Opportunities for Quantitative Translational Modeling in Oncology

• Published in: Clinical pharmacology and therapeutics Vol. 108; no. 3; pp. 447 - 457
• Main Authors: Yates, James W.T., Byrne, Helen, Chapman, Sonya C., Chen, Tao, Cucurull‐Sanchez, Lourdes, Delgado‐SanMartin, Juan, Di Veroli, Giovanni, Dovedi, Simon J., Dunlop, Carina, Jena, Rajesh, Jodrell, Duncan, Martin, Emma, Mercier, Francois, Ramos‐Montoya, Antonio, Struemper, Herbert, Vicini, Paolo
• Format: Journal Article
• Language: English
• Published: United States 01.09.2020
• Subjects: Animals; Antineoplastic Agents - adverse effects; Antineoplastic Agents - therapeutic use; Cell Line, Tumor; Clinical Trials as Topic; Dose-Response Relationship, Drug; Drug Development; Endpoint Determination; Humans; Medical Oncology; Models, Theoretical; Neoplasms, Experimental - drug therapy; Neoplasms, Experimental - genetics; Neoplasms, Experimental - metabolism; Neoplasms, Experimental - pathology; Research Design; Response Evaluation Criteria in Solid Tumors; Translational Research, Biomedical; Tumor Burden - drug effects; Xenograft Model Antitumor Assays
• ISSN: 0009-9236; 1532-6535
• DOI: 10.1002/cpt.1963

A 2‐day meeting was held by members of the UK Quantitative Systems Pharmacology Network (<http://www.qsp‐uk.net/>) in November 2018 on the topic of Translational Challenges in Oncology. Participants from a wide range of backgrounds were invited to discuss current and emerging modeling applications in nonclinical and clinical drug development, and to identify areas for improvement. This resulting perspective explores opportunities for impactful quantitative pharmacology approaches. Four key themes arose from the presentations and discussions that were held, leading to the following recommendations: Evaluate the predictivity and reproducibility of animal cancer models through precompetitive collaboration. Apply mechanism of action (MoA) based mechanistic models derived from nonclinical data to clinical trial data. Apply MoA reflective models across trial data sets to more robustly quantify the natural history of disease and response to differing interventions. Quantify more robustly the dose and concentration dependence of adverse events through mathematical modelling techniques and modified trial design.