Discovery of novel therapeutic properties of drugs from transcriptional responses based on multi-label classification.
ABSTRACT: Drug repositioning strategies have improved substantially in recent years. At present, two advances are poised to facilitate new strategies. First, the LINCS project can provide rich transcriptome data that reflect the responses of cells upon exposure to various drugs. Second, machine learning algorithms have been applied successfully in biomedical research. In this paper, we developed a systematic method to discover novel indications for existing drugs by approaching drug repositioning as a multi-label classification task and used a Softmax regression model to predict previously unrecognized therapeutic properties of drugs based on LINCS transcriptome data. This approach to complete the said task has not been achieved in previous studies. By performing in silico comparison, we demonstrated that the proposed Softmax method showed markedly superior performance over those of other methods. Once fully trained, the method showed a training accuracy exceeding 80% and a validation accuracy of approximately 70%. We generated a highly credible set of 98 drugs with high potential to be repositioned for novel therapeutic purposes. Our case studies included zonisamide and brinzolamide, which were originally developed to treat indications of the nervous system and sensory organs, respectively. Both drugs were repurposed to the cardiovascular category.
Project description:Current pharmacological treatments for major depressive disorder (MDD) are ineffective in a significant proportion of patients, and the identification of new antidepressant compounds has been difficult. 'Connectivity mapping' is a method that can be used to identify drugs that elicit similar downstream effects on mRNA levels when compared to current treatments, and thus may point towards possible repositioning opportunities. We investigated genome-wide transcriptomic changes to human hippocampal progenitor cells treated with therapeutically relevant concentrations of a tricyclic antidepressant (nortriptyline) and a selective serotonin reuptake inhibitor (escitalopram). We identified mRNA changes common to both drugs to create an 'antidepressant mRNA signature'. We used this signature to probe the Library of Integrated Network-based Cellular Signatures (LINCS) and to identify other compounds that elicit similar changes to mRNA in neural progenitor cells. Results from LINCS revealed that the tricyclic antidepressant clomipramine elicited mRNA changes most similar to our mRNA signature, and we identified W-7 and vorinostat as functionally relevant drug candidates, which may have repositioning potential. Our results are encouraging and represent the first attempt to use connectivity mapping for drug repositioning in MDD.
Project description:Reversal of cancer gene expression is predictive of therapeutic potential and can be used to find new indications for existing drugs (drug repositioning). Gene expression reversal potential is currently calculated, in almost all studies, by pre-aggregating all tumour samples into a single group signature or a limited number of molecular subtype signatures. Here, we investigate whether drug repositioning based on individual tumour sample gene expression signatures outperforms the use of tumour group and subtype signatures. The tumour signatures were created using 534 tumour samples and 72 matched normal samples from 530 clear cell renal cell carcinoma (ccRCC) patients. More than 20,000 drug signatures were extracted from the CMAP and LINCS databases. We show that negative enrichment of individual tumour samples correlated (Spearman's rho?=?0.15) much better with the amount of differentially expressed genes in drug signatures than with the tumour group signature (Rho?=?0.08) and the 4 tumour subtype signatures (Rho 0.036-0.11). Targeted drugs used against ccRCC, such as sirolimus and temsirolimus, which could not be identified with the pre-aggregated tumour signatures could be recovered using individual sample analysis. Thus, drug repositioning can be personalized by taking into account the gene expression profile of the individual's tumour sample.
Project description:Inferring potential drug indications, for either novel or approved drugs, is a key step in drug development. Previous computational methods in this domain have focused on either drug repositioning or matching drug and disease gene expression profiles. Here, we present a novel method for the large-scale prediction of drug indications (PREDICT) that can handle both approved drugs and novel molecules. Our method is based on the observation that similar drugs are indicated for similar diseases, and utilizes multiple drug-drug and disease-disease similarity measures for the prediction task. On cross-validation, it obtains high specificity and sensitivity (AUC=0.9) in predicting drug indications, surpassing existing methods. We validate our predictions by their overlap with drug indications that are currently under clinical trials, and by their agreement with tissue-specific expression information on the drug targets. We further show that disease-specific genetic signatures can be used to accurately predict drug indications for new diseases (AUC=0.92). This lays the computational foundation for future personalized drug treatments, where gene expression signatures from individual patients would replace the disease-specific signatures.
Project description:BACKGROUND:Due to the high cost and low success rate in new drug development, systematic drug repositioning methods are exploited to find new indications for existing drugs. OBJECTIVE:We sought to propose a new computational drug repositioning method to identify repositioning drugs for Parkinson disease (PD). METHODS:We developed a novel heterogeneous network mining repositioning method that constructed a 3-layer network of disease, drug, and adverse drug reaction and involved user-generated data from online health communities to identify potential candidate drugs for PD. RESULTS:We identified 44 non-Parkinson drugs by using the proposed approach, with data collected from both pharmaceutical databases and online health communities. Based on the further literature analysis, we found literature evidence for 28 drugs. CONCLUSIONS:In summary, the proposed heterogeneous network mining repositioning approach is promising for identifying repositioning candidates for PD. It shows that adverse drug reactions are potential intermediaries to reveal relationships between disease and drug.
Project description:Objective:Drug repositioning is a promising methodology for reducing the cost and duration of the drug discovery pipeline. We sought to develop a computational repositioning method leveraging annotations in the literature, such as Medical Subject Heading (MeSH) terms. Methods:We developed software to determine significantly co-occurring drug-MeSH term pairs and a method to estimate pair-wise literature-derived distances between drugs. Results:We found that literature-based drug-drug similarities predicted the number of shared indications across drug-drug pairs. Clustering drugs based on their similarity revealed both known and novel drug indications. We demonstrate the utility of our approach by generating repositioning hypotheses for the commonly used diabetes drug metformin. Conclusion:Our study demonstrates that literature-derived similarity is useful for identifying potential repositioning opportunities. We provided open-source code and deployed a free-to-use, interactive application to explore our database of similarity-based drug clusters (available at http://apps.chiragjpgroup.org/MeSHDD/ ).
Project description:Drug repositioning helps fully explore indications for marketed drugs and clinical candidates. Here we show that the clinical side-effects (SEs) provide a human phenotypic profile for the drug, and this profile can suggest additional disease indications. We extracted 3,175 SE-disease relationships by combining the SE-drug relationships from drug labels and the drug-disease relationships from PharmGKB. Many relationships provide explicit repositioning hypotheses, such as drugs causing hypoglycemia are potential candidates for diabetes. We built Naïve Bayes models to predict indications for 145 diseases using the SEs as features. The AUC was above 0.8 in 92% of these models. The method was extended to predict indications for clinical compounds, 36% of the models achieved AUC above 0.7. This suggests that closer attention should be paid to the SEs observed in trials not just to evaluate the harmful effects, but also to rationally explore the repositioning potential based on this "clinical phenotypic assay".
Project description:The application of established drug compounds to new therapeutic indications, known as drug repositioning, offers several advantages over traditional drug development, including reduced development costs and shorter paths to approval. Recent approaches to drug repositioning use high-throughput experimental approaches to assess a compound's potential therapeutic qualities. Here, we present a systematic computational approach to predict novel therapeutic indications on the basis of comprehensive testing of molecular signatures in drug-disease pairs. We integrated gene expression measurements from 100 diseases and gene expression measurements on 164 drug compounds, yielding predicted therapeutic potentials for these drugs. We recovered many known drug and disease relationships using computationally derived therapeutic potentials and also predict many new indications for these 164 drugs. We experimentally validated a prediction for the antiulcer drug cimetidine as a candidate therapeutic in the treatment of lung adenocarcinoma, and demonstrate its efficacy both in vitro and in vivo using mouse xenograft models. This computational method provides a systematic approach for repositioning established drugs to treat a wide range of human diseases.
Project description:The identification of the modes of action of bioactive compounds is a major challenge in chemical systems biology of diseases. Genome-wide expression profiling of transcriptional responses to compound treatment for human cell lines is a promising unbiased approach for the mode-of-action analysis. Here we developed a novel approach to elucidate the modes of action of bioactive compounds in a cell-specific manner using large-scale chemically-induced transcriptome data acquired from the Library of Integrated Network-based Cellular Signatures (LINCS), and analyzed 16,268 compounds and 68 human cell lines. First, we performed pathway enrichment analyses of regulated genes to reveal active pathways among 163 biological pathways. Next, we explored potential target proteins (including primary targets and off-targets) with cell-specific transcriptional similarity using chemical-protein interactome. Finally, we predicted new therapeutic indications for 461 diseases based on the target proteins. We showed the usefulness of the proposed approach in terms of prediction coverage, interpretation, and large-scale applicability, and validated the new prediction results experimentally by an in vitro cellular assay. The approach has a high potential for advancing drug discovery and repositioning.
Project description:Drug repositioning, the process of discovering, validating, and marketing previously approved drugs for new indications, is of growing interest to academia and industry due to reduced time and costs associated with repositioned drugs. Computational methods for repositioning are appealing because they putatively nominate the most promising candidate drugs for a given indication. Comparing the wide array of computational repositioning methods, however, is a challenge due to inconsistencies in method validation in the field. Furthermore, a common simplifying assumption, that all novel predictions are false, is intellectually unsatisfying and hinders reproducibility. We address this assumption by providing a gold standard database, repoDB, that consists of both true positives (approved drugs), and true negatives (failed drugs). We have made the full database and all code used to prepare it publicly available, and have developed a web application that allows users to browse subsets of the data (http://apps.chiragjpgroup.org/repoDB/).
Project description:<h4>Background</h4>Drug repositioning, finding new indications for existing drugs, has gained much recent attention as a potentially efficient and economical strategy for accelerating new therapies into the clinic. Although improvement in the sensitivity of computational drug repositioning methods has identified numerous credible repositioning opportunities, few have been progressed. Arguably the "black box" nature of drug action in a new indication is one of the main blocks to progression, highlighting the need for methods that inform on the broader target mechanism in the disease context.<h4>Results</h4>We demonstrate that the analysis of co-expressed genes may be a critical first step towards illumination of both disease pathology and mode of drug action. We achieve this using a novel framework, co-expressed gene-set enrichment analysis (cogena) for co-expression analysis of gene expression signatures and gene set enrichment analysis of co-expressed genes. The cogena framework enables simultaneous, pathway driven, disease and drug repositioning analysis. Cogena can be used to illuminate coordinated changes within disease transcriptomes and identify drugs acting mechanistically within this framework. We illustrate this using a psoriatic skin transcriptome, as an exemplar, and recover two widely used Psoriasis drugs (Methotrexate and Ciclosporin) with distinct modes of action. Cogena out-performs the results of Connectivity Map and NFFinder webservers in similar disease transcriptome analyses. Furthermore, we investigated the literature support for the other top-ranked compounds to treat psoriasis and showed how the outputs of cogena analysis can contribute new insight to support the progression of drugs into the clinic. We have made cogena freely available within Bioconductor or https://github.com/zhilongjia/cogena .<h4>Conclusions</h4>In conclusion, by targeting co-expressed genes within disease transcriptomes, cogena offers novel biological insight, which can be effectively harnessed for drug discovery and repositioning, allowing the grouping and prioritisation of drug repositioning candidates on the basis of putative mode of action.