In silico methods for drug repurposing and pharmacology

Abstract

Data in the biological, chemical, and clinical domains are accumulating at ever-increasing rates and have the potential to accelerate and inform drug development in new ways. Challenges and opportunities now lie in developing analytic tools to transform these often complex and heterogeneous data into testable hypotheses and actionable insights. This is the aim of computational pharmacology, which uses in silico techniques to better understand and predict how drugs affect biological systems, which can in turn improve clinical use, avoid unwanted side effects, and guide selection and development of better treatments. One exciting application of computational pharmacology is drug repurposing- finding new uses for existing drugs. Already yielding many promising candidates, this strategy has the potential to improve the efficiency of the drug development process and reach patient populations with previously unmet needs such as those with rare diseases. While current techniques in computational pharmacology and drug repurposing often focus on just a single data modality such as gene expression or drug-target interactions, we rationalize that methods such as matrix factorization that can integrate data within and across diverse data types have the potential to improve predictive performance and provide a fuller picture of a drug's pharmacological action.

Search for in-Silico Applications in Drug Discovery and Applications of Different Disciplines in It: A Survey

“…Pharmacological studies are performed in target and ligand screening, drug repourposing and clinical testing of a drug. Some of the computer based methods regarding these uses of pharmacology can be found in [49] - [52].…”

Revisiting Cross-Validation of Drug Similarity Based Classifiers Using Paired Data

“…A number of studies use chemical, target and side effect similarity between drugs to build knowledge-based models that predict drug indications and drug-drug interactions [1][2][3]. The proposed models are typically benchmarked using cross-validation, in which the known drug-disease or drug-drug associations are split into training and test sets.…”

“…The proposed models are typically benchmarked using cross-validation, in which the known drug-disease or drug-drug associations are split into training and test sets. Though these methods report areas under receiver operating characteristic (ROC) curves around 90% under cross-validation, their applicability in translational medicine and, thus, ability to reduce drug development costs has been controversial [2,4,5].…”

“…The proposed models are typically benchmarked using cross-validation, in which the known drug-disease or drug-drug associations are split into training and test sets. Though these methods report areas under receiver operating characteristic (ROC) curves around 90% under cross-validation, their applicability in translational medicine and, thus, ability to reduce drug development costs has been controversial [2,4,5].In light of previous works highlighting the perils of cross-validation using paired data [6,7], we recently investigated the effect of using drug-wise disjoint cross-validation in predicting drug-disease pairs, where none of the drugs in the training set appeared in the test set [8]. We showed that the prediction accuracy of the classifier drops dramatically under such cross-validation setting, suggesting that the existing approaches are prone to over-fitting due to the inherent relationships in the data.…”

Reproducible Drug Repurposing: When Similarity Does Not Suffice

“…The areas under reciver operating characteristic (ROC) curves in the cross validation analysis reported for these models range between 75-95%, suggesting that some of these models can accurately identify novel drugdisease associations. Nevertheless, in reality, the applicability of these methods for discovery of novel drug-disease associations has been limited due to "the reliance on data existing nearby in pharmacological space" as highlighted by Hodos et al 2 Moreover, Vilar and colleagues alert the community about the potential "upstream bias introduced with the information provided in the construction of the similarity measurement" in similarity-based predictors. 12 Yet, since many studies do not provide the data and code used to build the models for repurposing, it is often cumbersome to validate, reproduce and reuse the underlying methodology.…”