Prediction of Promiscuous P-Glycoprotein Inhibition Using a Novel Machine Learning Scheme

BACKGROUND: P-glycoprotein (P-gp) is an ATP-dependent membrane transporter that plays a pivotal role in eliminating xenobiotics by active extrusion of xenobiotics from the cell. Multidrug resistance (MDR) is highly associated with the over-expression of P-gp by cells, resulting in increased efflux of chemotherapeutical agents and reduction of intracellular drug accumulation. It is of clinical importance to develop a P-gp inhibition predictive model in the process of drug discovery and development. METHODOLOGY/PRINCIPAL FINDINGS: An in silico model was derived to predict the inhibition of P-gp using the newly invented pharmacophore ensemble/support vector machine (PhE/SVM) scheme based on the data compiled from the literature. The predictions by the PhE/SVM model were found to be in good agreement with the observed values for those structurally diverse molecules in the training set (n = 31, r(2) = 0.89, q(2) = 0.86, RMSE = 0.40, s = 0.28), the test set (n = 88, r(2) = 0.87, RMSE = 0.39, s = 0.25) and the outlier set (n = 11, r(2) = 0.96, RMSE = 0.10, s = 0.05). The generated PhE/SVM model also showed high accuracy when subjected to those validation criteria generally adopted to gauge the predictivity of a theoretical model. CONCLUSIONS/SIGNIFICANCE: This accurate, fast and robust PhE/SVM model that can take into account the promiscuous nature of P-gp can be applied to predict the P-gp inhibition of structurally diverse compounds that otherwise cannot be done by any other methods in a high-throughput fashion to facilitate drug discovery and development by designing drug candidates with better metabolism profile

Association of predicted 10 years cardiovascular mortality risk with duration of HIV infection and antiretroviral therapy among HIV-infected individuals in Durban, South Africa

Background: South Africa has the largest population of human immunodeficiency virus (HIV) infected patients on antiretroviral therapy (ART) realising the benefits of increased life expectancy. However, this population may be susceptible to cardiovascular disease (CVD) development, due to the chronic consequences of a lifestyle-related combination of risk factors, HIV infection and ART. We predicted a 10-year cardiovascular mortality risk in an HIV-infected population on long-term ART, based on their observed metabolic risk factor profile. Methods: We extracted data from hospital medical charts for 384 randomly selected HIV-infected patients aged ≥ 30 years. We defined metabolic syndrome (MetS) subcomponents using the International Diabetes Federation definition. A validated non-laboratory-based model for predicting a 10-year CVD mortality risk was applied and categorised into five levels, with the thresholds ranging from very low-risk ( 30%). Results: Among the 384 patients, with a mean (± standard deviation) age of 42.90 ± 8.20 years, the proportion of patients that were overweight/obese was 53.3%, where 50.9% had low high-density lipoprotein (HDL) cholesterol and 21 (17.5%) had metabolic syndrome. A total of 144 patients with complete data allowed a definitive prediction of a 10-year CVD mortality risk. 52% (95% CI 44-60) of the patients were stratified to very low risk ( 30%) of 10-year CVD mortality. The CVD risk grows with increasing age (years), 57.82 ± 6.27 among very high risk and 37.52 ± 4.50; p < 0.001 in very low risk patients. Adjusting for age and analysing CVD risk mortality as a continuous risk score, increasing duration of HIV infection (p = 0.002) and ART (p = 0.007) were significantly associated with increased predicted 10 year CVD mortality risk. However, there was no association between these factors and categorised CVD mortality risk as per recommended scoring thresholds. Conclusions: Approximately 1 in 10 HIV-infected patients is at very high risk of predicted 10-year CVD mortality in our study population. Like uninfected individuals, our study found increased age as a major predictor of 10-year mortality risk and high prevalence of metabolic syndrome. Additional CVD mortality risk due to the duration of HIV infection and ART was seen in our population, further studies in larger and more representative study samples are encouraged. It recommends an urgent need for early planning, prevention and management of metabolic risk factors in HIV populations, at the point of ART initiation