Tipranavir 100mg/ml oral solution sugar free

Background: A coronavirus identified in 2019, SARS-CoV-2, has caused a pandemic of respiratory illness, called COVID-19. Most people with COVID-19 experience mild to moderate symptoms and recover without the need for special treatments. The SARS‑CoV‑2 RNA‑dependent RNA polymerase (RdRp) plays a crucial role in the viral life cycle. The active site of the RdRp is a very accessible region, so targeting this region to study the inhibition of viral replication may be an effective therapeutic approach. For this reason, this study has selected and analysed a series of ligands used as SARS-CoV-2 virus inhibitors, namely: Darunavir (Daru), Dexamethasona (Dexame), Dolutegravir (Dolu), Fosamprenavir (Fosam), Ganciclovir (Gan), Insoine (Inso), Lopinavir (Lop), Ritonavir (Rito) and Tipranavir (Tipra). Methods: These ligands were analyzed using molecular docking, molecular quantum similarity using four similarity indices like overlap, Coulomb and their Euclidean distances. On the other hand, these outcomes were supported with chemical reactivity indices defined within a conceptual density functional theory framework. Results: The results show the conformations with the highest root-mean-square deviation (RMSD), have π-π stacking interaction with residue LYS621, ARG555 and ASP623, CYS622, ASP760, among others. In the molecular quantum similarity, the highest indices have been obtained in the electronic similarity in comparison with the structural similarity. Conclusions: These studies allow the identification of the main stabilizing interactions using the crystal structure of SARS‑CoV‑2 RNA‑dependent RNA polymerase. In this order of ideas, this study provides new insights into these ligands that can be used in the design of new COVID-19 treatments. The studies allowed us to find an explanation supported in the Density Functional Theory about the chemical reactivity and the stabilization in the active site of the ligands.

Abstract licence: CC BY

BACKGROUND: Outbreak of COVID-19 has been recognized as a global health concern since it causes high rates of morbidity and mortality. No specific antiviral drugs are available for the treatment of COVID-19 till date. Drug repurposing strategy helps to find out the drugs for COVID-19 treatment from existing FDA approved antiviral drugs. In this study, FDA approved small molecule antiviral drugs were repurposed against the major viral proteins of SARS-CoV-2. METHODS: The 3D structures of FDA approved small molecule antiviral drugs were retrieved from PubChem. Virtual screening was performed to find out the lead antiviral drug molecules against main protease (Mpro) and RNA-dependent RNA polymerase (RdRp) using COVID-19 Docking Server. Furthermore, lead molecules were individually docked against protein targets using AutoDock 4.0.1 software and their drug-likeness and ADMET properties were evaluated. RESULTS: Out of 65 FDA approved small molecule antiviral drugs screened, Raltegravir showed highest interaction energy value of -9 kcal/mol against Mpro of SARS-CoV-2 and Indinavir, Tipranavir, and Pibrentasvir exhibited a binding energy value of ≥-8 kcal/mol. Similarly Indinavir showed the highest binding energy of -11.5 kcal/mol against the target protein RdRp and Dolutegravir, Elbasvir, Tipranavir, Taltegravir, Grazoprevir, Daclatasvir, Glecaprevir, Ledipasvir, Pibrentasvir and Velpatasvir showed a binding energy value in range from -8 to -11.2 kcal/mol. The antiviral drugs Raltegravir, Indinavir, Tipranavir, Dolutegravir, and Etravirine also exhibited good bioavailability and drug-likeness properties. CONCLUSION: This study suggests that the screened small molecule antiviral drugs Raltegravir, Indinavir, Tipranavir, Dolutegravir, and Etravirine could serve as potential drugs for the treatment of COVID-19 with further validation studies.

Abstract licence: CC BY-NC-ND

Drug resistance of pathogens, including viruses, is one of the reasons for decreased efficacy of therapy. Considering the impact of HIV type 1 (HIV-1) on the development of progressive immune dysfunction and the rapid development of drug resistance, the analysis of HIV-1 resistance is of high significance. Currently, a substantial amount of data has been accumulated on HIV-1 drug resistance that can be used to build both qualitative and quantitative models of HIV-1 drug resistance. Quantitative models of drug resistance can enrich the information about the efficacy of a particular drug in the scheme of antiretroviral therapy. In our study, we investigated the possibility of developing models for quantitative prediction of HIV-1 resistance to eight protease inhibitors based on the analysis of amino acid sequences of HIV-1 protease for 900 virus variants. We developed random forest regression (RFR), support vector regression (SVR), and self-consistent regression (SCR) models using binary vectors containing values from 0 or 1, depending on the presence of a specific peptide fragment in each amino acid sequence as independent variables, while fold ratio, reflecting the level of resistance, was the predicted variable. The SVR and SCR models showed the highest predictive performances. The models built demonstrate reasonable performances for eight out of nine (R2 varied from 0.828 to 0.909) protease inhibitors, while R2 for predicting tipranavir fold ratio was lower (R2 was 0.642). We believe that the developed approach can be applied to evaluate drug resistance of molecular targets of other viruses where appropriate experimental data are available.

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We focus on drug repurposing in the Ras signaling pathway, considering structural similarities of protein-protein interfaces. The interfaces formed by physically interacting proteins are found from PDB if available and via PRISM (PRotein Interaction by Structural Matching) otherwise. The structural coverage of these interactions has been increased from 21 to 92% using PRISM. Multiple conformations of each protein are used to include protein dynamics and diversity. Next, we find FDA-approved drugs bound to structurally similar protein-protein interfaces. The results suggest that HIV protease inhibitors tipranavir, indinavir, and saquinavir may bind to EGFR and ERBB3/HER3 interface. Tipranavir and indinavir may also bind to EGFR and ERBB2/HER2 interface. Additionally, a drug used in Alzheimer's disease can bind to RAF1 and BRAF interface. Hence, we propose a methodology to find drugs to be potentially used for cancer using a dataset of structurally similar protein-protein interface clusters rather than pockets in a systematic way.

Abstract licence: CC BY