Harnessing machine learning to find synergistic combinations for FDA-approved cancer drugs

Combination therapy is a fundamental strategy in cancer chemotherapy. It involves administering two or more anti-cancer agents to increase efficacy and overcome multidrug resistance compared to monotherapy. However, drug combinations can exhibit synergy, additivity, or antagonism. This study present...

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Bibliographic Details
Published in:Scientific reports Vol. 14; no. 1; pp. 2428 - 23
Main Authors: Abd El-Hafeez, Tarek, Shams, Mahmoud Y., Elshaier, Yaseen A. M. M., Farghaly, Heba Mamdouh, Hassanien, Aboul Ella
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 29.01.2024
Nature Publishing Group
Nature Portfolio
Subjects:
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631/532
631/67
639/705/1046
639/705/117
639/705/258
692/699
692/700
Antagonism
Antitumor agents
Cancer therapies
Chemotherapy
Colorectal cancer
Colorectal carcinoma
Data collection
DNA damage
Drug interaction
Drug interactions
Gemcitabine
Histone deacetylase
Humanities and Social Sciences
Inhibitors
Learning algorithms
Lung carcinoma
Machine learning
Melanoma
multidisciplinary
Multidrug resistance
Prostate carcinoma
Regression analysis
Science
Science (multidisciplinary)
Synergism
TOR protein
ISSN:2045-2322, 2045-2322
Online Access:Get full text
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Summary:Combination therapy is a fundamental strategy in cancer chemotherapy. It involves administering two or more anti-cancer agents to increase efficacy and overcome multidrug resistance compared to monotherapy. However, drug combinations can exhibit synergy, additivity, or antagonism. This study presents a machine learning framework to classify and predict cancer drug combinations. The framework utilizes several key steps including data collection and annotation from the O’Neil drug interaction dataset, data preprocessing, stratified splitting into training and test sets, construction and evaluation of classification models to categorize combinations as synergistic, additive, or antagonistic, application of regression models to predict combination sensitivity scores for enhanced predictions compared to prior work, and the last step is examination of drug features and mechanisms of action to understand synergy behaviors for optimal combinations. The models identified combination pairs most likely to synergize against different cancers. Kinase inhibitors combined with mTOR inhibitors, DNA damage-inducing drugs or HDAC inhibitors showed benefit, particularly for ovarian, melanoma, prostate, lung and colorectal carcinomas. Analysis highlighted Gemcitabine, MK-8776 and AZD1775 as frequently synergizing across cancer types. This machine learning framework provides a valuable approach to uncover more effective multi-drug regimens.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-52814-w