Computer aided screening of inhibitors to 5-? reductase type 2 for prostate cancer.
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ABSTRACT:
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]
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Project description:Importance5-alpha-reductase-inhibitors (5-ARIs) are approved for treating benign prostatic hyperplasia (BPH) and have been found to reduce prostate cancer (PCa) risk by 25%. However, trials also have shown 5-ARIs to be associated with high-grade PCa. Whether 5-ARIs increase mortality among those with a diagnosis of PCa remains unclear.ObjectiveTo determine long-term outcomes of clinically localized PCa arising in individuals taking 5-ARIs compared with nonusers.Design, setting, and participantsThis population-based cohort study was conducted between January 2003 and October 2017. Eligible participants were men aged 65 years or older in Ontario, Canada, who developed clinically localized PCa with complete pathological abstraction from the Ontario Health Administrative Databases. Data analysis occurred from November 2017 to November 2022.Exposure5-ARIs before PCa diagnosis.Main outcomes and measuresThe primary outcomes were overall mortality and PCa-specific mortality. Cause-specific hazard models with inverse probability treatment weights (IPTW) were used to examine associations of 5-ARI use with mortality outcomes. Sensitivity analyses based on prediagnostic 5-ARI use, Gleason score, comorbidity, 5-ARI indication, prostate-specific antigen modeling, and statin use were also performed.ResultsThe cohort included 19 938 patients with PCa. Of these, 2112 (10.6%; median [IQR] age, 74 [70-79] years) were 5-ARI users and 17 826 (89.4%; median [IQR] age, 71 [68-76] years) were nonusers. During a median (IQR) follow-up of 8.96 (6.28-12.17) years, 6053 (30.4%) died, including 1047 (5.3%) from PCa. 5-ARI use appeared to be associated with increased overall and PCa specific mortality in crude analyses; however, after IPTW, 5-ARI use was not associated with overall mortality (hazard ratio, 0.98; 95% CI, 0.90-1.07; P = .77) or PCa-specific mortality (hazard ratio, 1.02; 95% CI, 0.83-1.25; P = .84).Conclusions and relevanceIn this population-based cohort study of 5-ARI use prior to PCa diagnosis including long-term follow-up and clinicopathologic details, prediagnostic 5-ARI use was not associated with PCa-specific or all-cause mortality. This study offers reassuring safety data for patients using 5-ARIs before PCa diagnosis for both BPH and chemopreventive reasons.
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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.
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Project description:Breast cancer (BCa) is one of the most predominantly diagnosed cancers in women. Notably, 70% of BCa diagnoses are Estrogen Receptor α positive (ERα+) making it a critical therapeutic target. With that, the two subtypes of ER, ERα and ERβ, have contrasting effects on BCa cells. While ERα promotes cancerous activities, ERβ isoform exhibits inhibitory effects on the same. ER-directed small molecule drug discovery for BCa has provided the FDA approved drugs tamoxifen, toremifene, raloxifene and fulvestrant that all bind to the estrogen binding site of the receptor. These ER-directed inhibitors are non-selective in nature and may eventually induce resistance in BCa cells as well as increase the risk of endometrial cancer development. Thus, there is an urgent need to develop novel drugs with alternative ERα targeting mechanisms that can overcome the limitations of conventional anti-ERα therapies. Several functional sites on ERα, such as Activation Function-2 (AF2), DNA binding domain (DBD), and F-domain, have been recently considered as potential targets in the context of drug research and discovery. In this review, we summarize methods of computer-aided drug design (CADD) that have been employed to analyze and explore potential targetable sites on ERα, discuss recent advancement of ERα inhibitor development, and highlight the potential opportunities and challenges of future ERα-directed drug discovery.
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Project description:The ability to design effective enzymes is one of the most fundamental challenges in biotechnology and in some respects in biochemistry. In fact, such ability would be one of the most convincing manifestations of a full understanding of the origin of enzyme catalysis. In this work, we explore the reliability of different simulation approaches, in terms of their ability to rank different possible active site constructs. This validation is done by comparing the ability of different approaches to evaluate the catalytic contributions of various residues in chorismate mutase. It is demonstrated that the empirical valence bond (EVB) model can serve as a practical yet accurate tool in the final stages of computer-aided enzyme design (CAED). Other approaches for fast screening are also examined and found to be less accurate and mainly useful for qualitative screening of ionized residues. It is pointed out that accurate ranking of different options for enzyme design cannot be accomplished by approaches that cannot capture the electrostatic preorganization effect. This is in particular true with regard to current design approaches that use gas phase or small cluster calculations and then estimate the interaction between the enzyme and the transition state (TS) model rather than the TS binding free energy or the relevant activation free energy. The ability of the EVB model to provide a tool for quantitative ranking in the final stage of CAED may help in progressing toward the design of enzymes whose catalytic power is closer to that of native enzymes than to that of the current generation of designer enzymes.
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Project description:PurposeThe primary aim of this study was to evaluate if exposure to 5-alpha-reductase inhibitors (5-ARIs) modifies the effect of MRI for the diagnosis of clinically significant Prostate Cancer (csPCa) (ISUP Gleason grade ≥ 2).MethodsThis study is a multicenter cohort study including patients undergoing prostate biopsy and MRI at 24 institutions between 2013 and 2022. Multivariable analysis predicting csPCa with an interaction term between 5-ARIs and PIRADS score was performed. Sensitivity, specificity, and negative (NPV) and positive (PPV) predictive values of MRI were compared in treated and untreated patients.Results705 patients (9%) were treated with 5-ARIs [median age 69 years, Interquartile range (IQR): 65, 73; median PSA 6.3 ng/ml, IQR 4.0, 9.0; median prostate volume 53 ml, IQR 40, 72] and 6913 were 5-ARIs naïve (age 66 years, IQR 60, 71; PSA 6.5 ng/ml, IQR 4.8, 9.0; prostate volume 50 ml, IQR 37, 65). MRI showed PIRADS 1-2, 3, 4, and 5 lesions in 141 (20%), 158 (22%), 258 (37%), and 148 (21%) patients treated with 5-ARIs, and 878 (13%), 1764 (25%), 2948 (43%), and 1323 (19%) of untreated patients (p < 0.0001). No difference was found in csPCa detection rates, but diagnosis of high-grade PCa (ISUP GG ≥ 3) was higher in treated patients (23% vs 19%, p = 0.013). We did not find any evidence of interaction between PIRADS score and 5-ARIs exposure in predicting csPCa. Sensitivity, specificity, PPV, and NPV of PIRADS ≥ 3 were 94%, 29%, 46%, and 88% in treated patients and 96%, 18%, 43%, and 88% in untreated patients, respectively.ConclusionsExposure to 5-ARIs does not affect the association of PIRADS score with csPCa. Higher rates of high-grade PCa were detected in treated patients, but most were clearly visible on MRI as PIRADS 4 and 5 lesions.Trial registrationThe present study was registered at ClinicalTrials.gov number: NCT05078359.
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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.
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Project description:BackgroundProstate cancer (PC) is a silent but potent killer among men. In 2018, PC accounted for more than 350, 000 death cases while more than 1.2 million cases were diagnosed. Docetaxel, a chemotherapeutic drug belonging to the taxane family of drugs, is one of the most potent drugs in combating advanced PC. However, PC cells often evolve resistance against the regimen. Hence, necessitating the search for complementary and alternative therapies. Quercetin, a ubiquitous phytocompound with numerous pharmacological properties, has been reported to reverse docetaxel resistance (DR) in docetaxel-resistant prostate cancer (DRPC). Therefore, this study aimed to explore the mechanism via which quercetin reverses DR in DRPC using an integrative functional network and exploratory cancer genomic data analyses.ResultsThe putative targets of quercetin were retrieved from relevant databases, while the differentially expressed genes (DEGs) in docetaxel-resistant prostate cancer (DRPC) were identified by analysing microarray data retrieved from the Gene Expression Omnibus (GEO) database. Subsequently, the protein-protein interaction (PPI) network of the overlapping genes between the DEGs and quercetin targets was retrieved from STRING, while the hub genes, which represent the key interacting genes of the network, were identified using the CytoHubba plug-in of Cytoscape. The hub genes were further subjected to a comprehensive analysis aimed at identifying their contribution to the immune microenvironment and overall survival (OS) of PC patients, while their alterations in PC patients were also revealed. The biological roles played by the hub genes in chemotherapeutic resistance include the positive regulation of developmental process, positive regulation of gene expression, negative regulation of cell death, and epithelial cell differentiation among others.ConclusionFurther analysis revealed epidermal growth factor receptor (EGFR) as the most pertinent target of quercetin in reversing DR in DRPC, while molecular docking simulation revealed an effective interaction between quercetin and EGFR. Ultimately, this study provides a scientific rationale for the further exploration of quercetin as a combinational therapy with docetaxel.
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