Discovery of Subtype Selective Janus Kinase (JAK) Inhibitors by Structure-Based Virtual Screening

Molecular Dynamics and Other HPC Simulations for Drug Discovery

High performance computing (HPC) is taking an increasingly important place in drug discovery. It makes possible the simulation of complex biochemical systems with high precision in a short time, thanks to the use of sophisticated algorithms. It promotes the advancement of knowledge in fields that are inaccessible or difficult to access through experimentation and it contributes to accelerating the discovery of drugs for unmet medical needs while reducing costs. Herein, we report how computational performance has evolved over the past years, and then we detail three domains where HPC is essential. Molecular dynamics (MD) is commonly used to explore the flexibility of proteins, thus generating a better understanding of different possible approaches to modulate their activity. Modeling and simulation of biopolymer complexes enables the study of protein-protein interactions (PPI) in healthy and disease states, thus helping the identification of targets of pharmacological interest. Virtual screening (VS) also benefits from HPC to predict in a short time, among millions or billions of virtual chemical compounds, the best potential ligands that will be tested in relevant assays to start a rational drug design process.

Identification of potent inhibitors for Leishmania donovani homoserine kinase: an integrated in silico and kinetic study

Leishmaniasis is caused by ∼20 species of Leishmania that affects millions in endemic areas. Available therapies are not sufficient to effectively control the disease, cause severe side effects and eventually lead to drug resistance, making the discovery of novel therapeutic molecules an immediate need. Molecular target-based drug discovery, where the target is a defined molecular gene, protein or a mechanism, is a rationale driven approach for novel therapeutics. Humans obtain the essential amino acid such as threonine from dietary sources, while Leishmania synthesize it de-novo. Enzymes of the threonine biosynthesis pathway, including the rate limiting Homoserine kinase (HSK) which converts L-homoserine into ortho-phospho homoserine are thus attractive targets for rationale driven therapy. The absence of HSK in humans and its presence in Leishmania donovani enhances the opportunity to exploit HSK as a molecular target for anti-leishmanials therapeutic development. In this study, we utilize structure-based high throughput drug discovery (SBDD), followed by biochemical validation and identified two potential inhibitors (RH00038 and S02587) from Maybridge chemical library that targets L. donovani HSK. These two inhibitors effectively induced the mortality of Leishmania donovani in both amastigote and promastigote stages, with one of them being specific to parasite and twice as effective as the standard therapeutic molecule.

Development of machine learning models based on molecular fingerprints for selection of small molecule inhibitors against JAK2 protein

Janus kinase 2 (JAK2) is emerging as a potential therapeutic target for many inflammatory diseases such as myeloproliferative disorders (MPD), cancer and rheumatoid arthritis (RA). In this study, we have collected experimental data of JAK2 protein containing 6021 unique inhibitors. We then characterized them based on Morgan (ECFP6) fingerprints followed by clustering into training and test set based on their molecular scaffolds. These data were used to build the classification models with various supervised machine learning (ML) algorithms that could prioritize novel inhibitors for future drug development against JAK2 protein. The best model built by Random Forest (RF) and Morgan fingerprints achieved the G-mean value of 0.84 on the external test set. As an application of our classification model, virtual screening was performed against Drugbank molecules in order to identify the potential inhibitors based on the confidence score by RF model. Nine potential molecules were identified, which were further subject to molecular docking studies to evaluate the virtual screening results of the best RF model. This proposed method can prove useful for developing novel target-specific JAK2 inhibitors.

2-Aminopyrimidine derivatives as selective dual inhibitors of JAK2 and FLT3 for the treatment of acute myeloid leukemia

Dual inhibitors of JAK2 and FLT3 can synergistically control the development of acute myeloid leukemia (AML), and overcome secondary drug resistance of AML that is associated with FLT3 inhibition. We therefore designed and synthesized a series of 4-piperazinyl-2-aminopyrimidines as dual inhibitors of JAK2 and FLT3, and improved their selectivity for JAK2. Screening cascades revealed that compound 11r exhibited inhibitory activity with IC₅₀ values of 2.01, 0.51, and 104.40 nM against JAK2, FLT3, and JAK3, respectively. Compound 11r achieved a high selectivity for JAK2 at a ratio of 51.94, and also showed potent antiproliferative activity in HEL (IC₅₀ = 1.10 μM) and MV4-11 (IC₅₀ = 9.43 nM) cell lines. In an in vitro metabolism assay, 11r exhibited moderate stability in human liver microsomes (HLMs), with a half-life time of 44.4 min, and in rat liver microsomes (RLMs), with a half-life of 143 min. In pharmacokinetic studies, compound 11r showed moderate absorption (Tmax = 5.33 h), with a peak concentration of 38.7 ng/mL and an AUC of 522 ng h/mL in rats, and an oral bioavailability of 25.2%. In addition, 11r induced MV4-11 cell apoptosis in a dose-dependent manner. These results indicate that 11r is a promising selective JAK2/FLT3 dual inhibitor.

Pharmacophore-Based Virtual Screening and Experimental Validation of Pyrazolone-Derived Inhibitors toward Janus Kinases

Janus kinases (JAKs) are nonreceptor protein tyrosine kinases that play a role in a broad range of cell signaling. JAK2 and JAK3 have been involved in the pathogenesis of common lymphoid-derived diseases and leukemia cancer. Thus, inhibition of both JAK2 and JAK3 can be a potent strategy to reduce the risk of these diseases. In the present study, the pharmacophore models built based on the commercial drug tofacitinib and the JAK2/3 proteins derived from molecular dynamics (MD) trajectories were employed to search for a dual potent JAK2/3 inhibitor by a pharmacophore-based virtual screening of 54 synthesized pyrazolone derivatives from an in-house data set. Twelve selected compounds from the virtual screening procedure were then tested for their inhibitory potency against both JAKs in the kinase assay. The in vitro kinase inhibition experiment indicated that compounds 3h, TK4g, and TK4b can inhibit both JAKs in the low nanomolar range. Among them, the compound TK4g showed the highest protein kinase inhibition with the half-maximal inhibitory concentration (IC₅₀) value of 12.61 nM for JAK2 and 15.80 nM for JAK3. From the MD simulations study, it could be found that the sulfonamide group of TK4g can form hydrogen bonds in the hinge region at residues E930 and L932 of JAK2 and E903 and L905 of JAK3, while van der Waals interaction also plays a dominant role in ligand binding. Altogether, TK4g, found by virtual screening and biological tests, could serve as a novel therapeutical lead candidate.

Maximizing the integration of virtual and experimental screening in hit discovery

INTRODUCTION

Experimental and virtual screening contributes to the discovery of more than 50% of clinical candidates. Considering the similar concept and goals, early-phase drug discovery would benefit from the effective integration of these approaches.

AREAS COVERED

After reviewing the recent trends in both experimental and virtual screening, the authors discuss different integration strategies from parallel, focused, sequential, and iterative screening. Strategic considerations are demonstrated in a number of real-life case studies.

EXPERT OPINION

Experimental and virtual screening are complementary approaches that should be integrated in lead discovery settings. Virtual screening can access extremely large synthetically feasible chemical space that can be effectively searched on GPU clusters or cloud architectures. Experimental screening provides reliable datasets by quantitative HTS applications, and DNA-encoded libraries (DEL) have enlarged the chemical space covered by these technologies. These developments, together with the use of artificial intelligence methods, represent new options for their efficient integration. The case studies discussed here demonstrate the benefits of complementary strategies, such as focused and iterative screening.

A multitask GNN-based interpretable model for discovery of selective JAK inhibitors

The Janus kinase (JAK) family plays a pivotal role in most cytokine-mediated inflammatory and autoimmune responses via JAK/STAT signaling, and administration of JAK inhibitors is a promising therapeutic strategy for several diseases including COVID-19. However, to screen and design selective JAK inhibitors is a daunting task due to the extremely high homology among four JAK isoforms. In this study, we aimed to simultaneously predict pIC₅₀ values of compounds for all JAK subtypes by constructing an interpretable GNN multitask regression model. The final model performance was positive, with R² values of 0.96, 0.79 and 0.78 on the training, validation and test sets, respectively. Meanwhile, we calculated and visualized atom weights, followed by the rank sum tests and local mean comparisons to obtain key atoms and substructures that could be fine-tuned to design selective JAK inhibitors. Several successful case studies have demonstrated that our approach is feasible and our model could learn the interactions between proteins and small molecules well, which could provide practitioners with a novel way to discover and design JAK inhibitors with selectivity.

Docking-generated multiple ligand poses for bootstrapping bioactivity classifying Machine Learning: Repurposing covalent inhibitors for COVID-19-related TMPRSS2 as case study

In the present work we introduce the use of multiple docked poses for bootstrapping machine learning-based QSAR modelling. Ligand-receptor contact fingerprints are implemented as descriptor variables. We implemented this method for the discovery of potential inhibitors of the serine protease enzyme TMPRSS2 involved the infectivity of coronaviruses. Several machine learners were scanned, however, Xgboost, support vector machines (SVM) and random forests (RF) were the best with testing set accuracies reaching 90%. Three potential hits were identified upon using the method to scan known untested FDA approved drugs against TMPRSS2. Subsequent molecular dynamics simulation and covalent docking supported the results of the new computational approach.

Current progress, challenges and future prospects of indazoles as protein kinase inhibitors for the treatment of cancer

The indazole core is an interesting pharmacophore due to its applications in medicinal chemistry. In the past few years, this moiety has been used for the synthesis of kinase inhibitors. Many researchers have demonstrated the use of indazole derivatives as specific kinase inhibitors, including tyrosine kinase and serine/threonine kinases. A number of anticancer drugs with an indazole core are commercially available, e.g. axitinib, linifanib, niraparib, and pazopanib. Indazole derivatives are applied for the targeted treatment of lung, breast, colon, and prostate cancers. In this review, we compile the current development of indazole derivatives as kinase inhibitors and their application as anticancer agents in the past five years.

Novel 1,3,4-oxadiazole Targets STAT3 Signaling to Induce Antitumor Effect in Lung Cancer

Lung cancer is the leading type of malignancy in terms of occurrence and mortality in the global context. STAT3 is an oncogenic transcription factor that is persistently activated in many types of human malignancies, including lung cancer. In the present report, new oxadiazole conjugated indazoles were synthesized and examined for their anticancer potential in a panel of cancer cell lines. Among the new compounds, 2-(3-(6-chloro-5-methylpyridin-3-yl)phenyl)-5-(1-methyl-1H-indazol-3-yl)-1,3,4-oxadiazole (CHK9) showed consistently good cytotoxicity towards lung cancer cells with IC50 values ranging between 4.8-5.1 µM. The proapoptotic effect of CHK9 was further demonstrated by Annexin-FITC staining and TUNEL assay. In addition, the effect of CHK9 on the activation of STAT3 in lung cancer cells was examined. CHK9 reduced the phosphorylation of STAT3Y705 in a dose-dependent manner. CHK9 had no effect on the activation and expression of JAK2 and STAT5. It also reduced the STAT3-dependent luciferase reporter gene expression. CHK9 increased the expression of proapoptotic (p53 and Bax) proteins and decreased the expression of the antiapoptotic (Bcl-2, Bcl-xL, BID, and ICAM-1) proteins. CHK9 displayed a significant reduction in the number of tumor nodules in the in vivo lung cancer model with suppression of STAT3 activation in tumor tissues. CHK9 did not show substantial toxicity in the normal murine model. Overall, CHK9 inhibits the growth of lung cancer cells and tumors by interfering with the STAT3 signaling pathway.

Inside Perspective of the Synthetic and Computational Toolbox of JAK Inhibitors: Recent Updates

The mechanisms of inflammation and cancer are intertwined by complex networks of signaling pathways. Dysregulations in the Janus kinase/signal transducer and activator of transcription (JAK/STAT) pathway underlie several pathogenic conditions related to chronic inflammatory states, autoimmune diseases and cancer. Historically, the potential application of JAK inhibition has been thoroughly explored, thus triggering an escalation of favorable results in this field. So far, five JAK inhibitors have been approved by the Food and Drug Administration (FDA) for the treatment of different diseases. Considering the complexity of JAK-depending processes and their involvement in multiple disorders, JAK inhibitors are the perfect candidates for drug repurposing and for the assessment of multitarget strategies. Herein we reviewed the recent progress concerning JAK inhibition, including the innovations provided by the release of JAKs crystal structures and the improvement of synthetic strategies aimed to simplify of the industrial scale-up.

Discovery of 4-piperazinyl-2-aminopyrimidine derivatives as dual inhibitors of JAK2 and FLT3

Hybridization strategy is an effective strategy to obtain multi-target inhibitors in drug design. In this study, we assembled the pharmacophores of momelotinib and tandutinib to get a series of 4-piperazinyl-2-aminopyrimidine derivatives. All compounds were tested for the inhibition of JAK2 and FLT3 enzymes, of which, compounds with potent enzyme activities were assayed for antiproliferative activities against three cancer cell lines (HEL, MV4-11, and HL60). The structure-activity relationship studies were conducted through variations in two regions, the "A" phenyl ring and "B" phenyl ring. Compound 14j showed the most balanced in vitro inhibitory activity against JAK2 and FLT3 (JAK2 IC₅₀ = 27 nM, FLT3 IC₅₀ = 30 nM), and it also showed potent inhibition against the above tested cell lines. In the cellular context, 14j strongly induced apoptosis by arresting cell cycle in the G₁/S phase, and was selected as a promising JAK2/FLT3 dual inhibitor.

Comparison of Data Fusion Methods as Consensus Scores for Ensemble Docking

Ensemble docking is a widely applied concept in structure-based virtual screening-to at least partly account for protein flexibility-usually granting a significant performance gain at a modest cost of speed. From the individual, single-structure docking scores, a consensus score needs to be produced by data fusion: this is usually done by taking the best docking score from the available pool (in most cases- and in this study as well-this is the minimum score). Nonetheless, there are a number of other fusion rules that can be applied. We report here the results of a detailed statistical comparison of seven fusion rules for ensemble docking, on five case studies of current drug targets, based on four performance metrics. Sevenfold cross-validation and variance analysis (ANOVA) allowed us to highlight the best fusion rules. The results are presented in bubble plots, to unite the four performance metrics into a single, comprehensive image. Notably, we suggest the use of the geometric and harmonic means as better alternatives to the generally applied minimum fusion rule.

The compromise of virtual screening and its impact on drug discovery

Introduction: Docking and structure-based virtual screening (VS) have been standard approaches in structure-based design for over two decades. However, our understanding of the limitations, potential, and strength of these techniques has enhanced, raising expectations. Areas covered: Based on a survey of reports in the past five years, we assess whether VS: (1) predicts binding poses in agreement with crystallographic data (when available); (2) is a superior screening tool, as often claimed; (3) is successful in identifying chemical scaffolds that can be starting points for subsequent lead optimization cycles. Data shows that knowledge of the target and its chemotypes in postprocessing lead to viable hits in early drug discovery endeavors. Expert opinion: VS is capable of accurate placements in the pocket for the most part, but does not consistently score screening collections accurately. What matters is capitalization on available resources to get closer to a viable lead or optimizable series. Integration of approaches, subjective hit selection guided by knowledge of the receptor or endogenous ligand, libraries driven by experimental guides, validation studies to identify the best docking/scoring that reproduces experimental findings, constraints regarding receptor-ligand interactions, thoroughly designed methodologies, and predefined cutoff scoring criteria strengthen VS's position in pharmaceutical research.

The impact of binding site waters on the activity/selectivity trade-off of Janus kinase 2 (JAK2) inhibitors

Structure based optimization of B39, an indazole-based low micromolar JAK2 virtual screening hit is reported. Analysing the effect of certain modifications on the activity and selectivity of the analogues suggested that these parameters are influenced by water molecules available in the binding site. Simulation of water networks in combination with docking enabled us to identify the key waters and to optimize our primary hit into a low nanomolar JAK2 lead with promising selectivity over JAK1.

Life beyond the Tanimoto coefficient: similarity measures for interaction fingerprints

Background

Interaction fingerprints (IFP) have been repeatedly shown to be valuable tools in virtual screening to identify novel hit compounds that can subsequently be optimized to drug candidates. As a complementary method to ligand docking, IFPs can be applied to quantify the similarity of predicted binding poses to a reference binding pose. For this purpose, a large number of similarity metrics can be applied, and various parameters of the IFPs themselves can be customized. In a large-scale comparison, we have assessed the effect of similarity metrics and IFP configurations to a number of virtual screening scenarios with ten different protein targets and thousands of molecules. Particularly, the effect of considering general interaction definitions (such as Any Contact, Backbone Interaction and Sidechain Interaction), the effect of filtering methods and the different groups of similarity metrics were studied.

Results

The performances were primarily compared based on AUC values, but we have also used the original similarity data for the comparison of similarity metrics with several statistical tests and the novel, robust sum of ranking differences (SRD) algorithm. With SRD, we can evaluate the consistency (or concordance) of the various similarity metrics to an ideal reference metric, which is provided by data fusion from the existing metrics. Different aspects of IFP configurations and similarity metrics were examined based on SRD values with analysis of variance (ANOVA) tests.

Conclusion

A general approach is provided that can be applied for the reliable interpretation and usage of similarity measures with interaction fingerprints. Metrics that are viable alternatives to the commonly used Tanimoto coefficient were identified based on a comparison with an ideal reference metric (consensus). A careful selection of the applied bits (interaction definitions) and IFP filtering rules can improve the results of virtual screening (in terms of their agreement with the consensus metric). The open-source Python package FPKit was introduced for the similarity calculations and IFP filtering; it is available at: https://github.com/davidbajusz/fpkit.

Electronic supplementary material

The online version of this article (10.1186/s13321-018-0302-y) contains supplementary material, which is available to authorized users.

The Development of Target-Specific Pose Filter Ensembles To Boost Ligand Enrichment for Structure-Based Virtual Screening

Structure-based virtual screening (SBVS) has become an indispensable technique for hit identification at the early stage of drug discovery. However, the accuracy of current scoring functions is not high enough to confer success to every target and thus remains to be improved. Previously, we had developed binary pose filters (PFs) using knowledge derived from the protein-ligand interface of a single X-ray structure of a specific target. This novel approach had been validated as an effective way to improve ligand enrichment. Continuing from it, in the present work we attempted to incorporate knowledge collected from diverse protein-ligand interfaces of multiple crystal structures of the same target to build PF ensembles (PFEs). Toward this end, we first constructed a comprehensive data set to meet the requirements of ensemble modeling and validation. This set contains 10 diverse targets, 118 well-prepared X-ray structures of protein-ligand complexes, and large benchmarking actives/decoys sets. Notably, we designed a unique workflow of two-layer classifiers based on the concept of ensemble learning and applied it to the construction of PFEs for all of the targets. Through extensive benchmarking studies, we demonstrated that (1) coupling PFE with Chemgauss4 significantly improves the early enrichment of Chemgauss4 itself and (2) PFEs show greater consistency in boosting early enrichment and larger overall enrichment than our prior PFs. In addition, we analyzed the pairwise topological similarities among cognate ligands used to construct PFEs and found that it is the higher chemical diversity of the cognate ligands that leads to the improved performance of PFEs. Taken together, the results so far prove that the incorporation of knowledge from diverse protein-ligand interfaces by ensemble modeling is able to enhance the screening competence of SBVS scoring functions.

Development of efficient docking strategies and structure-activity relationship study of the c-Met type II inhibitors

c-Met is a transmembrane receptor tyrosine kinase and an important therapeutic target for anticancer drugs. In the present study, we systematically investigated the influence of a range of parameters on the correlation between experimental and calculated binding free energies of type II c-Met inhibitors. We especially focused on evaluating the impact of different force fields, binding energy calculation methods, docking protocols, conformation sampling strategies, and conformations of the binding site captured in several crystallographic structures. Our results suggest that the force fields, the protein flexibility, and the selected conformation of the binding site substantially influence the correlation coefficient, while the sampling strategies and ensemble docking only mildly affect the prediction accuracy. Structure-activity relationship study suggests that the structural determinants to the high binding affinity of the type II inhibitors originate from its overall linear shape, hydrophobicity, and two conserved hydrogen bonds. Results from this study will form the basis for establishing an efficient computational docking approach for c-Met type II inhibitors design.

Ensemble docking-based virtual screening yields novel spirocyclic JAK1 inhibitors

Small molecule inhibition of Janus kinases (JAKs) has been demonstrated as a viable strategy for the treatment of various inflammatory conditions and continues to emerge in cancer-related indications. In this study, a large supplier database was screened to identify novel chemistry starting points for JAK1. The docking-based screening was followed up by testing ten hit compounds experimentally, out of which five have displayed single-digit micromolar and submicromolar IC₅₀ values on JAK1. Thus, the study was concluded with the discovery of five novel JAK inhibitors from a tiny screening deck with a remarkable hitrate of 50%. The results have highlighted spirocyclic pyrrolopyrimidines with submicromolar JAK1 IC₅₀ values and a preference for JAK1 over JAK2 as potential starting points in developing a novel class of JAK1 inhibitors.