Combining 3-D quantitative structure-activity relationship with ligand based and structure based alignment procedures for in silico screening of new hepatitis C virus NS5B polymerase inhibitors

Transcriptomic and genomic studies classify NKL54 as a histone deacetylase inhibitor with indirect influence on MEF2-dependent transcription

In leiomyosarcoma class IIa HDACs (histone deacetylases) bind MEF2 and convert these transcription factors into repressors to sustain proliferation. Disruption of this complex with small molecules should antagonize cancer growth. NKL54, a PAOA (pimeloylanilide o-aminoanilide) derivative, binds a hydrophobic groove of MEF2, which is used as a docking site by class IIa HDACs. However, NKL54 could also act as HDAC inhibitor (HDACI). Therefore, it is unclear which activity is predominant. Here, we show that NKL54 and similar derivatives are unable to release MEF2 from binding to class IIa HDACs. Comparative transcriptomic analysis classifies these molecules as HDACIs strongly related to SAHA/vorinostat. Low expressed genes are upregulated by HDACIs, while abundant genes are repressed. This transcriptional resetting correlates with a reorganization of H3K27 acetylation around the transcription start site (TSS). Among the upregulated genes there are several BH3-only family members, thus explaining the induction of apoptosis. Moreover, NKL54 triggers the upregulation of MEF2 and the downregulation of class IIa HDACs. NKL54 also increases the binding of MEF2D to promoters of genes that are upregulated after treatment. In summary, although NKL54 cannot outcompete MEF2 from binding to class IIa HDACs, it supports MEF2-dependent transcription through several actions, including potentiation of chromatin binding.

Machine Learning Data Augmentation as a Tool to Enhance Quantitative Composition-Activity Relationships of Complex Mixtures. A New Application to Dissect the Role of Main Chemical Components in Bioactive Essential Oils

Scientific investigation on essential oils composition and the related biological profile are continuously growing. Nevertheless, only a few studies have been performed on the relationships between chemical composition and biological data. Herein, the investigation of 61 assayed essential oils is reported focusing on their inhibition activity against Microsporum spp. including development of machine learning models with the aim of highlining the possible chemical components mainly related to the inhibitory potency. The application of machine learning and deep learning techniques for predictive and descriptive purposes have been applied successfully to many fields. Quantitative composition-activity relationships machine learning-based models were developed for the 61 essential oils tested as Microsporum spp. growth modulators. The models were built with in-house python scripts implementing data augmentation with the purpose of having a smoother flow between essential oils' chemical compositions and biological data. High statistical coefficient values (Accuracy, Matthews correlation coefficient and F1 score) were obtained and model inspection permitted to detect possible specific roles related to some components of essential oils' constituents. Robust machine learning models are far more useful tools to reveal data augmentation in comparison with raw data derived models. To the best of the authors knowledge this is the first report using data augmentation to highlight the role of complex mixture components, in particular a first application of these data will be for the development of ingredients in the dermo-cosmetic field investigating microbial species considering the urge for the use of natural preserving and acting antimicrobial agents.

StackHCV: a web-based integrative machine-learning framework for large-scale identification of hepatitis C virus NS5B inhibitors

Fast and accurate identification of inhibitors with potency against HCV NS5B polymerase is currently a challenging task. As conventional experimental methods is the gold standard method for the design and development of new HCV inhibitors, they often require costly investment of time and resources. In this study, we develop a novel machine learning-based meta-predictor (termed StackHCV) for accurate and large-scale identification of HCV inhibitors. Unlike the existing method, which is based on single-feature-based approach, we first constructed a pool of various baseline models by employing a wide range of heterogeneous molecular fingerprints with five popular machine learning algorithms (k-nearest neighbor, multi-layer perceptron, partial least squares, random forest and support vectors machine). Secondly, we integrated these baseline models in order to develop the final meta-based model by means of the stacking strategy. Extensive benchmarking experiments showed that StackHCV achieved a more accurate and stable performance as compared to its constituent baseline models on the training dataset and also outperformed the existing predictor on the independent test dataset. To facilitate the high-throughput identification of HCV inhibitors, we built a web server that can be freely accessed at http://camt.pythonanywhere.com/StackHCV . It is expected that StackHCV could be a useful tool for fast and precise identification of potential drugs against HCV NS5B particularly for liver cancer therapy and other clinical applications.

Discovery of the First Human Arylsulfatase A Reversible Inhibitor Impairing Mouse Oocyte Fertilization

Arylsulfatase A (ARSA) plays a crucial role in the reproduction of mammals due to its involvement in the specific gamete interaction preceding sperm and egg fusion leading to fertilization. Recently, it has been shown that zona pellucida (ZP) sperm binding and in vivo fertilization in mice are markedly hampered by using a specific anti-ARSA antibody. Herein, the design and discovery of the first ARSA small molecule inhibitor based on a coumarin-containing polycycle are presented. Through a structure-based approach applied on our in-house library, compound 1r was identified as an ARSA reversible inhibitor (ARSAi); then its activity was validated through both surface plasmon resonance and biochemical inhibition experiments, the first providing a K_D value of 21 μM and the latter an IC₅₀ value of 13.2 μM. Further investigations highlighted that compound 1r induced 20% sperm death at 25 μM and also impaired sperm motility; nevertheless both the effects were mediated by ROS production, since they were rescued by the cotreatment of 1r and N-acetyl cysteine (NAC). Interestingly, while 1r was not able to hamper the ZP/sperm binding, it markedly decreased the in vitro oocyte fertilization by mouse sperm up to 60%. Notably, this effect was not hampered by 1r/NAC coadministration, hence allowing the ruling out of an ROS-dependent mechanism. In conclusion, herein is reported the first ever hit of ARSAi as a chemical tool that will enable better exploration of ARSA's biological role in fertilization as well as provide a starting point for developing 1r structure optimization aimed at increasing enzyme inhibition potency but also providing a deeper understanding of the involvement of ARSA in the fertilization pathway mechanism.

Discovery of novel Hepatitis C virus inhibitor targeting multiple allosteric sites of NS5B polymerase

HCV is a viral infection posing a severe global threat when left untreated progress to end-stage liver disease, including cirrhosis and HCC. The NS5B polymerase of HCV is the most potent target that harbors four allosteric binding sites that could interfere with the HCV infection. We present the discovery of a novel synthetic compound that harbors the potential of NS5B polymerase inhibition. All eight compounds belonging to the benzothiazine family of heterocycles displayed no cellular cytotoxicity in HepG2 cells at nontoxic dose concentration (200 μM). Subsequently, among eight compounds of the series, merely compound 5b exhibited significant inhibition of the expression of the HCV NS5B gene as compared to DMSO control in semi-quantitative PCR. Based on our western blot result, 5b at the range of 50, 100 and 200 μM induced 20, 40, and 70% inhibition of NS5B protein respectively. To estimate the binding potential, 5b was docked at respective allosteric sites followed by molecular dynamics (MD) simulations for a period of 20 ns. In addition, binding free energy calculation by MM-GB/PBSA method revealed a conserved interaction profile of residues lining the allosteric sites in agreement with the reported NS5B co-crystallized inhibitors. The presented results provide important information about a novel compound 5b which may facilitate the the discovery of novel inhibitors that tends to target multiple sites on NS5B polymerase.

HCVpred: A web server for predicting the bioactivity of hepatitis C virus NS5B inhibitors

Hepatitis C virus (HCV) is one of the major causes of liver disease affecting an estimated 170 million people culminating in 300,000 deaths from cirrhosis or liver cancer. NS5B is one of three potential therapeutic targets against HCV (i.e., the other two being NS3/4A and NS5A) that is central to viral replication. In this study, we developed a classification structure-activity relationship (CSAR) model for identifying substructures giving rise to anti-HCV activities among a set of 578 non-redundant compounds. NS5B inhibitors were described by a set of 12 fingerprint descriptors and predictive models were constructed from 100 independent data splits using the random forest algorithm. The modelability (MODI index) of the data set was determined to be robust with a value of 0.88 exceeding established threshold of 0.65. The predictive performance was deduced by the accuracy, sensitivity, specificity, and Matthews correlation coefficient, which was found to be statistically robust (i.e., the former three parameters afforded values in excess of 0.8 while the latter statistical parameter provided a value >0.7). An in-depth analysis of the top 20 important descriptors revealed that aromatic ring and alkyl side chains are important for NS5B inhibition. Finally, the predictive model is deployed as a publicly accessible HCVpred web server (available at http://codes.bio/hcvpred/) that would allow users to predict the biological activity as being active or inactive against HCV NS5B. Thus, the knowledge and web server presented herein can be used in the design of more potent and specific drugs against the HCV NS5B.

Evolution of efficacious pangenotypic hepatitis C virus therapies

Hepatitis C compromises the quality of life of more than 350 million individuals worldwide. Over the last decade, therapeutic regimens for treating hepatitis C virus (HCV) infections have undergone rapid advancements. Initially, structure-based drug design was used to develop molecules that inhibit viral enzymes. Subsequently, establishment of cell-based replicon systems enabled investigations into various stages of HCV life cycle including its entry, replication, translation, and assembly, as well as role of host proteins. Collectively, these approaches have facilitated identification of important molecules that are deemed essential for HCV life cycle. The expanded set of putative virus and host-encoded targets has brought us one step closer to developing robust strategies for efficacious, pangenotypic, and well-tolerated medicines against HCV. Herein, we provide an overview of the development of various classes of virus and host-directed therapies that are currently in use along with others that are undergoing clinical evaluation.

Quantum molecular modeling of hepatitis C virus inhibition through non-structural protein 5B polymerase receptor binding of C5-arylidene rhodanines

We have carried out high-level quantum chemical computations followed by molecular docking studies on a set of 17C₅-arylidene rhodanine isomers to provide insights into the binding modes with different reported binding pockets of the nonstructural protein 5B (NS5B) polymerase that contribute to the hepatitis C virus (HCV) inhibition. We optimized the multi-target profile of the selected rhodanine analogs to investigate potential non-nucleotide inhibitors (NNIs) by quantum chemical optimization of the 18 isomers followed by docking with quantum chemically optimized structures of each isomer with NS5B polymerase at multiple binding pockets. The binding affinities of the PP-I, PP-II and TP-II pockets of NS5B polymerase were analyzed for all the 17 isomers of 2-[(5Z)-5-(2,4-dichlorobenzylidene)-4-oxo-2-thioxo-1,3-thiazolidin-3-yl]-3-phenylpropanoic acid. On the basis of binding propensity at the different pockets and inhibitor constants, we ranked these isomers as potential candidates for the HCV inhibition. We have identified four isomers as promising NNIs of NS5B polymerase with comparable binding and inhibition to the standard (1,3) dichloro substituted isomer that exhibits in vitro activity and several other isomers as candidates in a "multi-targeted drug" approach.

Disruptor of telomeric silencing 1-like (DOT1L): disclosing a new class of non-nucleoside inhibitors by means of ligand-based and structure-based approaches

Chemical inhibition of chromatin-mediated signaling involved proteins is an established strategy to drive expression networks and alter disease progression. Protein methyltransferases are among the most studied proteins in epigenetics and, in particular, disruptor of telomeric silencing 1-like (DOT1L) lysine methyltransferase plays a key role in MLL-rearranged acute leukemia Selective inhibition of DOT1L is an established attractive strategy to breakdown aberrant H3K79 methylation and thus overexpression of leukemia genes, and leukemogenesis. Although numerous DOT1L inhibitors have been several structural data published no pronounced computational efforts have been yet reported. In these studies a first tentative of multi-stage and LB/SB combined approach is reported in order to maximize the use of available data. Using co-crystallized ligand/DOT1L complexes, predictive 3-D QSAR and COMBINE models were built through a python implementation of previously reported methodologies. The models, validated by either modeled or experimental external test sets, proved to have good predictive abilities. The application of these models to an internal library led to the selection of two unreported compounds that were found able to inhibit DOT1L at micromolar level. To the best of our knowledge this is the first report of quantitative LB and SB DOT1L inhibitors models and their application to disclose new potential epigenetic modulators.

Integrated in Silico Methods for the Design and Optimization of Novel Drug Candidates

Although almost fully automated, the discovery of novel, effective, and safe drugs is still a long-term and highly expensive process. Consequently, the need for fleet, rational, and cost-efficient development of novel drugs is crucial, and nowadays the advanced in silico drug design methodologies seem to effectively meet these issues. The aim of this chapter is to provide a comprehensive overview of some of the current trends and advances in the in silico design of novel drug candidates with a special emphasis on 6-fluoroquinolone (6-FQ) antibacterials as potential novel Mycobacterium tuberculosis DNA gyrase inhibitors. In particular, the chapter covers some of the recent aspects of a wide range of in silico drug discovery approaches including multidimensional machine-learning methods, ligand-based and structure-based methodologies, as well as their proficient combination and integration into an intelligent virtual screening protocol for design and optimization of novel 6-FQ analogs.

Computational Approaches for the Discovery of Novel Hepatitis C Virus NS3/4A and NS5B Inhibitors

Nonstructural 5B (NS5B) polymerase and Nonstructural 3/4A (NS3/4A) protease have proven to be promising targets for the development of anti-HCV (Hepatitis C Virus) agents. The NS5B polymerase is of paramount importance in HCV viral replication; therefore, employing NS5B inhibitors was considered an effective way for the treatment of HCV. Identifying inhibitors against NS3/4A serine protease represents another attractive approach applied in anti-HCV drug discovery, which is evidenced by its crucial role of in the biogenesis of the viral replication activity. In this chapter, many different computational approaches including Quantitative Structure-Activity Relationship (QSAR) and virtual screening in anti-HCV drug discovery were considered and discussed in detail. Virtual Screening (VS) techniques, including ligand-based and structure-based, and QSAR have been utilized for the discovery of NS5B inhibitors. Moreover, using various in silico protocols and workflows, a number of studies have been conducted with an aim of identifying potential NS3/4A blockage agents.

Discovery of Novel Hepatitis C Virus NS5B Polymerase Inhibitors by Combining Random Forest, Multiple e-Pharmacophore Modeling and Docking

The NS5B polymerase is one of the most attractive targets for developing new drugs to block Hepatitis C virus (HCV) infection. We describe the discovery of novel potent HCV NS5B polymerase inhibitors by employing a virtual screening (VS) approach, which is based on random forest (RB-VS), e-pharmacophore (PB-VS), and docking (DB-VS) methods. In the RB-VS stage, after feature selection, a model with 16 descriptors was used. In the PB-VS stage, six energy-based pharmacophore (e-pharmacophore) models from different crystal structures of the NS5B polymerase with ligands binding at the palm I, thumb I and thumb II regions were used. In the DB-VS stage, the Glide SP and XP docking protocols with default parameters were employed. In the virtual screening approach, the RB-VS, PB-VS and DB-VS methods were applied in increasing order of complexity to screen the InterBioScreen database. From the final hits, we selected 5 compounds for further anti-HCV activity and cellular cytotoxicity assay. All 5 compounds were found to inhibit NS5B polymerase with IC50 values of 2.01-23.84 μM and displayed anti-HCV activities with EC50 values ranging from 1.61 to 21.88 μM, and all compounds displayed no cellular cytotoxicity (CC50 > 100 μM) except compound N2, which displayed weak cytotoxicity with a CC50 value of 51.3 μM. The hit compound N2 had the best antiviral activity against HCV, with a selective index of 32.1. The 5 hit compounds with new scaffolds could potentially serve as NS5B polymerase inhibitors through further optimization and development.

Newly discovered chroman-2,4-diones neutralize the in vivo DNA damage induced by alkylation through the inhibition of Topoisomerase IIα: A story behind the molecular modeling approach

Eight chroman-2,4-diones, namely 2a-h, previously investigated as anticoagulants, of which 2a and 2f as the most active, were evaluated as in vivo genotoxic agents in Wistar rat livers and kidneys using the comet assay. Compounds 2a, 2b, and 2f without genotoxic activity were applied prior to ethyl methanesulfonate (EMS) and diminished EMS-induced DNA damage according to the total score and percentage of reduction. EMS produce harmful O(6)-ethylguanine lesion which is incorporated in aberrant genotoxic GT and TG pairing after ATP-dependent DNA strand breaks have been catalyzed by rat Topoisomerase IIα (rTopIIα, EC 5.99.1.3). Therefore, the mechanism of 2a, 2b, and 2f antigenotoxic activity was investigated on the enzyme level using molecular docking and molecular dynamics simulations insamuch as it had been determined that compounds do not intercalate DNA but instead inhibit the ATPase activity. Calculations predicted that compounds inhibit ATP hydrolysis before the DNA-EMS cleavage is being catalyzed by rTopIIα, prevent EMS mutagenic and carcinogenic effects, and beside anticoagulant activity can even be applied in the cancer treatment to control the rate of anticancer alkylation drugs.

Vascular endothelial growth factor receptor-2 (VEGFR-2) inhibitors: development and validation of predictive 3-D QSAR models through extensive ligand- and structure-based approaches

Vascular endothelial growth factor receptor-2, (VEGFR-2), is a key element in angiogenesis, the process by which new blood vessels are formed, and is thus an important pharmaceutical target. Here, 3-D quantitative structure-activity relationship (3-D QSAR) were used to build a quantitative screening and pharmacophore model of the VEGFR-2 receptors for design of inhibitors with improved activities. Most of available experimental data information has been used as training set to derive optimized and fully cross-validated eight mono-probe and a multi-probe quantitative models. Notable is the use of 262 molecules, aligned following both structure-based and ligand-based protocols, as external test set confirming the 3-D QSAR models' predictive capability and their usefulness in design new VEGFR-2 inhibitors. From a survey on literature, this is the first generation of a wide-ranging computational medicinal chemistry application on VEGFR2 inhibitors.

Searching for anthranilic acid-based thumb pocket 2 HCV NS5B polymerase inhibitors through a combination of molecular docking, 3D-QSAR and virtual screening

A combination of the following computational methods: (i) molecular docking, (ii) 3-D Quantitative Structure Activity Relationship Comparative Molecular Field Analysis (3D-QSAR CoMFA), (iii) similarity search and (iv) virtual screening using PubChem database was applied to identify new anthranilic acid-based inhibitors of hepatitis C virus (HCV) replication. A number of known inhibitors were initially docked into the "Thumb Pocket 2" allosteric site of the crystal structure of the enzyme HCV RNA-dependent RNA polymerase (NS5B GT1b). Then, the CoMFA fields were generated through a receptor-based alignment of docking poses to build a validated and stable 3D-QSAR CoMFA model. The proposed model can be first utilized to get insight into the molecular features that promote bioactivity, and then within a virtual screening procedure, it can be used to estimate the activity of novel potential bioactive compounds prior to their synthesis and biological tests.

Hsp90 inhibitors, part 2: combining ligand-based and structure-based approaches for virtual screening application

Hsp90 continues to be an important target for pharmaceutical discovery. In this project, virtual screening (VS) for novel Hsp90 inhibitors was performed using a combination of Autodock and Surflex-Sim (LB) scoring functions with the predictive ability of 3-D QSAR models, previously generated with the 3-D QSAutogrid/R procedure. Extensive validation of both structure-based (SB) and ligand-based (LB), through realignments and cross-alignments, allowed the definition of LB and SB alignment rules. The mixed LB/SB protocol was applied to virtually screen potential Hsp90 inhibitors from the NCI Diversity Set composed of 1785 compounds. A selected ensemble of 80 compounds were biologically tested. Among these molecules, preliminary data yielded four derivatives exhibiting IC50 values ranging between 18 and 63 μM as hits for a subsequent medicinal chemistry optimization procedure.

Hsp90 inhibitors, part 1: definition of 3-D QSAutogrid/R models as a tool for virtual screening

The multichaperone heat shock protein (Hsp) 90 complex mediates the maturation and stability of a variety of oncogenic signaling proteins. For this reason, Hsp90 has emerged as a promising target for anticancer drug development. Herein, we describe a complete computational procedure for building several 3-D QSAR models used as a ligand-based (LB) component of a comprehensive ligand-based (LB) and structure-based (SB) virtual screening (VS) protocol to identify novel molecular scaffolds of Hsp90 inhibitors. By the application of the 3-D QSAutogrid/R method, eight SB PLS 3-D QSAR models were generated, leading to a final multiprobe (MP) 3-D QSAR pharmacophoric model capable of recognizing the most significant chemical features for Hsp90 inhibition. Both the monoprobe and multiprobe models were optimized, cross-validated, and tested against an external test set. The obtained statistical results confirmed the models as robust and predictive to be used in a subsequent VS.

Multiple e-pharmacophore modeling, 3D-QSAR, and high-throughput virtual screening of hepatitis C virus NS5B polymerase inhibitors

The hepatitis C virus (HCV) NS5B RNA-dependent RNA polymerase (RdRP) is a crucial and unique component of the HCV RNA replication machinery and a validated target for drug discovery. Multiple crystal structures of NS5B inhibitor complexes have facilitated the identification of novel compound scaffolds through in silico analysis. With the goal of discovering new NS5B inhibitor leads, HCV NS5B crystal structures bound with inhibitors in the palm and thumb allosteric pockets in combination with ligands with known inhibitory potential were explored for a comparative pharmacophore analyses. The energy-based and 3D-QSAR-based pharmacophore models were validated using enrichment analysis, and the six models thus developed were employed for high-throughput virtual screening and docking to identify nonpeptidic leads. The hits derived at each stage were analyzed for diversity based on the six pharmacophore models, followed by molecular docking and filtering based on their interaction with amino acids in the NS5B allosteric pocket and 3D-QSAR predictions. The resulting 10 hits displaying diverse scaffold were then screened employing biochemical and cell-based NS5B and anti-HCV inhibition assays. Of these, two molecules H-5 and H-6 were the most promising, exhibiting IC50 values of 28.8 and 47.3 μM against NS5B polymerase and anti-HCV inhibition of 96% and 86% at 50 μM, respectively. The identified leads comprised of benzimidazole (H-5) and pyridine (H-6) scaffolds thus constitute prototypical molecules for further optimization and development as NS5B inhibitors.

Quantitative structure and bioactivity relationship study on HCV NS5B polymerase inhibitors

Several QSAR (quantitative structure-activity relationship) models for predicting the inhibitory activity of 333 hepatitis C virus (HCV) NS5B polymerase inhibitors were developed. All the inhibitors are HCV polymerase non-nucleoside analogue inhibitors (NNIs) fitting into the pocket of the NNI III binding site. For each molecule, global descriptors and 2D property autocorrelation descriptors were calculated from the program ADRIANA.Code. Pearson correlation analysis was used to select the significant descriptors for building models. The whole dataset was split into a training set and a test set randomly or using a Kohonen's self-organizing map (SOM). Then, the inhibitory activity of 333 HCV NS5B polymerase inhibitors was predicted using multilinear regression (MLR) analysis and support vector machine (SVM) method, respectively. For the test set of the best model (Model 2B), correlation coefficient of 0.91 was achieved. Some molecular descriptors, such as molecular complexity (Complexity), the number of hydrogen bonding donors (HDon) and the solubility of the molecule in water (log S) were found to be very important factors which determined the bioactivity of the HCV NS5B inhibitors. Some other molecular properties such as electrostatic and charge properties also played important roles in the interaction between the ligand and the protein. The selected molecular descriptors were further confirmed by analysing the interaction between two representative inhibitors and the polymerase in their crystal structures.

Inhibitors for the hepatitis C virus RNA polymerase explored by SAR with advanced machine learning methods

Hepatitis C virus (HCV) is a global health challenge, affecting approximately 200 million people worldwide. In this study we developed SAR models with advanced machine learning classifiers Random Forest and k Nearest Neighbor Simulated Annealing for 679 small molecules with measured inhibition activity for NS5B genotype 1b. The activity was expressed as a binary value (active/inactive), where actives were considered molecules with IC50 ≤0.95 μM. We applied our SAR models to various drug-like databases and identified novel chemical scaffolds for NS5B inhibitors. Subsequent in vitro antiviral assays suggested a new activity for an existing prodrug, Candesartan cilexetil, which is currently used to treat hypertension and heart failure but has not been previously tested for anti-HCV activity. We also identified NS5B inhibitors with two novel non-nucleoside chemical motifs.

Structure-based discovery of pyrazolobenzothiazine derivatives as inhibitors of hepatitis C virus replication

The NS5B RNA-dependent RNA polymerase is an attractive target for the development of novel and selective inhibitors of hepatitis C virus replication. To identify novel structural hits as anti-HCV agents, we performed structure-based virtual screening of our in-house library followed by rational drug design, organic synthesis, and biological testing. These studies led to the identification of pyrazolobenzothiazine scaffold as a suitable template for obtaining novel anti-HCV agents targeting the NS5B polymerase. The best compound of this series was the meta-fluoro-N-1-phenyl pyrazolobenzothiazine derivative 4a, which exhibited an EC50 = 3.6 μM, EC90 = 25.6 μM, and CC50 > 180 μM in the Huh 9-13 replicon system, thus providing a good starting point for further hit evolution.

Multiple virtual screening approaches for finding new hepatitis C virus RNA-dependent RNA polymerase inhibitors: structure-based screens and molecular dynamics for the pursue of new poly pharmacological inhibitors

The RNA polymerase NS5B of Hepatitis C virus (HCV) is a well-characterised drug target with an active site and four allosteric binding sites. This work presents a workflow for virtual screening and its application to Drug Bank screening targeting the Hepatitis C Virus (HCV) RNA polymerase non-nucleoside binding sites. Potential polypharmacological drugs are sought with predicted active inhibition on viral replication, and with proven positive pharmaco-clinical profiles. The approach adopted was receptor-based. Docking screens, guided with contact pharmacophores and neural-network activity prediction models on all allosteric binding sites and MD simulations, constituted our analysis workflow for identification of potential hits. Steps included: 1) using a two-phase docking screen with Surflex and Glide Xp. 2) Ranking based on scores, and important H interactions. 3) a machine-learning target-trained artificial neural network PIC prediction model used for ranking. This provided a better correlation of IC50 values of the training sets for each site with different docking scores and sub-scores. 4) interaction pharmacophores-through retrospective analysis of protein-inhibitor complex X-ray structures for the interaction pharmacophore (common interaction modes) of inhibitors for the five non-nucleoside binding sites were constructed. These were used for filtering the hits according to the critical binding feature of formerly reported inhibitors. This filtration process resulted in identification of potential new inhibitors as well as formerly reported ones for the thumb II and Palm I sites (HCV-81) NS5B binding sites. Eventually molecular dynamics simulations were carried out, confirming the binding hypothesis and resulting in 4 hits.

On scaffold hopping: challenges in the discovery of sulfated small molecules as mimetics of glycosaminoglycans

The design of sulfated, small, nonsaccharide molecules as modulators of proteins is still in its infancy as standard drug discovery tools such as library of diverse sulfated molecules and in silico docking and scoring protocol have not been firmly established. Databases, such as ZINC, contain too few sulfate-containing nonsaccharide molecules, which severely limits the identification of new hits. Lack of a generally applicable protocol for scaffold hopping limits the development of sulfated small molecules as synthetic mimetics of the highly sulfated glycosaminoglycans. We explored a sequential ligand-based (LBVS) and structure-based virtual screening (SBVS) approach starting from our initial discovery of monosulfated benzofurans to discover alternative scaffolds as allosteric modulators of thrombin, a key coagulation enzyme. Screening the ZINC database containing nearly 1 million nonsulfated small molecules using a pharmacophore developed from the parent sulfated benzofurans followed by a genetic algorithm-based dual-filter docking and scoring screening identified a group of 10 promising hits, of which three top-scoring hits were synthesized. Each was found to selectively inhibit human alpha-thrombin suggesting the possibility of this approach for scaffold hopping. Michaelis-Menten kinetics showed allosteric inhibition mechanism for the best molecule and human plasma studies confirmed good anticoagulation potential as expected. Our simple sequential LBVS and SBVS approach is likely to be useful as a general strategy for identification of sulfated small molecules hits as modulators of glycosaminoglycan-protein interactions.

Discovery of new scaffolds for rational design of HCV NS5B polymerase inhibitors

Hepatitis C virus (HCV) NS5B polymerase is a key target for the development of anti-HCV drugs. Here we report on the identification of novel allosteric inhibitors of HCV NS5B through a combination of structure-based virtual screening and in vitro NS5B inhibition assays. One hundred and sixty thousand compounds from the Otava database were virtually screened against the thiazolone inhibitor binding site on NS5B (thumb pocket-2, TP-2), resulting in a sequential down-sizing of the library by 2.7 orders of magnitude to yield 59 NS5B non-nucleoside inhibitor (NNI) candidates. In vitro evaluation of the NS5B inhibitory activity of the 59 selected compounds resulted in a 14% hit rate, yielding 8 novel structural scaffolds. Of these, compound 1 bearing a 4-hydrazinoquinazoline scaffold was the most active (IC(50) = 16.0 μM). The binding site of all 8 NNIs was mapped to TP-2 of NS5B as inferred by a decrease in their inhibition potency against the M423T NS5B mutant, employed as a screen for TP-2 site binders. At 100 μM concentration, none of the eight compounds exhibited any cytotoxicity, and all except compound 8 exhibited between 40 and 60% inhibition of intracellular NS5B polymerase activity in BHK-NS5B-FRLuc reporter cells. These inhibitor scaffolds will form the basis for future optimization and development of more potent NS5B inhibitors.

3-D QSAutogrid/R: an alternative procedure to build 3-D QSAR models. Methodologies and applications

Since it first appeared in 1988 3-D QSAR has proved its potential in the field of drug design and activity prediction. Although thousands of citations now exist in 3-D QSAR, its development was rather slow with the majority of new 3-D QSAR applications just extensions of CoMFA. An alternative way to build 3-D QSAR models, based on an evolution of software, has been named 3-D QSAutogrid/R and has been developed to use only software freely available to academics. 3-D QSAutogrid/R covers all the main features of CoMFA and GRID/GOLPE with implementation by multiprobe/multiregion variable selection (MPGRS) that improves the simplification of interpretation of the 3-D QSAR map. The methodology is based on the integration of the molecular interaction fields as calculated by AutoGrid and the R statistical environment that can be easily coupled with many free graphical molecular interfaces such as UCSF-Chimera, AutoDock Tools, JMol, and others. The description of each R package is reported in detail, and, to assess its validity, 3-D QSAutogrid/R has been applied to three molecular data sets of which either CoMFA or GRID/GOLPE models were reported in order to compare the results. 3-D QSAutogrid/R has been used as the core engine to prepare more that 240 3-D QSAR models forming the very first 3-D QSAR server ( www.3d-qsar.com ) with its code freely available through R-Cran distribution.

Inhibition of hepatitis C virus NS5B polymerase by S-trityl-L-cysteine derivatives

Structure-based studies led to the identification of a constrained derivative of S-trityl-l-cysteine (STLC) scaffold as a candidate inhibitor of hepatitis C virus (HCV) NS5B polymerase. A panel of STLC derivatives were synthesized and investigated for their activity against HCV NS5B. Three STLC derivatives, 9, F-3070, and F-3065, were identified as modest HCV NS5B inhibitors with IC(50) values between 22.3 and 39.7 μM. F-3070 and F-3065 displayed potent inhibition of intracellular NS5B activity in the BHK-NS5B-FRLuc reporter and also inhibited HCV RNA replication in the Huh7/Rep-Feo1b reporter system. Binding mode investigations suggested that the STLC scaffold can be used to develop new NS5B inhibitors by further chemical modification at one of the trityl phenyl group.

In silico virtual screening approaches for anti-viral drug discovery

Despite the considerable advances in medical and pharmaceutical research during the past years, diseases caused by viruses have remained a major burden to public health. Virtual in silico screening has repeatedly proven to be useful to meet the special challenges of antiviral drug discovery. Large virtual compound libraries are filtered by different computational screening methods such as docking, ligand-based similarity searches or pharmacophore-based screening, reducing the number of candidate molecules to a smaller set of promising candidates that are then tested biologically. This rational approach makes the drug discovery process more goal-oriented and saves resources in terms of time and money. In this review we discuss how different virtual screening techniques can be applied to antiviral drug discovery, present recent success stories in this field and finally address the main differences between the methods.:

Structural insights into the inhibition of cytosolic 5'-nucleotidase II (cN-II) by ribonucleoside 5'-monophosphate analogues

Cytosolic 5′-nucleotidase II (cN-II) regulates the intracellular nucleotide pools within the cell by catalyzing the dephosphorylation of 6-hydroxypurine nucleoside 5′-monophosphates. Beside this physiological function, high level of cN-II expression is correlated with abnormal patient outcome when treated with cytotoxic nucleoside analogues. To identify its specific role in the resistance phenomenon observed during cancer therapy, we screened a particular class of chemical compounds, namely ribonucleoside phosphonates to predict them as potential cN-II inhibitors. These compounds incorporate a chemically and enzymatically stable phosphorus-carbon linkage instead of a regular phosphoester bond. Amongst them, six compounds were predicted as better ligands than the natural substrate of cN-II, inosine 5′-monophosphate (IMP). The study of purine and pyrimidine containing analogues and the introduction of chemical modifications within the phosphonate chain has allowed us to define general rules governing the theoretical affinity of such ligands. The binding strength of these compounds was scrutinized in silico and explained by an impressive number of van der Waals contacts, highlighting the decisive role of three cN-II residues that are Phe 157, His 209 and Tyr 210. Docking predictions were confirmed by experimental measurements of the nucleotidase activity in the presence of the three best available phosphonate analogues. These compounds were shown to induce a total inhibition of the cN-II activity at 2 mM. Altogether, this study emphasizes the importance of the non-hydrolysable phosphonate bond in the design of new competitive cN-II inhibitors and the crucial hydrophobic stacking promoted by three protein residues.

Nucleotidase activity is part of a biological process that allows the cell to regulate the intracellular pools of nucleotides involved in many signaling pathways. During cancer therapy with cytotoxic nucleoside analogues, the role of cN-II is unclear. Therefore, the development of specific inhibitors against this enzyme is of great interest for understanding its implication in cancer biology and drug resistance. Ribonucleoside phosphonates are of major importance because they behave as bioisosteric analogues of the natural cN-II substrates and contain a chemically and enzymatically stable phosphorus-carbon linkage. Taking the advantages of docking methods, we predicted the inhibitory potential of these compounds. Their binding strength was explained by an impressive interaction network involving mainly three residues of the enzyme (acting as hydrophobic tweezers). These new characterized inhibitors will constitute a valuable tool for elucidating the role of cN-II in cancer cells and may be used in combination with cytotoxic nucleosidic drugs in order to increase their antitumor activity. Furthermore, the strategy taking into account the hydrophobic clamp for designing new inhibitors may be applied to other nucleotidases of the HAD family as two of the three identified residues are present in the substrate binding site of cytosolic 5′-nucleotidase III and 5′-deoxynucleotidase-I.

Allosteric inhibition of the hepatitis C virus NS5B polymerase: in silico strategies for drug discovery and development

Chronic infection by hepatitis C virus (HCV) often leads to severe liver disease including cirrhosis, hepatocellular carcinoma and liver failure. Despite it being more than 20 years since the identification of HCV, the current standard of care for treating the infection is based on aspecific therapy often associated with severe side effects and low-sustained virological response. Research is ongoing to develop new and better medications, including a broad range of allosteric NS5B polymerase inhibitors. This article reviews traditional computational methodologies and more recently developed in silico strategies aimed at identifying and optimizing non-nucleoside inhibitors targeting allosteric sites of HCV NS5B polymerase. The drug-discovery approaches reviewed could provide take-home lessons for general computer-aided research projects.

Novel cinnamyl hydroxyamides and 2-aminoanilides as histone deacetylase inhibitors: apoptotic induction and cytodifferentiation activity

Four novel series of cinnamyl-containing histone deacetylase (HDAC) inhibitors 1-4 are described, containing hydroxamate (1 and 3) or 2-aminoanilide (2 and 4) derivatives. When screened against class I (maize HD1-B and human HDAC1) and class II (maize HD1-A and human HDAC4) HDACs, most hydroxamates and 2-aminoanilides displayed potent and selective inhibition toward class I enzymes. Immunoblotting analyses performed in U937 leukemia cells generally revealed high acetyl-H3 and low acetyl-α-tubulin levels. Exceptions are compounds 3 f-i, 3 m-o, and 4 k, which showed higher tubulin acetylation than SAHA. In U937 cells, cell-cycle blockade in either the G₂/M or G₁/S phase was observed with 1-4. Five hydroxamates (compounds 1 h-l) effected a two- to greater than threefold greater percent apoptosis than SAHA, and in the CD11c cytodifferentiation test some 2-aminoanilides belonging to both series 2 and 4 were more active than MS-275. The highest-scoring derivatives in terms of apoptosis (1 k, 1 l) or cytodifferentiation (2 c, 4 n) also showed antiproliferative activity in U937 cells, thus representing valuable tools for study in other cancer contexts.

Discovery and development of anti-HBV agents and their resistance

Hepatitis B virus (HBV) infection is a prime cause of liver diseases such as hepatitis, cirrhosis and hepatocellular carcinoma. The current drugs clinically available are nucleot(s)ide analogues that inhibit viral reverse transcriptase activity. Most drugs of this class are reported to have viral resistance with breakthrough. Recent advances in methods for in silico virtual screening of chemical libraries, together with a better understanding of the resistance mechanisms of existing drugs have expedited the discovery and development of novel anti-viral drugs. This review summarizes the current status of knowledge about and viral resistance of HBV drugs, approaches for the development of novel drugs as well as new viral and host targets for future drugs.