Retaining Patients with Drug-Resistant Tuberculosis on Treatment During the COVID-19 Pandemic - Dharavi, Mumbai, India, 2020-2022.

Mumbai, India's second largest city, has one of the highest prevalences of drug-resistant tuberculosis* (DRTB) in the world. Treatment for DRTB takes longer and is more complicated than treatment for drug-susceptible tuberculosis (TB). Approximately 300 persons receive a new DRTB diagnosis each year in Mumbai's Dharavi slum†; historically, fewer than one half of these patients complete DRTB treatment. As nationwide restrictions to mitigate the COVID-19 pandemic were implemented, a program to facilitate uninterrupted DRTB care for patients receiving treatment was also implemented. A comprehensive tool and risk assessment provided support to DRTB patients and linked those who relocated outside of Dharavi during the pandemic to DRTB care at their destination. During May 2020-September 2022, a total of 973 persons received DRTB treatment in Dharavi, including 255 (26%) who relocated during treatment. Overall, 25 (3%) DRTB patients were lost to follow-up, a rate substantially lower than the rate before the pandemic (18%). Proactive planning and implementation of simple tools retained patients on treatment during periods of travel restrictions and relocations, improving programmatic outcomes. This approach might aid public health programs serving migrant populations or patients receiving treatment for DRTB during public health emergencies.

AI-Accelerated Design of Targeted Covalent Inhibitors for SARS-CoV-2.

Direct-acting antivirals for the treatment of the COVID-19 pandemic caused by the SARS-CoV-2 virus are needed to complement vaccination efforts. Given the ongoing emergence of new variants, automated experimentation, and active learning based fast workflows for antiviral lead discovery remain critical to our ability to address the pandemic's evolution in a timely manner. While several such pipelines have been introduced to discover candidates with noncovalent interactions with the main protease (Mpro), here we developed a closed-loop artificial intelligence pipeline to design electrophilic warhead-based covalent candidates. This work introduces a deep learning-assisted automated computational workflow to introduce linkers and an electrophilic "warhead" to design covalent candidates and incorporates cutting-edge experimental techniques for validation. Using this process, promising candidates in the library were screened, and several potential hits were identified and tested experimentally using native mass spectrometry and fluorescence resonance energy transfer (FRET)-based screening assays. We identified four chloroacetamide-based covalent inhibitors of Mpro with micromolar affinities (KI of 5.27 µM) using our pipeline. Experimentally resolved binding modes for each compound were determined using room-temperature X-ray crystallography, which is consistent with the predicted poses. The induced conformational changes based on molecular dynamics simulations further suggest that the dynamics may be an important factor to further improve selectivity, thereby effectively lowering KI and reducing toxicity. These results demonstrate the utility of our modular and data-driven approach for potent and selective covalent inhibitor discovery and provide a platform to apply it to other emerging targets.

Effectiveness of BBV152/Covaxin and AZD1222/Covishield vaccines against severe COVID-19 and B.1.617.2/Delta variant in India, 2021: a multi-centric hospital-based case-control study.

OBJECTIVES: India introduced BBV152/Covaxin and AZD1222/Covishield vaccines in January 2021. We estimated the effectiveness of these vaccines against severe COVID-19 among individuals aged ≥45 years. METHODS: We did a multi-centric, hospital-based, case-control study between May and July 2021. Cases were severe COVID-19 patients, and controls were COVID-19 negative individuals from 11 hospitals. Vaccine effectiveness (VE) was estimated for complete (2 doses ≥ 14 days) and partial (1 dose ≥ 21 days) vaccination; interval between two vaccine doses and vaccination against the Delta variant. We used the random effects logistic regression model to calculate the adjusted odds ratios (aOR) with a 95% confidence interval (CI) after adjusting for relevant known confounders. RESULTS: We enrolled 1143 cases and 2541 control patients. The VE of complete vaccination was 85% (95% CI: 79-89%) with AZD1222/Covishield and 71% (95% CI: 57-81%) with BBV152/Covaxin. The VE was highest for 6-8 weeks between two doses of AZD1222/Covishield (94%, 95% CI: 86-97%) and BBV152/Covaxin (93%, 95% CI: 34-99%). The VE estimates were similar against the Delta strain and sub-lineages. CONCLUSION: BBV152/Covaxin and AZD1222/Covishield were effective against severe COVID-19 among the Indian population during the period of dominance of the highly transmissible Delta variant in the second wave of the pandemic. An escalation of two-dose coverage with COVID-19 vaccines is critical to reduce severe COVID-19 and further mitigate the pandemic in the country.

3D-Scaffold: A Deep Learning Framework to Generate 3D Coordinates of Drug-like Molecules with Desired Scaffolds.

The prerequisite of therapeutic drug design and discovery is to identify novel molecules and developing lead candidates with desired biophysical and biochemical properties. Deep generative models have demonstrated their ability to find such molecules by exploring a huge chemical space efficiently. An effective way to generate new molecules with desired target properties is by constraining the critical fucntional groups or the core scaffolds in the generation process. To this end, we developed a domain aware generative framework called 3D-Scaffold that takes 3D coordinates of the desired scaffold as an input and generates 3D coordinates of novel therapeutic candidates as an output while always preserving the desired scaffolds in generated structures. We demonstrated that our framework generates predominantly valid, unique, novel, and experimentally synthesizable molecules that have drug-like properties similar to the molecules in the training set. Using domain specific data sets, we generate covalent and noncovalent antiviral inhibitors targeting viral proteins. To measure the success of our framework in generating therapeutic candidates, generated structures were subjected to high throughput virtual screening via docking simulations, which shows favorable interaction against SARS-CoV-2 main protease (Mpro) and nonstructural protein endoribonuclease (NSP15) targets. Most importantly, our deep learning model performs well with relatively small 3D structural training data and quickly learns to generalize to new scaffolds, highlighting its potential application to other domains for generating target specific candidates.

An insight into the inhibitory mechanism of phytochemicals and FDA-approved drugs on the ACE2-Spike complex of SARS-CoV-2 using computational methods.

The S-glycoprotein (Spike) of the SARS-CoV-2 forms a complex with the human transmembrane protein angiotensin-converting enzyme 2 (ACE2) during infection. It forms the first line of contact with the human cell. The FDA-approved drugs and phytochemicals from Indian medicinal plants were explored. Molecular docking and simulations of these molecules targeting the ACE2-Spike complex were performed. Rutin DAB10 and Swertiapuniside were obtained as the top-scored drugs as per the docking protocol. The MD simulations of ligand-free, Rutin DAB10-bound, and Swertiapuniside-bound ACE2-Spike complex revealed abrogation of the hydrogen bonding network between the two proteins. The principal component and dynamic cross-correlation analysis pointed out conformational changes in both the proteins unique to the ligand-bound systems. The interface residues, His34, and Lys353 from ACE2 and Arg403, and Tyr495 from the Spike protein formed significant strong interactions with the ligand molecules, inferring the inhibition of ACE2-Spike complex. Few novel interactions specific to Rutin-DAB10 and Swertiapuniside were also identified. The conformational flexibility of the drug-binding pocket was captured using the RMSD-based clustering of the ligand-free simulations. Ensemble docking was performed wherein the FDA-approved database and phytochemical dataset were docked on each of the cluster representatives of the ACE2-Spike. The phytochemicals identified belonged to Withania somnifera, Swertia chirayita, Tinospora cordifolia and Rutin DAB10, fulvestrant, elbasvir from FDA. Supplementary Information: The online version contains supplementary material available at 10.1007/s11696-021-01680-1.

Natural plant products as potential inhibitors of RNA dependent RNA polymerase of Severe Acute Respiratory Syndrome Coronavirus-2.

Drug repurposing studies targeting inhibition of RNA dependent RNA polymerase (RdRP) of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) have exhibited the potential effect of small molecules. In the present work a detailed interaction study between the phytochemicals from Indian medicinal plants and the RdRP of SARS-CoV-2 has been performed. The top four phytochemicals obtained through molecular docking were, swertiapuniside, cordifolide A, sitoindoside IX, and amarogentin belonging to Swertia chirayita, Tinospora cordifolia and Withania somnifera. These ligands bound to the RdRP were further studied using molecular dynamics simulations. The principal component analysis of these systems showed significant conformational changes in the finger and thumb subdomain of the RdRP. Hydrogen bonding, salt-bridge and water mediated interactions supported by MM-GBSA free energy of binding revealed strong binding of cordifolide A and sitoindoside IX to RdRP. The ligand-interacting residues belonged to either of the seven conserved motifs of the RdRP. These residues were polar and charged amino acids, namely, ARG 553, ARG 555, ASP 618, ASP 760, ASP 761, GLU 811, and SER 814. The glycosidic moieties of the phytochemicals were observed to form favourable interactions with these residues. Hence, these phytochemicals may hold the potential to act as RdRP inhibitors owing to their stability in binding to the druggable site.

Structural insight into the binding interactions of NTPs and nucleotide analogues to RNA dependent RNA polymerase of SARS-CoV-2.

RNA dependent RNA polymerase (RdRP) from positive-stranded RNA viruses has always been a hot target for designing of new drugs. Major class of drugs that are targeted against RdRP are nucleotide analogues. Extensive docking and molecular dynamics study describing the binding of natural nucleotides (NTPs) and its analogues leading to significant structural variation in the RdRP has been presented here. RdRP simulations in its apo, NTP-bound, and analogue-bound form have been performed. Nucleotide analogues included in this study were, favipiravir, galidesivir, lamivudine, ribavirin, remdesivir and sofosbuvir. The conformational flexibility of the RdRP molecule has been explored using principal component (PCA) and Markov state modeling (MSM) analysis. PCA inferred the presence of correlated motions among the conserved motifs of RdRP. Inter-domain distances between the finger and thumb subdomain flanking the nascent RNA template entry site sampled open and closed conformations. The ligand and template binding motifs F and G showed negatively correlated motions. K551, R553, and R555, a part of motif F appear to form strong interactions with the ligand molecules. R836, a primer binding residue was observed to strongly bind to the analogues. MSM analysis helped to extract statistically distinct conformations explored by the RdRP. Ensemble docking of the ligands on the Markov states also suggested the involvement of the above residues in ligand interactions. Markov states obtained clearly demarcated the open/closed conformations of the template entry site. These observations on residues from the conserved motifs involved in binding to the ligands may provide an insight into designing new inhibitors.Communicated by Ramaswamy H. Sarma.

Remdesivir-bound and ligand-free simulations reveal the probable mechanism of inhibiting the RNA dependent RNA polymerase of severe acute respiratory syndrome coronavirus 2.

The efforts towards developing a potential drug against the current global pandemic, COVID-19, have increased in the past few months. Drug development strategies to target the RNA dependent RNA polymerase (RdRP) of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) are being tried worldwide. The gene encoding this protein, is known to be conserved amongst positive strand RNA viruses. This enables an avenue to repurpose the drugs designed against earlier reported inhibitors of RdRP. One such strong inhibitor is remdesivir which has been used against EBOLA infections. The binding of remdesivir to RdRP of SARS-CoV-2 has been studied using the classical molecular dynamics and ensemble docking approach. A comparative study of the simulations of RdRP in the apo and remdesivir-bound form revealed blocking of the template entry site in the presence of remdesivir. The conformation changes leading to this event were captured through principal component analysis. The conformational and thermodynamic parameters supported the experimental information available on the involvement of crucial arginine, serine and aspartate residues belonging to the conserved motifs in RdRP functioning. The catalytic site comprising of SER 759, ASP 760, and ASP 761 (SDD) was observed to form strong contacts with remdesivir. The significantly strong interactions of these residues with remdesivir may infer the latter's binding similar to the normal nucleotides thereby remaining unidentified by the exonuclease activity of RdRP. The ensemble docking of remdesivir too, comprehended the involvement of similar residues in interaction with the inhibitor. This information on crucial interactions between conserved residues of RdRP with remdesivir through in silico approaches may be useful in designing inhibitors.

Drug repurposing studies targeting SARS-CoV-2: an ensemble docking approach on drug target 3C-like protease (3CLpro).

The COVID-19 pandemic has been responsible for several deaths worldwide. The causative agent behind this disease is the Severe Acute Respiratory Syndrome - novel Coronavirus 2 (SARS-CoV-2). SARS-CoV-2 belongs to the category of RNA viruses. The main protease, responsible for the cleavage of the viral polyprotein is considered as one of the hot targets for treating COVID-19. Earlier reports suggest the use of HIV anti-viral drugs for targeting the main protease of SARS-CoV, which caused SARS in the year 2002-2003. Hence, drug repurposing approach may prove to be useful in targeting the main protease of SARS-CoV-2. The high-resolution crystal structure of the main protease of SARS-CoV-2 (PDB ID: 6LU7) was used as the target. The Food and Drug Administration approved and SWEETLEAD database of drug molecules were screened. The apo form of the main protease was simulated for a cumulative of 150 ns and 10 µs open-source simulation data was used, to obtain conformations for ensemble docking. The representative structures for docking were selected using RMSD-based clustering and Markov State Modeling analysis. This ensemble docking approach for the main protease helped in exploring the conformational variation in the drug-binding site of the main protease leading to the efficient binding of more relevant drug molecules. The drugs obtained as top hits from the ensemble docking possessed anti-bacterial and anti-viral properties. This in silico ensemble docking approach would support the identification of potential candidates for repurposing against COVID-19.Communicated by Ramaswamy H. Sarma.