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Designing of promiscuous inhibitors against pancreatic cancer cell lines.

ABSTRACT: Pancreatic cancer remains the most devastating disease with worst prognosis. There is a pressing need to accelerate the drug discovery process to identify new effective drug candidates against pancreatic cancer. We have developed QSAR models for predicting promiscuous inhibitors using the pharmacological data. Our models achieved maximum Pearson correlation coefficient of 0.86, when evaluated on 10-fold cross-validation. Our models have also successfully validated the drug-to-oncogene relationship and further we used these models to screen FDA approved drugs and tested them in vitro. We have integrated these models in a webserver named as DiPCell, which will be useful for screening and designing novel promiscuous drug molecules. We have also identified the most and least effective drugs for pancreatic cancer cell lines. On the other side, we have identified resistant pancreatic cancer cell lines, which need investigative scanner on them to put light on resistant mechanism in pancreatic cancer.

SUBMITTER: Kumar R

PROVIDER: S-EPMC3985076 | biostudies-literature | 2014 Apr

REPOSITORIES: biostudies-literature

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Designing of promiscuous inhibitors against pancreatic cancer cell lines.

Kumar Rahul R Chaudhary Kumardeep K Singla Deepak D Gautam Ankur A Raghava Gajendra P S GP

Scientific reports 20140414

Pancreatic cancer remains the most devastating disease with worst prognosis. There is a pressing need to accelerate the drug discovery process to identify new effective drug candidates against pancreatic cancer. We have developed QSAR models for predicting promiscuous inhibitors using the pharmacological data. Our models achieved maximum Pearson correlation coefficient of 0.86, when evaluated on 10-fold cross-validation. Our models have also successfully validated the drug-to-oncogene relationsh ...[more]

PMID: 24728108

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Project description:BackgroundMycobacterium tuberculosis (M.tb) is the causative agent of tuberculosis, killing ~1.7 million people annually. The remarkable capacity of this pathogen to escape the host immune system for decades and then to cause active tuberculosis disease, makes M.tb a successful pathogen. Currently available anti-mycobacterial therapy has poor compliance due to requirement of prolonged treatment resulting in accelerated emergence of drug resistant strains. Hence, there is an urgent need to identify new chemical entities with novel mechanism of action and potent activity against the drug resistant strains.ResultsThis study describes novel computational models developed for predicting inhibitors against both replicative and non-replicative phase of drug-tolerant M.tb under carbon starvation stage. These models were trained on highly diverse dataset of 2135 compounds using four classes of binary fingerprint namely PubChem, MACCS, EState, SubStructure. We achieved the best performance Matthews correlation coefficient (MCC) of 0.45 using the model based on MACCS fingerprints for replicative phase inhibitor dataset. In case of non-replicative phase, Hybrid model based on PubChem, MACCS, EState, SubStructure fingerprints performed better with maximum MCC value of 0.28. In this study, we have shown that molecular weight, polar surface area and rotatable bond count of inhibitors (replicating and non-replicating phase) are significantly different from non-inhibitors. The fragment analysis suggests that substructures like hetero_N_nonbasic, heterocyclic, carboxylic_ester, and hetero_N_basic_no_H are predominant in replicating phase inhibitors while hetero_O, ketone, secondary_mixed_amine are preferred in the non-replicative phase inhibitors. It was observed that nitro, alkyne, and enamine are important for the molecules inhibiting bacilli residing in both the phases. In this study, we introduced a new algorithm based on Matthews correlation coefficient called MCCA for feature selection and found that this algorithm is better or comparable to frequency based approach.ConclusionIn this study, we have developed computational models to predict phase specific inhibitors against drug resistant strains of M.tb grown under carbon starvation. Based on simple molecular properties, we have derived some rules, which would be useful in robust identification of tuberculosis inhibitors. Based on these observations, we have developed a webserver for predicting inhibitors against drug tolerant M.tb H37Rv available at http://crdd.osdd.net/oscadd/mdri/.

Dataset Information

Designing of promiscuous inhibitors against pancreatic cancer cell lines.

Publications

Designing of promiscuous inhibitors against pancreatic cancer cell lines.

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