Systematic review of computational methods for drug combination prediction

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Kong , W , Midena , G , Chen , Y , Athanasiadis , P , Wang , T , Rousu , J , He , L & Aittokallio , T 2022 , ' Systematic review of computational methods for drug combination prediction ' , Computational and Structural Biotechnology Journal , vol. 20 , pp. 2807-2814 . https://doi.org/10.1016/j.csbj.2022.05.055

Title: Systematic review of computational methods for drug combination prediction
Author: Kong, Weikaixin; Midena, Gianmarco; Chen, Yingjia; Athanasiadis, Paschalis; Wang, Tianduanyi; Rousu, Juho; He, Liye; Aittokallio, Tero
Contributor organization: Institute for Molecular Medicine Finland
Helsinki Institute of Life Science HiLIFE
Computational Systems Medicine
Helsinki Institute for Information Technology
University of Helsinki
Tero Aittokallio / Principal Investigator
Bioinformatics
Date: 2022
Language: eng
Number of pages: 8
Belongs to series: Computational and Structural Biotechnology Journal
ISSN: 2001-0370
DOI: https://doi.org/10.1016/j.csbj.2022.05.055
URI: http://hdl.handle.net/10138/346573
Abstract: Synergistic effects between drugs are rare and highly context-dependent and patient-specific. Hence, there is a need to develop novel approaches to stratify patients for optimal therapy regimens, especially in the context of personalized design of combinatorial treatments. Computational methods enable systematic in-silico screening of combination effects, and can thereby prioritize most potent combinations for further testing, among the massive number of potential combinations. To help researchers to choose a prediction method that best fits for various real-world applications, we carried out a systematic literature review of 117 computational methods developed to date for drug combination prediction, and classified the methods in terms of their combination prediction tasks and input data requirements. Most current methods focus on prediction or classification of combination synergy, and only a few methods consider the efficacy and potential toxicity of the combinations, which are the key determinants of therapeutic success of drug treatments. Furthermore, there is a need to further develop methods that enable dose-specific predictions of combination effects across multiple doses, which is important for clinical translation of the predictions, as well as model-based identification of biomarkers predictive of heterogeneous drug combination responses. Even if most of the computational methods reviewed focus on anticancer applications, many of the modelling approaches are also applicable to antiviral and other diseases or indications.(c) 2022 The Author(s). Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.
Subject: Drug combinations
Synergistic effect
Selective effect
Literature review
Dose-response assay
Cancer
Viral infection
CANCER
THERAPY
MODELS
1182 Biochemistry, cell and molecular biology
318 Medical biotechnology
11832 Microbiology and virology
Peer reviewed: Yes
Rights: cc_by_nc_nd
Usage restriction: openAccess
Self-archived version: publishedVersion


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