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Computer aided screening of inhibitors to 5-? reductase type 2 for prostate cancer.

ABSTRACT: Traditionally, drugs are discovered by testing compounds synthesized in time consuming multi-step processes against a battery of invivo biological screens. Promising compounds are then further studied in development, where their pharmacokinetic properties, metabolism and potential toxicity were investigated. Here, we present a study on herbal lead compounds and their potential binding affinity to the effectors molecules of major disease like Prostate Cancer. Clinical studies demonstrate a positive correlation between the extent of 5-? reductase type 2 (isoform 2) and malignant progression of precancerous lesions in prostate. Therefore, identification of effective, well-tolerated 5-? reductase inhibitors represents a rational chemo preventive strategy. This study has investigated the effects of naturally occurring nonprotein compounds berberine and monocaffeyltartaric acid that inhibits 5-? reductase type 2. Our results reveal that these compounds use less energy to bind to 5-? reductase and inhibit its activity. Their high ligand binding affinity to 5-? reductase introduces the prospect for their use in chemopreventive applications. In addition, they are freely available natural compounds that can be safely used to prevent prostate cancer.

SUBMITTER: Bhattacharjee B

PROVIDER: S-EPMC3124690 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature

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Computer aided screening of inhibitors to 5-α reductase type 2 for prostate cancer.

Bhattacharjee Biplab B Talambedu Usha U Sadegh Saremy S Goyal Arvind Kumar AK Pande Veena V Nagaveni Madhugiri Bhujangarao MB Patil Vijayakumari Mali VM Jayadev Joshi J Middha Sushil Kumar SK

Bioinformation 20110623 7

Traditionally, drugs are discovered by testing compounds synthesized in time consuming multi-step processes against a battery of invivo biological screens. Promising compounds are then further studied in development, where their pharmacokinetic properties, metabolism and potential toxicity were investigated. Here, we present a study on herbal lead compounds and their potential binding affinity to the effectors molecules of major disease like Prostate Cancer. Clinical studies demonstrate a positi ...[more]

PMID: 21738326

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Project description:BackgroundWith the growth of machine learning applications, the practice of medicine is evolving. Computer-aided detection (CAD) is a software technology that has become widespread in radiology practices, particularly in breast cancer screening for improving detection rates at earlier stages. Many studies have investigated the diagnostic accuracy of CAD, but its implementation in clinical settings has been largely overlooked.ObjectiveThe aim of this scoping review was to summarize recent literature on the adoption and implementation of CAD during breast cancer screening by radiologists and to describe barriers and facilitators for CAD use.MethodsThe MEDLINE database was searched for English, peer-reviewed articles that described CAD implementation, including barriers or facilitators, in breast cancer screening and were published between January 2010 and March 2018. Articles describing the diagnostic accuracy of CAD for breast cancer detection were excluded. The search returned 526 citations, which were reviewed in duplicate through abstract and full-text screening. Reference lists and cited references in the included studies were reviewed.ResultsA total of nine articles met the inclusion criteria. The included articles showed that there is a tradeoff between the facilitators and barriers for CAD use. Facilitators for CAD use were improved breast cancer detection rates, increased profitability of breast imaging, and time saved by replacing double reading. Identified barriers were less favorable perceptions of CAD compared to double reading by radiologists, an increase in recall rates of patients for further testing, increased costs, and unclear effect on patient outcomes.ConclusionsThere is a gap in the literature between CAD's well-established diagnostic accuracy and its implementation and use by radiologists. Generally, the perceptions of radiologists have not been considered and details of implementation approaches for adoption of CAD have not been reported. The cost-effectiveness of CAD has not been well established for breast cancer screening in various populations. Further research is needed on how to best facilitate CAD in radiology practices in order to optimize patient outcomes, and the views of radiologists need to be better considered when advancing CAD use.

Project description:Computer-aided detection identifies suspicious findings on mammograms to assist radiologists. Since the Food and Drug Administration approved the technology in 1998, it has been disseminated into practice, but its effect on the accuracy of interpretation is unclear.We determined the association between the use of computer-aided detection at mammography facilities and the performance of screening mammography from 1998 through 2002 at 43 facilities in three states. We had complete data for 222,135 women (a total of 429,345 mammograms), including 2351 women who received a diagnosis of breast cancer within 1 year after screening. We calculated the specificity, sensitivity, and positive predictive value of screening mammography with and without computer-aided detection, as well as the rates of biopsy and breast-cancer detection and the overall accuracy, measured as the area under the receiver-operating-characteristic (ROC) curve.Seven facilities (16%) implemented computer-aided detection during the study period. Diagnostic specificity decreased from 90.2% before implementation to 87.2% after implementation (P<0.001), the positive predictive value decreased from 4.1% to 3.2% (P=0.01), and the rate of biopsy increased by 19.7% (P<0.001). The increase in sensitivity from 80.4% before implementation of computer-aided detection to 84.0% after implementation was not significant (P=0.32). The change in the cancer-detection rate (including invasive breast cancers and ductal carcinomas in situ) was not significant (4.15 cases per 1000 screening mammograms before implementation and 4.20 cases after implementation, P=0.90). Analyses of data from all 43 facilities showed that the use of computer-aided detection was associated with significantly lower overall accuracy than was nonuse (area under the ROC curve, 0.871 vs. 0.919; P=0.005).The use of computer-aided detection is associated with reduced accuracy of interpretation of screening mammograms. The increased rate of biopsy with the use of computer-aided detection is not clearly associated with improved detection of invasive breast cancer.

Dataset Information

Computer aided screening of inhibitors to 5-? reductase type 2 for prostate cancer.

Publications

Computer aided screening of inhibitors to 5-α reductase type 2 for prostate cancer.

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