Growth‐rate model predicts in vivo tumor response from in vitro data

• Main Authors: Rocky Diegmiller (Author), Laurent Salphati (Author), Bruno Alicke (Author), Timothy R. Wilson (Author), Thomas J. Stout (Author), Marc Hafner (Author)
• Format: Book
• Published: Wiley, 2022-09-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 2163-8306; 10.1002/psp4.12836
• Keywords: Therapeutics. Pharmacology; RM1-950
• Source: CPT: Pharmacometrics & Systems Pharmacology, Vol 11, Iss 9, Pp 1183-1193 (2022)

Abstract A major challenge in oncology drug development is to elucidate why drugs that show promising results in cancer cell lines in vitro fail in mouse studies or human trials. One of the fundamental steps toward solving this problem is to better predict how in vitro potency translates into in vivo efficacy. A common approach to infer whether a model will respond in vivo is based on in vitro half‐maximal inhibitory concentration values (IC50), but yields limited quantitative comparison between cell lines and drugs, potentially because cell division and death rates differ between cell lines and in vivo models. Other methods based either on mechanistic modeling or machine learning require molecular insights or extensive training data, limiting their use for early drug development. To address these challenges, we propose a mathematical model integrating in vitro growth rate inhibition values with pharmacokinetic parameters to estimate in vivo drug response. Upon calibration with a drug‐specific factor, our model yields precise estimates of tumor growth rate inhibition for in vivo studies based on in vitro data. We then demonstrate how our model can be used to study dosing schedules and perform sensitivity analyses. In addition, it provides meaningful metrics to assess association with genotypes and guide clinical trial design. By relying on commonly collected data, our approach shows great promise for optimizing drug development, better characterizing the efficacy of novel molecules targeting proliferation, and identifying more robust biomarkers of sensitivity while limiting the number of in vivo experiments.