Discovery of new Cdc2-like kinase 4 (CLK4) inhibitors via pharmacophore exploration combined with flexible docking-based ligand/receptor contact fingerprints and machine learning

N1-Benzoylated 5-(4-pyridinyl)indazole-based kinase inhibitors: Attaining haspin and Clk4 selectivity via modulation of the benzoyl substituents

Haspin and Clk4 are both understudied protein kinases (PKs), offering potential targets for the development of new anticancer agents. Thus, the identification of new inhibitors targeting these PKs is of high interest. However, the inhibitors targeting haspin or Clk4 developed to date show a poor selectivity profile over other closely related PKs, increasing the risk of side effects. Herein, we present two newly developed N1-benzyolated 5-(4-pyridinyl)indazole-based inhibitors (18 and 19), derived from a newly identified indazole hit. These inhibitors exhibit an exceptional inhibitory profile toward haspin and/or Clk4. Compound 18 (2-acetyl benzoyl) showed a preference to inhibit Clk4 and haspin over a panel of closely related kinases, with sixfold selectivity for Clk4 (IC50 = 0.088 and 0.542 μM, respectively). Compound 19 (4-acetyl benzoyl) showed high selectivity against haspin over the common off-target kinases (Dyrks and Clks) with an IC50 of 0.155 μM for haspin. Molecular docking studies explained the remarkable selectivity of 18 and 19, elucidating how the new scaffold can be modified to toggle between inhibition of haspin or Clk4, despite the high homology of the ATP-binding sites. Their distinguished profile allows these compounds to be marked as interesting chemical probes to assess the selective inhibition of haspin and/or Clk4.

Synthesis and evaluation of novel N1-acylated 5-(4-pyridinyl)indazole derivatives as potent and selective haspin inhibitors

Protein kinase dysregulation was strongly linked to cancer pathogenesis. Moreover, histone alterations were found to be among the most important post-translational modifications that could contribute to cancer growth and development. In this context, haspin, an atypical serine/threonine kinase, phosphorylates histone H3 at threonine-3 and is notably overexpressed in various common cancer types. Herein, we report novel 5-(4-pyridinyl)indazole derivatives as potent and selective haspin inhibitors. Amide coupling at N1 of the indazole ring with m-hydroxyphenyl acetic acid yielded compound 21 with an IC50 value of 78 nM against haspin. This compound showed a meaningful selectivity over 15 of the most common off-targets, including Clk 1-3 and Dyrk1A, 1B, and 2. The most potent haspin inhibitors 5 and 21 effectively inhibited the growth of the NCI-60 cancer cell lines, further emphasizing the success of our scaffold as a new selective lead for the development of anti-cancer therapeutic agents.

Ligand-based pharmacophore modeling and machine learning for the discovery of potent aurora A kinase inhibitory leads of novel chemotypes

Aurora-A (AURKA) is serine/threonine protein kinase involved in the regulation of numerous processes of cell division. Numerous studies have demonstrated strong association between AURKA and cancer. AURKA is overexpressed in many cancers, such as colon, breast and prostate cancers. Consequently, AURKA has emerged as promising target for therapeutic intervention in cancer management. Herein, we describe a computational workflow for the discovery of novel anti-AURKA inhibitory leads starting with ligand-based assessment of the pharmacophoric space of six diverse sets of inhibitors. Subsequently, machine learning/QSAR modeling was coupled with genetic function algorithm to search for the best possible combination of machine learner, ligand-based pharmacophore(s) and molecular descriptors capable of explaining variation in anti-AURKA bioactivities within a collected list of inhibitors. Two learners succeeded in achieving acceptable structure/activity correlations, namely, random forests and extreme gradient boosting (XGBoost). Three pharmacophores emerged in the successful ML models. These were then used as 3D search queries to mine the National Cancer Institute database for novel anti-AURKA leads. Top-ranking 38 hits were assessed in vitro for their anti-AURKA bioactivities. Among them, three compounds exhibited promising dose-response curves, demonstrating experimental IC50 values ranging from sub-micromolar to low micromolar values. Remarkably, two of these compounds are of novel chemotypes.

Machine Learning-Guided Discovery of AcrB and MexB Efflux Pump Inhibitors

Multidrug efflux pump is one of the reasons behind the antimicrobial inactivity related to infection caused by Gram-negative pathogens. The inner membrane resistance-nodulation-cell division transporter proteins, AcrB and MexB, in association with outer membrane proteins, TolC and OprM, are responsible for the extrusion of a broad range of substrates, followed by recognizing them. Although various inhibitors were proposed to stop the efflux activity of the transporter protein, none of them had been approved clinically. Our study aims to identify potent inhibitor-like molecules employing supervised classification models trained upon the molecular descriptors of previously known inhibitors. Based on the intrinsic minimum inhibitory concentration (MIC) values of the reported inhibitors, they were classified into highly potent and less potent categories. A total of 10 different classification models were built using various molecular descriptors; among them, support vector machine, Random Forest, AdaBoost, and LightGBM models appeared to deliver promising results with >80% accuracy. These top four models were implemented on a library of 5043 to obtain 8 hit molecules after the multistep filtering process. To assess their activity toward AcrB and MexB, several molecular dynamics simulations of their ligand-bound structures were performed. We also calculated the binding free-energy values and analyzed other structural properties. Mol.3488 of the unknown molecules showed higher binding affinities for both AcrB and MexB. Also, the presence of "pyridopyrimidone" and "benzothiazole" moieties in the molecules and "V"-shaped orientation of ligands inside the deep binding pocket increase the binding affinity, thereby higher inhibitory properties.

Computational workflow for discovering small molecular binders for shallow binding sites by integrating molecular dynamics simulation, pharmacophore modeling, and machine learning: STAT3 as case study

STAT3 belongs to a family of seven transcription factors. It plays an important role in activating the transcription of various genes involved in a variety of cellular processes. High levels of STAT3 are detected in several types of cancer. Hence, STAT3 inhibition is considered a promising therapeutic anti-cancer strategy. However, since STAT3 inhibitors bind to the shallow SH2 domain of the protein, it is expected that hydration water molecules play significant role in ligand-binding complicating the discovery of potent binders. To remedy this issue, we herein propose to extract pharmacophores from molecular dynamics (MD) frames of a potent co-crystallized ligand complexed within STAT3 SH2 domain. Subsequently, we employ genetic function algorithm coupled with machine learning (GFA-ML) to explore the optimal combination of MD-derived pharmacophores that can account for the variations in bioactivity among a list of inhibitors. To enhance the dataset, the training and testing lists were augmented nearly a 100-fold by considering multiple conformers of the ligands. A single significant pharmacophore emerged after 188 ns of MD simulation to represent STAT3-ligand binding. Screening the National Cancer Institute (NCI) database with this model identified one low micromolar inhibitor most likely binds to the SH2 domain of STAT3 and inhibits this pathway.

Cdc2-like kinases: structure, biological function, and therapeutic targets for diseases

The CLKs (Cdc2-like kinases) belong to the dual-specificity protein kinase family and play crucial roles in regulating transcript splicing via the phosphorylation of SR proteins (SRSF1-12), catalyzing spliceosome molecular machinery, and modulating the activities or expression of non-splicing proteins. The dysregulation of these processes is linked with various diseases, including neurodegenerative diseases, Duchenne muscular dystrophy, inflammatory diseases, viral replication, and cancer. Thus, CLKs have been considered as potential therapeutic targets, and significant efforts have been exerted to discover potent CLKs inhibitors. In particular, clinical trials aiming to assess the activities of the small molecules Lorecivivint on knee Osteoarthritis patients, and Cirtuvivint and Silmitasertib in different advanced tumors have been investigated for therapeutic usage. In this review, we comprehensively documented the structure and biological functions of CLKs in various human diseases and summarized the significance of related inhibitors in therapeutics. Our discussion highlights the most recent CLKs research, paving the way for the clinical treatment of various human diseases.