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

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

@article{69b83a29636f4e5b8e7328b640367833,
title = "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",
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.",
keywords = "Animals, Drug Combinations, Drug Interactions, Drug Therapy, Combination, Humans, Pharmacology, Clinical, Translational Medical Research",
author = "{El Hassouni}, Btissame and Giulia Mantini and {Li Petri}, Giovanna and Mjriam Capula and Lenka Boyd and Weinstein, {Hannah N W} and Andrea Vall{\'e}s-Marti and Kouwenhoven, {Mathilde C M} and Elisa Giovannetti and Westerman, {Bart A} and Peters, {Godefridus J} and {EORTC PAMM Group}",
note = "Copyright{\circledC} 2019, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.",
year = "2019",
month = "1",
day = "1",
doi = "10.21873/anticanres.13472",
language = "English",
volume = "39",
pages = "3303--3309",
journal = "Anticancer Research",
issn = "0250-7005",
publisher = "International Institute of Anticancer Research",
number = "7",

}

TY - JOUR

T1 - To Combine or Not Combine

T2 - Drug Interactions and Tools for Their Analysis. Reflections from the EORTC-PAMM Course on Preclinical and Early-phase Clinical Pharmacology

AU - El Hassouni, Btissame

AU - Mantini, Giulia

AU - Li Petri, Giovanna

AU - Capula, Mjriam

AU - Boyd, Lenka

AU - Weinstein, Hannah N W

AU - Vallés-Marti, Andrea

AU - Kouwenhoven, Mathilde C M

AU - Giovannetti, Elisa

AU - Westerman, Bart A

AU - Peters, Godefridus J

AU - EORTC PAMM Group

N1 - Copyright© 2019, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.

PY - 2019/1/1

Y1 - 2019/1/1

N2 - 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.

AB - 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.

KW - Animals

KW - Drug Combinations

KW - Drug Interactions

KW - Drug Therapy, Combination

KW - Humans

KW - Pharmacology, Clinical

KW - Translational Medical Research

UR - http://www.scopus.com/inward/record.url?scp=85068255528&partnerID=8YFLogxK

U2 - 10.21873/anticanres.13472

DO - 10.21873/anticanres.13472

M3 - Review article

VL - 39

SP - 3303

EP - 3309

JO - Anticancer Research

JF - Anticancer Research

SN - 0250-7005

IS - 7

ER -