Computational repositioning of the anticonvulsant topiramate for inflammatory bowel disease

By Joel T. Dudley, Marina Sirota, M. Shenoy, R. Pai, S. Roedder, Annie P. Chiang, Alexander A. Morgan, M. Sarwal, P. Pasricha, Atul J. Butte. 2011

561 citations recorded by the source corpus.

Computationally predicted repositioning of an anticonvulsant for inflammatory bowel disease is confirmed experimentally. Greening Drug Discovery Recycling is good for the environment—and for drug development too. Repurposing existing, approved drugs can speed their adoption in the clinic because they can often take advantage of the existing rigorous safety testing required by the Food and Drug Administration and other regulatory agencies. In a pair of papers, Sirota et al. and Dudley et al. examined publicly available gene expression data and determined the genes affected in 100 diseases and 164 drugs. By pairing drugs that correct abnormal gene expression in diseases, they confirm known effective drug-disease pairs and predict new indications for already approved agents. Experimental validation that an antiulcer drug and an antiepileptic can be reused for lung cancer and inflammatory bowel disease reinforces the promise of this approach. The authors scrutinized the data in Gene Expression Omnibus and identified a disease signature for 100 diseases, which they defined as the set of mRNAs that reliably increase or decrease in patients with that disease compared to normal individuals. They compared each of these disease signatures to each of the gene expression signatures for 164 drugs from the Connectivity Map, a collection of mRNA expression data from cultured human cells treated with bioactive small molecules that is maintained at the Broad Institute at Massachusetts Institute of Technology. A similarity score calculated by the authors for every possible pair of drug and disease ranged from +1 (a perfect correlation of signatures) to −1 (exactly opposite signatures). The investigators suggested that a similarity score of −1 would predict that the drug would ameliorate the abnormalities in the disease and thus be an effective therapy. This proved to be true for a number of drugs already on the market. The corticosteroid prednisolone, a common treatment for Crohn’s disease and ulcerative colitis, showed a strong similarity score for these two diseases. The histone deacetylase inhibitors trichostatin A, valproic acid, and vorinostat were predicted to work against brain tumors and other cancers (esophagus, lung, and colon), and there is experimental evidence that this is indeed the case. But in the ultimate test of method, the authors confirmed two new predictions in animal experiments: Cimetidine, an antiulcer drug, predicted by the authors to be effective against lung cancer, inhibited tumor cells in vitro and in vivo in mice. In addition, the antiepileptic topiramate, predicted to improve inflammatory bowel disease by similarity score, improved damage in colon tissue of rats treated with trinitrobenzenesulfonic acid, a model of the disease. These two drugs are therefore good candidates for recycling to treat two diseases in need of better therapies—lung cancer and inflammatory bowel disease—and we now have a way to mine available data for fast routes to new disease therapies. Inflammatory bowel disease (IBD) is a chronic inflammatory disorder of the gastrointestinal tract for which there are few safe and effective therapeutic options for long-term treatment and disease maintenance. Here, we applied a computational approach to discover new drug therapies for IBD in silico, using publicly available molecular data reporting gene expression in IBD samples and 164 small-molecule drug compounds. Among the top compounds predicted to be therapeutic for IBD by our approach were prednisolone, a corticosteroid used to treat IBD, and topiramate, an anticonvulsant drug not previously described to have efficacy for IBD or any related disorders of inflammation or the gastrointestinal tract. Using a trinitrobenzenesulfonic acid (TNBS)–induced rodent model of IBD, we experimentally validated our topiramate prediction in vivo. Oral administration of topiramate significantly reduced gross pathological signs and microscopic damage in primary affected colon tissue in the TNBS-induced rodent model of IBD. These findings suggest that topiramate might serve as a therapeutic option for IBD in humans and support the use of public molecular data and computational approaches to discover new therapeutic options for disease.

Subfields: Genetics, Infectious Diseases, Oncology

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In Genetics

In Infectious Diseases

Pankaj Agarwal

In Oncology

Atul J. Butte

Citations in the corpus

SWEETLEAD: an In Silico Database of Approved Drugs, Regulated Chemicals, and Herbal Isolates for Computer-Aided Drug Discovery (2013, neutral): “promising new therapeutic strategies[15-20].”
The Emerging Paradigm of Network Medicine in the Study of Human Disease (2012, background): “84 As a result, the use of existing gene coexpression networks in combination with the known molecular interactions of existing drugs has yielded more comprehensive insight into the pleiotropic effects of various medicinal compounds.”
A survey of current trends in computational drug repositioning (2016, result): “[19] inferred several new interesting drug–disease pairs and validated one pair in IBD preclinical models.”
Drug repositioning for non-small cell lung cancer by using machine learning algorithms and topological graph theory (2016, methodology): “Many works on identifying repositioned drugs have been based on various computational methods, such as mapping gene expression profiles using drug response profiles [10–14], the use of side-effect-based similarities [15–17], heterogeneous network clustering [18], and the graph-based inference method”
Genomic responses in mouse models poorly mimic human inflammatory diseases (2013, background): “Furthermore, new genomic information, such as the availability of personal genomes (32) or exomes (33) to capture the disease heterogeneity directly from patients or systematic interpretation of genome-wide signatures in human diseases (34, 35), will likely complement or short circuit the need for m”
Predicting drug-target interactions using restricted Boltzmann machines (2013, unknown): “(2011), He et al .”
ImmPort: disseminating data to the public for the future of immunology (2014, background): “We and others have shown that integrating across publicly available datasets can yield important new diagnostics [19], drug targets [20], causal factors [21], and even new therapeutic uses for drugs [22, 23].”
Prediction of Drug-Target Interactions and Drug Repositioning via Network-Based Inference (2012, background): “Recently Butte group also reported two successful examples of drug repositioning based on public gene expression data [15,16].”
Drug repositioning: a machine-learning approach through data integration (2013, unknown): “Remarkably, this kind of approach has already led to the identification and experimental validation of novel therapeutic indications for the antiepileptic topiramate in inflammatory bowel disease (IBD) [6].”
Computational Drug Repositioning: From Data to Therapeutics (2013, methodology): “17 The CMap database can also be used to identify connections between the drugs themselves.”
Axes of a revolution: challenges and promises of big data in healthcare (2020, methodology): “Another example is the use of publicly available molecular data for the discovery of new candidate therapies for inflammatory bowel disease 111 .”
A review of connectivity map and computational approaches in pharmacogenomics (2017, background): “Put simply, the goal of the CMap in genomic drug discovery studies is to identify disease or drug-associated gene signatures that correlate with perturbations on the transcriptomics level as a response to administrated small molecules or drugs [14].”