COVID-19 is a viral disease caused by a new severe acute respiratory syndrome (SARS-CoV-2), which has quickly resulted in a pandemic. As a great threat to global public health, the development of a treatment has become vital, and a rush to find a cure has mobilized researchers from all areas across the world. Synthetic drugs, such as hydroxychloroquine, have gained attention. However, the efficacy of repositioned drugs is still under evaluation, and besides, some severe side effects are a cause for concern. This emphasizes the urgency for treatment options, which can be both safe and effective. With this in mind, natural products could be an important resource in the development of COVID-19 treatment, as they have already contributed in the past to treatments against other viruses, such as HIV, MERS-CoV, and influenza. Natural products are described long term as bioactive substances and some phytochemical classes such as flavonoids, alkaloids, and peptides are known antiviral bioproducts, and have been virtually tested with success against COVID-19. However, important issues still need to be addressed as to their bioavailability and true efficacy in vivo. This review intends to systematically evaluate the natural metabolites that could potentially be used against this new disease looking at their natural sources, mechanism of action and previous pharmacological usages. The aim is to provide a starting point for this research area in order to speed up the establishment of anti-SARS-CoV-2 bioproducts.

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Natural products' role against COVID-19

To improve the quality control of drugs, we predicted the absorption, distribution, metabolism, excretion, and toxicity (ADMET) of ceftazidime (CAZ) and its impurities via in silico methods. We used three types of quantitative structure-activity relationship and docking software for precise prediction: Discovery Studio 4.0, OECD QSAR Toolbox 4.1, Toxtree, and the pkCSM approach. The pharmacokinetics and toxicity of ceftazidime and impurity A (Δ-2-CAZ) are similar. The biological properties of impurity B (CAZ E-isomer) are different from CAZ. Therefore, we focused on drug stability to analyze impurity B. Impurities D and I have strong lipophilicity, good intestinal absorption, and poor excretion in the body. Impurity D is particularly neurotoxic and genotoxic. It is important to control the content of impurity D. The toxicity of impurity F is low, but the toxicity is enhanced when it becomes the C-3 side chain of CAZ and forms a quaternary amine group. We conclude that the beta-lactam ring of nucleus, the quaternary amine group at the C-3 side chain, and the acetates at the C-7 side chain of CAZ are the main toxic functional groups. Impurities B and D may be the genetic impurity in CAZ and may also have neurotoxicity. This in silico approach can predict the toxicity of other cephalosporins and impurities.

/pdf/in-silico-adme-and-toxicity-prediction-of-ceftazidime-and-d9t93uo1dd.pdf

In silico ADME and Toxicity Prediction of Ceftazidime and Its Impurities

Advances in nanomedicine, coupled with novel methods of creating advanced materials at the nanoscale, have opened new perspectives for the development of healthcare and medical products Special attention must be paid toward safe design approaches for nanomaterial-based products Recently, artificial intelligence (AI) and machine learning (ML) gifted the computational tool for enhancing and improving the simulation and modeling process for nanotoxicology and nanotherapeutics In particular, the correlation of in vitro generated pharmacokinetics and pharmacodynamics to in vivo application scenarios is an important step toward the development of safe nanomedicinal products This review portrays how in vitro and in vivo datasets are used in in silico models to unlock and empower nanomedicine Physiologically based pharmacokinetic (PBPK) modeling and absorption, distribution, metabolism, and excretion (ADME)-based in silico methods along with dosimetry models as a focus area for nanomedicine are mainly described The computational OMICS, colloidal particle determination, and algorithms to establish dosimetry for inhalation toxicology, and quantitative structure-activity relationships at nanoscale (nano-QSAR) are revisited The challenges and opportunities facing the blind spots in nanotoxicology in this computationally dominated era are highlighted as the future to accelerate nanomedicine clinical translation

Artificial Intelligence and Machine Learning in Computational Nanotoxicology: Unlocking and Empowering Nanomedicine.

The novel coronavirus SARS-CoV-2 disease "COVID-19" emerged in China and rapidly spread to other countries; due to its rapid worldwide spread, the WHO has declared this as a global emergency. As there is no specific treatment prescribed to treat COVID-19, the seeking of suitable therapeutics among existing drugs seems valuable. The structure availability of coronavirus macromolecules has encouraged the finding of conceivable anti-SARS-CoV-2 therapeutics through in silico analysis. The results reveal that quinoline,1,2,3,4-tetrahydro-1-[(2-phenylcyclopropyl)sulfonyl]-trans-(8CI) and saquinavir strongly interact with the active site (Cys-His catalytic dyad), thereby are predicted to hinder the activity of SARS-CoV-2 3CLpro. Out of 113 quinoline-drugs, elvitegravir and oxolinic acid are able to interact with the NTP entry-channel and thus interfere with the RNA-directed 5'-3' polymerase activity of SARS-CoV-2 RdRp. The bioactivity-prediction results also validate the outcome of the docking study. Moreover, as SARS-CoV-2 Spike-glycoprotein uses human ACE2-receptor for viral entry, targeting the Spike-RBD-ACE2 has been viewed as a promising strategy to control the infection. The result shows rilapladib is the only quinoline that can interrupt the Spike-RBD-ACE2 complex. In conclusion, owing to their ability to target functional macromolecules of SARS-CoV-2, along with positive ADMET properties, quinoline,1,2,3,4-tetrahydro-1-[(2-phenylcyclopropyl)sulfonyl]-trans-(8CI), saquinavir, elvitegravir, oxolinic acid, and rilapladib are suggested for the treatment of COVID-19.

Quinolines-Based SARS-CoV-2 3CLpro and RdRp Inhibitors and Spike-RBD-ACE2 Inhibitor for Drug-Repurposing Against COVID-19: An in silico Analysis

hERG toxicity assessment: Useful guidelines for drug design.

Chemical absorption, distribution, metabolism, excretion, and toxicity (ADMET), play key roles in drug discovery and development. A high-quality drug candidate should not only have sufficient efficacy against the therapeutic target, but also show appropriate ADMET properties at a therapeutic dose. A lot of in silico models are hence developed for prediction of chemical ADMET properties. However, it is still not easy to evaluate the drug-likeness of compounds in terms of so many ADMET properties. In this study, we proposed a scoring function named the ADMET-score to evaluate drug-likeness of a compound. The scoring function was defined on the basis of 18 ADMET properties predicted via our web server admetSAR. The weight of each property in the ADMET-score was determined by three parameters: the accuracy rate of the model, the importance of the endpoint in the process of pharmacokinetics, and the usefulness index. The FDA-approved drugs from DrugBank, the small molecules from ChEMBL and the old drugs withdrawn from the market due to safety concerns were used to evaluate the performance of the ADMET-score. The indices of the arithmetic mean and p-value showed that the ADMET-score among the three data sets differed significantly. Furthermore, we learned that there was no obvious linear correlation between the ADMET-score and QED (quantitative estimate of drug-likeness). These results suggested that the ADMET-score would be a comprehensive index to evaluate chemical drug-likeness, and might be helpful for users to select appropriate drug candidates for further development.

Longfei Guan

Papers

ADMET-score - a comprehensive scoring function for evaluation of chemical drug-likeness.