressing the challenges of AD and similar conditions.