Drug-disease modeling in the pharmaceutical industry - where mechanistic systems pharmacology and statistical pharmacometrics meet

Quantitative Systems Pharmacology: An Exemplar Model‐Building Workflow With Applications in Cardiovascular, Metabolic, and Oncology Drug Development

“…In the pharmaceutical industry, the pace of innovation in translational biology, pharmacological modalities, predictive personalized biomarkers, multiomics measurement technologies, and optimal therapeutic drug combinations in nearly all diseases combined with the ever‐present questions on dose posology in patient populations that are increasingly genetically and phenotypically characterized has continued to accelerate. In such a context, quantitative systems pharmacology (QSP), a mechanistically oriented form of drug and disease modeling that integrates data and knowledge, is proving to be increasingly impactful in model‐informed drug discovery and development . In this context, the impact of QSP continues to grow and is increasingly recognized within the pharmaceutical industry, from the early stages in drug discovery to late‐stage development and life‐cycle management up to support of regulatory submission …”

“…QSP modeling has found multiple domains of use and impact in the industry. QSP models are often used to generate hypotheses and support a quantitative understanding of novel compound mechanism(s) of action, in a specific tissue, disease, or nonclinical experimental or clinical patient population context …”

“…QSP models are often used to generate hypotheses and support a quantitative understanding of novel compound mechanism(s) of action, in a specific tissue, disease, or nonclinical experimental or clinical patient population context. 1,2,[4][5][6] QSP may further be used in optimizing doses and dosing regimens 4,7,8 or in support of dose-sequencing decisions for drug combinations 9 given that a QSP model typically contains multiple effectors and at least one pharmacodynamic marker of interest-often the pharmacodynamic endpoint in a given study-downstream of the drug or compound target.Mechanistically oriented QSP models also prove useful in placing biomarkers of efficacy, safety, or disease pathophysiology and phenotype in the appropriate quantitative and dynamic context for a therapeutic treatment of choice. 5,[10][11][12][13] In the course of QSP model development and testing, QSP modeling may help reconcile (or not) what, at a first glance, may appear as discrepancies in data, e.g., as obtained from different animal models or trials or discrepancies between in vitro and in vivo (nonhuman) findings or in vivo and clinical findings.…”

“…QSP models are often used to generate hypotheses and support a quantitative understanding of novel compound mechanism(s) of action, in a specific tissue, disease, or nonclinical experimental or clinical patient population context. 1,2,[4][5][6] QSP may further be used in optimizing doses and dosing regimens 4,7,8 or in support of dose-sequencing decisions for drug combinations 9 given that a QSP model typically contains multiple effectors and at least one pharmacodynamic marker of interest-often the pharmacodynamic endpoint in a given study-downstream of the drug or compound target.…”

“…In such a context, quantitative systems pharmacology (QSP), a mechanistically oriented form of drug and disease modeling that integrates data and knowledge, is proving to be increasingly impactful in model-informed drug discovery and development. 1 In this context, the impact of QSP continues to grow and is increasingly recognized within the pharmaceutical industry, from the early stages in drug discovery 2,3 to late-stage development and life-cycle management up to support of regulatory submission. 4 QSP models integrate features of the drug (dose, dosing regimen, exposure or concentration at target site, potency, or a full pharmacokinetic submodel) with target biology; downstream effectors at the molecular, cellular, and pathophysiological levels; and possibly functional effector(s) of interest, such as a physiologically based pharmacodynamic study end point (Figure 1a).…”