The DHODH Inhibitor PTC299 Arrests SARS-CoV-2 Replication and Suppresses Induction of Inflammatory Cytokines

Ligand-based approach for predicting drug targets and for virtual screening against COVID-19

Discovering efficient drugs and identifying target proteins are still an unmet but urgent need for curing coronavirus disease 2019 (COVID-19). Protein structure-based docking is a widely applied approach for discovering active compounds against drug targets and for predicting potential targets of active compounds. However, this approach has its inherent deficiency caused by e.g. various different conformations with largely varied binding pockets adopted by proteins, or the lack of true target proteins in the database. This deficiency may result in false negative results. As a complementary approach to the protein structure-based platform for COVID-19, termed as D3Docking in our previous work, we developed in this study a ligand-based method, named D3Similarity, which is based on the molecular similarity evaluation between the submitted molecule(s) and those in an active compound database. The database is constituted by all the reported bioactive molecules against the coronaviruses, viz., severe acute respiratory syndrome coronavirus (SARS), Middle East respiratory syndrome coronavirus (MERS), severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), human betacoronavirus 2c EMC/2012 (HCoV-EMC), human CoV 229E (HCoV-229E) and feline infectious peritonitis virus (FIPV), some of which have target or mechanism information but some do not. Based on the two-dimensional (2D) and three-dimensional (3D) similarity evaluation of molecular structures, virtual screening and target prediction could be performed according to similarity ranking results. With two examples, we demonstrated the reliability and efficiency of D3Similarity by using 2D × 3D value as score for drug discovery and target prediction against COVID-19. The database, which will be updated regularly, is available free of charge at https://www.d3pharma.com/D3Targets-2019-nCoV/D3Similarity/index.php.

The role of chemical biology in the fight against SARS-CoV-2

Since late 2019, biomedical labs all over the world have been struggling to cope with the 'new normal' and to find ways in which they can contribute to the fight against COVID-19. In this unique situation where a biomedical issue dominates people's lives and the news cycle, chemical biology has a great deal to contribute. This review will describe the importance of science at the chemistry/biology interface to both understand and combat the SARS-CoV-2 pandemic.

Modeling Parent-Specific Genetic Nurture in Families with Missing Parental Genotypes: Application to Birthweight and BMI

Disaggregation and estimation of genetic effects from offspring and parents has long been of interest to statistical geneticists. Recently, technical and methodological advances have made the genome-wide and loci-specific estimation of direct offspring and parental genetic nurture effects more possible. However, unbiased estimation using these methods requires datasets where both parents and at least one child have been genotyped, which are relatively scarce. Our group has recently developed a method and accompanying software (IMPISH; Hwang et al. in PLoS Genet 16:e1009154, 2020) which is able to impute missing parental genotypes from observed data on sibships and estimate their effects on an offspring phenotype conditional on the effects of genetic transmission. However, this method is unable to disentangle maternal and paternal effects, which may differ in magnitude and direction. Here, we introduce an extension to the original IMPISH routine which takes advantage of all available nuclear families to impute parent-specific missing genotypes and obtain asymptotically unbiased estimates of genetic effects on offspring phenotypes. We apply this this method to data from related individuals in the UK Biobank, showing concordance with previous estimates of maternal genetic effects on offspring birthweight. We also conduct the first GWAS jointly estimating offspring-, maternal-, and paternal-specific genetic effects on body-mass index.

A SARS-CoV-2 antiviral therapy score card

The COVID-19 pandemic has unleashed an unprecedented effort to identify efficacious treatments for persons infected with SARS-CoV-2. As of September 2020, more than 750 completed, ongoing, or planned clinical trials of drugs intended to inhibit SARS-CoV-2 replication have been registered on the ClinicalTrials.gov or WHO International Clinical Trials Platform websites. Most of the treatments studied in these trials are repurposed licensed or investigational drugs targeting viral proteins or cellular pathways required for virus replication. The use of repurposed compounds is understandable because with the exception of monoclonal antibodies, it will be several months before novel SARS-CoV-2-specific drugs will be available for human testing. This editorial describes those compounds that I believe should be prioritized for clinical testing: i) viral RNA polymerase inhibitors including GS-441524, its prodrug remdesivir, and EIDD-2801; ii) entry inhibitors including monoclonal antibodies, ACE2 molecular decoys, and peptide fusion inhibitors; iii) parenteral and inhalational preparations of interferon β and λ; and iv) inhibitors of host transmembrane protease serine 2 (TMPRSS2), endosomal trafficking, and pyrimidine synthesis. As SARS-CoV-2 is pandemic and as its most severe consequences result from a dysregulated immunological response to infection, the ideal therapies should be inexpensive and should be able to be administered to non-hospitalized persons at the time of their initial diagnosis.

Coronavirus Antiviral Research Database (CoV-RDB): An Online Database Designed to Facilitate Comparisons between Candidate Anti-Coronavirus Compounds

BACKGROUND

To prioritize the development of antiviral compounds, it is necessary to compare their relative preclinical activity and clinical efficacy.

METHODS

We reviewed in vitro, animal model, and clinical studies of candidate anti-coronavirus compounds and placed extracted data in an online relational database.

RESULTS

As of August 2020, the Coronavirus Antiviral Research Database (CoV-RDB; covdb.stanford.edu) contained over 2800 cell culture, entry assay, and biochemical experiments, 259 animal model studies, and 73 clinical studies from over 400 published papers. SARS-CoV-2, SARS-CoV, and MERS-CoV account for 85% of the data. Approximately 75% of experiments involved compounds with known or likely mechanisms of action, including monoclonal antibodies and receptor binding inhibitors (21%), viral protease inhibitors (17%), miscellaneous host-acting inhibitors (10%), polymerase inhibitors (9%), interferons (7%), fusion inhibitors (5%), and host protease inhibitors (5%). Of 975 compounds with known or likely mechanism, 135 (14%) are licensed in the U.S. for other indications, 197 (20%) are licensed outside the U.S. or are in human trials, and 595 (61%) are pre-clinical investigational compounds.

CONCLUSION

CoV-RDB facilitates comparisons between different candidate antiviral compounds, thereby helping scientists, clinical investigators, public health officials, and funding agencies prioritize the most promising compounds and repurposed drugs for further development.