A Review of Modeling Approaches to Predict Drug Response in Clinical Oncology

Abstract

Model-based approaches have emerged as important tools for quantitatively understanding temporal relationships between drug dose, concentration, and effect over the course of treatment, and have now become central to optimal drug development and tailored drug treatment. In oncology, the therapeutic index of a chemotherapeutic drug is typically narrow and a full dose–response relationship is not available, often because of treatment failure. Noting the benefits of model-based approaches and the low therapeutic index of oncology drugs, in recent years, modeling approaches have been increasingly used to streamline oncologic drug development through early identification and quantification of dose–response relationships. With this background, this report reviews publications that used model-based approaches to evaluate drug treatment outcome variables in oncology therapeutics, ranging from tumor size dynamics to tumor/biomarker time courses and survival response.

Exposure-Response Analysis of Necitumumab Efficacy in Squamous Non-Small Cell Lung Cancer Patients

“…Utilizing the complete time‐course curve for tumor size was found superior to using single dimension metrics such as baseline tumor size, rate of tumor shrinkage, time to regrowth, or best percent change in tumor size. Historically, the majority of oncology PKPD modeling efforts of OS have been focusing on linking survival to a single dimension tumor size metric 30, 31. It can be argued that looking at single dimension metrics ignores information, and may introduce bias as well as limit extrapolation of data 32, 33.…”

“…Historically, the majority of oncology PKPD modeling efforts of OS have been focusing on linking survival to a single dimension tumor size metric. 30,31 It can be argued that looking at single dimension metrics ignores information, and may introduce bias as well as limit extrapolation of data. 32,33 Utilizing a complete time-course should be more suitable at describing the complexity of interplay of tumor burden and OS, while still containing all single-dimension metrics.…”

Mathematical models applied to thyroid cancer

“…A second fundamental characteristic related to modeling concerns the quantity and quality of the variables used. In general, the choice of the set of variables refers to the aspect to be elucidated and to the degree of complexity or realism to be obtained with the model (Park 2016).…”

“…The approach of clinical oncology in terms of mathematical modeling is revised in Park (2016); in this review article, the author presents several texts published with mathematical models about cancer. The different models deal with aspects of the disease such as tumor size, which discusses the use of analytical or differential equations, models for tumor markers, biomarkers, adverse effects after treatments, and progression-free survival.…”

“…Faced with the complexity of cancer, mathematical models are currently seen as a valuable research tool, and the use of differential equations highlight as the main means of study from several papers (Abduvaliev et al 2015;Hori and Gambhir 2011;Park 2016;Jackson 2004;Rodrigues et al 2016). Ordinary differential equations (ODE) contain functions of one variable, commonly time, and derivatives from this same variable, whereas partial differential equations (PDE) have functions with two or more variables and their partial derivatives.…”