Discovery and characterization of novel FGFR1 inhibitors in triple-negative breast cancer via hybrid virtual screening and molecular dynamics simulations.

The overexpression of FGFR1 is thought to significantly contribute to the progression of triple-negative breast cancer (TNBC), impacting aspects such as tumorigenesis, growth, metastasis, and drug resistance. Consequently, the pursuit of effective inhibitors for FGFR1 is a key area of research interest. In response to this need, our study developed a hybrid virtual screening method. Utilizing KarmaDock, an innovative algorithm that blends deep learning with molecular docking, alongside Schrödinger's Residue Scanning. This strategy led us to identify compound 6, which demonstrated promising FGFR1 inhibitory activity, evidenced by an IC50 value of approximately 0.24 nM in the HTRF bioassay. Further evaluation revealed that this compound also inhibits the FGFR1 V561M variant with an IC50 value around 1.24 nM. Our subsequent investigations demonstrate that Compound 6 robustly suppresses the migration and invasion capacities of TNBC cell lines, through the downregulation of p-FGFR1 and modulation of EMT markers, highlighting its promise as a potent anti-metastatic therapeutic agent. Additionally, our use of molecular dynamics simulations provided a deeper understanding of the compound's specific binding interactions with FGFR1.

Identification and assessment of new PIM2 inhibitors for treating hematologic cancers: A combined approach of energy-based virtual screening and machine learning evaluation.

PIM2, part of the PIM kinase family along with PIM1 and PIM3, is often overexpressed in hematologic cancers, fueling tumor growth. Despite its significance, there are no approved drugs targeting it. In response to this challenge, we devised a thorough virtual screening workflow for discovering novel PIM2 inhibitors. Our process includes molecular docking and diverse scoring methods like molecular mechanics generalized born surface area, XGBOOST, and DeepDock to rank potential inhibitors by binding affinities and interaction potential. Ten compounds were selected and subjected to an adequate evaluation of their biological activity. Compound 2 emerged as the most potent inhibitor with an IC50 of approximately 135.7 nM. It also displayed significant activity against various hematological cancers, including acute myeloid leukemia, mantle cell lymphoma, and anaplastic large cell lymphoma (ALCL). Molecular dynamics simulations elucidated the binding mode of compound 2 with PIM2, offering insights for drug development. These results highlight the reliability and efficacy of our virtual screening workflow, promising new drugs for hematologic cancers, notably ALCL.

Discovery of potential WEE1 inhibitors via hybrid virtual screening.

G2/M cell cycle checkpoint protein WEE1 kinase is a promising target for inhibiting tumor growth. Although various WEE1 inhibitors have entered clinical investigations, their therapeutic efficacy and safety profile remain unsatisfactory. In this study, we employed a comprehensive virtual screening workflow, which included Schrödinger-Glide molecular docking at different precision levels, as well as the utilization of tools such as MM/GBSA and Deepdock to predict the binding affinity between targets and ligands, in order to identify potential WEE1 inhibitors. Out of ten molecules screened, 50% of these molecules exhibited strong inhibitory activity against WEE1. Among them, compounds 4 and 5 showed excellent inhibitory activity with IC50 values of 1.069 and 3.77 nM respectively, which was comparable to AZD1775. Further investigations revealed that compound 4 displayed significant anti-proliferative effects in A549, PC9, and HuH-7 cells and could also induce apoptosis and G1 phase arrest in PC9 cells. Additionally, molecular dynamics simulations unveiled the binding details of compound 4 with WEE1, notably the crucial hydrogen bond interactions formed with Cys379. In summary, this comprehensive virtual screening workflow, combined with in vitro testing and computational modeling, holds significant importance in the development of promising WEE1 inhibitors.

Discovery of novel 2H-chromene-3-carbonyl derivatives as selective estrogen receptor degraders (SERDs): Design, synthesis and biological evaluation.

Selective estrogen receptor degraders (SERDs) not only block ERα activity but degrade this receptor at the same time and are effective in relapsed ERα positive breast cancer patients who have accepted other endocrine therapies. Herein, through scaffold hopping of coumarin skeleton, a series of 2H-chromene-3-carbonyl-based SERDs with phenyl acrylic acid group as the side chain were designed and synthesized. Compound XH04 containing 7-hydroxy-2H-chromene-3-carbonyl skeleton exhibited the most potent activities in 2D (IC50 = 0.8 µM) and 3D cells culture models (MCF-7) and had the best ERα binding affinity as well. Furthermore, the significant antiestrogen property of compound XH04 was confirmed by inhibiting the expression of progesterone receptor (PgR) mRNA in MCF-7 cells. On the other hand, the outgoing ERα degradation property of compound XH04 was qualitatively and quantificationally verified by immunofluorescence analysis and Western blot assay in MCF-7 cells. Besides, compound XH04 repressed the expression level of Ki67 in MCF-7 cells and induced the apoptosis increase of this tumor cells in a dose-dependent manner like approved-SERD fulvestrant (2), while compound XH04 exhibited better preliminary pharmacokinetics in human and rat liver microsomes in vitro and a lower LogD7.4 value than fulvestrant. And further molecular docking study revealed that compound XH04 possessed a proverbial and typical binding model with ERα like other reported SERD. All these results confirmed that 7-hydroxy-2H-chromene-3-carbonyl structure could be a feasible skeleton for design of ERα antagonists including SERDs and compound XH04 is a promising candidate for further development of ERα + breast cancer therapy agents.