Identification of N-Acyl Hydrazones as New Non-Zinc-Binding MMP-13 Inhibitors by Structure-Based Virtual Screening Studies and Chemical Optimization.

Matrix metalloproteinase 13 plays a central role in osteoarthritis (OA), as its overexpression induces an excessive breakdown of collagen that results in an imbalance between collagen synthesis and degradation in the joint, leading to progressive articular cartilage degradation. Therefore, MMP-13 has been proposed as a key therapeutic target for OA. Here we have developed a virtual screening workflow aimed at identifying selective non-zinc-binding MMP-13 inhibitors by targeting the deep S1' pocket of MMP-13. Three ligands were found to inhibit MMP-13 in the µM range, and one of these showed selectivity over other MMPs. A structure-based analysis guided the chemical optimization of the hit compound, leading to the obtaining of a new N-acyl hydrazone-based derivative with improved inhibitory activity and selectivity for the target enzyme.

Haste makes waste: A critical review of docking-based virtual screening in drug repurposing for SARS-CoV-2 main protease (M-pro) inhibition.

This review makes a critical evaluation of 61 peer-reviewed manuscripts that use a docking step in a virtual screening (VS) protocol to predict SARS-CoV-2 M-pro (M-pro) inhibitors in approved or investigational drugs. Various manuscripts predict different compounds, even when they use a similar initial dataset and methodology, and most of them do not validate their methodology or results. In addition, a set of known 150 SARS-CoV-2 M-pro inhibitors extracted from the literature and a second set of 81 M-pro inhibitors and 113 inactive compounds obtained from the COVID Moonshot project were used to evaluate the reliability of using docking scores as feasible predictors of the potency of a SARS-CoV-2 M-pro inhibitor. Using two SARS-CoV-2 M-pro structures and five protein-ligand docking programs, we proved that the correlation between the pIC50 and docking scores is not good. Neither was any correlation found between the pIC50 and the ∆G calculated with an MM-GBSA method. When a group of experimentally known inactive compounds was added, neither the docking scores or the ∆G were able to distinguish between compounds with or without M-pro experimental inhibitory activity. Performances improved when covalent and noncovalent inhibitors were treated separately, but were not good enough to fully support using a docking score as a cutoff value for selecting new putative M-pro inhibitors or predicting the relative bioactivity of a compound by comparison with a reference compound. The two sets of known SARS-CoV-2 M-pro inhibitors presented here could be used for validating future VS protocols which aim to predict M-pro inhibitors.

Identification of Broad-Spectrum MMP Inhibitors by Virtual Screening.

Matrix metalloproteinases (MMPs) are the family of proteases that are mainly responsible for degrading extracellular matrix (ECM) components. In the skin, the overexpression of MMPs as a result of ultraviolet radiation triggers an imbalance in the ECM turnover in a process called photoaging, which ultimately results in skin wrinkling and premature skin ageing. Therefore, the inhibition of different enzymes of the MMP family at a topical level could have positive implications for photoaging. Considering that the MMP catalytic region is mostly conserved across different enzymes of the MMP family, in this study we aimed to design a virtual screening (VS) workflow to identify broad-spectrum MMP inhibitors that can be used to delay the development of photoaging. Our in silico approach was validated in vitro with 20 VS hits from the Specs library that were not only structurally different from one another but also from known MMP inhibitors. In this bioactivity assay, 18 of the 20 compounds inhibit at least one of the assayed MMPs at 100 µM (with 5 of them showing around 50% inhibition in all the tested MMPs at this concentration). Finally, this VS was used to identify natural products that have the potential to act as broad-spectrum MMP inhibitors and be used as a treatment for photoaging.

Anti-Inflammatory and Immunomodulatory Effects of the Grifola frondosa Natural Compound o-Orsellinaldehyde on LPS-Challenged Murine Primary Glial Cells. Roles of NF-κß and MAPK.

In response to foreign or endogenous stimuli, both microglia and astrocytes adopt an activated phenotype that promotes the release of pro-inflammatory mediators. This inflammatory mechanism, known as neuroinflammation, is essential in the defense against foreign invasion and in normal tissue repair; nevertheless, when constantly activated, this process can become detrimental through the release of neurotoxic factors that amplify underlying disease. In consequence, this study presents the anti-inflammatory and immunomodulatory properties of o-orsellinaldehyde, a natural compound found by an in silico approach in the Grifola frondosa mushroom, in astrocytes and microglia cells. For this purpose, primary microglia and astrocytes were isolated from mice brain and cultured in vitro. Subsequently, cells were exposed to LPS in the absence or presence of increasing concentrations of this natural compound. Specifically, the results shown that o-orsellinaldehyde strongly inhibits the LPS-induced inflammatory response in astrocytes and microglia by decreasing nitrite formation and downregulating iNOS and HO-1 expression. Furthermore, in microglia cells o-orsellinaldehyde inhibits NF-κB activation; and potently counteracts LPS-mediated p38 kinase and JNK phosphorylation (MAPK). In this regard, o-orsellinaldehyde treatment also induces a significant cell immunomodulation by repolarizing microglia toward the M2 anti-inflammatory phenotype. Altogether, these results could partially explain the reported beneficial effects of G. frondosa extracts on inflammatory conditions.

Bioactivity Profile Similarities to Expand the Repertoire of COVID-19 Drugs.

Until a vaccine becomes available, the current repertoire of drugs is our only therapeutic asset to fight the SARS-CoV-2 outbreak. Indeed, emergency clinical trials have been launched to assess the effectiveness of many marketed drugs, tackling the decrease of viral load through several mechanisms. Here, we present an online resource, based on small-molecule bioactivity signatures and natural language processing, to expand the portfolio of compounds with potential to treat COVID-19. By comparing the set of drugs reported to be potentially active against SARS-CoV-2 to a universe of 1 million bioactive molecules, we identify compounds that display analogous chemical and functional features to the current COVID-19 candidates. Searches can be filtered by level of evidence and mechanism of action, and results can be restricted to drug molecules or include the much broader space of bioactive compounds. Moreover, we allow users to contribute COVID-19 drug candidates, which are automatically incorporated to the pipeline once per day. The computational platform, as well as the source code, is available at https://sbnb.irbbarcelona.org/covid19.

Prediction of Novel Inhibitors of the Main Protease (M-pro) of SARS-CoV-2 through Consensus Docking and Drug Reposition.

Since the outbreak of the COVID-19 pandemic in December 2019 and its rapid spread worldwide, the scientific community has been under pressure to react and make progress in the development of an effective treatment against the virus responsible for the disease. Here, we implement an original virtual screening (VS) protocol for repositioning approved drugs in order to predict which of them could inhibit the main protease of the virus (M-pro), a key target for antiviral drugs given its essential role in the virus' replication. Two different libraries of approved drugs were docked against the structure of M-pro using Glide, FRED and AutoDock Vina, and only the equivalent high affinity binding modes predicted simultaneously by the three docking programs were considered to correspond to bioactive poses. In this way, we took advantage of the three sampling algorithms to generate hypothetic binding modes without relying on a single scoring function to rank the results. Seven possible SARS-CoV-2 M-pro inhibitors were predicted using this approach: Perampanel, Carprofen, Celecoxib, Alprazolam, Trovafloxacin, Sarafloxacin and ethyl biscoumacetate. Carprofen and Celecoxib have been selected by the COVID Moonshot initiative for in vitro testing; they show 3.97 and 11.90% M-pro inhibition at 50 µM, respectively.

Understanding the variability of the S1' pocket to improve matrix metalloproteinase inhibitor selectivity profiles.

Matrix metalloproteinases (MMPs) are a family of proteins involved in a range of pathologies. Given that MMP broad-spectrum inhibition is associated with severe adverse effects, selectivity has become a priority in the design of MMP inhibitors, and is often achieved by targeting the variable S1' pocket. However, the specific characteristics of the S1' pocket that determine inhibitor selectivity are often not described and, in many cases, challenging to identify. In this review, we investigate the variability of the S1' pocket across the MMP family, and propose explanations for the selectivity of previously described inhibitors. These analyses provide valuable insights into how to design novel inhibitors selective for a given MMP.

Mining large databases to find new leads with low similarity to known actives: application to find new DPP-IV inhibitors.

Aim: Fragment-based drug design or bioisosteric replacement is used to find new actives with low (or no) similarity to existing ones but requires the synthesis of nonexisting compounds to prove their predicted bioactivity. Protein-ligand docking or pharmacophore screening are alternatives but they can become computationally expensive when applied to very large databases such as ZINC. Therefore, fast strategies are necessary to find new leads in such databases. Materials & methods: We designed a computational strategy to find lead molecules with very low (or no) similarity to existing actives and applied it to DPP-IV. Results: The bioactivity assays confirm that this strategy finds new leads for DPP-IV inhibitors. Conclusion: This computational strategy reduces the time of finding new lead molecules.

The Light and Dark Sides of Virtual Screening: What Is There to Know?

Virtual screening consists of using computational tools to predict potentially bioactive compounds from files containing large libraries of small molecules. Virtual screening is becoming increasingly popular in the field of drug discovery as in silico techniques are continuously being developed, improved, and made available. As most of these techniques are easy to use, both private and public organizations apply virtual screening methodologies to save resources in the laboratory. However, it is often the case that the techniques implemented in virtual screening workflows are restricted to those that the research team knows. Moreover, although the software is often easy to use, each methodology has a series of drawbacks that should be avoided so that false results or artifacts are not produced. Here, we review the most common methodologies used in virtual screening workflows in order to both introduce the inexperienced researcher to new methodologies and advise the experienced researcher on how to prevent common mistakes and the improper usage of virtual screening methodologies.

Anti-inflammatory and Proapoptotic Properties of the Natural Compound o-Orsellinaldehyde.

Metabolic syndrome is a cluster of medical conditions that increases the risk of developing cardiovascular disease and type 2 diabetes. Numerous studies have shown that inflammation is directly involved in the onset of metabolic syndrome and related pathologies. In this study, in silico techniques were applied to a natural products database containing molecules isolated from mushrooms from the Catalan forests to predict molecules that can act as human nuclear-factor κß kinase 2 (IKK-2) inhibitors. IKK-2 is the main component responsible for activating the nuclear-factor κß transcription factor (NF-κß). One of these predicted molecules was o-orsellinaldehyde, a molecule present in the mushroom Grifola frondosa. This study shows that o-orsellinaldehyde presents anti-inflammatory and pro-apoptotic properties by acting as IKK-2 inhibitor. Additionally, we suggest that the anti-inflammatory and pro-apoptotic properties of Grifola frondosa mushroom could partially be explained by the presence of o-orsellinaldehyde on its composition.

Combined Ligand- and Receptor-Based Virtual Screening Methodology to Identify Structurally Diverse Protein Tyrosine Phosphatase 1B Inhibitors.

Protein tyrosine phosphatase 1B (PTP1B) is a potential drug target for diabetes and obesity. However, the design of PTP1B inhibitors that combine potency and bioavailability is a great challenge, and new leads are needed to circumvent this problem. Virtual screening (VS) workflows can be used to find new PTP1B inhibitors with little chemical similarity to existing inhibitors. Unfortunately, previous VS workflows for the identification of PTP1B inhibitors have several limitations, such as a small number of experimentally tested compounds and the low bioactivity of those compounds. We developed a VS workflow capable of identifying 15 structurally diverse PTP1B inhibitors from 20 compounds, the bioactivity of which was tested in vitro. Moreover, we identified two PTP1B inhibitors with the highest bioactivity reported by any VS campaign (i.e., IC50 values of 1.4 and 2.1 µm), which could be used as new lead compounds.

Activity and selectivity cliffs for DPP-IV inhibitors: Lessons we can learn from SAR studies and their application to virtual screening.

The inhibition of dipeptidyl peptidase-IV (DPP-IV) has emerged over the last decade as one of the most effective treatments for type 2 diabetes mellitus, and consequently (a) 11 DPP-IV inhibitors have been on the market since 2006 (three in 2015), and (b) 74 noncovalent complexes involving human DPP-IV and drug-like inhibitors are available at the Protein Data Bank (PDB). The present review aims to (a) explain the most important activity cliffs for DPP-IV noncovalent inhibition according to the binding site structure of DPP-IV, (b) explain the most important selectivity cliffs for DPP-IV noncovalent inhibition in comparison with other related enzymes (i.e., DPP8 and DPP9), and (c) use the information deriving from this activity/selectivity cliff analysis to suggest how virtual screening protocols might be improved to favor the early identification of potent and selective DPP-IV inhibitors in molecular databases (because they have not succeeded in identifying selective DPP-IV inhibitors with IC50 ≤ 100 nM). All these goals are achieved with the help of available homology models for DPP8 and DPP9 and an analysis of the structure-activity studies used to develop the noncovalent inhibitors that form part of some of the complexes with human DPP-IV available at the PDB.

Ephedrine as a lead compound for the development of new DPP-IV inhibitors.

AIM: Extracts from Ephedra species have been reported to be effective as antidiabetics. A previous in silico study predicted that ephedrine and five ephedrine derivatives could contribute to the described antidiabetic effect of Ephedra extracts by inhibiting dipeptidyl peptidase IV (DPP-IV). Finding selective DPP-IV inhibitors is a current therapeutic strategy for Type 2 diabetes mellitus management. Therefore, the main aim of this work is to experimentally determine whether these alkaloids are DPP-IV inhibitors. Materials & methods: The DPP-IV inhibition of Ephedra's alkaloids was determined via a competitive-binding assay. Then, computational analyses were used in order to find out the protein-ligand interactions and to perform a lead optimization. RESULTS: Our results show that all six molecules are DPP-IV inhibitors, with IC50 ranging from 124 µM for ephedrine to 28 mM for N-methylpseudoephedrine. CONCLUSION: Further computational analysis shows how Ephedra's alkaloids could be used as promising lead molecules for designing more potent and selective DPP-IV inhibitors.

Chronic lymphocytic leukemia cells express CD38 in response to Th1 cell-derived IFN-γ by a T-bet-dependent mechanism.

Chronic lymphocytic leukemia (CLL) is a B cell malignancy associated with increased levels of inflammatory cytokines. Similarly, expression of CD38 on CLL cells correlates with CLL cell survival and proliferation, but the mechanisms that regulate CD38 expression and inflammatory cytokines remain unclear. We have recently demonstrated that patients have CLL-specific Th cells that support CLL proliferation. In this article, we show that CLL cells attract such Th cells, thereby establishing an Ag-dependent collaboration. Blocking experiments performed in vitro as wells as in vivo, using a xenograft model, revealed that secretion of IFN-γ was a major mechanism by which CLL-specific Th cells increased CD38 on CLL cells. The expression of the transcription factor T-bet in peripheral blood CLL cells significantly correlated with CD38 expression, and transient transfection of CLL cells with T-bet resulted in T-bet(hi)CD38(hi) cells. Finally, chromatin immunoprecipitation experiments revealed that T-bet can bind to regulatory regions of the CD38 gene. These data suggest that CLL cells attract CLL-specific Th cells and initiate a positive feedback loop with upregulation of T-bet, CD38, and type 1 chemokines allowing further recruitment of Th cells and increased type 1 cytokine secretion. This insight provides a cellular and molecular mechanism that links the inflammatory signature observed in CLL pathogenesis with CD38 expression and aggressive disease and suggests that targeting the IFN-γ/IFN-γR/JAK/STAT/T-bet/CD38 pathway could play a role in the therapy of CLL.