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.

(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.