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Cobanoglu, Murat Can (2015) PROBABILISTIC LATENT FACTOR MODELS FOR TRANSFORMATIVE DRUG DISCOVERY. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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The cost of discovering a new drug has doubled every 9 years since the 1950s. This can change by using machine learning to guide experimentation. The idea I have developed over the course of my PhD is that using latent factor modeling (LFM) of the drug-target interaction network, we can guide drug repurposable efforts to achieve transformative improvements. By better characterizing the drug-target interaction network, it is possible to use currently approved drugs to achieve therapies for diseases that currently are not optimally treated. These drugs might be directly used through repurposing, or they can serve as a starting point for new drug discovery efforts where they are optimized through medicinal chemistry methods. To achieve this goal, I have developed LFM-based techniques applicable to existing databases of drug-target interaction networks. Specifically, I have started out by establishing that probabilistic matrix factorization (PMF; one type of LFM algorithm) can be used as descriptors by showing they capture therapeutic function similarities that state-of-the-art 3D chemical similarity methods could not capture. Then I have shown that PMF can effectively predict unknown drug-target interactions. Furthermore, I have used newly developed computational techniques for discovering repurposable drugs for two diseases, α1 antitrypsin (1-AT) deficiency (ATD) and Huntington’s disease (HD) leading to successful discoveries in both. For ATD, two sets of data generated by the David Perlmutter and Gary Silverman laboratories have been used as input to deduce potential targets and repurposable drugs: (i) a high throughput screening data from a genome-wide RNAi knockdown in a C. elegans model for studying ATZ (Z-allele of 1-AT), and (ii) data from Prestwick library screen for the same model. We have predicted that the antidiabetic drug glibenclamide would be beneficial against ATZ aggregation, and data collected to date in Mus musculus models are promising. We have worked on HD with the Robert Friedlander lab, by examining the potential drugs and implicated pathways for 15 neuroprotective (repurposable) drugs that they have identified in a two-stage screening study. Based on LFM-based analysis of the targets of these drugs, we have developed a number of hypotheses to be tested. Among them, the antihypertensive drug sodium nitroprusside appears to be effective against HD based on neuronal cell death inhibition experiments that were conducted at the University of Pittsburgh Drug Discovery Institute as well as the Friedlander lab. Finally, we have built a web server, named BalestraWeb, for facilitating the use of PMF in repurposable drug identification by the broader community. BalestraWeb enables users to extract information on known and potential targets (or drugs) for any approved drug (or target), simply by entering the name of the query drug (or target). I have also laid out the framework for developing an integrated resource for quantitative systems pharmacology, Balestra toolkit (BalestraTK), which would take advantage of existing databases such as STITCH, UniProt, and PubChem. Collectively, our results provide firm evidence for the potential utility of machine learning techniques for assisting in drug discovery.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Cobanoglu, Murat Canmuc3@pitt.eduMUC3
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairOltvai, Zoltán N.oltvai@pitt.eduOLTVAI
Thesis AdvisorBahar, Ivetbahar@pitt.eduBAHAR
Thesis AdvisorTaylor, D. Lansingdltaylor@pitt.eduDLTAYLOR
Date: 10 July 2015
Date Type: Publication
Defense Date: 9 June 2015
Approval Date: 10 July 2015
Submission Date: 7 July 2015
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 234
Institution: University of Pittsburgh
Schools and Programs: School of Medicine > Computational and Systems Biology
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: machine learning, drug discovery
Date Deposited: 10 Jul 2015 19:53
Last Modified: 15 Nov 2016 14:29


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