Although late-stage clinical attrition has been repeatedly highlighted as the most significant issue facing the pharmaceutical industry, the probability of technical success in the clinic is largely related to decisions made years earlier in the preclinical stages of R&D. It’s at these stages that decisions are made regarding the molecular target, modality of intervention, drug design and clinical candidate selection. Accordingly, model-informed drug development approaches that have proven useful in the clinic (e.g. PBPK, PKPD, QSP) are increasingly be leveraged to support decisions in the earlier, preclinical stages of R&D.

There have been numerous studies published to date about applying modeling and simulation techniques on various preclinical research topics in drug discovery. Among these preclinical modeling publications, only a few focus on decision making. Studies about modeling methodology per se (e.g., establishing a mechanistic but retrospective model to recapitulate clinical observations) are interesting and important, however it is also critical to test these modeling methods in decision-making scenarios (e.g., estimating required selectivity between on-target and off-target potency for a drug candidate based on translational modeling). Publishing model-informed decision-making case examples will allow us to better understand modeling’s impacts and limitations in the real-world drug discovery, and provide insights and guidance towards future preclinical modeling research. Under this topic, Frontiers in Pharmacology will promote publication of in silico studies that have an experimental component ( in vitro or in vivo approaches), which illustrates such efforts with an emphasis on how modeling and simulation is being used to advance hypothesis driven research and support decision making in preclinical research.

This Research Topic welcomes Original Research, Perspective, or Review article submissions that cover the following topics:

• Target evaluation: Model-based evaluation of therapeutic hypotheses as they relate to the molecular target or pathway of interest. Highlighting model-based assessments of potential efficacy and safety which led to decisions to either accelerate or discontinue investment in a target or pathway.
• Modality selection: Model-based prioritization of interventional modalities in disease (e.g. competitive, allosteric, covalent, mAb, multispecific mAbs, PROTACs, molecular glues DUBs, RNAi, etc.). Submissions should exemplify models which codify target/disease biology, molecular mechanism of action and the desired mechanistic/therapeutic response.
• Drug Design: Model-based guidance of drug design by providing a line of sight into a clinically relevant outcome (response, dose, therapeutic index). Submissions should highlight how modeling and simulation informed drug design through identification of desired properties or direct guidance regarding options for lead optimization.
• Candidate selection and early clinical development: Application of preclinical modeling and simulation to quantitatively predict aspects of human pharmacology and safety in support of candidate selection and early clinical trial design. Ideal examples would be those in which model-based predictions were particularly impactful in making the case for advancement to the clinic or significantly influenced the design of early, decision-making clinical trials.
• Preclinical modeling and simulation methods: Approaches of broad relevance to preclinical R&D. Models and methods should be described in enough detail to allow recapitulation of results. Ideally, one or more case examples will be provided to illustrate application to decision making. Anonymization of proprietary information (e.g. chemical structure) is acceptable so long as it does not prevent recapitulation of the proposed model.

Important note: In order for readers to understand the key modeling steps and to reproduce the results described in the article, authors are required to supply sufficient technical details (including example model code and datasets) either as supplementary information submitted with the main article, or archived in an appropriate public repository.

Topic editors Rui Li and Tristan S. Maurer are employed by Pfizer Inc. Topic Editor Alison Betts is employed by Applied BioMath. All other Topic Editors declare no competing interests with regards to the Research Topic subject.

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