Literature DB >> 26360051

Modelling of compound combination effects and applications to efficacy and toxicity: state-of-the-art, challenges and perspectives.

Krishna C Bulusu1, Rajarshi Guha2, Daniel J Mason3, Richard P I Lewis3, Eugene Muratov4, Yasaman Kalantar Motamedi3, Murat Cokol5, Andreas Bender6.   

Abstract

The development of treatments involving combinations of drugs is a promising approach towards combating complex or multifactorial disorders. However, the large number of compound combinations that can be generated, even from small compound collections, means that exhaustive experimental testing is infeasible. The ability to predict the behaviour of compound combinations in biological systems, whittling down the number of combinations to be tested, is therefore crucial. Here, we review the current state-of-the-art in the field of compound combination modelling, with the aim to support the development of approaches that, as we hope, will finally lead to an integration of chemical with systems-level biological information for predicting the effect of chemical mixtures.
Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

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Year:  2015        PMID: 26360051     DOI: 10.1016/j.drudis.2015.09.003

Source DB:  PubMed          Journal:  Drug Discov Today        ISSN: 1359-6446            Impact factor:   7.851


  45 in total

Review 1.  Predictive approaches for drug combination discovery in cancer.

Authors:  Seyed Ali Madani Tonekaboni; Laleh Soltan Ghoraie; Venkata Satya Kumar Manem; Benjamin Haibe-Kains
Journal:  Brief Bioinform       Date:  2018-03-01       Impact factor: 11.622

Review 2.  Collaborative drug discovery for More Medicines for Tuberculosis (MM4TB).

Authors:  Sean Ekins; Anna Coulon Spektor; Alex M Clark; Krishna Dole; Barry A Bunin
Journal:  Drug Discov Today       Date:  2016-11-22       Impact factor: 7.851

3.  Computational Pipeline for Rational Drug Combination Screening in Patient-Derived Cells.

Authors:  Paschalis Athanasiadis; Aleksandr Ianevski; Sigrid S Skånland; Tero Aittokallio
Journal:  Methods Mol Biol       Date:  2022

Review 4.  Chemical-Genetic Interactions as a Means to Characterize Drug Synergy.

Authors:  Hamid Gaikani; Guri Giaever; Corey Nislow
Journal:  Methods Mol Biol       Date:  2021

Review 5.  Charting the Fragmented Landscape of Drug Synergy.

Authors:  Christian T Meyer; David J Wooten; Carlos F Lopez; Vito Quaranta
Journal:  Trends Pharmacol Sci       Date:  2020-02-26       Impact factor: 14.819

6.  Design of a Multicompartment Hydrogel that Facilitates Time-Resolved Delivery of Combination Therapy and Synergized Killing of Glioblastoma.

Authors:  Poulami Majumder; Ulrich Baxa; Scott T R Walsh; Joel P Schneider
Journal:  Angew Chem Int Ed Engl       Date:  2018-10-18       Impact factor: 15.336

Review 7.  Machine learning approaches for drug combination therapies.

Authors:  Betül Güvenç Paltun; Samuel Kaski; Hiroshi Mamitsuka
Journal:  Brief Bioinform       Date:  2021-11-05       Impact factor: 11.622

8.  TranSynergy: Mechanism-driven interpretable deep neural network for the synergistic prediction and pathway deconvolution of drug combinations.

Authors:  Qiao Liu; Lei Xie
Journal:  PLoS Comput Biol       Date:  2021-02-12       Impact factor: 4.475

Review 9.  Artificial intelligence and machine learning methods in predicting anti-cancer drug combination effects.

Authors:  Kunjie Fan; Lijun Cheng; Lang Li
Journal:  Brief Bioinform       Date:  2021-11-05       Impact factor: 13.994

10.  Synergy Maps: exploring compound combinations using network-based visualization.

Authors:  Richard Lewis; Rajarshi Guha; Tamás Korcsmaros; Andreas Bender
Journal:  J Cheminform       Date:  2015-08-01       Impact factor: 5.514

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