Bromocriptine monotherapy overcomes prostate cancer chemoresistance in preclinical models

Bai, Lijuan; Li, Xin; Yang, Yang; Zhao, Rui; White, Elshaddai; Bowen, Nathan J.; Hinton, Cimona V.; Cook, Nicholas; Li, Dehong; Wu, Annie M.; Qui, Min; Du, Yuhong; Fu, Haian; Küçük, Ömer; Wu, Daqing

doi:10.1016/j.tranon.2023.101707

Cited by 4 publications

(1 citation statement)

References 84 publications

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“…We found 11 drugs with logIC 50 = < −2 μM in at least five cell lines out of 12 (Table ). Of these 11 drugs, six were known anticancer drugs, three were antimicrobial, which have been shown to have an antineoplastic effect, − and two drugs, i.e., viomycin (antimicrobial agent) and diamorphine (analgesic), have yet not been experimentally validated as anticancer drugs. , …”

Section: Resultsmentioning

confidence: 99%

Support Vector Machine-Based Prediction Models for Drug Repurposing and Designing Novel Drugs for Colorectal Cancer

Sengupta,

Singh,

Kumar

2024

ACS Omega

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Colorectal cancer (CRC) has witnessed a concerning increase in incidence and poses a significant therapeutic challenge due to its poor prognosis. There is a pressing demand to identify novel drug therapies to combat CRC. In this study, we addressed this need by utilizing the pharmacological profiles of anticancer drugs from the Genomics of Drug Sensitivity in Cancer (GDSC) database and developed QSAR models using the Support Vector Machine (SVM) algorithm for prediction of alternative and promiscuous anticancer compounds for CRC treatment. Our QSAR models demonstrated their robustness by achieving a high correlation of determination (R 2 ) after 10-fold cross-validation. For 12 CRC cell lines, R 2 ranged from 0.609 to 0.827. The highest performance was achieved for SW1417 and GP5d cell lines with R 2 values of 0.827 and 0.786, respectively. Further, we listed the most common chemical descriptors in the drug profiles of the CRC cell lines and we also further reported the correlation of these descriptors with drug activity. The KRFP314 fingerprint was the predominantly occurring descriptor, with the KRFPC314 fingerprint following closely in prevalence within the drug profiles of the CRC cell lines. Beyond predictive modeling, we also confirmed the applicability of our developed QSAR models via in silico methods by conducting descriptor-drug analyses and recapitulating drug-to-oncogene relationships. We also identified two potential anti-CRC FDA-approved drugs, viomycin and diamorphine, using QSAR models. To ensure the easy accessibility and utility of our research findings, we have incorporated these models into a user-friendly prediction Web server named "ColoRecPred", available at https://project.iith.ac.in/cgntlab/colorecpred. We anticipate that this Web server can be used for screening of chemical libraries to identify potential anti-CRC drugs.

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