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 - EORTC PAMM Group
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
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 - https://doi.org/10.21873/anticanres.13472
DO - https://doi.org/10.21873/anticanres.13472
M3 - Review article
C2 - 31262850
SN - 0250-7005
VL - 39
SP - 3303
EP - 3309
JO - Anticancer research
JF - Anticancer research
IS - 7
ER -