Optimization of novel reversible Bruton's tyrosine kinase inhibitors identified using Tethering-fragment-based screens

Pyrrole-containing hybrids as potential anticancer agents: An insight into current developments and structure-activity relationships

Cancer poses a significant threat to human health. Therefore, it is urgent to develop potent anti-cancer drugs with excellent inhibitory activity and no toxic side effects. Pyrrole and its derivatives are privileged heterocyclic compounds with significant diverse pharmacological effects. These compounds can target various aspects of cancer cells and have been applied in clinical settings or are undergoing clinical trials. As a result, pyrrole has emerged as a promising drug scaffold and has been further probed to get novel entities for the treatment of cancer. This article reviews recent research progress on anti-cancer drugs containing pyrrole. It focuses on the mechanism of action, biological activity, and structure-activity relationships of pyrrole derivatives, aiming to assist in designing and synthesizing innovative pyrrole-based anti-cancer compounds.

Machine learning-based classification models for non-covalent Bruton's tyrosine kinase inhibitors: predictive ability and interpretability

In this study, we built classification models using machine learning techniques to predict the bioactivity of non-covalent inhibitors of Bruton's tyrosine kinase (BTK) and to provide interpretable and transparent explanations for these predictions. To achieve this, we gathered data on BTK inhibitors from the Reaxys and ChEMBL databases, removing compounds with covalent bonds and duplicates to obtain a dataset of 3895 inhibitors of non-covalent. These inhibitors were characterized using MACCS fingerprints and Morgan fingerprints, and four traditional machine learning algorithms (decision trees (DT), random forests (RF), support vector machines (SVM), and extreme gradient boosting (XGBoost)) were used to build 16 classification models. In addition, four deep learning models were developed using deep neural networks (DNN). The best model, Model D_4, which was built using XGBoost and MACCS fingerprints, achieved an accuracy of 94.1% and a Matthews correlation coefficient (MCC) of 0.75 on the test set. To provide interpretable explanations, we employed the SHAP method to decompose the predicted values into the contributions of each feature. We also used K-means dimensionality reduction and hierarchical clustering to visualize the clustering effects of molecular structures of the inhibitors. The results of this study were validated using crystal structures, and we found that the interaction between the BTK amino acid residue and the important features of clustered scaffold was consistent with the known properties of the complex crystal structures. Overall, our models demonstrated high predictive ability and a qualitative model can be converted to a quantitative model to some extent by SHAP, making them valuable for guiding the design of new BTK inhibitors with desired activity.

Discovery of structural diverse reversible BTK inhibitors utilized to develop a novel in vivo CD69 and CD86 PK/PD mouse model

For the past two decades, BTK a tyrosine kinase and member of the Tec family has been a drug target of significant interest due to its potential to selectively treat various B cell-mediated diseases such as CLL, MCL, RA, and MS. Owning to the challenges encountered in identifying drug candidates exhibiting the potency block B cell activation via BTK inhibition, the pharmaceutical industry has relied on the use of covalent/irreversible inhibitors to address this unmet medical need. Herein, we describe a medicinal chemistry campaign to identify structurally diverse reversible BTK inhibitors originating from HITS identified using a fragment base screen. The leads were optimized to improve the potency and in vivo ADME properties resulting in a structurally distinct chemical series used to develop and validate a novel in vivo CD69 and CD86 PD assay in rodents.

In-silico probing of AML related RUNX1 cancer-associated missense mutations: Predicted relationships to DNA binding and drug interactions

The molecular consequences of cancer associated mutations in Acute myeloid leukemia (AML) linked factors are not very well understood. Here, we interrogated the COSMIC database for missense mutations associated with the RUNX1 protein, that is frequently mis-regulated in AML, where we sought to identify recurrently mutated positions at the DNA-interacting interface. Indeed, six of the mutated residues, out of a total 417 residues examined within the DNA binding domain, evidenced reduced DNA association in in silico predictions. Further, given the prominence of RUNX1’s compromised function in AML, we asked the question if the mutations themselves might alter RUNX1’s interaction (off-target) with known FDA-approved drug molecules, including three currently used in treating AML. We identified several AML-associated mutations in RUNX1 that were calculated to enhance RUNX1’s interaction with specific drugs. Specifically, we retrieved data from the COSMIC database for cancer-associated mutations of RUNX1 by using R package “data.table” and “ggplot2” modules. In the presence of DNA and/or drug, we used docking scores and energetics of the complexes as tools to evaluate predicted interaction strengths with RUNX1. For example, we performed predictions of drug binding pockets involving Enasidenib, Giltertinib, and Midostaurin (AML associated), as well as ten different published cancer associated drug compounds. Docking of wild type RUNX1 with these 13 different cancer-associated drugs indicates that wild-type RUNX1 has a lower efficiency of binding while RUNX1 mutants R142K, D171N, R174Q, P176H, and R177Q suggested higher affinity of drug association. Literature evidence support our prediction and suggests the mutation R174Q affects RUNX1 DNA binding and could lead to compromised function. We conclude that specific RUNX1 mutations that lessen DNA binding facilitate the binding of a number of tested drug molecules. Further, we propose that molecular modeling and docking studies for RUNX1 in the presence of DNA and/or drugs enables evaluation of the potential impact of RUNX1 cancer associated mutations in AML.

Allosteric inhibition of LRRK2, where are we now

Parkinson's disease (PD) is the second most common neurodegenerative disease. In recent years, it has been shown that leucine-rich repeat kinase 2 (LRRK2) has a crucial function in both familial and sporadic forms of PD. LRRK2 pathogenic mutations are thought to result in an increase in LRRK2 kinase activity. Thus, inhibiting LRRK2 kinase activity has become a main therapeutic target. Many compounds capable of inhibiting LRRK2 kinase activity with high selectivity and brain availability have been described. However, the safety of long-term use of these ATP-competitive LRRK2 kinase inhibitors has been challenged by several studies. Therefore, alternative ways of targeting LRRK2 activity will have a great benefit. In this review, we discuss the recent progress in the development of allosteric inhibitors of LRRK2, mainly via interfering with GTPase activity, and propose potential new intra and interprotein interactions targets that can lead to open doors toward new therapeutics.

Utilizing structure based drug design and metabolic soft spot identification to optimize the in vitro potency and in vivo pharmacokinetic properties leading to the discovery of novel reversible Bruton's tyrosine kinase inhibitors

Bruton's tyrosine kinase (BTK) is an essential node on the BCR signaling in B cells, which are clinically validated to play a critical role in B-cell lymphomas and various auto-immune diseases such as Multiple Sclerosis (MS), Pemphigus, and rheumatoid arthritis (RA). Although non-selective irreversible BTK inhibitors have been approved for oncology, due to the emergence of drug resistance in B-cell lymphoma associated with covalent inhibitor, there an unmet medical need to identify reversible, selective, potent BTK inhibitor as viable therapeutics for patients. Herein, we describe the identification of Hits and subsequence optimization to improve the physicochemical properties, potency and kinome selectivity leading to the discovery of a novel class of BTK inhibitors. Utilizing Met ID and structure base design inhibitors were synthesized with increased in vivo metabolic stability and oral exposure in rodents suitable for advancing to lead optimization.

Emerging small-molecule inhibitors of the Bruton's tyrosine kinase (BTK): Current development

Therapy based on Bruton's tyrosine kinase (BTK) inhibitors one of the major treatment options currently recommended for lymphoma patients. The first generation of BTK inhibitor, Ibrutinib, achieved remarkable progress in the treatment of B-cell malignancies, but still has problems with drug-resistance or off-target induced serious side effects. Therefore, numerous new BTK inhibitors were developed to address this unmet medical need. In parallel, the effect of BTK inhibitors against immune-related diseases has been evaluated in clinical trials. This review summarizes recent progress in the research and development of BTK inhibitors, with a focus on structural characteristics and structure-activity relationships. The structure-refinement process of representative pharmacophores as well as their effects on binding affinity, biological activity and pharmacokinetics profiles were analyzed. The advantages and disadvantages of reversible/irreversible BTK inhibitors and their potential implications were discussed to provide a reference for the rational design and development of novel potent BTK inhibitors.

Fragment-to-Lead Medicinal Chemistry Publications in 2019

Fragment-based drug discovery (FBDD) has grown and matured to a point where it is valuable to keep track of its extent and details of application. This Perspective summarizes successful fragment-to-lead stories published in 2019. It is the fifth in a series that started with literature published in 2015. The analysis of screening methods, optimization strategies, and molecular properties of hits and leads are presented in the hope of informing best practices for FBDD. Moreover, FBDD is constantly evolving, and the latest technologies and emerging trends are summarized. These include covalent FBDD, FBDD for the stabilization of proteins or protein-protein interactions, FBDD for enzyme activators, new screening technologies, and advances in library design and chemical synthesis.

Discovery of BIIB068: A Selective, Potent, Reversible Bruton's Tyrosine Kinase Inhibitor as an Orally Efficacious Agent for Autoimmune Diseases

Autoreactive B cell-derived antibodies form immune complexes that likely play a pathogenic role in autoimmune diseases. In systemic lupus erythematosus (SLE), these antibodies bind Fc receptors on myeloid cells and induce proinflammatory cytokine production by monocytes and NETosis by neutrophils. Bruton's tyrosine kinase (BTK) is a non-receptor tyrosine kinase that signals downstream of Fc receptors and plays a transduction role in antibody expression following B cell activation. Given the roles of BTK in both the production and sensing of autoreactive antibodies, inhibitors of BTK kinase activity may provide therapeutic value to patients suffering from autoantibody-driven immune disorders. Starting from an in-house proprietary screening hit followed by structure-based rational design, we have identified a potent, reversible BTK inhibitor, BIIB068 (1), which demonstrated good kinome selectivity with good overall drug-like properties for oral dosing, was well tolerated across preclinical species at pharmacologically relevant doses with good ADME properties, and achieved >90% inhibition of BTK phosphorylation (pBTK) in humans.

Application of Fragment-Based Drug Discovery to Versatile Targets

Fragment-based drug discovery (FBDD) is a powerful method to develop potent small-molecule compounds starting from fragments binding weakly to targets. As FBDD exhibits several advantages over high-throughput screening campaigns, it becomes an attractive strategy in target-based drug discovery. Many potent compounds/inhibitors of diverse targets have been developed using this approach. Methods used in fragment screening and understanding fragment-binding modes are critical in FBDD. This review elucidates fragment libraries, methods utilized in fragment identification/confirmation, strategies applied in growing the identified fragments into drug-like lead compounds, and applications of FBDD to different targets. As FBDD can be readily carried out through different biophysical and computer-based methods, it will play more important roles in drug discovery.