Biology subjects

Poroikov, V.

Publications and source records attributed to Poroikov, V..

4 recordsLinked to original sources

bioRxiv2021

HIV-1 drug resistance profiling using amino acid sequence space cartography

Human immunodeficiency virus (HIV) drug resistance is a global healthcare issue. The emergence of drug resistance demands treatment adaptation. Computational methods predicting the drug resistance profile from genomic data of HIV isolates are advantageous for monitoring drug resistance in patients. Yet, the currently existing computational methods for drug resistance prediction are either not suitable for complex mutational patterns in emerging HIV strains or lack interpretability of prediction results which is of paramount importance in clinical practice. Hence, to overcome these limitations, new approaches for the HIV drug resistance prediction combining high accuracy and interpretability are required. In this work, a new methodology for the analysis of protein sequence data based on the application of generative topographic mapping was developed and applied for HIV drug resistance profiling. It allowed achieving high accuracy of resistance predictions and intuitive interpretation of prediction results. The developed approach was successfully applied for the prediction of HIV resistance towards protease, reverse-transcriptase and integrase inhibitors and in-depth analysis of HIV resistance-inducing mutation patterns. Hence, it can serve as an efficient and interpretable tool to suggest optimal treatment regimens.

bioinformatics↗

bioRxiv2019

Assessment of the cardiovascular adverse effects of drug-drug interactions through a combined analysis of spontaneous reports and predicted drug-target interactions

Adverse drug effects (ADEs) are one of the leading causes of death in developed countries and are the main reason for drug recalls from the market, whereas the ADEs that are associated with action on the cardiovascular system are the most dangerous and widespread. The treatment of human diseases often requires the intake of several drugs, which can lead to undesirable drug-drug interactions (DDIs), thus causing an increase in the frequency and severity of ADEs. An evaluation of DDI-induced ADEs is a nontrivial task and requires numerous experimental and clinical studies. Therefore, we developed a computational approach to assess the cardiovascular ADEs of DDIs. This approach is based on the combined analysis of spontaneous reports (SRs) and predicted drug-target interactions to estimate the five cardiovascular ADEs that are induced by DDIs, namely, myocardial infarction, ischemic stroke, ventricular tachycardia, cardiac failure, and arterial hypertension. We applied a method based on least absolute shrinkage and selection operator (LASSO) logistic regression to SRs for the identification of interacting pairs of drugs causing corresponding ADEs, as well as noninteracting pairs of drugs. As a result, five datasets containing, on average, 3100 ADE-causing and non-ADE-causing drug pairs were created. The obtained data, along with information on the interaction of drugs with 1553 human targets predicted by PASS Targets software, were used to create five classification models using the Random Forest method. The average area under the ROC curve of the obtained models, sensitivity, specificity and balanced accuracy were 0.838, 0.764, 0.754 and 0.759, respectively. The predicted drug targets were also used to hypothesize the potential mechanisms of DDI-induced ventricular tachycardia for the top-scoring drug pairs. The created five classification models can be used for the identification of drug combinations that are potentially the most or least dangerous for the cardiovascular system. Author summaryAssessment of adverse drug effects as well as the influence of drug-drug interactions on their manifestation is a nontrivial task that requires numerous experimental and clinical studies. We developed a computational approach for the prediction of adverse effects that are induced by drug-drug interactions, which are based on a combined analysis of spontaneous reports and predicted drug-target interactions. Importantly, the approach requires only structural formulas to predict adverse effects, and, therefore, may be applied for new, insufficiently studied drugs. We applied the approach to predict five of the most important cardiovascular adverse effects, because they are the most dangerous and widespread. These effects are myocardial infarction, ischemic stroke, ventricular tachycardia, arterial hypertension and cardiac failure. The accuracies of predictive models were relatively high, in the range of 73-81%; therefore, we performed a prediction of the five cardiovascular adverse effects for the large number of drug pairs and revealed the combinations that are the most dangerous for the cardiovascular system. We consider that the developed approach can be used for the identification of pairwise drug combinations that are potentially the most or least dangerous for the cardiovascular system.

bioinformatics↗

bioRxiv2019

Identification of a novel therapeutic agent for Inflammatory Bowel Disease guided by systems medicine

ObjectiveInflammatory bowel diseases cause significant morbidity and mortality. Aberrant NF-{kappa}B signalling is strongly associated with these conditions, and several established drugs influence the NF-{kappa}B signalling network to exert their effect. This study aimed to identify drugs which alter NF-{kappa}B signalling and may be repositioned for use in inflammatory bowel disease. DesignThe SysmedIBD consortium established a novel drug-repurposing pipeline based on a combination of in-silico drug discovery and biological assays targeted at demonstrating an impact on NF-kappaB signalling, and a murine model of IBD. ResultsThe drug discovery algorithm identified several drugs already established in IBD, including corticosteroids. The highest-ranked drug was the macrolide antibiotic Clarithromycin, which has previously been reported to have anti-inflammatory effects in aseptic conditions. Clarithromycins effects were validated in several experiments: it influenced NF-{kappa}B mediated transcription in murine peritoneal macrophages and intestinal enteroids; it suppressed NF-{kappa}B protein shuttling in murine reporter enteroids; it suppressed NF-{kappa}B (p65) DNA binding in the small intestine of mice exposed to LPS, and it reduced the severity of dextran sulphate sodium-induced colitis in C57BL/6 mice. Clarithromycin also suppressed NF-{kappa}B (p65) nuclear translocation in human intestinal enteroids. ConclusionsThese findings demonstrate that in-silico drug repositioning algorithms can viably be allied to laboratory validation assays in the context of inflammatory bowel disease; and that further clinical assessment of clarithromycin in the management of inflammatory bowel disease is required.

systems biology↗

bioRxiv2018

Novel allosteric mechanism of p53 activation by small molecules for targeted anticancer therapy

Given the immense significance of p53 restoration for anti-cancer therapy, elucidation of the mechanisms of action of p53-activating molecules is of the utmost importance. Here we report a discovery of novel allosteric modulation of p53 by small molecules, which is an unexpected turn in the p53 story. We identified a structural element involved in p53 regulation, whose targeting by RITA, PpIX and licofelone block the binding of p53 inhibitors, MDM2 and MDMX. Deletion and mutation analysis followed by molecular modeling, identified the key p53 residues S33 and S37 targeted by RITA and PpIX. We propose that the binding of small molecules to the identified site induces a conformational trap preventing p53 from the interaction with MDM2 and MDMX. These results point to a high potential of allosteric activators. Our study provides the basis for the development of therapeutics with a novel mechanism of action, thus extending the p53 pharmacological potential.

cancer biology↗