Drug repurposing screening validated by experimental assays identifies two clinical drugs targeting SARS-CoV-2 main protease

The COVID-19 pandemic prompted several drug repositioning initiatives with the aim to rapidly deliver pharmacological candidates able to reduce SARSCoV- 2 dissemination and mortality. A major issue shared by many of the in silico studies addressing the discovery of compounds or drugs targeting SARS-CoV- 2 molecules is that they lacked experimental validation of the results. Here we present a computer-aided drug-repositioning campaign against the indispensable SARS-CoV-2 main protease (MPro or 3CLPro) that involved the development of ligand-based ensemble models and the experimental testing of a small subset of the identified hits. The search method explored random subspaces of molecular descriptors to obtain linear classifiers. The best models were then combined by selective ensemble learning to improve their predictive power. Both the individual models and the ensembles were validated by retrospective screening, and later used to screen the DrugBank, Drug Repurposing Hub and Sweetlead libraries for potential inhibitors of MPro. From the 4 in silico hits assayed, atpenin and tinostamustine inhibited MPro (IC50 1 μM and 4 μM, respectively) but not the papain-like protease of SARSCoV- 2 (drugs tested at 25 μM). Preliminary kinetic characterization suggests that tinostamustine and atpenin inhibit MPro by an irreversible and acompetitive mechanisms, respectively. Both drugs failed to inhibit the proliferation of SARSCoV- 2 in VERO cells. The virtual screening method reported here may be a powerful tool to further extent the identification of novel MPro inhibitors. Furthermore, the confirmed MPro hits may be subjected to optimization or retrospective search strategies to improve their molecular target and anti-viral potency.Laboratorio de Investigación y Desarrollo de Bioactivo

Need for a Standardized Translational Drug Development Platform: Lessons Learned from the Repurposing of Drugs for COVID-19

In the absence of drugs to treat or prevent COVID-19, drug repurposing can be a valuable strategy. Despite a substantial number of clinical trials, drug repurposing did not deliver on its promise. While success was observed with some repurposed drugs (e.g., remdesivir, dexamethasone, tocilizumab, baricitinib), others failed to show clinical efficacy. One reason is the lack of clear translational processes based on adequate preclinical profiling before clinical evaluation. Combined with limitations of existing in vitro and in vivo models, there is a need for a systematic approach to urgent antiviral drug development in the context of a global pandemic. We implemented a methodology to test repurposed and experimental drugs to generate robust preclinical evidence for further clinical development. This translational drug development platform comprises in vitro, ex vivo, and in vivo models of SARS-CoV-2, along with pharmacokinetic modeling and simulation approaches to evaluate exposure levels in plasma and target organs. Here, we provide examples of identified repurposed antiviral drugs tested within our multidisciplinary collaboration to highlight lessons learned in urgent antiviral drug development during the COVID-19 pandemic. Our data confirm the importance of assessing in vitro and in vivo potency in multiple assays to boost the translatability of pre-clinical data. The value of pharmacokinetic modeling and simulations for compound prioritization is also discussed. We advocate the need for a standardized translational drug development platform for mild-to-moderate COVID-19 to generate preclinical evidence in support of clinical trials. We propose clear prerequisites for progression of drug candidates for repurposing into clinical trials. Further research is needed to gain a deeper understanding of the scope and limitations of the presented translational drug development platform

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

Garbage in, garbage out: how reliable training data improved a virtual screening approach against SARS-CoV-2 MPro

Introduction: The identification of chemical compounds that interfere with SARS-CoV-2 replication continues to be a priority in several academic and pharmaceutical laboratories. Computational tools and approaches have the power to integrate, process and analyze multiple data in a short time. However, these initiatives may yield unrealistic results if the applied models are not inferred from reliable data and the resulting predictions are not confirmed by experimental evidence.Methods: We undertook a drug discovery campaign against the essential major protease (MPro) from SARS-CoV-2, which relied on an in silico search strategy –performed in a large and diverse chemolibrary– complemented by experimental validation. The computational method comprises a recently reported ligand-based approach developed upon refinement/learning cycles, and structure-based approximations. Search models were applied to both retrospective (in silico) and prospective (experimentally confirmed) screening.Results: The first generation of ligand-based models were fed by data, which to a great extent, had not been published in peer-reviewed articles. The first screening campaign performed with 188 compounds (46 in silico hits and 100 analogues, and 40 unrelated compounds: flavonols and pyrazoles) yielded three hits against MPro (IC50 ≤ 25 μM): two analogues of in silico hits (one glycoside and one benzo-thiazol) and one flavonol. A second generation of ligand-based models was developed based on this negative information and newly published peer-reviewed data for MPro inhibitors. This led to 43 new hit candidates belonging to different chemical families. From 45 compounds (28 in silico hits and 17 related analogues) tested in the second screening campaign, eight inhibited MPro with IC50 = 0.12–20 μM and five of them also impaired the proliferation of SARS-CoV-2 in Vero cells (EC50 7–45 μM).Discussion: Our study provides an example of a virtuous loop between computational and experimental approaches applied to target-focused drug discovery against a major and global pathogen, reaffirming the well-known “garbage in, garbage out” machine learning principle

Analysis of Water Quality Characteristics in Unit Watersheds in the Hangang Basin with Respect to TMDL Implementation

Spatiotemporal water quality tendencies before and after total maximum daily load (TMDL) implementation in the Hangang basin were analyzed to determine the water quality improvement resulting from the TMDL policy. The periodicities of water quality indicators were also analyzed and water quality characteristics corresponding to different unit watershed units were identified in terms of pollution source. Considering five water quality indicators, including biochemical oxygen demand and total phosphorus, it was observed that water quality indicator concentrations were low in the upstream areas of the Bukhangang and Namhangang watersheds. However, they were high between the downstream areas of the Namhangang watershed and the Imjingang watershed and in the Hangang downstream and Jinwicheon watersheds. Additionally, the concentrations of water quality indicators in most of the unit watersheds where TMDL had been implemented decreased after TMDL implementation. However, increasing tendencies in the concentrations of water quality indicators continued to be observed in some of the watershed units in the upstream areas of the Bukhangang and Namhangang watersheds, possibly because these watersheds are affected by nonpoint source pollution owing to rainfall. Therefore, in the future, it would be necessary to implement policies that take these findings into consideration

JULGI-mediated increment in phloem transport capacity relates to fruit yield in tomato

The continuous growth of the global population and the increase in the amount of arid land has severely constrained agricultural crop production. To solve this problem, many researchers have attempted to increase productivity through the efficient distribution of energy; however, the direct relationship between the plant vasculature, specifically phloem development, and crop yield is not well established. Here, we demonstrate that an optimum increase in phloem-transportation capacity by reducing SIJUL expression leads to improved sink strength in tomato (Solanum lycopersicum L.). SIJUL, a negative regulator of phloem development, suppresses the translation of a positive regulator of phloem development, SlSMXL5. The suppression of SlJUL increases the number of phloem cells and sucrose transport, but only an optimal reduction of SlJUL function greatly enhances sink strength in tomato, improving fruit setting, and yield contents by 37% and 60%, respectively. We show that the increment in phloem cell number confers spare transport capacity. Our results suggest that the control of phloem-transport capacity within the threshold could enhance the commitment of photosynthates to instigate yield improvement.11Ysciescopu

Immunogenicity and Durability of Antibody Responses to Homologous and Heterologous Vaccinations with BNT162b2 and ChAdOx1 Vaccines for COVID-19

During the COVID-19 pandemic, vaccines were developed based on various platform technologies and were approved for emergency use. However, the comparative analysis of immunogenicity and durability of vaccine-induced antibody responses depending on vaccine platforms or vaccination regimens has not been thoroughly examined for mRNA- or viral vector-based vaccines. In this study, we assessed spike-binding IgG levels and neutralizing capacity in 66 vaccinated individuals prime-boost immunized either by homologous (BNT162b2-BNT162b2 or ChAdOx1-ChAdOx1) or heterologous (ChAdOx1-BNT162b2) vaccination for six months after the first vaccination. Despite the discrepancy in intervals for the prime-boost vaccination regimen of different COVID-19 vaccines, we found stronger induction and relatively rapid waning of antibody responses by homologous vaccination of the mRNA vaccine, while weaker boost effect and stable maintenance of humoral immune responses were observed in the viral vector vaccine group over 6 months. Heterologous vaccination with ChAdOx1 and BNT162b2 resulted in an effective boost effect with the highest remaining antibody responses at six months post-primary vaccination

High-throughput screening of small-molecules libraries identified antibacterials against clinically relevant multidrug-resistant A. baumannii and K. pneumoniaeResearch in context

Summary: Background: The current pipeline for new antibiotics fails to fully address the significant threat posed by drug-resistant Gram-negative bacteria that have been identified by the World Health Organization (WHO) as a global health priority. New antibacterials acting through novel mechanisms of action are urgently needed. We aimed to identify new chemical entities (NCEs) with activity against Klebsiella pneumoniae and Acinetobacter baumannii that could be developed into a new treatment for drug-resistant infections. Methods: We developed a high-throughput phenotypic screen and selection cascade for generation of hit compounds active against multidrug-resistant (MDR) strains of K. pneumoniae and A. baumannii. We screened compound libraries selected from the proprietary collections of three pharmaceutical companies that had exited antibacterial drug discovery but continued to accumulate new compounds to their collection. Compounds from two out of three libraries were selected using “eNTRy rules” criteria associated with increased likelihood of intracellular accumulation in Escherichia coli. Findings: We identified 72 compounds with confirmed activity against K. pneumoniae and/or drug-resistant A. baumannii. Two new chemical series with activity against XDR A. baumannii were identified meeting our criteria of potency (EC50 ≤50 μM) and absence of cytotoxicity (HepG2 CC50 ≥100 μM and red blood cell lysis HC50 ≥100 μM). The activity of close analogues of the two chemical series was also determined against A. baumannii clinical isolates. Interpretation: This work provides proof of principle for the screening strategy developed to identify NCEs with antibacterial activity against multidrug-resistant critical priority pathogens such as K. pneumoniae and A. baumannii. The screening and hit selection cascade established here provide an excellent foundation for further screening of new compound libraries to identify high quality starting points for new antibacterial lead generation projects. Funding: BMBF and GARDP