Prediction of Drug-Target Interactions for Drug Repositioning Only Based on Genomic Expression Similarity

Abstract

Small drug molecules usually bind to multiple protein targets or even unintended off-targets. Such drug promiscuity has often led to unwanted or unexplained drug reactions, resulting in side effects or drug repositioning opportunities. So it is always an important issue in pharmacology to identify potential drug-target interactions (DTI). However, DTI discovery by experiment remains a challenging task, due to high expense of time and resources. Many computational methods are therefore developed to predict DTI with high throughput biological and clinical data. Here, we initiatively demonstrate that the on-target and off-target effects could be characterized by drug-induced in vitro genomic expression changes, e.g. the data in Connectivity Map (CMap). Thus, unknown ligands of a certain target can be found from the compounds showing high gene-expression similarity to the known ligands. Then to clarify the detailed practice of CMap based DTI prediction, we objectively evaluate how well each target is characterized by CMap. The results suggest that (1) some targets are better characterized than others, so the prediction models specific to these well characterized targets would be more accurate and reliable; (2) in some cases, a family of ligands for the same target tend to interact with common off-targets, which may help increase the efficiency of DTI discovery and explain the mechanisms of complicated drug actions. In the present study, CMap expression similarity is proposed as a novel indicator of drug-target interactions. The detailed strategies of improving data quality by decreasing the batch effect and building prediction models are also effectively established. We believe the success in CMap can be further translated into other public and commercial data of genomic expression, thus increasing research productivity towards valid drug repositioning and minimal side effects.

Some leopards can change their spots: potential repositioning of stem cell reprogramming compounds as anti-cancer agents

“…In this commentary, we have discussed the investigation of anti-cancer activity in cell reprogramming compounds, based on the Btraditionall aboratory approach of cell-based assays and animal model testing. However, there are alternative strategies to facilitate repositioning, such as in silico-based methodologies utilizing large-scale virtual screening of compound libraries or compounds approved for human use against large numbers of protein targets (highthroughput shotgun repurposing) (Wang et al 2013). We hope that this commentary illustrates the link between some cell reprogramming compounds and potential anti-cancer activity, based on the modulation of target proteins that are important regulators in both cell reprogramming and carcinogenesis.…”

Systematic drug safety evaluation based on public genomic expression (Connectivity Map) data: Myocardial and infectious adverse reactions as application cases

“…2A). To verify this theory, we adopted a previously published naïve model [20] that equally weights expression similarity to individual reference drugs and translates ADR risk into a 'risk score' (see Methods).…”

“…Since cell cultures treated with the same compound but in different batches should differ more in batch variation and less in drug action, we used the expression profiles of these cultures to estimate and normalize batch variation. The signatures of normalized expression profiles were compared to each other using the Gene Set Enrichment Analysis (GSEA) algorithm [19], so the similarity between different drugs could be measured by Bridge Adjusted Expression Similarity (BAES) [20].…”

Pred-binding: large-scale protein–ligand binding affinity prediction

“…These drug target interactions (DTIs) are meaningful to explain biological activities of these proteins. Indeed, some small chemical binders target to multiple proteins and sometimes unfortunately bind to unwanted off-targets 3 . Such unwanted DTIs could be severe and cause harmful side effects.…”

“…Among ligand-based methods, we can cite quantitative structure-activity relationships (QSAR) and a similarity search-based approach are most reported and used widely 1 . On the other hand, receptor-based methods, such as reverse docking, have also been applied in drug-target-binding affinity prediction, DTI prediction and drug repositioning 3,7 . In docking method, the structures are evaluated on the basis of a force field or a scoring function 8 .…”