De Novo Design of Anti-COVID Drugs Using Machine Learning-Based Equivariant Diffusion Model Targeting the Spike Protein.

The drug discovery and research for an anti-COVID-19 drug has been ongoing despite repurposed drugs in the market. Over time, these drugs were discontinued due to side effects. The search for effective drugs is still under process. The role of Machine Learning (ML) is critical in the search for novel drug compounds. In the current work, using the equivariant diffusion model, we built novel compounds targeting the spike protein of SARS-CoV-2. Using the ML models, 196 de novo compounds were generated which had no hits on any major chemical databases. These novel compounds fulfilled all the criteria of ADMET properties to be lead-like and drug-like compounds. Of the 196 compounds, 15 were docked with high confidence in the target. These compounds were further subjected to molecular docking, the best compound having an IUPAC name of (4aS,4bR,8aS,8bS)-4a,8a-dimethylbiphenylene-1,4,5,8(4aH,4bH,8aH,8bH)-tetraone and a binding score of -6.930 kcal/mol. The principal compound is labeled as CoECG-M1. Density Function Theory (DFT) and Quantum optimization was carried out along with the study of ADMET properties. This suggests that the compound has potential drug-like properties. The docked complex was further subjected to MD simulations, GBSA, and metadynamics simulations to gain insights into the stability of binding. The model can be in the future modified to improve the positive docking rate.

Repurposing of known drugs for COVID-19 using molecular docking and simulation analysis

We selected fifty one drugs already known for their potential disease treatment roles in various studies and subjected to docking and molecular docking simulation (MDS) analyses. Five of them showed promising features that are discussed and suggested as potential candidates for repurposing for COVID-19. These top five compounds were boswellic acid, pimecrolimus, GYY-4137, BMS-345541 and triamcinolone hexacetonide that interacted with the chosen receptors 1R42, 4G3D, 6VW1, 6VXX and 7MEQ, respectively with binding energies of -9.2 kcal/mol, -9.1 kcal/mol, -10.3 kcal/mol, -10.1 kcal/mol and -8.7 kcal/mol, respectively. The MDS studies for the top 5 best complexes revealed binding features for the chosen receptor, human NF-kappa B transcription factor as an important drug target in COVID-19-based drug development strategies.

SARS-CoV-2-encoded ORF8 protein possesses complement inhibitory properties.

Hyperactivation of the complement system, a major component of innate immunity, has been recognized as one of the core clinical features in severe covid-19 patients. However, how the virus escapes the targeted elimination by the network of activated complement pathways still remains an enigma. Here, we identified SARS-CoV-2-encoded ORF8 protein as one of the major binding partners of human complement C3/C3b components and their metabolites. Our results demonstrated that preincubation of ORF8 with C3/C3b in the fluid phase has two immediate functional consequences in the alternative pathway; this preincubation inhibits factor I-mediated proteolysis and blocks factor B zymogen activation into active Bb. ORF8 binding results in the occlusion of both factor H and factor B from C3b, rendering the complexes resistant to factor I-mediated proteolysis and inhibition of pro-C3-convertase (C3bB) formation, respectively. We also confirmed the complement inhibitory activity of ORF8 in our hemolysis-based assay, where ORF8 prevented human serum-induced lysis of rabbit erythrocytes with an IC50 value of about 2.3 µM. This inhibitory characteristic of ORF8 was also supported by in-silico protein-protein docking analysis, as it appeared to establish primary interactions with the ß-chain of C3b, orienting itself near the C3b CUB (C1r/C1s, Uegf, Bmp1) domain like a peptidomimetic compound, sterically hindering the binding of essential cofactors required for complement amplification. Thus, ORF8 has characteristics to act as an inhibitor of critical regulatory steps in the alternative pathway, converging to hasten the decay of C3-convertase and thereby, attenuating the complement amplification loop.

Molecular dynamics simulation and docking analysis of NF-κB protein binding with sulindac acid.

It is of interest to document the Molecular Dynamics Simulation and docking analysis of NF-κB target with sulindac sodium in combating COVID-19 for further consideration. Sulindac is a nonsteroidal anti-inflammatory drug (NSAID) of the arylalkanoic acid class that is marketed by Merck under the brand name Clinoril. We show the binding features of sulindac sodium with NF-κB that can be useful in drug repurposing in COVID-19 therapy.

Mitochondria-Targeted, Nanoparticle-Based Drug-Delivery Systems: Therapeutics for Mitochondrial Disorders.

Apart from ATP generation, mitochondria are involved in a wide range of functions, making them one of the most prominent organelles of the human cell. Mitochondrial dysfunction is involved in the pathophysiology of several diseases, such as cancer, neurodegenerative diseases, cardiovascular diseases, and metabolic disorders. This makes it a target for a variety of therapeutics for the diagnosis and treatment of these diseases. The use of nanoparticles to target mitochondria has significant importance in modern times because they provide promising ways to deliver drug payloads to the mitochondria by overcoming challenges, such as low solubility and poor bioavailability, and also resolve the issues of the poor biodistribution of drugs and pharmacokinetics with increased specificity. This review assesses nanoparticle-based drug-delivery systems, such as liposomes, DQAsome, MITO-Porters, micelles, polymeric and metal nanocarriers, as well as quantum dots, as mitochondria-targeted strategies and discusses them as a treatment for mitochondrial disorders.

Chapter 6 - Modeling of COVID-19 Outbreak in Reference to Physical Parameters

The spread of coronavirus disease 2019 (COVID-19) in various countries varies in different manners, depending on the demographic and climatic conditions. This needs expediting better strategies to combat the novel coronavirus and also the emergence of new viral strains in the future. Our in-depth analysis suggests the spread of COVID-19 cases worldwide, the timeline of spread among most affected countries as of September 3, 2020. Evaluation of the climatic conditions in 2-week interval is carried out. The study recorded most of the countries getting affected by COVID-19 belong to the range of 25–35°C maximum temperature, 20–30°C minimum temperature, and 60%–80% relative humidity. To get a more generalized view on the spread, the study explored the temperature distribution of affected countries and chronicled in both minimum and maximum temperatures so that the number of COVID-19 cases mostly follows a lognormal distribution. In different ranges of relative humidity, not a single distribution is obtained to explain the spread of COVID-19 cases worldwide. A comparison between theoretic and descriptive parameters is also done to support the spread of COVID-19.

Study of the trend pattern of COVID-19 using spline-based time series model: a Bayesian paradigm.

A vast majority of the countries are under economic and health crises due to the current epidemic of coronavirus disease 2019 (COVID-19). The present study analyzes the COVID-19 using time series, an essential gizmo for knowing the enlargement of infection and its changing behavior, especially the trending model. We consider an autoregressive model with a non-linear time trend component that approximately converts into the linear trend using the spline function. The spline function splits the series of COVID-19 into different piecewise segments between respective knots in the form of various growth stages and fits the linear time trend. First, we obtain the number of knots with their locations in the COVID-19 series to identify the transmission stages of COVID-19 infection. Then, the estimation of the model parameters is obtained under the Bayesian setup for the best-fitted model. The results advocate that the proposed model appropriately determines the location of knots based on different transmission stages and know the current transmission situation of the COVID-19 pandemic in a country.

Comparative molecular docking analysis of the SARS CoV-2 Spike glycoprotein with the human ACE-2 receptors and thrombin.

Comparative molecular docking and vixualization analysis of the human thrombin with the SARS CoV-2 Spike glycoprotein and the human ACE-2 receptors is of interest. The data shows that residues spanning positions 30-41 in the ACE-2 have interaction with the spike glycoprotein (UniProt ID: Q9BYF1). Results also shows that thrombin binds with SER494 in the spike protein, and GLU37 in the ACE2 receptor. SER494 in the viral receptor-binding domain provides support for hotspot-353 reported elsewhere. These preliminary data provide insights for further probe.

Problems arising from PPE when worn for long periods.

In the context of Covid-19, personal protective equipment is much needed and often in short supply as a protection against the virus, but nobody until recently was discussing the downside of its prolonged use by its wearers. Increasing numbers of health care workers feel unwell using it and are overheating and some have fainted. Will it impair their professional performance? This article considers this aspect of PPE based upon the personal experience of a Forensic team at AIIMS Bhopal in India who wore it during autopsy work and proposed recommendations to minimise it.