Literature DB >> 31262850

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 Hassouni1, Giulia Mantini1, Giovanna Li Petri1, Mjriam Capula2, Lenka Boyd1, Hannah N W Weinstein1, Andrea Vallés-Marti1, Mathilde C M Kouwenhoven3, Elisa Giovannetti1, Bart A Westerman4, Godefridus J Peters5.   

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

Entities:  

Keywords:  Calcusyn; Combination treatment; Compusyn; review; synergy

Mesh:

Substances:

Year:  2019        PMID: 31262850     DOI: 10.21873/anticanres.13472

Source DB:  PubMed          Journal:  Anticancer Res        ISSN: 0250-7005            Impact factor:   2.480


  4 in total

1.  Using Split Luciferase Assay and anti-HSV Glycoprotein Monoclonal Antibodies to Predict a Functional Binding Site Between gD and gH/gL.

Authors:  Doina Atanasiu; Wan Ting Saw; Tina M Cairns; Roselyn J Eisenberg; Gary H Cohen
Journal:  J Virol       Date:  2021-01-27       Impact factor: 5.103

2.  Study of Combinatorial Drug Synergy of Novel Acridone Derivatives With Temozolomide Using in-silico and in-vitro Methods in the Treatment of Drug-Resistant Glioma.

Authors:  Malobika Chakravarty; Piyali Ganguli; Manikanta Murahari; Ram Rup Sarkar; Godefridus Johannes Peters; Y C Mayur
Journal:  Front Oncol       Date:  2021-03-15       Impact factor: 6.244

3.  Dietary Flavone Baicalein Combinate with Genipin Attenuates Inflammation Stimulated by Lipopolysaccharide in RAW264.7 Cells or Pseudomonas aeruginosa in Mice via Regulating the Expression and Phosphorylation of AKT.

Authors:  Man Zhang; Lili Ye; Chuanjing Cheng; Fukui Shen; Lin Niu; Yuanyuan Hou; Gang Bai
Journal:  Nutrients       Date:  2021-12-14       Impact factor: 5.717

4.  Reversible Monoacylglycerol Lipase Inhibitors: Discovery of a New Class of Benzylpiperidine Derivatives.

Authors:  Giulia Bononi; Miriana Di Stefano; Giulio Poli; Gabriella Ortore; Philip Meier; Francesca Masetto; Isabella Caligiuri; Flavio Rizzolio; Marco Macchia; Andrea Chicca; Amir Avan; Elisa Giovannetti; Chiara Vagaggini; Annalaura Brai; Elena Dreassi; Massimo Valoti; Filippo Minutolo; Carlotta Granchi; Jürg Gertsch; Tiziano Tuccinardi
Journal:  J Med Chem       Date:  2022-05-06       Impact factor: 8.039

  4 in total

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