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

• Published in: Drug discovery today Vol. 21; no. 2; pp. 225 - 238
• Main Authors: Bulusu, Krishna C., Guha, Rajarshi, Mason, Daniel J., Lewis, Richard P.I., Muratov, Eugene, Kalantar Motamedi, Yasaman, Cokol, Murat, Bender, Andreas
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.02.2016
• Subjects: Animals; Drug Combinations; Drug Interactions; Drug Therapy, Combination; Gene Expression; Humans; Models, Biological; RNA - genetics
• ISSN: 1359-6446; 1878-5832
• DOI: 10.1016/j.drudis.2015.09.003

•Review of the state-of-the-art in the field of compound combination modelling.•Significance of quality control of large-scale combination screening data.•Strategies for modelling combination effects using publicly available resources.•Importance of chemical and biological data integration for predictions.•Technical and scientific challenges of data integration in this context are discussed. 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. An overview of the state-of-the-art in predictive modelling of compound combination activity and the value and significance of systems informatics in identifying combinations for therapeutic purposes.