CDCDB: A large and continuously updated drug combination database

Abstract

In recent years, due to the complementary action of drug combinations over mono-therapy, the multiple-drugs for multiple-targets paradigm has received increased attention to treat bacterial infections and complex diseases. Although new drug combinations screening has benefited from experimental tests like automated high throughput screening, it is limited due to the large number of possible drug combinations. The task of drug combination screening can be streamlined through computational methods and models. Such models require up-to-date databases; however, existing databases are static and consist of the data collected at the time of their creation. This paper introduces the Continuous Drug Combination Database (CDCDB), a continuously updated drug combination database. The CDCDB includes over 40,795 drug combinations, of which 17,107 are unique combinations consisting of more than 4,129 individual drugs, curated from ClinicalTrials.gov, the FDA Orange Book®, and patents. To create CDCDB, we use various methods, including natural language processing techniques, to improve the process of drug combination discovery, ensuring that our database can be used for drug synergy prediction. Website: https://icc.ise.bgu.ac.il/medical_ai/CDCDB/.

Multi-task learning for predicting synergistic drug combinations based on auto-encoding multi-relational graphs

“…We obtained a dataset from Continuous Drug Combination Database (CDCDB), 26 a comprehensive drug combination database containing more than 40,795 drug combinations. We extracted the DrugBank identifiers (IDs) that are related to the FDA Orange Book and the Medical Subject Headings (MeSH) terms that describe the conditions that are treated in the study from our database.…”

“… 26 1105 Diseases Shtar, G. et al. 26 1283 Training data Shtar, G. et al. 26 53694 Testing data Shtar, G. et al.…”

Predicting anti-cancer drug combination responses with a temporal cell state network model

“…Assumptions regarding the additivity (or not) of multi-drug action on transition probabilities would have to be asserted. The current availability of drug combination response data sets [ 74 , 75 , 94 , 95 ] could facilitate the testing of such methods. Such future work could explore drug combination features we did not consider here, such as combining drugs that are not effective as single agents.…”

Predicting Anti-Cancer Drug Combination Responses with a Temporal Cell State Network Model

“…Assumptions regarding the additivity (or not) of multi-drug action on transition rates would have to be asserted. The current availability of drug combination response data sets [79][80][81][82] could facilitate the testing of such methods. Such future work could explore drug combination features we did not here, such as combining drugs that are not effective as single agents.…”