Inhibition effects of eight anti-coronavirus drugs on glycosides metabolism and glycosidases in human gut microflora.

The effects of eight oral anti-coronavirus drugs (lopinavir, ritonavir, chloroquine, darunavir, ribavirin, arbidol, favipiravir, oseltamivir) on the metabolism of four specific glycosides (polydatin, geniposide, quercitrin, glycyrrhizin) and on the activities of three major glycosidases (ß-glucosidase, α-rhamnosidase, ß-glucuronidase) from gut microflora were explored in vitro and determined by LC-MS/MS. The metabolism of polydatin, geniposide, quercitrin and glycyrrhizin was significantly inhibited by one or several anti-coronavirus drugs of 100 µM around 1 h and 4 h (P<0.05), among which darunavir could strongly reduce the production of genipin (70.6% reduction), quercitin (80.6% reduction) and glycyrrhetinic acid (37.9% reduction), which may cause a high risk of herb-drug interactions (HDI). Additionally, chloroquine reduced the production of genipin and quercitin by more than 75% (P<0.05), whereas arbidol had no significant influence on the metabolism of polydatin, quercitrin and glycyrrhizin (P>0.05) so that its risk may be lower. The inhibition of darunavir on ß-glucosidase was relatively strong (IC50 = 193±23 µM), and the inhibition became weaker on ß-glucuronidase and α-rhamnosidase (IC50>500 µM). The consistency between gut microflora and glycosidase system indicated that the inhibition of darunavir on the activity of ß-glucosidase and ß-glucuronidase may be the main reason for affecting the metabolism of geniposide, glycyrrhizin and polydatin in gut microflora. However, for the inhibition of darunavir and chloroquine on the metabolism of quercetrin, there was no correlation between gut microflora and α-rhamnosidase system. Assessing the risk of HDI mediated by glycosidases in gut microflora may be conducive to the safety and efficacy of combining traditional herbal and Western medicine for the treatment of patients with Covid-19.

Molecular Docking and Virtual Screening Based Prediction of Drugs for COVID-19.

AIMS: To predict potential drugs for COVID-19 by using molecular docking for virtual screening of drugs approved for other clinical applications. BACKGROUND: SARS-CoV-2 is the betacoronavirus responsible for the COVID-19 pandemic. It was listed as a potential global health threat by the WHO due to high mortality, high basic reproduction number, and lack of clinically approved drugs and vaccines. The genome of the virus responsible for COVID-19 has been sequenced. In addition, the three-dimensional structure of the main protease has been determined experimentally. OBJECTIVE: To identify potential drugs that can be repurposed for treatment of COVID-19 by using molecular docking based virtual screening of all approved drugs. METHODS: A list of drugs approved for clinical use was obtained from the SuperDRUG2 database. The structure of the target in the apo form, as well as structures of several target-ligand complexes, were obtained from RCSB PDB. The structure of SARS-CoV-2 Mpro determined from X-ray diffraction data was used as the target. Data regarding drugs in clinical trials for COVID-19 was obtained from clinicaltrials.org. Input for molecular docking based virtual screening was prepared by using Obabel and customized python, bash, and awk scripts. Molecular docking calculations were carried out with Vina and SMINA, and the docked conformations were analyzed and visualized with PLIP, Pymol, and Rasmol. RESULTS: Among the drugs that are being tested in clinical trials for COVID-19, Danoprevir and Darunavir were predicted to have the highest binding affinity for the Main protease (Mpro) target of SARS-CoV-2. Saquinavir and Beclabuvir were identified as the best novel candidates for COVID-19 therapy by using Virtual Screening of drugs approved for other clinical indications. CONCLUSION: Protease inhibitors approved for treatment of other viral diseases have the potential to be repurposed for treatment of COVID-19.

A Modified P1 Moiety Enhances In Vitro Antiviral Activity against Various Multidrug-Resistant HIV-1 Variants and In Vitro Central Nervous System Penetration Properties of a Novel Nonpeptidic Protease Inhibitor, GRL-10413.

We report here that GRL-10413, a novel nonpeptidic HIV-1 protease inhibitor (PI) containing a modified P1 moiety and a hydroxyethylamine sulfonamide isostere, is highly active against laboratory HIV-1 strains and primary clinical isolates (50% effective concentration [EC50] of 0.00035 to 0.0018 µM), with minimal cytotoxicity (50% cytotoxic concentration [CC50] = 35.7 µM). GRL-10413 blocked the infectivity and replication of HIV-1NL4-3 variants selected by use of atazanavir, lopinavir, or amprenavir (APV) at concentrations of up to 5 µM (EC50 = 0.0021 to 0.0023 µM). GRL-10413 also maintained its strong antiviral activity against multidrug-resistant clinical HIV-1 variants isolated from patients who no longer responded to various antiviral regimens after long-term antiretroviral therapy. The development of resistance against GRL-10413 was significantly delayed compared to that against APV. In addition, GRL-10413 showed favorable central nervous system (CNS) penetration properties as assessed with an in vitro blood-brain barrier (BBB) reconstruction system. Analysis of the crystal structure of HIV-1 protease in complex with GRL-10413 demonstrated that the modified P1 moiety of GRL-10413 has a greater hydrophobic surface area and makes greater van der Waals contacts with active site amino acids of protease than in the case of darunavir. Moreover, the chlorine substituent in the P1 moiety interacts with protease in two distinct configurations. The present data demonstrate that GRL-10413 has desirable features for treating patients infected with wild-type and/or multidrug-resistant HIV-1 variants, with favorable CNS penetration capability, and that the newly modified P1 moiety may confer desirable features in designing novel anti-HIV-1 PIs.

Computational Prediction of HIV-1 Resistance to Protease Inhibitors.

The development of mutations in HIV-1 protease (PR) hinders the activity of antiretroviral drugs, forcing changes in drug prescription. Most resistance assessments used to date rely on expert-based rules on predefined sets of stereotypical mutations; such an information-driven approach cannot capture new polymorphisms or be applied for new drugs. Computational modeling could provide a more general assessment of drug resistance and could be made available to clinicians through the Internet. We have created a protocol involving sequence comparison and all-atom protein-ligand induced fit simulations to predict resistance at the molecular level. We first compared our predictions with the experimentally determined IC50 values of darunavir, amprenavir, ritonavir, and indinavir from reference PR mutants displaying different resistance levels. We then performed analyses on a large set of variants harboring more than 10 mutations. Finally, several sequences from real patients were analyzed for amprenavir and darunavir. Our computational approach detected all of the genotype changes triggering high-level resistance, even those involving a large number of mutations.

Improved darunavir genotypic mutation score predicting treatment response for patients infected with HIV-1 subtype B and non-subtype B receiving a salvage regimen.

OBJECTIVES: The objective of this study was to improve the prediction of the impact of HIV-1 protease mutations in different viral subtypes on virological response to darunavir. METHODS: Darunavir-containing treatment change episodes (TCEs) in patients previously failing PIs were selected from large European databases. HIV-1 subtype B-infected patients were used as the derivation dataset and HIV-1 non-B-infected patients were used as the validation dataset. The adjusted association of each mutation with week 8 HIV RNA change from baseline was analysed by linear regression. A prediction model was derived based on best subset least squares estimation with mutational weights corresponding to regression coefficients. Virological outcome prediction accuracy was compared with that from existing genotypic resistance interpretation systems (GISs) (ANRS 2013, Rega 9.1.0 and HIVdb 7.0). RESULTS: TCEs were selected from 681 subtype B-infected and 199 non-B-infected adults. Accompanying drugs were NRTIs in 87%, NNRTIs in 27% and raltegravir or maraviroc or enfuvirtide in 53%. The prediction model included weighted protease mutations, HIV RNA, CD4 and activity of accompanying drugs. The model's association with week 8 HIV RNA change in the subtype B (derivation) set was R(2) = 0.47 [average squared error (ASE) = 0.67, P < 10(-6)]; in the non-B (validation) set, ASE was 0.91. Accuracy investigated by means of area under the receiver operating characteristic curves with a binary response (above the threshold value of HIV RNA reduction) showed that our final model outperformed models with existing interpretation systems in both training and validation sets. CONCLUSIONS: A model with a new darunavir-weighted mutation score outperformed existing GISs in both B and non-B subtypes in predicting virological response to darunavir.

Drug susceptibility to etravirine and darunavir among Human Immunodeficiency Virus Type 1-derived pseudoviruses in treatment-experienced patients with HIV/AIDS in South Korea.

BACKGROUND: In South Korea, about 20 types of antiretroviral drugs are used in the treatment of patients with human immunodeficiency virus/acquired immune deficiency syndrome. Since 2010, raltegravir, etravirine, and darunavir have been spotlighted as new drugs for highly active antiretroviral therapy (HAART)-experienced adults with resistant HIV-1 in South Korea. In this study, we investigated potential susceptibility of pseudoviruses derived from treatment-experienced Korean patients to etravirine vs efavirenz and to darunavir vs amprenavir and indinavir using a modified single-round assay. METHODS: Pseudoviruses derived from nine treatment-experienced patients infected with HIV-1 were investigated by comparison with the wild-type strain pNL4-3. The 50% inhibitory concentration (IC50) values were calculated and drug susceptibility was compared. The intensity of genotypic drug resistance was classified based on the 'SIR' interpretation of the Stanford data base. RESULTS: Drug susceptibility was generally higher for etravirine and darunavir compared with efavirenz, amprenavir, and indinavir in pseudoviruses derived from treatment-experienced patients. Pseudoviruses derived from patients KRB4025 and KRB8014, who exhibited long-term use of protease inhibitors, showed an outside of tested drug concentration, especially for amprenavir and indinavir. However, they exhibited a lower fold-change in resistance to darunavir. CONCLUSIONS: Etravirine and darunavir have been used in HAART since 2010 in South Korea. Therefore, these antiretroviral drugs together with other newly introduced antiretroviral drugs are interesting for the optimal treatment of patients with treatment failure. This study may help to find a more effective HAART in the case of HIV-1 infected patients that have difficulty being treated.