Motivation Identification of new molecules promising for treatment of HIV-infection and HIV-associated disorders remains an important task in order to provide safer and more effective therapies. Utilization of prior knowledge by application of computer-aided drug discovery approaches reduces time and financial expenses and increases the chances of positive results in anti-HIV R&D. To provide the scientific community with a tool that allows estimating of potential agents for treatment of HIV-infection and its comorbidities, we have created a freely-available web-resource for prediction of relevant biological activities based on the structural formulae of drug-like molecules. Results Over 50 000 experimental records for anti-retroviral agents from ChEMBL database were extracted for creating the training sets. After careful examination, about seven thousand molecules inhibiting five HIV-1 proteins were used to develop regression and classification models with the GUSAR software. The average values of R2 = 0.95 and Q2 = 0.72 in validation procedure demonstrated the reasonable accuracy and predictivity of the obtained (Q)SAR models. Prediction of 81 biological activities associated with the treatment of HIV-associated comorbidities with 92% mean accuracy was realized using the PASS program. Availability and implementation Freely available on the web at http://www.way2drug.com/hiv/. Supplementary information Supplementary data are available at Bioinformatics online.
Aims To develop a quantitative systems pharmacology model to describe the effect of dapagliflozin (a sodium‐glucose co‐transporter‐2 [SGLT2] inhibitor) on glucose‐insulin dynamics in type 2 diabetes mellitus (T2DM) patients, and to identify key determinants of treatment‐mediated glycated haemoglobin (HbA1c) reduction. Materials and methods Glycaemic control during dapagliflozin treatment was mechanistically characterized by integrating components representing dapagliflozin pharmacokinetics (PK), glucose‐insulin homeostasis, renal glucose reabsorption, and HbA1c formation. The model was developed using PK variables, glucose, plasma insulin, and urinary glucose excretion (UGE) from a phase IIa dapagliflozin trial in patients with T2DM (NCT00162305). The model was used to predict dapagliflozin‐induced HbA1c reduction; model predictions were compared to actual data from phase III trials (NCT00528879, NCT00683878, NCT00680745 and NCT00673231). Results The integrated glucose‐insulin‐dapagliflozin model successfully described plasma glucose and insulin levels, as well as UGE in response to oral glucose tolerance tests and meal intake. HbA1c reduction was also well predicted. The results show that dapagliflozin‐mediated glycaemic control is anticorrelated to steady‐state insulin concentration and insulin sensitivity. Conclusions The developed model framework is the first to integrate SGLT2 inhibitor mechanism of action with both short‐term glucose‐insulin dynamics and long‐term glucose control (HbA1c). The results suggest that dapagliflozin treatment is beneficial in patients with inadequate glycaemic control from insulin alone and this benefit increases as insulin control diminishes.
To improve the discovery of more effective and less toxic pharmaceutical agents, large virtual repositories of synthesizable molecules have been generated to increase the explored chemicalpharmacological space diversity. Such libraries include billions of structural formulae of druglike molecules associated with data on synthetic schemes, required building blocks, estimated physical-chemical parameters, etc. Clearly, such repositories are "Big Data". Thus, to identify the most promising compounds with the required pharmacological properties (hits) among billions of available opportunities, special computational methods are necessary. We have proposed using a combined computational approach, which combines structural similarity assessment, machine learning, and molecular modeling. Our approach has been validated in a project aimed at finding new pharmaceutical agents against HIV/AIDS and associated comorbidities from the Synthetically Accessible Virtual Inventory (SAVI), a 1.75 billion compound database. Potential inhibitors of HIV-1 protease and reverse transcriptase and agonists of toll-like receptors and STING, affecting innate immunity, were computationally identified. The activity of the three synthesized compounds has been confirmed in a cell-based assay. These compounds belong to the chemical classes, in which the agonistic effect on TLR 7/8 had not been previously shown. Synthesis and biological testing of several dozens of compounds with predicted antiretroviral activity are currently taking place at the NCI/NIH. We also carried out virtual screening among one billion substances to find compounds potentially possessing anti-SARS-CoV-2 activity. The selected hits' information has been accepted by the European Initiative "JEDI Grand Challenge against COVID-19" for synthesis and further biological evaluation. The possibilities and limitations of the approach are discussed.
Despite the achievements of antiretroviral therapy, discovery of new anti-HIV medicines remains an essential task because the existing drugs do not provide a complete cure for the infected patients, exhibit severe adverse effects, and lead to the appearance of resistant strains. To predict the interaction of drug-like compounds with multiple targets for HIV treatment, ligand-based drug design approach is widely applied. In this study, we evaluated the possibilities and limitations of (Q)SAR analysis aimed at the discovery of novel antiretroviral agents inhibiting the vital HIV enzymes. Local (Q)SAR models are based on the analysis of structure–activity relationships for molecules from the same chemical class, which significantly restrict their applicability domain. In contrast, global (Q)SAR models exploit data from heterogeneous sets of drug-like compounds, which allows their application to databases containing diverse structures. We compared the information for HIV-1 integrase, protease and reverse transcriptase inhibitors available in the EBI ChEMBL, NIAID HIV/OI/TB Therapeutics, and Clarivate Analytics Integrity databases as the sources for (Q)SAR training sets. Using the PASS and GUSAR software, we developed and validated a variety of (Q)SAR models, which can be further used for virtual screening of new antiretrovirals in the SAVI library. The developed models are implemented in the freely available web resource AntiHIV-Pred.
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