Drug and Alcohol Rehabilitation: Protein-Chemical Interaction Prediction via Kernelized Sparse Learning Svm.

Protein-chemical interaction prediction via kernelized sparse learning svm.

Filed under: Drug and Alcohol Rehabilitation

Pac Symp Biocomput. 2013; 41-52
Shi Y, Zhang X, Liao X, Lin G, Schuurmans D

Given the difficulty of experimental determination of drug-protein interactions, there is a significant motivation to develop effective in silico prediction methods that can provide both new predictions for experimental verification and supporting evidence for experimental results. Most recently, classification methods such as support vector machines (SVMs) have been applied to drug-target prediction. Unfortunately, these methods generally rely on measures of the maximum “local similarity” between two protein sequences, which could mask important drug-protein interaction information since drugs are much smaller molecules than proteins and drug-target binding regions must comprise only small local regions of the proteins. We therefore develop a novel sparse learning method that considers sets of short peptides. Our method integrates feature selection, multi-instance learning, and Gaussian kernelization into an L(1) norm support vector machine classifier. Experimental results show that it not only outperformed the previous methods but also pointed to an optimal subset of potential binding regions. Supplementary materials are available at “www.cs.ualberta.ca/~ys3/drug_target”.
HubMed – drug

 

A novel multi-modal drug repurposing approach for identification of potent ack1 inhibitors.

Filed under: Drug and Alcohol Rehabilitation

Pac Symp Biocomput. 2013; 29-40
Phatak SS, Zhang S

Exploiting drug polypharmacology to identify novel modes of actions for drug repurposing has gained significant attentions in the current era of weak drug pipelines. From a serendipitous to systematic or rational ways, a variety of unimodal computational approaches have been developed but the complexity of the problem clearly needs multi-modal approaches for better solutions. In this study, we propose an integrative computational framework based on classical structure-based drug design and chemical-genomic similarity methods, combined with molecular graph theories for this task. Briefly, a pharmacophore modeling method was employed to guide the selection of docked poses resulting from our high-throughput virtual screening. We then evaluated if complementary results (hits missed by docking) can be obtained by using a novel chemo-genomic similarity approach based on chemical/sequence information. Finally, we developed a bipartite-graph based on the extensive data curation of DrugBank, PDB, and UniProt. This drug-target bipartite graph was used to assess similarity of different inhibitors based on their connections to other compounds and targets. The approaches were applied to the repurposing of existing drugs against ACK1, a novel cancer target significantly overexpressed in breast and prostate cancers during their progression. Upon screening of ?1,447 marketed drugs, a final set of 10 hits were selected for experimental testing. Among them, four drugs were identified as potent ACK1 inhibitors. Especially the inhibition of ACK1 by Dasatinib was as strong as IC(50)=1nM. We anticipate that our novel, integrative strategy can be easily extended to other biological targets with a more comprehensive coverage of known bio-chemical space for repurposing studies.
HubMed – drug

 

Predictive systems biology approach to broad-spectrum, host-directed drug target discovery in infectious diseases.

Filed under: Drug and Alcohol Rehabilitation

Pac Symp Biocomput. 2013; 17-28
Felciano RM, Bavari S, Richards DR, Billaud JN, Warren T, Panchal R, Krämer A

Knowledge of immune system and host-pathogen pathways can inform development of targeted therapies and molecular diagnostics based on a mechanistic understanding of disease pathogenesis and the host response. We investigated the feasibility of rapid target discovery for novel broad-spectrum molecular therapeutics through comprehensive systems biology modeling and analysis of pathogen and host-response pathways and mechanisms. We developed a system to identify and prioritize candidate host targets based on strength of mechanistic evidence characterizing the role of the target in pathogenesis and tractability desiderata that include optimal delivery of new indications through potential repurposing of existing compounds or therapeutics. Empirical validation of predicted targets in cellular and mouse model systems documented an effective target prediction rate of 34%, suggesting that such computational discovery approaches should be part of target discovery efforts in operational clinical or biodefense research initiatives. We describe our target discovery methodology, technical implementation, and experimental results. Our work demonstrates the potential for in silico pathway models to enable rapid, systematic identification and prioritization of novel targets against existing or emerging biological threats, thus accelerating drug discovery and medical countermeasures research.
HubMed – drug

 

Evaluation of analytical methods for connectivity map data.

Filed under: Drug and Alcohol Rehabilitation

Pac Symp Biocomput. 2013; 5-16
Cheng J, Xie Q, Kumar V, Hurle M, Freudenberg JM, Yang L, Agarwal P

Connectivity map data and associated methodologies have become a valuable tool in understanding drug mechanism of action (MOA) and discovering new indications for drugs. However, few systematic evaluations have been done to assess the accuracy of these methodologies. One of the difficulties has been the lack of benchmarking data sets. Iskar et al. (PLoS. Comput. Biol. 6, 2010) predicted the Anatomical Therapeutic Chemical (ATC) drug classification based on drug-induced gene expression profile similarity (DIPS), and quantified the accuracy of their method by computing the area under the curve (AUC) of the Receiver Operating Characteristic (ROC) curve. We adopt the same data and extend the methodology, by using a simpler eXtreme cosine (XCos) method, and find it does better in this limited setting than the Kolmogorov-Smirnov (KS) statistic. In fact, for partial AUC (a more relevant statistic for actual application to repositioning) XCos does 17% better than the DIPS method (p=1.2e-7). We also observe that smaller gene signatures (with 100 probes) do better than larger ones (with 500 probes), and that DMSO controls from within the same batch obviate the need for mean centering. As expected there is heterogeneity in the prediction accuracy amongst the various ATC codes. We find that good transcriptional response to drug treatment appears necessary but not sufficient to achieve high AUCs. Certain ATC codes, such as those corresponding to corticosteroids, had much higher AUCs possibly due to strong transcriptional responses and consistency in MOA.
HubMed – drug

 

Computational drug repositioning.

Filed under: Drug and Alcohol Rehabilitation

Pac Symp Biocomput. 2013; 1-4
Lu Z, Agarwal P, Butte AJ

Despite increasing investments in pharmaceutical R&D, there is a continuing paucity of new drug approvals. Drug discovery continues to be a lengthy and resource-consuming process in spite of all the advances in genomics, life sciences, and technology. Indeed, it is estimated that about 90% of the drugs fail during development in phase 1 clinical trials(1) and that it takes billions of dollars in investment and an average of 15 years to bring a new drug to the market(2).…
HubMed – drug

 

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