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Food and Drug Administration submissions of physiologically based pharmacokinetic (PBPK) modeling and simulation of small‐molecule drugs document the relevance of pediatric drug development and, in particular, information on dosing strategies in children. The most relevant prerequisite for reliable PBPK‐based translation of adult pharmacokinetics of a small molecule to children is knowledge of the drug‐specific absorption, distribution, metabolism, and elimination (ADME) processes in adults together with existing information about ontogeny of ADME processes relevant for the drug. All mechanisms driving a drug's clearance are of specific importance. For other drug modalities, our knowledge of ADME processes and ontogeny is still limited. More research is required, for example, to understand why some therapeutic proteins show complex differences in pharmacokinetics between adults and children, whereas other proteins seem to follow simple allometric scaling rules. Ontogeny information originates from various sources, such as (semi)quantitative mRNA expression, in vitro activity data, and deconvolution of in vivo pharmacokinetic data. The workflow for pediatric predictions is well described in several articles documenting successful translation from adults to children. The technical hurdles for PBPK modeling are low. State‐of‐the‐art PBPK modeling software tools provide integrated pediatric translation workflows. For example, PK‐Sim and MoBi are freely available as fully transparent open‐source software via Open Systems Pharmacology (OSP). With the latest 2019 software release, version 8.0, OSP even provides a fully integrated technical framework for the qualification (and requalification) of any specific intended PBPK use in line with Food and Drug Administration and European Medicines Agency PBPK guidance. Qualification packages for pediatric translation are available on the OSP platform.

Early indication of late-stage failure of novel candidate drugs could be facilitated by continuous integration, assessment, and transfer of knowledge acquired along pharmaceutical development programs. We here present a translational systems pharmacology workflow that combines drug cocktail probing in a specifically designed clinical study, physiologically based pharmacokinetic modeling, and Bayesian statistics to identify and transfer (patho-)physiological and drug-specific knowledge across distinct patient populations. Our work builds on two clinical investigations, one with 103 healthy volunteers and one with 79 diseased patients from which we systematically derived physiological information from pharmacokinetic data for a reference probe drug (midazolam) at the single-patient level. Taking into account the acquired knowledge describing (patho-)physiological alterations in the patient cohort allowed the successful prediction of the population pharmacokinetics of a second, candidate probe drug (torsemide) in the patient population. In addition, we identified significant relations of the acquired physiological processes to patient metadata from liver biopsies. The presented prototypical systems pharmacology approach is a proof of concept for model-based translation across different stages of pharmaceutical development programs. Applied consistently, it has the potential to systematically improve predictivity of pharmacokinetic simulations by incorporating the results of clinical trials and translating them to subsequent studies.

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