A mechanistic modeling platform of SGLT2 inhibition: Implications for type 1 diabetes.

Type 1 diabetes mellitus (T1DM) is an autoimmune disease characterized by abnormally high blood glucose concentrations due to dysfunction of the insulin-producing beta-cells in the pancreas. Dapagliflozin, an inhibitor of renal glucose reabsorption, has the potential to improve often suboptimal glycemic control in patients with T1DM through insulin-independent mechanisms and to partially mitigate the adverse effects associated with long-term insulin administration. In this work, we have adapted a systems pharmacology model of type 2 diabetes mellitus to describe the T1DM condition and characterize the effect of dapagliflozin on short- and long-term glycemic markers under various treatment scenarios. The developed platform serves as a quantitative tool for the in silico evaluation of the insulin-glucose-dapagliflozin crosstalk, optimization of the treatment regimens, and it can be further expanded to include additional therapies or other aspects of the disease.

Novel mitochondria-targeting compounds selectively kill human leukemia cells.

Acute myeloid leukemia (AML) is a heterogeneous group of aggressive hematological malignancies commonly associated with treatment resistance, high risk of relapse, and mitochondrial dysregulation. We identified six mitochondria-affecting compounds (PS compounds) that exhibit selective cytotoxicity against AML cells in vitro. Structure-activity relationship studies identified six analogs from two original scaffolds that had over an order of magnitude difference between LD50 in AML and healthy peripheral blood mononuclear cells. Mechanistically, all hit compounds reduced ATP and selectively impaired both basal and ATP-linked oxygen consumption in leukemic cells. Compounds derived from PS127 significantly upregulated production of reactive oxygen species (ROS) in AML cells and triggered ferroptotic, necroptotic, and/or apoptotic cell death in AML cell lines and refractory/relapsed AML primary samples. These compounds exhibited synergy with several anti-leukemia agents in AML, acute lymphoblastic leukemia (ALL), or chronic myelogenous leukemia (CML). Pilot in vivo efficacy studies indicate anti-leukemic efficacy in a MOLM14/GFP/LUC xenograft model, including extended survival in mice injected with leukemic cells pre-treated with PS127B or PS127E and in mice treated with PS127E at a dose of 5 mg/kg. These compounds are promising leads for development of future combinatorial therapeutic approaches for mitochondria-driven hematologic malignancies such as AML, ALL, and CML.

A model-based approach to investigating the relationship between glucose-insulin dynamics and dapagliflozin treatment effect in patients with type 2 diabetes.

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.

AntiHIV-Pred: web-resource for in silico prediction of anti-HIV/AIDS activity.

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.

(Q)SAR Models of HIV-1 Protein Inhibition by Drug-Like Compounds.

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.