Abstract
Combinatorial therapies that target multiple pathways have shown great promises for treating complex diseases. DrugComb (https://drugcomb.org/) is a web-based portal for the deposition and analysis of drug combination screening datasets. Since its first release, DrugComb has received continuous updates on the coverage of data resources, as well as on the functionality of the web server to improve the analysis, visualization and interpretation of drug combination screens. Here, we report significant updates of DrugComb, including: (i) manual curation and harmonization of more comprehensive drug combination and monotherapy screening data, not only for cancers but also for other diseases such as malaria and COVID-19; (ii) enhanced algorithms for assessing the sensitivity and synergy of drug combinations; (iii) network modelling tools to visualize the mechanisms of action of drugs or drug combinations for a given cancer sample and (iv) state-of-the-art machine learning models to predict drug combination sensitivity and synergy. These improvements have been provided with more user-friendly graphical interface and faster database infrastructure, which make DrugComb the most comprehensive web-based resources for the study of drug sensitivities for multiple diseases.
Highlights
Despite the scientific advances in the understanding of complex diseases such as cancer, there remains a major gap between the vast knowledge of molecular biology and effective treatments
We provide a baseline model based on CatBoost to predict the sensitivity and synergy of drug combinations, with which the machine learning community may develop novel algorithms to improve our understanding of drug responses in cancer cells
The drug targets as well as the gene expressions of the signalling pathways for a given cancer cell line can be annotated as a network model
Summary
Despite the scientific advances in the understanding of complex diseases such as cancer, there remains a major gap between the vast knowledge of molecular biology and effective treatments. To facilitate the discovery of drug combination therapies, highthroughput drug screening techniques have been developed to allow for a large scale of drug combinations to be tested for their sensitivity (percentage inhibition of cell growth) and synergy (degree of interaction) in-vitro [3]. Patient-derived cancer cell cultures and xenograft models have been developed, which make the drug discovery closer to the actual patients [4,5,6]. With the increasing amount of drug sensitivity screening data, the challenge of translating them into actual drug discovery remains, as recent studies showed that most of clinically approved drug combinations work independently [7], that the efficacy and synergy observed in a pre-clinical setting may not be translated into a clinical trial [8,9]. The challenge of utilizing the results from drug combination screens largely resides from un-harmonized metrics for syn-
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