Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

Computational Advancements in Drug Repurposing for Cancer Combination Therapy Prediction

Version 1 : Received: 22 May 2023 / Approved: 23 May 2023 / Online: 23 May 2023 (10:19:47 CEST)
Version 2 : Received: 19 July 2023 / Approved: 20 July 2023 / Online: 20 July 2023 (05:02:16 CEST)

A peer-reviewed article of this Preprint also exists.

Flanary, V.L.; Fisher, J.L.; Wilk, E.J.; Howton, T.C.; Lasseigne, B.N. Computational Advancements in Cancer Combination Therapy Prediction. JCO Precision Oncology 2023, doi:10.1200/po.23.00261. Flanary, V.L.; Fisher, J.L.; Wilk, E.J.; Howton, T.C.; Lasseigne, B.N. Computational Advancements in Cancer Combination Therapy Prediction. JCO Precision Oncology 2023, doi:10.1200/po.23.00261.

Abstract

As cancer remains resistant to several modes of treatment, novel therapeutics are still under active investigation to overcome treatment inefficacy in cancer. Given the high attrition rate of de novo drug discovery, drug screening, and drug repurposing have offered time- and cost-effective alternative strategies for the identification of potentially effective therapeutics. In contrast to large-scale drug screens, computational approaches for drug repurposing leverage the increasing amounts of biomedical data to predict candidate therapeutic agents prior to testing in biological models. Current studies in drug repurposing for cancer therapy prediction have increasingly focused on the prediction of combination therapies, as combination therapies have numerous advantages over monotherapies. These include increased effect from synergistic interactions, reduced toxicity from lowered drug doses, and a reduced risk of resistance due to multiple non-overlapping mechanisms of action. This review provides a summary of several classes of computational methods used for drug combination therapy prediction in cancer research, including networks, regression-based machine learning, classifier machine learning models, and deep learning approaches, with the goal of presenting current progress in the field, particularly to non-computational cancer biologists. We conclude by discussing the need for further advancements in technologies that incorporate disease mechanisms, drug characteristics, multi-omics data, and clinical considerations to generate effective patient-specific drug combinations, as holistic data integration will inevitably result in optimal targeted therapeutics for cancer.

Keywords

computational; drug repurposing; drug repositioning; cancer; combination therapy; network biology; machine learning; deep learning; precision oncology

Subject

Medicine and Pharmacology, Oncology and Oncogenics

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