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.

Identification of New Carbonic Anhydrase VII Inhibitors by Structure-Based Virtual Screening.

Human carbonic anhydrase VII (hCA VII) constitutes a promising molecular target for the treatment of epileptic seizures and other central nervous system disorders due to its almost exclusive expression in neurons. Achieving isoform selectivity is one of the main challenges for the discovery of new hCA inhibitors, since nonspecific inhibition may lead to tolerance and side effects. In the present work, we report the development of a molecular docking protocol based on AutoDock4Zn for the search of new hCA VII inhibitors by virtual screening. The docking protocol was applied to the screening of two sets of compounds: a ZINC15 subset of sulfur-containing structures and an in-house library consisting of synthetic and commercial candidates (including approved drugs). Five compounds were selected from the first screening campaign and three from the second one, and they were tested in vitro against the enzyme. Among the eight selected structures, four showed Ki values in the low nanomolar range. These confirmed hits include three approved drugs: meloxicam, piroxicam, and nitrofurantoin, which also showed good selectivity for hCA VII versus hCA II.

Strengths and Weaknesses of Docking Simulations in the SARS-CoV-2 Era: the Main Protease (Mpro) Case Study.

The scientific community is working against the clock to arrive at therapeutic interventions to treat patients with COVID-19. Among the strategies for drug discovery, virtual screening approaches have the capacity to search potential hits within millions of chemical structures in days, with the appropriate computing infrastructure. In this article, we first analyzed the published research targeting the inhibition of the main protease (Mpro), one of the most studied targets of SARS-CoV-2, by docking-based methods. An alarming finding was the lack of an adequate validation of the docking protocols (i.e., pose prediction and virtual screening accuracy) before applying them in virtual screening campaigns. The performance of the docking protocols was tested at some level in 57.7% of the 168 investigations analyzed. However, we found only three examples of a complete retrospective analysis of the scoring functions to quantify the virtual screening accuracy of the methods. Moreover, only two publications reported some experimental evaluation of the proposed hits until preparing this manuscript. All of these findings led us to carry out a retrospective performance validation of three different docking protocols, through the analysis of their pose prediction and screening accuracy. Surprisingly, we found that even though all tested docking protocols have a good pose prediction, their screening accuracy is quite limited as they fail to correctly rank a test set of compounds. These results highlight the importance of conducting an adequate validation of the docking protocols before carrying out virtual screening campaigns, and to experimentally confirm the predictions made by the models before drawing bold conclusions. Finally, successful structure-based drug discovery investigations published during the redaction of this manuscript allow us to propose the inclusion of target flexibility and consensus scoring as alternatives to improve the accuracy of the methods.

Can drug repurposing strategies be the solution to the COVID-19 crisis?

Introduction: The COVID-19 pandemic resulted in disastrous human and economic costs, mainly due to the initial lack of specific treatments. Complementary to immunotherapies, drug repurposing is possibly the best option to arrive at COVID-19 treatments in the short term.Areas covered: Repurposing prospects undergoing clinical trials or with some level of evidence emerging from clinical studies are overviewed. The authors discuss some possible intellectual property and commercial barriers to drug repurposing, and strategies to facilitate equitable access to incoming therapeutic solutions, highlighting the importance of collaborative drug discovery models. Based on a critical analysis of the available literature about in silico screens against SARS-CoV-2 main protease, the authors illustrate how frequently overconfident conclusions are being drawn in COVID-19-related literature.Expert opinion: Most of the current clinical trials on potential COVID-19 treatments are, in fact, drug repurposing examples. In October 2020, the FDA approved a repurposed antiviral, remdesivir, as the first treatment for COVID-19. Considering the high expectations invested in approaching therapeutic solutions, the scientific community must be careful not to raise unrealistic expectations. Today more than ever, the conclusions drawn in scientific reports have to be fully supported by the level of evidence, avoiding any sort of unfounded speculation.

Development and Validation of a Computational Model Ensemble for the Early Detection of BCRP/ABCG2 Substrates during the Drug Design Stage.

Breast Cancer Resistance Protein (BCRP) is an ATP-dependent efflux transporter linked to the multidrug resistance phenomenon in many diseases such as epilepsy and cancer and a potential source of drug interactions. For these reasons, the early identification of substrates and nonsubstrates of this transporter during the drug discovery stage is of great interest. We have developed a computational nonlinear model ensemble based on conformational independent molecular descriptors using a combined strategy of genetic algorithms, J48 decision tree classifiers, and data fusion. The best model ensemble consists in averaging the ranking of the 12 decision trees that showed the best performance on the training set, which also demonstrated a good performance for the test set. It was experimentally validated using the ex vivo everted rat intestinal sac model. Five anticonvulsant drugs classified as nonsubstrates for BRCP by the model ensemble were experimentally evaluated, and none of them proved to be a BCRP substrate under the experimental conditions used, thus confirming the predictive ability of the model ensemble. The model ensemble reported here is a potentially valuable tool to be used as an in silico ADME filter in computer-aided drug discovery campaigns intended to overcome BCRP-mediated multidrug resistance issues and to prevent drug-drug interactions.