To Combine or Not Combine: Drug Interactions and Tools for Their Analysis. Reflections from the EORTC-PAMM Course on Preclinical and Early-phase Clinical Pharmacology

Btissame El Hassouni, Giulia Mantini, Giovanna Li Petri, Mjriam Capula, Lenka Boyd, Hannah N W Weinstein, Andrea Vallés-Marti, Mathilde C M Kouwenhoven, Elisa Giovannetti, Bart A Westerman, Godefridus J Peters, EORTC PAMM Group

Research output: Contribution to journalReview articleAcademicpeer-review

Abstract

Combination therapies are used in the clinic to achieve cure, better efficacy and to circumvent resistant disease in patients. Initial assessment of the effect of such combinations, usually of two agents, is frequently performed using in vitro assays. In this review, we give a short summary of the types of analyses that were presented during the Preclinical and Early-phase Clinical Pharmacology Course of the Pharmacology and Molecular Mechanisms Group, European Organization for Research and Treatment on Cancer, that can be used to determine the efficacy of drug combinations. The effect of a combination treatment can be calculated using mathematical equations based on either the Loewe additivity or Bliss independence model, or a combination of both, such as Chou and Talalay's median-drug effect model. Interactions can be additive, synergistic (more than additive), or antagonistic (less than additive). Software packages CalcuSyn (also available as CompuSyn) and Combenefit are designed to calculate the extent of the combined effects. Interestingly, the application of machine-learning methods in the prediction of combination treatments, which can include pharmacogenomic, genetic, metabolomic and proteomic profiles, might contribute to further refinement of combination regimens. However, more research is needed to apply appropriate rules of machine learning methods to ensure correct predictive models.

Original languageEnglish
Pages (from-to)3303-3309
Number of pages7
JournalAnticancer Research
Volume39
Issue number7
DOIs
Publication statusPublished - 1 Jan 2019

Cite this