Pharmacophore feature-based virtual screening for finding potent GSK-3 inhibitors using molecular docking and dynamics simulations

Discovery of novel PDGFR inhibitors targeting non-small cell lung cancer using a multistep machine learning assisted hybrid virtual screening approach

Non-Small Cell Lung Cancer (NSCLC) is a formidable global health challenge, responsible for the majority of cancer-related deaths worldwide. The Platelet-Derived Growth Factor Receptor (PDGFR) has emerged as a promising therapeutic target in NSCLC, given its crucial involvement in cell growth, proliferation, angiogenesis, and tumor progression. Among PDGFR inhibitors, avapritinib has garnered attention due to its selective activity against mutant forms of PDGFR, particularly PDGFRA D842V and KIT exon 17 D816V, linked to resistance against conventional tyrosine kinase inhibitors. In recent years, Machine Learning has emerged as a powerful tool in pharmaceutical research, offering data-driven insights and accelerating lead identification for drug discovery. In this research article, we focus on the application of Machine Learning, alongside the RDKit toolkit, to identify potential anti-cancer drug candidates targeting PDGFR in NSCLC. Our study demonstrates how smart algorithms efficiently narrow down large screening collections to target-specific sets of just a few hundred small molecules, streamlining the hit discovery process. Employing a Machine Learning-assisted virtual screening strategy, we successfully preselected 220 compounds with potential PDGFRA inhibitory activity from a vast library of 1.048 million compounds, representing a mere 0.013% of the original library. To validate these candidates, we employed traditional genetic algorithm-based virtual screening and docking methods. Remarkably, we found that ZINC000002931631 exhibited comparable or even superior inhibitory potential against PDGFRA compared to Avapritinib, which highlights the value of our Machine Learning approach. Moreover, as part of our lead validation studies, we conducted molecular dynamic simulations, revealing critical molecular-level interactions responsible for the conformational changes in PDGFRA necessary for substrate binding. Our study exemplifies the potential of Machine Learning in the drug discovery process, providing a more efficient and cost-effective means of identifying promising drug candidates for NSCLC treatment. The success of this approach in preselecting compounds with potent PDGFRA inhibitory potential highlights its significance in advancing personalized and targeted therapies for cancer treatment.

GSK-3β Allosteric Inhibition: A Dead End or a New Pharmacological Frontier?

Most kinase inhibitors are designed to bind to highly homologous ATP-binding sites, which leads to promiscuity and possible off-target effects. Allostery is an alternative approach to pursuing selectivity. However, allostery is difficult to exploit due to the wide variety of underlying mechanisms and the potential involvement of long-range conformational effects that are difficult to pinpoint. GSK-3β is involved in several pathologies. This critical target has an ATP-binding site that is highly homologous with the orthosteric sites of other kinases. Unsurprisingly, there is also great similarity between the ATP-binding sites of GSK-3β and its isomer, which is not redundant and thus would benefit from selective inhibition. Allostery would also allow for a moderate and tunable inhibition, which is ideal for GSK-3β, because this target is involved in multiple pathways, some of which must be preserved. However, despite considerable research efforts, only one allosteric GSK-3β inhibitor has reached the clinic. Moreover, unlike other kinases, there are no X-ray structures of GSK-3β in complex with allosteric inhibitors in the PDB data bank. This review aims to summarize the state of the art in allosteric GSK-3β inhibitor investigations, highlighting the aspects that make this target challenging for an allosteric approach.

Pathobiology and Therapeutic Relevance of GSK-3 in Chronic Hematological Malignancies

Glycogen synthase kinase-3 (GSK-3) is an evolutionarily conserved, ubiquitously expressed, multifunctional serine/threonine protein kinase involved in the regulation of a variety of physiological processes. GSK-3 comprises two isoforms (α and β) which were originally discovered in 1980 as enzymes involved in glucose metabolism via inhibitory phosphorylation of glycogen synthase. Differently from other proteins kinases, GSK-3 isoforms are constitutively active in resting cells, and their modulation mainly involves inhibition through upstream regulatory networks. In the early 1990s, GSK-3 isoforms were implicated as key players in cancer cell pathobiology. Active GSK-3 facilitates the destruction of multiple oncogenic proteins which include β-catenin and Master regulator of cell cycle entry and proliferative metabolism (c-Myc). Therefore, GSK-3 was initially considered to be a tumor suppressor. Consistently, GSK-3 is often inactivated in cancer cells through dysregulated upstream signaling pathways. However, over the past 10–15 years, a growing number of studies highlighted that in some cancer settings GSK-3 isoforms inhibit tumor suppressing pathways and therefore act as tumor promoters. In this article, we will discuss the multiple and often enigmatic roles played by GSK-3 isoforms in some chronic hematological malignancies (chronic myelogenous leukemia, chronic lymphocytic leukemia, multiple myeloma, and B-cell non-Hodgkin’s lymphomas) which are among the most common blood cancer cell types. We will also summarize possible novel strategies targeting GSK-3 for innovative therapies of these disorders.