Current mathematical models for cancer drug discovery

ABSTRACT

Introduction: Pharmacometric models represent the most comprehensive approaches for extracting, summarizing and integrating information obtained in the often sparse, limited, and less-than-optimally designed experiments performed in the early phases of oncology drug discovery. Whilst empirical methodologies may be enough for screening and ranking candidate drugs, modeling approaches are needed for optimizing and making economically viable the learn-confirm cycles within an oncology research program and anticipating the dose regimens to be investigated in the subsequent clinical development.

Areas covered: Papers appearing in the literature of approximately the last decade reporting modeling approaches applicable to anticancer drug discovery have been listed and commented. Papers were selected based on the interest in the proposed methodology or in its application.

Expert opinion: The number of modeling approaches used in the discovery of anticancer drugs is consistently increasing and new models are developed based on the current directions of research of new candidate drugs. These approaches have contributed to a better understanding of new oncological targets and have allowed for the exploitation of the relatively sparse information generated by preclinical experiments. In addition, they are used in translational approaches for guiding and supporting the choice of dosing regimens in early clinical development.

KEYWORDS:

• Combination therapy
• drug candidates
• in vitro
• in vivo
• mathematical modeling
• oncology
• pharmacometrics
• PK-PD
• single agent
• translational pharmacology

Article highlights

• Pharmacometric models are increasingly applied for summarizing and integrating the relatively sparse information from preclinical pharmacological studies in anticancer candidate drug discovery.

• This paper provides a review of the modeling approaches proposed in the literature approximately of the last decade.

• Numerous mathematical models have been proposed for describing tumor growth and the effect of candidate drugs on tumor inhibition, both in in vitro and in vivo experiments, using both single agent and combination therapies.

• Following the advent of the targeted therapies, the integration of additional markers of pharmacological activity (e.g. target engagement, activation, modulation, and downstream effects) has been studied and implemented. This provided more solid mechanistic grounds to modeling approaches.

• The adoption of mechanistic models provides the best chance to translate the preclinical results into the clinical situation, allowing to anticipate the active clinical doses and streamlining the early clinical development of new anticancer candidate drugs.

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Declaration of interest

I Poggesi is an employee and shares holder with Janssen Pharmaceuticals. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

Additional information

Funding

This manuscript has not been funded.