Unlocking The Mysteries of DNA Adducts with Artificial Intelligence.

Cellular genome is considered a dynamic blueprint of a cell since it encodes genetic information that gets temporally altered due to various endogenous and exogenous insults. Largely, the extent of genomic dynamicity is controlled by the trade-off between DNA repair processes and the genotoxic potential of the causative agent (genotoxins or potential carcinogens). A subset of genotoxins form DNA adducts by covalently binding to the cellular DNA, triggering structural or functional changes that lead to significant alterations in cellular processes via genetic (e. g., mutations) or non-genetic (e. g., epigenome) routes. Identification, quantification, and characterization of DNA adducts are indispensable for their comprehensive understanding and could expedite the ongoing efforts in predicting carcinogenicity and their mode of action. In this review, we elaborate on using Artificial Intelligence (AI)-based modeling in adducts biology and present multiple computational strategies to gain advancements in decoding DNA adducts. The proposed AI-based strategies encompass predictive modeling for adduct formation via metabolic activation, novel adducts' identification, prediction of biochemical routes for adduct formation, adducts' half-life predictions within biological ecosystems, and, establishing methods to predict the link between adducts chemistry and its location within the genomic DNA. In summary, we discuss some futuristic AI-based approaches in DNA adduct biology.

Amelioration of Amyloid-ß Induced Alzheimer's Disease by Bacopa monnieri through Modulation of Mitochondrial Dysfunction and GSK-3ß/Wnt/ß-Catenin Signaling.

BACKGROUND: Alzheimer's disease (AD) is the most prevalent dementia, affecting a large number of populations. Despite being under scrutiny for decades, an effective therapeutic option is still not available. METHODS AND RESULTS: This study explores the therapeutic role of a nootropic herb Bacopa monnieri (BM) in AD-like pathological conditions produced by injecting preformed amyloid-ß42 (Aß42 ) fibril bilaterally into hippocampus of Wistar rats, and ethanolic extract of BM is orally administered for 4 weeks. Assessment of behavioral changes reveals that BM treatment ameliorates Aß42 -induced cognitive impairment and compromised explorative behavior. Supplementation of BM also reduces oxidative stress biomarkers, proinflammatory cytokines, and cholinesterase activity in the AD rats. Additionally, BM treatment restores Bcl-2-associated X protein (Bax)/ B-cell lymphoma 2 (Bcl-2) imbalance, increases neurotrophic factors expression, and prevents neurodegeneration validated by quantifying Nissl-positive hippocampal neurons. Interestingly, BM administration eliminates amyloid plaques in the hippocampal region and normalizes the Aß42 -induced increase in phospho-tau and total tau expression. Mechanistic investigations reveal that BM interacts with glycogen synthase kinase (GSK-3ß) and restores Wnt/ß-catenin signaling. CONCLUSION: BM has been used in diet as a nootropic herb for several centuries. This study highlights the anti-Alzheimer activity of BM from the behavioral to the molecular level by modulating mitochondrial dysfunction, and GSK-3ß mediates the Wnt/ß-catenin signaling pathway.

deepGraphh: AI-driven web service for graph-based quantitative structure-activity relationship analysis.

Artificial intelligence (AI)-based computational techniques allow rapid exploration of the chemical space. However, representation of the compounds into computational-compatible and detailed features is one of the crucial steps for quantitative structure-activity relationship (QSAR) analysis. Recently, graph-based methods are emerging as a powerful alternative to chemistry-restricted fingerprints or descriptors for modeling. Although graph-based modeling offers multiple advantages, its implementation demands in-depth domain knowledge and programming skills. Here we introduce deepGraphh, an end-to-end web service featuring a conglomerate of established graph-based methods for model generation for classification or regression tasks. The graphical user interface of deepGraphh supports highly configurable parameter support for model parameter tuning, model generation, cross-validation and testing of the user-supplied query molecules. deepGraphh supports four widely adopted methods for QSAR analysis, namely, graph convolution network, graph attention network, directed acyclic graph and Attentive FP. Comparative analysis revealed that deepGraphh supported methods are comparable to the descriptors-based machine learning techniques. Finally, we used deepGraphh models to predict the blood-brain barrier permeability of human and microbiome-generated metabolites. In summary, deepGraphh offers a one-stop web service for graph-based methods for chemoinformatics.

Applying polypharmacology approach for drug repurposing for SARS-CoV2.

Exploring the new therapeutic indications of known drugs for treating COVID-19, popularly known as drug repurposing, is emerging as a pragmatic approach especially owing to the mounting pressure to control the pandemic. Targeting multiple targets with a single drug by employing drug repurposing known as the polypharmacology approach may be an optimised strategy for the development of effective therapeutics. In this study, virtual screening has been carried out on seven popular SARS-CoV-2 targets (3CLpro, PLpro, RdRp (NSP12), NSP13, NSP14, NSP15, and NSP16). A total of 4015 approved drugs were screened against these targets. Four drugs namely venetoclax, tirilazad, acetyldigitoxin, and ledipasvir have been selected based on the docking score, ability to interact with four or more targets and having a reasonably good number of interactions with key residues in the targets. The MD simulations and MM-PBSA studies showed reasonable stability of protein-drug complexes and sustainability of key interactions between the drugs with their respective targets throughout the course of MD simulations. The identified four drug molecules were also compared with the known drugs namely elbasvir and nafamostat. While the study has provided a detailed account of the chosen protein-drug complexes, it has explored the nature of seven important targets of SARS-CoV-2 by evaluating the protein-drug complexation process in great detail. Graphical abstract: Drug repurposing strategy against SARS-CoV2 drug targets. Computational analysis was performed to identify repurposable approved drug candidates against SARS-CoV2 using approaches such as virtual screening, molecular dynamics simulation and MM-PBSA calculations. Four drugs namely venetoclax, tirilazad, acetyldigitoxin, and ledipasvir have been selected as potential candidates. Supplementary Information: The online version contains supplementary material available at 10.1007/s12039-022-02046-0.

Improved Binding Affinity of Omicron's Spike Protein for the Human Angiotensin-Converting Enzyme 2 Receptor Is the Key behind Its Increased Virulence.

The new variant of severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), Omicron, has been quickly spreading in many countries worldwide. Compared to the original virus, Omicron is characterized by several mutations in its genomic region, including the spike protein's receptor-binding domain (RBD). We have computationally investigated the interaction between the RBD of both the wild type and Omicron variant of SARS-CoV-2 with the human angiotensin-converting enzyme 2 (hACE2) receptor using molecular dynamics and molecular mechanics-generalized Born surface area (MM-GBSA)-based binding free energy calculations. The mode of the interaction between Omicron's RBD with the hACE2 receptor is similar to the original SARS-CoV-2 RBD except for a few key differences. The binding free energy difference shows that the spike protein of Omicron has an increased affinity for the hACE2 receptor. The mutated residues in the RBD showed strong interactions with a few amino acid residues of hACE2. More specifically, strong electrostatic interactions (salt bridges) and hydrogen bonding were observed between R493 and R498 residues of the Omicron RBD with D30/E35 and D38 residues of the hACE2, respectively. Other mutated amino acids in the Omicron RBD, e.g., S496 and H505, also exhibited hydrogen bonding with the hACE2 receptor. A pi-stacking interaction was also observed between tyrosine residues (RBD-Tyr501: hACE2-Tyr41) in the complex, which contributes majorly to the binding free energies and suggests that this is one of the key interactions stabilizing the formation of the complex. The resulting structural insights into the RBD:hACE2 complex, the binding mode information within it, and residue-wise contributions to the free energy provide insight into the increased transmissibility of Omicron and pave the way to design and optimize novel antiviral agents.

A Review on Parallel Virtual Screening Softwares for High-Performance Computers.

Drug discovery is the most expensive, time-demanding, and challenging project in biopharmaceutical companies which aims at the identification and optimization of lead compounds from large-sized chemical libraries. The lead compounds should have high-affinity binding and specificity for a target associated with a disease, and, in addition, they should have favorable pharmacodynamic and pharmacokinetic properties (grouped as ADMET properties). Overall, drug discovery is a multivariable optimization and can be carried out in supercomputers using a reliable scoring function which is a measure of binding affinity or inhibition potential of the drug-like compound. The major problem is that the number of compounds in the chemical spaces is huge, making the computational drug discovery very demanding. However, it is cheaper and less time-consuming when compared to experimental high-throughput screening. As the problem is to find the most stable (global) minima for numerous protein-ligand complexes (on the order of 106 to 1012), the parallel implementation of in silico virtual screening can be exploited to ensure drug discovery in affordable time. In this review, we discuss such implementations of parallelization algorithms in virtual screening programs. The nature of different scoring functions and search algorithms are discussed, together with a performance analysis of several docking softwares ported on high-performance computing architectures.

Improved binding affinity of the Omicron's spike protein with hACE2 receptor is the key factor behind its increased virulence

The new variant of SARS-CoV-2, Omicron, has been quickly spreading in many countries worldwide. Compared to the original virus, Omicron is characterized by several mutations in its genomic region, including spike proteins receptor-binding domain (RBD). We have computationally investigated the interaction between RBD of both wild-type and omicron variants with hACE2 receptor using molecular dynamics and MM-GBSA based binding free energy calculations. The mode of the interaction between Omicrons RBD to the human ACE2 (hACE2) receptor is similar to the original SARS-CoV-2 RBD except for a few key differences. The binding free energy difference shows that the spike protein of Omicron has increased binding affinity for the hACE-2 receptor. The mutated residues in the RBD showed strong interactions with a few amino acid residues of the hACE2. More specifically, strong electrostatic interactions (salt bridges) and hydrogen bonding were observed between R493 and R498 residues of the Omicron RBD with D30/E35 and D38 residues of the hACE2, respectively. Other mutated amino acids in the Omicron RBD, e.g. S496 and H505, also exhibited hydrogen bonding with the hACE2 receptor. The pi-stacking interaction was also observed between tyrosine residues (RBD-Tyr501: hACE2-Tyr41) in the complex, which contributes majorly to binding free energies suggesting this as one of the key interactions stabilizing the complex formation. The structural insights of RBD:hACE2 complex, their binding mode information and residue wise contributions to binding free energy provide insight on the increased transmissibility of Omicron and pave the way to design and optimize novel antiviral agents.

Rodenticide ingestion is an important cause of acute hepatotoxicity in Tamil Nadu, southern India.

BACKGROUND AND AIM: Though rodenticidal hepatotoxicity is reported from India, there is no systematic study to assess its magnitude. This study aimed to assess exposure to rodenticide as a risk factor for acute hepatotoxicity in Tamil Nadu, India. METHODS: We retrospectively analyzed acute hepatotoxicity caused by ingestion of hepatotoxin or potentially hepatotoxic drug overdose across 15 hospitals in 6 districts of Tamil Nadu from 1 January 2019 to 30 June 2019. Study exclusion criteria were idiosyncratic drug-induced liver injury and chronic liver diseases. RESULTS: Of the 702 patients, 685 gave history of consuming rodenticide; hepatotoxicity in the other patients resulted from paracetamol overdose (n=10) and due to other drugs (n=7); 97% patients had a suicidal intent. Of 671 patients with complete data, ratio of number of patients with hepatotoxicity due to rodenticide to paracetamol overdose was 450:6 (i.e. 75:1). The 451 rodenticidal hepatotoxicity patients (255 males, 75% were 15-34 years old) underwent conservative management (n=396), plasma exchange (n=54) and plasma exchange followed by liver transplant (n=1); 159 patients (35%) had poor outcome (131 died, 28 discharged in moribund state). Based on our observations, we estimate a case burden of 1584 rodenticidal hepatotoxicity patients (95% CI: 265-6119) with poor outcome in 554 patients in Tamil Nadu from January 2019 to June 2019. Population attributable risk for rodenticide as cause of hepatotoxicity was 22.7%. CONCLUSION: Rodenticide ingestion was an important cause of acute hepatotoxicity in Tamil Nadu. Most patients were young and one-third had poor outcome. Public health interventions are needed to address this.

Assessment of Amyloid Forming Tendency of Peptide Sequences from Amyloid Beta and Tau Proteins Using Force-Field, Semi-Empirical, and Density Functional Theory Calculations.

A wide variety of neurodegenerative diseases are characterized by the accumulation of protein aggregates in intraneuronal or extraneuronal brain regions. In Alzheimer's disease (AD), the extracellular aggregates originate from amyloid-ß proteins, while the intracellular aggregates are formed from microtubule-binding tau proteins. The amyloid forming peptide sequences in the amyloid-ß peptides and tau proteins are responsible for aggregate formation. Experimental studies have until the date reported many of such amyloid forming peptide sequences in different proteins, however, there is still limited molecular level understanding about their tendency to form aggregates. In this study, we employed umbrella sampling simulations and subsequent electronic structure theory calculations in order to estimate the energy profiles for interconversion of the helix to ß-sheet like secondary structures of sequences from amyloid-ß protein (KLVFFA) and tau protein (QVEVKSEKLD and VQIVYKPVD). The study also included a poly-alanine sequence as a reference system. The calculated force-field based free energy profiles predicted a flat minimum for monomers of sequences from amyloid and tau proteins corresponding to an α-helix like secondary structure. For the parallel and anti-parallel dimer of KLVFFA, double well potentials were obtained with the minima corresponding to α-helix and ß-sheet like secondary structures. A similar double well-like potential has been found for dimeric forms for the sequences from tau fibril. Complementary semi-empirical and density functional theory calculations displayed similar trends, validating the force-field based free energy profiles obtained for these systems.

Computational investigation on Andrographis paniculata phytochemicals to evaluate their potency against SARS-CoV-2 in comparison to known antiviral compounds in drug trials.

The outbreak due to SARS-CoV-2 (or Covid-19) is spreading alarmingly and number of deaths due to infection is aggressively increasing every day. Due to the rapid human to human transmission of Covid-19, we are in need to find a potent drug at the earliest by ruling-out the traditional time-consuming approach of drug development. This is only possible if we use reliable computational approaches for screening compounds from chemical space or by drug repurposing or by finding the phytochemicals and nutraceuticals from plants as they can be immediately used without the need for carrying out drug-trials to test safety and efficacy. A number of plant products were routinely suggested as drugs in traditional Indian and Chinese medicine. Here using molecular docking approach, and combined molecular dynamics and MM-GBSA based free energy calculations approach, we study the potency of the four selected phytochemicals namely andrographolide (AGP1), 14-deoxy 11,12-didehydro andrographolide (AGP2), neoandrographolide (AGP3) and 14-deoxy andrographolide (AGP4) from A. paniculata plant against the four key targets including three non-structural proteins (3 L main protease (3CLpro), Papain-like proteinase (PLpro) and RNA-directed RNA polymerase (RdRp)) and a structural protein (spike protein (S)) of the virus which are responsible for replication, transcription and host cell recognition. The therapeutic potential of the selected phytochemicals against Covid-19 were also evaluated in comparison with a few commercially available drugs. The binding free energy data suggest that AGP3 could be used as a cost-effective drug-analog for treating covid-19 infection in developing countries.Communicated by Ramaswamy H. Sarma.

Searching for target-specific and multi-targeting organics for Covid-19 in the Drugbank database with a double scoring approach.

The current outbreak of Covid-19 infection due to SARS-CoV-2, a virus from the coronavirus family, has become a major threat to human healthcare. The virus has already infected more than 44 M people and the number of deaths reported has reached more than 1.1 M which may be attributed to lack of medicine. The traditional drug discovery approach involves many years of rigorous research and development and demands for a huge investment which cannot be adopted for the ongoing pandemic infection. Rather we need a swift and cost-effective approach to inhibit and control the viral infection. With the help of computational screening approaches and by choosing appropriate chemical space, it is possible to identify lead drug-like compounds for Covid-19. In this study, we have used the Drugbank database to screen compounds against the most important viral targets namely 3C-like protease (3CLpro), papain-like protease (PLpro), RNA-dependent RNA polymerase (RdRp) and the spike (S) protein. These targets play a major role in the replication/transcription and host cell recognition, therefore, are vital for the viral reproduction and spread of infection. As the structure based computational screening approaches are more reliable, we used the crystal structures for 3C-like main protease and spike protein. For the remaining targets, we used the structures based on homology modeling. Further, we employed two scoring methods based on binding free energies implemented in AutoDock Vina and molecular mechanics-generalized Born surface area approach. Based on these results, we propose drug cocktails active against the three viral targets namely 3CLpro, PLpro and RdRp. Interestingly, one of the identified compounds in this study i.e. Baloxavir marboxil has been under clinical trial for the treatment of Covid-19 infection. In addition, we have identified a few compounds such as Phthalocyanine, Tadalafil, Lonafarnib, Nilotinib, Dihydroergotamine, R-428 which can bind to all three targets simultaneously and can serve as multi-targeting drugs. Our study also included calculation of binding energies for various compounds currently under drug trials. Among these compounds, it is found that Remdesivir binds to targets, 3CLpro and RdRp with high binding affinity. Moreover, Baricitinib and Umifenovir were found to have superior target-specific binding while Darunavir is found to be a potential multi-targeting drug. As far as we know this is the first study where the compounds from the Drugbank database are screened against four vital targets of SARS-CoV-2 and illustrates that the computational screening using a double scoring approach can yield potential drug-like compounds against Covid-19 infection.

Performance of Force-Field- and Machine Learning-Based Scoring Functions in Ranking MAO-B Protein-Inhibitor Complexes in Relevance to Developing Parkinson's Therapeutics.

Monoamine oxidase B (MAOB) is expressed in the mitochondrial membrane and has a key role in degrading various neurologically active amines such as benzylamine, phenethylamine and dopamine with the help of Flavin adenine dinucleotide (FAD) cofactor. The Parkinson's disease associated symptoms can be treated using inhibitors of MAO-B as the dopamine degradation can be reduced. Currently, many inhibitors are available having micromolar to nanomolar binding affinities. However, still there is demand for compounds with superior binding affinity and binding specificity with favorable pharmacokinetic properties for treating Parkinson's disease and computational screening methods can be majorly recruited for this. However, the accuracy of currently available force-field methods for ranking the inhibitors or lead drug-like compounds should be improved and novel methods for screening compounds need to be developed. We studied the performance of various force-field-based methods and data driven approaches in ranking about 3753 compounds having activity against the MAO-B target. The binding affinities computed using autodock and autodock-vina are shown to be non-reliable. The force-field-based MM-GBSA also under-performs. However, certain machine learning approaches, in particular KNN, are found to be superior, and we propose KNN as the most reliable approach for ranking the complexes to reasonable accuracy. Furthermore, all the employed machine learning approaches are also computationally less demanding.

Discovery of biphenyl pyrazole scaffold for neurodegenerative diseases: A novel class of acetylcholinesterase-centered multitargeted ligands.

Multitargeted ligands have demonstrated remarkable efficiency as potential therapeutics for neurodegenerative diseases as they target multiple pathways involved in the progression of these diseases. Herein, we report first-in-class dual inhibitor of acetylcholinesterase (AChE) and tau aggregation as a novel class of multitargeted ligands for neurodegenerative diseases. The reported biphenyl pyrazole scaffold binds monomeric tau with submicromolar affinity and impedes the formation of tau oligomers at early stages. Additionally, the lead compound inhibited AChE activity with an IC50 value of 0.35 ± 0.02 µM. Remarkably, the neuroprotective effect of this lead in induced cytotoxicity model of SH-SY5Y neuroblastoma cells is superior to single-targeted AChE and tau-aggregation inhibitors. This scaffold would enable development of new generation of multitargeted ligands for neurodegenerative diseases that function through dual targeting of AChE and monomeric tau.

Structure-based drug designing and immunoinformatics approach for SARS-CoV-2.

The prevalence of respiratory illness caused by the novel SARS-CoV-2 virus associated with multiple organ failures is spreading rapidly because of its contagious human-to-human transmission and inadequate globalhealth care systems. Pharmaceutical repurposing, an effective drug development technique using existing drugs, could shorten development time and reduce costs compared to those of de novo drug discovery. We carried out virtual screening of antiviral compounds targeting the spike glycoprotein (S), main protease (Mpro), and the SARS-CoV-2 receptor binding domain (RBD)-angiotensin-converting enzyme 2 (ACE2) complex of SARS-CoV-2. PC786, an antiviral polymerase inhibitor, showed enhanced binding affinity to all the targets. Furthermore, the postfusion conformation of the trimeric S protein RBD with ACE2 revealed conformational changes associated with PC786 drug binding. Exploiting immunoinformatics to identify T cell and B cell epitopes could guide future experimental studies with a higher probability of discovering appropriate vaccine candidates with fewer experiments and higher reliability.

Glycosmis pentaphylla (Rutaceae): A Natural Candidate for the Isolation of Potential Bioactive Arborine and Skimmianine Compounds for Controlling Multidrug-Resistant Staphylococcus aureus.

Several multidrug-resistant organisms have emerged, which increases the threat to public health around the world and a limited number of therapeutics were available to counteract these issues. Thus, researchers are trying to find out novel antimicrobials to overcome multidrug-resistant issues. The present study aimed to isolate antibacterial principles against the clinical isolates of multidrug-resistant (MDR) Staphylococcus aureus from the ethyl acetate extract of Gycosmis pentaphylla. The isolation and structural characterization of bioactive compounds were carried out using various chromatographic techniques (TLC, column, HPLC, and LC-MS) and spectral studies such as FT-IR, CHNS analysis, 1H-NMR, and 13C-NMR. The antimicrobial potential of isolated compounds was assessed according to the standard methods. The isolated compounds were identified as arborine and skimmianine, which exhibited a significant antibacterial effect with the lowest MIC and MBC values against MDR S. aureus and in vitro kinetic and protein leakage assays supported the antimicrobial activity. Significant morphological changes such as uneven cell surfaces and morphology, cell shrinkage, and cell membrane damages were observed in the MDR S. aureus upon the treatment of arborine and skimmianine. The present investigation concludes that the isolated arborine and skimmianine compounds from G. pentaphylla harbor a strong antibacterial activity against MDR S. aureus and may be used as alternative natural drugs in the treatment of MDR S. aureus.

Destabilization of amyloid fibrils on interaction with MoS2-based nanomaterials.

The present work is motivated by the established concept that the structure and energetics of biomacromolecules can be modulated by confining their dimensions in the nanoscale. In particular, here we use force-field methods to understand the stability of amyloid fibrils at nanostructured interfaces, which can be useful for the development of new therapeutics for Alzheimer's disease. We explore the binding modes and structural properties of fibrils at the interface of molybdenum disulphide nanotubes and the nanosurface using classical molecular dynamics simulations. We find that in general the MoS2 materials induces disruptions in the structure of the amyloid fibrils where the beta sheet conformation of the fibrils changes to a turned conformation, and it is large in the case of nanotubes in comparison to the nanosurfaces. The intermolecular hydrogen bonds, hydrophilic and hydrophobic contacts between the monomer peptides in the fibril are reduced due to their adsorption onto the MoS2 materials, which results in a destabilization of the fibril. The destabilization of fibril is to some extent compensated for by the van der Waals interactions between the fibril and MoS2. Overall the results indicate that MoS2-based materials can be useful in inhibiting the aggregation of smaller protofibrils to matured fibrils and to bust the already formed fibrils. Therapeutic materials should not exhibit any cross interaction with other off-targets compounds. In order to test whether the MoS2 nanomaterial has any such effect we have studied its interaction with two additional biomacromolecules, the human serum albumin and p53 protein, and we report no significant changes in the secondary structure of these biomolecules. Through molecular docking studies we also established that the drug binding ability of HSA is not altered by its surface binding to MoS2 nanosurface.

INASL Guidelines on Management of Hepatitis B Virus Infection in Patients receiving Chemotherapy, Biologicals, Immunosupressants, or Corticosteroids.

Hepatitis B Virus (HBV) reactivation in patients receiving chemotherapy, biologicals, immunosupressants, or corticosteroids is emerging to be an important cause of morbidity and mortality in patients with current or prior exposure to HBV infection. These patients suffer a dual onslaught of illness: one from the primary disease for which they are receiving the culprit drug that led to HBV reactivation, and the other from HBV reactivation itself. The HBV reactivation not only leads to a compromised liver function, which may culminate into hepatic failure; it also adversely impacts the treatment outcome of the primary illness. Hence, identification of patients at risk of reactivation before starting these drugs, and starting treatment aimed at prevention of HBV reactivation is the best strategy of managing these patients. There are no Indian guidelines on management of HBV infection in patients receiving chemotherapy, biologicals, immunosupressants, or corticosteroids for the treatment of rheumatologic conditions, malignancies, inflammatory bowel disease, dermatologic conditions, or solid-organ or bone marrow transplantation. The Indian National Association for Study of the Liver (INASL) had set up a taskforce on HBV in 2016, with a mandate to develop consensus guidelines for management of various aspects of HBV infection, relevant to India. In 2017 the taskforce had published the first INASL guidelines on management of HBV infection in India. In the present guidelines, which are in continuation with the previous guidelines, the issues on management of HBV infection in patients receiving chemotherapy, biologicals, immunosupressants, or corticosteroids are addressed.

Structure-Based Optimization of N-Substituted Oseltamivir Derivatives as Potent Anti-Influenza A Virus Agents with Significantly Improved Potency against Oseltamivir-Resistant N1-H274Y Variant.

Due to the emergence of highly pathogenic and oseltamivir-resistant influenza viruses, there is an urgent need to develop new anti-influenza agents. Herein, five subseries of oseltamivir derivatives were designed and synthesized to improve their activity toward drug-resistant viral strains by further exploiting the 150-cavity in the neuraminidases (NAs). The bioassay results showed that compound 21h exhibited antiviral activities similar to or better than those of oseltamivir carboxylate (OSC) against H5N1, H5N2, H5N6, and H5N8. Besides, 21h was 5- to 86-fold more potent than OSC toward N1, N8, and N1-H274Y mutant NAs in the inhibitory assays. Computational studies provided a plausible rationale for the high potency of 21h against group-1 and N1-H274Y NAs. In addition, 21h demonstrated acceptable oral bioavailability, low acute toxicity, potent antiviral activity in vivo, and high metabolic stability. Overall, the above excellent profiles make 21h a promising drug candidate for the treatment of influenza virus infection.

Optimization of N-Substituted Oseltamivir Derivatives as Potent Inhibitors of Group-1 and -2 Influenza A Neuraminidases, Including a Drug-Resistant Variant.

On the basis of our earlier discovery of N1-selective inhibitors, the 150-cavity of influenza virus neuraminidases (NAs) could be further exploited to yield more potent oseltamivir derivatives. Among the synthesized compounds, 15b and 15c were exceptionally active against both group-1 and -2 NAs. Especially for 09N1, N2, N6, and N9 subtypes, they showed 6.80-12.47 and 1.20-3.94 times greater activity than oseltamivir carboxylate (OSC). They also showed greater inhibitory activity than OSC toward H274Y and E119V variant. In cellular assays, they exhibited greater potency than OSC toward H5N1, H5N2, H5N6, and H5N8 viruses. 15b demonstrated high metabolic stability, low cytotoxicity in vitro, and low acute toxicity in mice. Computational modeling and molecular dynamics studies provided insights into the role of R group of 15b in improving potency toward group-1 and -2 NAs. We believe the successful exploitation of the 150-cavity of NAs represents an important breakthrough in the development of more potent anti-influenza agents.

Nonempirical Simulations of Inhomogeneous Broadening of Electronic Transitions in Solution: Predicting Band Shapes in One- and Two-Photon Absorption Spectra of Chalcones.

We have examined several approaches relying on the Polarizable Embedding (PE) scheme to predict optical band shapes for two chalcone molecules in methanol solution. The PE-TDDFT and PERI-CC2 methods were combined with molecular dynamics simulations, where the solute geometry was kept either as rigid, flexible or partly-flexible (restrained) body. The first approach, termed RBMD-PE-TDDFT, was employed to estimate the inhomogeneous broadening for subsequent convolution with the vibrationally-resolved spectra of the molecule in solution determined quantum-mechanically (QM). As demonstrated, the RBMD-PE-TDDFT/QM-PCM approach delivers accurate band widths, also reproducing their correct asymmetric shapes. Further refinement can be obtained by the estimation of the inhomogeneous broadening using the RBMD-PERI-CC2 method. On the other hand, the remaining two approaches (FBMD-PE-TDDFT and ResBMD-PE-TDDFT), which lack quantum-mechanical treatment of molecular vibrations, lead to underestimated band widths. In this study, we also proposed a simple strategy regarding the rapid selection of the exchange-correlation functional for the simulations of vibrationally-resolved one- and two-photon absorption spectra based on two easy-to-compute metrics.