Discovery and preclinical validation of drug indications using compendia of public gene expression data

By Marina Sirota, Joel T. Dudley, Jeewon Kim, Annie P. Chiang, Alexander A. Morgan, A. Sweet-Cordero, Julien Sage, Atul J. Butte. Science Translational Medicine, 2011

746 citations recorded by the source corpus.

A systematic computational method predicts new uses for existing drugs by integrating public gene expression signatures of drugs and diseases. 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. 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.

Subfields: Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/b2b8f9ae9c48f7183fb2e52ed014ba5f048a8ad4

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

In Molecular Biology

Citations in the corpus

A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information (2017, background): “In addition to chemical and genomic data, previous works have incorporated pharmacological or phenotypic information, such as side-effect [7, 41], transcriptional response data [24], drug-disease associations [70], public gene expression data [18, 53] and functional data [77] for DTI prediction.”
The Emerging Paradigm of Network Medicine in the Study of Human Disease (2012, background): “83 In a separate set of experiments, another prediction regarding the potential for the anticonvulsant topiramate in treatment of inflammatory bowel disease was successfully validated in a rodent model of inflammatory bowel disease.”
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”
Systems biology approaches for advancing the discovery of effective drug combinations (2015, methodology): “An advantage of this approach is the ability to query CMap with publicly available gene expression data from sources such as Gene Expression Omnibus (GEO) [53], therefore facilitating rapid drug combinations prediction for experimental validation [54].”
Network-based in silico drug efficacy screening (2016, neutral): “Gene expression proﬁle consistency based approaches aim to overcome these limitations by investigating correlations between the expression signatures of drug perturbations and the expression proﬁles in diseases 22,23 .”
Automatic construction of a large-scale and accurate drug-side-effect association knowledge base from biomedical literature (2014, background): “The majority of these approaches leverage on known drug properties such as chemical similarity [15], molecular activity similarity [20], molecular docking [17], and gene expression profile similarity [13, 24].”
Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities (2018, methodology): “egorized into four groups: (1) methods that predict new uses for existing drugs on the basis of protein target interaction networks [307, 308, 309, 310, 272], (2) methods that make predictions by analyzing gene expression activation following various drug treatment regimes [311, 312], (3) methods th”
Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review (2012, background): “On the contrary, positively correlated profiles may reveal side effects (Dudley et al., 2011; Sirota et al., 2011; Iskar et al., 2012; Pacini et al., 2013).”
A review of connectivity map and computational approaches in pharmacogenomics (2017, background): “[94] integrated a new gene expression database from 100 diseases and 164 drug compounds, yielding predictions for all drug compounds that show a high consistency with already known therapeutics.”
Computational repositioning of the anticonvulsant topiramate for inflammatory bowel disease (2011, background): “Computational prediction and assessment of novel IBD therapies Computational prediction and assessment of novel IBD therapies was performed as described in a paper appearing in this issue (4).”
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”
Computational Drug Repositioning: From Data to Therapeutics (2013, background): “These contain raw data and processed results from thousands of individual experiments, making it possible to probe the CMap database with collections of disease signatures 21,22 produced by other researchers.”
Predicting drug-target interactions using restricted Boltzmann machines (2013, neutral): “(2010) and Perlman et al .”