A phenome-guided drug repositioning through a latent variable model

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• 作者：Halil Bisgin ; Zhichao Liu ; Hong Fang ; Reagan Kelly ; Xiaowei Xu…
• 关键词：Drug repositioning ; Bayesian methods ; Latent dirichlet allocation ; Data mining ; Phenome ; Side effects ; Indications
• 刊名：BMC Bioinformatics
• 出版年：2014
• 出版时间：December 2014
• 年：2014
• 卷：15
• 期：1
• 全文大小：1,240 KB
• 参考文献：1. Mahner, M, Kary, M (1997) What exactly are genomes, genotypes and phenotypes? And what about phenomes?. J Theor Biol 186: pp. 55-63 CrossRef
  2. Freimer, N, Sabatti, C (2003) The Human Phenome Project. Nat Genet 34: pp. 15-21 CrossRef
  3. Rzhetsky, A, Wajngurt, D, Park, N, Zheng, T (2007) Probing genetic overlap among complex human phenotypes. Proc Natl Acad Sci U S A 104: pp. 11694-11699 CrossRef
  4. van Driel, MA, Bruggeman, J, Vriend, G, Brunner, HG, Leunissen, JA (2006) A text-mining analysis of the human phenome. Eur J Hum Genet 14: pp. 535-542 CrossRef
  5. Wu, X, Jiang, R, Zhang, MQ, Li, S (2008) Network-based global inference of human disease genes. Mol Syst Biol 4: pp. 189 CrossRef
  6. Wu, X, Liu, Q, Jiang, R (2009) Align human interactome with phenome to identify causative genes and networks underlying disease families. Bioinformatics 25: pp. 98-104 CrossRef
  7. Dudley, JT, Deshpande, T, Butte, AJ (2011) Exploiting drug disease relationships for computational drug repositioning. Brief Bioinform 12: pp. 303-311 CrossRef
  8. Ekins, S, Williams, AJ, Krasowski, MD, Freundlich, JS (2011) In silico repositioning of approved drugs for rare and neglected diseases. Drug Discov Today 16: pp. 298-310 CrossRef
  9. Iorio, F, Bosotti, R, Scacheri, E, Belcastro, V, Mithbaokar, P, Ferriero, R, Murino, L, Tagliaferri, R, Brunetti-Pierri, N, Isacchi, A, Bernardoa, D (2010) Discovery of drug mode of action and drug repositioning from transcriptional responses. Proc Natl Acad Sci 107: pp. 14621-14626 CrossRef
  10. Keiser, MJ, Setola, V, Irwin, JJ, Laggner, C, Abbas, AI, Hufeisen, SJ, Jensen, NH, Kuijer, MB, Matos, RC, Tran, TB, Whaley, R, Glennon, RA, Hert, J, Thomas, KLH, Edwards, DD, Shoichet, BK, Roth, BL (2009) Predicting new molecular targets for known drugs. Nature 462: pp. 175-181 CrossRef
  11. Sardana, D, Zhu, C, Zhang, M, Gudivada, RC, Jegga, AG (2011) Yang L. Jegga AG, Drug repositioning for orphan diseases. Briefings in Bioinformatics
  12. Liu, Z, Fang, H, Reagan, K, Xu, X, Mendrick, DL, Slikker, W, Tong, W (2013) In silico drug repositioning: what we need to know. Drug Discov Today 18: pp. 110-115 CrossRef
  13. Yang, L, Agarwal, P (2011) Systematic Drug Repositioning Based on Clinical Side-Effects. PLoS ONE 6: pp. e28025 CrossRef
  14. Napolitano, F, Zhao, Y, Moreira, V, Tagliaferri, R, Kere, J, D’Amato, M, Greco, D (2013) Drug repositioning: a machine-learning approach through data integration. J Cheminformatics C7 - 30 5: pp. 1-9 CrossRef
  15. Sirota, M, Dudley, JT, Kim, J, Chiang, AP, Morgan, AA, Sweet-Cordero, A, Sage, J, Butte, AJ (2011) Discovery and preclinical validation of drug indications using compendia of public gene expression data. Sci Transl Med 3: pp. 96ra77 CrossRef
  16. Dudley, JT, Sirota, M, Shenoy, M, Pai, RK, Roedder, S, Chiang, AP, Morgan, AA, Sarwal, MM, Pasricha, PJ, Butte, AJ (2011) Computational repositioning of the anticonvulsant topiramate for inflammatory bowel disease. Sci Transl Med 3: pp. 96ra76 CrossRef
  17. Campillos, M, Kuhn, M, Gavin, A-C, Jensen, LJ, Bork, P (2008) Drug Target Identification Using Side-Effect Similarity. Science 32
• 刊物主题：Bioinformatics; Microarrays; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Combinatorial Libraries; Algorithms;
• 出版者：BioMed Central
• ISSN：1471-2105

文摘

Background The phenome represents a distinct set of information in the human population. It has been explored particularly in its relationship with the genome to identify correlations for diseases. The phenome has been also explored for drug repositioning with efforts focusing on the search space for the most similar candidate drugs. For a comprehensive analysis of the phenome, we assumed that all phenotypes (indications and side effects) were inter-connected with a probabilistic distribution and this characteristic may offer an opportunity to identify new therapeutic indications for a given drug. Correspondingly, we employed Latent Dirichlet Allocation (LDA), which introduces latent variables (topics) to govern the phenome distribution. Results We developed our model on the phenome information in Side Effect Resource (SIDER). We first developed a LDA model optimized based on its recovery potential through perturbing the drug-phenotype matrix for each of the drug-indication pairs where each drug-indication relationship was switched to “unknown-one at the time and then recovered based on the remaining drug-phenotype pairs. Of the probabilistically significant pairs, 70% was successfully recovered. Next, we applied the model on the whole phenome to narrow down repositioning candidates and suggest alternative indications. We were able to retrieve approved indications of 6 drugs whose indications were not listed in SIDER. For 908 drugs that were present with their indication information, our model suggested alternative treatment options for further investigations. Several of the suggested new uses can be supported with information from the scientific literature. Conclusions The results demonstrated that the phenome can be further analyzed by a generative model, which can discover probabilistic associations between drugs and therapeutic uses. In this regard, LDA serves as an enrichment tool to explore new uses of existing drugs by narrowing down the search space.