Malnutrition in children is a global health problem, particularly in developing countries. The effects of an insufficient supply of nutrients on body composition and physiological functions may have implications for drug disposition and ultimately affect the clinical outcome in this vulnerable population. Physiologically based pharmacokinetic (PBPK) modeling can be used to predict the effect of malnutrition as it links physiological changes to pharmacokinetic (PK) consequences. However, the absence of detailed information on body composition and the limited availability of controlled clinical trials in malnourished children complicates the establishment and evaluation of a generic PBPK model in this population. In this manuscript we describe the creation of physiologically-based bridge to a malnourished pediatric population, by combining information on a) the differences in body composition between healthy and malnourished adults and b) the differences in physiology between healthy adults and children. Model performance was confirmed using clinical reference data. This study presents a physiologically-based translational framework for prediction of drug disposition in malnourished children. The model is readily applicable for dose recommendation strategies to address the urgent medicinal needs of this vulnerable population.

The most common method for establishing bioequivalence (BE) is to demonstrate similarity of concentration-time profiles in the systemic circulation, as a surrogate to the site of action. However, the similarity, or otherwise, of profiles from two different formulation in the gastrointestinal tract (GIT) may not be associated with same conclusions based on systemic circulation. Here, we have explored the concordance of BE conclusions (or otherwise) for a set of hypothetical formulations based on drug concentration-profiles in various segments of gut vs those in systemic circulation using virtual trials powered by physiologically-based (PBPK) models. Moreover, by changing physiological and biological attributes of the GIT, the impact of Crohn’s Disease on the BE conclusions was explored. The virtual BE (VBE) was applied to an Entocort EC, a pH/time-dependent release formulation of budesonide, as model drug that is targeted at ileum and colon. Substantial “discordance” between the local and systemic outcomes of VBE was observed. Upper GIT segments were much more sensitive to formulation changes than systemic circulation, where the latter led to false conclusions for BE. The ileum and colon showed a lower frequency of discordance with systemic-derived VBE. Nonetheless, a higher f2 value than the default similarity standard (50) was required to achieve local BE. In the case of Crohn’s Disease, a product-specific f2 value might be needed for drugs such as Entocort EC to ensure local BE, knowing the insensitivity of systemic data to show lack of BE. The results, being limited to the single model drug product, Entocort EC, may not provide a a general and conclusive picture. However, for the first time, it demonstrated the potential discordances between the local gut vs systemic BE.

Good eyesight belongs to the most-valued attributes of health, and diseases of the eye are a significant health care burden. Case numbers are expected to further increase in the next decades due to an aging society. The development of drugs in ophthalmology, however, is difficult due to limited accessibility of the eye, in terms of drug administration and in terms of sampling of tissues for drug pharmacokinetics (PK) and pharmacodynamics (PD). Ocular quantitative systems pharmacology (QSP) models provide the opportunity to describe the distribution of drugs in the eye as well as the resulting drug-response in specific segments of the eye. In particular, ocular physiologically-based pharmacokinetic (PBPK) models are necessary to describe drug concentration levels in different regions of the eye. Further, ocular effect models employing molecular data from specific cellular systems are needed to develop dose-response correlations. We here describe the current status of PK/PBPK as well as PD models for the eye and describe cellular systems, data repositories as well as animal models in ophthalmology. The application of the various concepts is highlighted for the development of new treatments for post-operative fibrosis after glaucoma surgery.

Physiologically Based Pharmacokinetic Models (PBPK) are mechanistical tools generally employed in the pharmaceutical industry and environmental health risk assessment. These models are recognised by regulatory authorities for predicting organ concentration-time profile, pharmacokinetic and daily intake dose of xenobiotics. Extension of PBPK models to capture sensitive populations like pediatric, geriatric, pregnant females, fetus etc. and diseased population like renal impairment, liver cirrhosis etc. is a must. However, the current modelling practice and existing models are not mature enough to confidently predict the risk in these populations. A multidisciplinary collaboration between clinicians, experimental and modeler scientist is vital to improve the physiology, and calculation of biochemical parameters for integrating the knowledge and refining existing PBPK models. Specific PBPK covering compartments like cerebrospinal fluid, and hippocampus are required to gain mechanistic understanding about xenobiotic disposition in these sub-parts. The PBPK model assists in building quantitative adverse outcome pathways (qAOPs) for several endpoints like developmental neurotoxicity (DNT), hepatotoxicity and cardiotoxicity. Machine learning algorithms can predict physicochemical parameters required to develop in-silico models where experimental data is unavailable. Integrating machine learning with PBPK carries the potential to revolutionize the field of drug discovery and development and environmental risk. Overall, this review tried to summarize the recent developments in the in-silico models, building qAOPs, use of machine learning for improving existing models along with a regulatory perspective. This review can act as a guide for toxicologists who wish to build their careers in kinetic modeling.

Dynamic variables in the non-equilibrium systems of life are determined by catalytic activities. These relate to the expression of the genome. The extent to which such a variable depends on the catalytic activity defined by a gene has become more and more important in view of the possibilities to modulate gene expression or intervene with enzyme function through the use of medicinal drugs. With all the complexity of cellular systems biology, there are still some very simple principles that guide the control of variables such as fluxes, concentrations, and half-times. Using time-unit invariance we here derive a multitude of laws governing the sums of the control coefficients that quantify the control of multiple variables by all the catalytic activities. We show that the sum of the control coefficients of any dynamic variable over all catalytic activities is determined by the control of the same property by time. When the variable is at a maximum, minimum or steady, this limits the sums to simple integers like 0, -1, 1, and -2, depending on the variable under consideration. Some of the implications for biological control are discussed as is the dependence of these results on the precise definition of control.