Case-only Methods Identified Genetic Loci Predicting a Subgroup of Men with Reduced Risk of High-grade Prostate Cancer by Finasteride

Dai, James Y.; LeBlanc, Michael; Goodman, Phyllis J.; Lucia, M. Scott; Thompson, Ian M.; Tangen, Catherine M.

doi:10.1158/1940-6207.capr-18-0284

Cited by 2 publications

(1 citation statement)

References 26 publications

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“…[2][3][4] Finasteride have three crystal structures and six solvates, [5][6][7][8] while form I is pharmaceutically preferred due to its better stability. 7,9 The present study showed that finasteride has new pharmacodynamic activities in polycystic ovary syndrome (PCOS) therapy and the ability of reducing the risk of bladder cancer and high-grade prostate cancer, [10][11][12] which could demonstrate the great research value of finasteride.…”

Section: Introductionmentioning

confidence: 64%

VIBRATIONAL SPECTROSCOPIC INVESTIGATION AND MOLECULAR STRUCTURE OF a 5α-Reductase INHIBITOR: FINASTERIDE

Wang¹,

Zeng²,

Gao³

2020

Quim. Nova

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By way of Density Functional Theory (DFT)-based computational methods with commercially available software, the computational work of which about the molecular properties, the vibrational modes and vibrational frequencies of finasteride were accomplished for the first time. In order to gain a deeper and more thorough understanding of the molecular structure and infrared spectrum of finasteride, the equilibrium geometry harmonic vibrational frequencies and geometric parameters (bond lengths, bond angles and dihedral angles) were calculated by Generalized Gradient Approximations (GGAs) with five different density functional methods (PBE, RPBE, HCTH, PW91 and BLYP), using Material Studio 8.0 program. Theoretical vibrational frequencies and theoretical optimized geometric parameters were compared with the corresponding experimental data, which concluded that the GGA/PW91 method were shown to be in a good agreement with the results of experiment. In addition, the highest occupied molecular orbital (HOMO) energies, the lowest unoccupied molecular orbital (LUMO) energies and electron density isosurface calculations were done with an aim at a better understanding of the stability and reactivity of the molecule, indicating that the alkene and lactam conjugated system on the ring A is more likely to interact with other species. And, the atomic charge distribution was also calculated to understand the charge effect caused by electronegative atoms, which shows that the possible active-sites in chemical reactions are N 1 and O 1 .

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Section: Introductionmentioning

confidence: 64%

VIBRATIONAL SPECTROSCOPIC INVESTIGATION AND MOLECULAR STRUCTURE OF a 5α-Reductase INHIBITOR: FINASTERIDE

Wang¹,

Zeng²,

Gao³

2020

Quim. Nova

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Machine Learning SNP Based Prediction for Precision Medicine

et al. 2019

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In the past decade, precision genomics based medicine has emerged to provide tailored and effective healthcare for patients depending upon their genetic features. Genome Wide Association Studies have also identified population based risk genetic variants for common and complex diseases. In order to meet the full promise of precision medicine, research is attempting to leverage our increasing genomic understanding and further develop personalized medical healthcare through ever more accurate disease risk prediction models. Polygenic risk scoring and machine learning are two primary approaches for disease risk prediction. Despite recent improvements, the results of polygenic risk scoring remain limited due to the approaches that are currently used. By contrast, machine learning algorithms have increased predictive abilities for complex disease risk. This increase in predictive abilities results from the ability of machine learning algorithms to handle multi-dimensional data. Here, we provide an overview of polygenic risk scoring and machine learning in complex disease risk prediction. We highlight recent machine learning application developments and describe how machine learning approaches can lead to improved complex disease prediction, which will help to incorporate genetic features into future personalized healthcare. Finally, we discuss how the future application of machine learning prediction models might help manage complex disease by providing tissue-specific targets for customized, preventive interventions.

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Case-only Methods Identified Genetic Loci Predicting a Subgroup of Men with Reduced Risk of High-grade Prostate Cancer by Finasteride

Cited by 2 publications

References 26 publications

VIBRATIONAL SPECTROSCOPIC INVESTIGATION AND MOLECULAR STRUCTURE OF a 5α-Reductase INHIBITOR: FINASTERIDE

VIBRATIONAL SPECTROSCOPIC INVESTIGATION AND MOLECULAR STRUCTURE OF a 5α-Reductase INHIBITOR: FINASTERIDE

Machine Learning SNP Based Prediction for Precision Medicine

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