Bringing Model-Based Prediction to Oncology Clinical Practice: A Review of Pharmacometrics Principles and Applications

Abstract

Despite much investment and progress, oncology is still an area with significant unmet medical needs, with new therapies and more effective use of current therapies needed. The emergent field of pharmacometrics combines principles from pharmacology (pharmacokinetics [PK] and pharmacodynamics [PD]), statistics, and computational modeling to support drug development and optimize the use of already marketed drugs. Although it has gained a role within drug development, its use in clinical practice remains scarce. The aim of the present study was to review the principal pharmacometric concepts and provide some examples of its use in oncology. Integrated population PK/PD/disease progression models as part of the pharmacometrics platform provide a powerful tool to predict outcomes so that the right dose can be given to the right patient to maximize drug efficacy and reduce drug toxicity. Population models often can be developed with routinely collected medical record data; therefore, we encourage the application of such models in the clinical setting by generating close collaborations between physicians and pharmacometricians. The Oncologist 2016;21:220-232Implications for Practice: The present review details how the emerging field of pharmacometrics can integrate medical record data with predictive pharmacological and statistical models of drug response to optimize and individualize therapies. In order to make this routine practice in the clinic, greater awareness of the potential benefits of the field is required among clinicians, together with closer collaboration between pharmacometricians and clinicians to ensure the requisite data are collected in a suitable format for pharmacometrics analysis.

Reverse Translation of US Food and Drug Administration Reviews of Oncology New Molecular Entities Approved in 2011–2017: Lessons Learned for Anticancer Drug Development

“…This may reflect the increased availability of targeted therapies, such as tyrosine kinase inhibitors, that can be administered on a continuous dosing schedule and/or for a longer duration than traditional cytotoxic therapies, thus prompting an increased focus on long‐term tolerability in the overall benefit:risk assessment. In particular, there has been a greater emphasis on understanding sources of pharmacokinetic (PK) variability and characterizing exposure‐response relationships to ensure optimal dose selection …”

“…In particular, there has been a greater emphasis on understanding sources of pharmacokinetic (PK) variability and characterizing exposure-response relationships to ensure optimal dose selection. 7,[18][19][20] We conducted a comprehensive analysis of publicly available documents that summarize clinical pharmacology studies/analyses included in initial NDA/BLA submissions of oncology new molecular entities (NMEs) approved by the FDA during the January 2011 to April 2017 timeframe. The main purpose of this translational research was to distill knowledge from a clinical pharmacology perspective from the FDA reviews of oncology drugs that can be valuable for clinical development of future oncology drugs.…”

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

“…Modeling in support of preclinical and clinical drug development in oncology has traditionally been focused on the optimization of dose and scheduling using semiempirical, semimechanistic models primarily focused on the characterization of tumor‐size dynamics as an end point . Part of the challenge in achieving successful QSP modeling applications in oncology is the weak preclinical‐to‐clinical translatability—or direct scalability—of experimental data, from mouse (the typical preclinical study system of choice for efficacy) to human in particular.…”

“…Modeling in support of preclinical and clinical drug development in oncology has traditionally been focused on the optimization of dose and scheduling using semiempirical, semimechanistic models primarily focused on the characterization of tumor-size dynamics as an end point. [69][70][71] Part of the challenge in achieving successful QSP modeling applications in oncology is the weak preclinical-to-clinical translatability-or direct scalability-of experimental data, from mouse (the typical preclinical study system of choice for efficacy) to human in particular. This creates a systematic knowledge gap between the detailed mechanistic understanding around an oncology target in a rodent experimental model and its alignment within the proper pathophysiological context in a human cancer indication of interest.…”

A Review of Mathematical Models for Tumor Dynamics and Treatment Resistance Evolution of Solid Tumors

“…2 To rationalize the treatment personalization and address treatment failure, the use of modeling and simulation, which can quantitatively characterize and predict the relationships between drug exposure/pharmacokinetics (PK), drug effects/pharmacodynamics (PD), and disease progression, is widely accepted to support drug decision making. [3][4][5][6] Mathematical models that characterize the effects of anticancer drug treatment for solid tumors based on tumor size dynamics, which is typically quantified with measurements of tumor diameter and volume, represent one key class of models applied in cancer pharmacology. Various tumor growth modeling strategies have been previously reviewed, including agent-based models, 7 image-based models, 8 multiscale models, 9 and PK/PD models.…”