Opportunities for Quantitative Translational Modeling in Oncology

Abstract

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.

Clone Wars: Quantitatively Understanding Cancer Drug Resistance

“…Bringing this knowledge back from the bedside to the bench would enable more focused nonclinical investigations to optimize future therapies. In particular, the following types of analyses should be considered: It is important to understand the patient journey in greater detail, 98 and there are existing clinical data for this purpose.…”

The Changing Face of Oncology Research, Drug Development, and Clinical Practice: Toward Patient‐Focused Precision Therapeutics

“…19,20 With increasing biological complexity of the underlying mechanisms of action, the question at hand is one of multidimensional optimization (dose, schedule, combination, and patient population), necessitating application of holistic mechanism-based quantitative translational frameworks. 21,22 It will be impossible to evaluate all possible combination partners and sequences of administration in all tumor immunophenotypes. Quantitative systems pharmacology (QSP) models permit simulations of expectations in virtual patient populations that recapitulate the heterogeneity of the tumor microenvironment, enabling rational pursuit of precision medicine, and combination hypotheses in clinical development.…”

How translational modeling in oncology needs to get the mechanism just right

“…4,5 The application of these approaches in oncology is precedented 6 but less mature than in the broader pharmaceutical field 7 with further work required to integrate nonclinical and clinical modeling. 8 The majority of these reviews have concentrated on the determination of exposure-response relationships. 9,10 These relationships are the foundations of any transitional strategy and are informative for an efficacious dose/exposure setting.…”