Discovering Dually Active Anti-cancer Compounds with a Hybrid AI-structure-based Approach.

Cancer's persistent growth often relies on its ability to maintain telomere length and tolerate the accumulation of DNA damage. This study explores a computational approach to identify compounds that can simultaneously target both G-quadruplex (G4) structures and poly(ADP-ribose) polymerase (PARP)1 enzyme, offering a potential multipronged attack on cancer cells. We employed a hybrid virtual screening (VS) protocol, combining the power of machine learning with traditional structure-based methods. PyRMD, our AI-powered tool, was first used to analyze vast chemical libraries and to identify potential PARP1 inhibitors based on known bioactivity data. Subsequently, a structure-based VS approach selected compounds from these identified inhibitors for their G4 stabilization potential. This two-step process yielded 50 promising candidates, which were then experimentally validated for their ability to inhibit PARP1 and stabilize G4 structures. Ultimately, four lead compounds emerged as promising candidates with the desired dual activity and demonstrated antiproliferative effects against specific cancer cell lines. This study highlights the potential of combining Artificial Intelligence and structure-based methods for the discovery of multitarget anticancer compounds, offering a valuable approach for future drug development efforts.

Disulfide bond replacement with non-reducible side chain to tail macrolactamization for the development of potent and selective CXCR4 peptide antagonists endowed with flanking binding sites.

The present study describes a small library of peptides derived from a potent and selective CXCR4 antagonist (3), wherein the native disulfide bond is replaced using a side-chain to tail macrolactamization technique to vary ring size and amino acid composition. The peptides were preliminary assessed for their ability to interfere with the interaction between the receptor and anti-CXCR4 PE-conjugated antibody clone 12G5. Two promising candidates (13 and 17) were identified and further evaluated in a125I-CXCL12 competition binding assay, exhibiting IC50 in the low-nanomolar range. Furthermore, both candidates displayed high selectivity towards CXCR4 with respect to the cognate receptor CXCR7, ability to block CXCL12-dependent cancer cell migration, and receptor internalization, albeit at a higher concentration compared to 3. Molecular modeling studies on 13 and 17 produced a theoretical model that may serve as a guide for future modifications, aiding in the development of analogs with improved affinity. Finally, the study provides valuable insights into developing therapeutic agents targeting CXCR4-mediated processes, demonstrating the adaptability of our lead peptide 3 to alternative cyclization approaches and offering prospects for comprehensive investigations into the receptor region's interaction with its C-terminal region.

Identification of Dual Inhibitors Targeting Main Protease (Mpro) and Cathepsin L as Potential Anti-SARS-CoV-2 Agents.

In this structure-activity relationship (SAR) study, we report the development of dual inhibitors with antiviral properties targeting the SARS-CoV-2 main protease (Mpro) and human cathepsin L (hCatL). The novel molecules differ in the aliphatic amino acids at the P2 site and the fluorine position on the phenyl ring at the P3 site. The identified dual inhibitors showed Ki values within 1.61 and 10.72 µM against SARS-CoV-2 Mpro; meanwhile, Ki values ranging from 0.004 to 0.701 µM toward hCatL were observed. A great interdependency between the nature of the side chain at the P2 site and the position of the fluorine atom was found. Three dual-targeting inhibitors exhibited antiviral activity in the low micromolar range with CC50 values >100 µM. Docking simulations were executed to gain a deeper understanding of the SAR profile. The findings herein collected should be taken into consideration for the future development of dual SARS-CoV-2 Mpro/hCatL inhibitors.

Streamlining Large Chemical Library Docking with Artificial Intelligence: the PyRMD2Dock Approach.

The present contribution introduces a novel computational protocol called PyRMD2Dock, which combines the Ligand-Based Virtual Screening (LBVS) tool PyRMD with the popular docking software AutoDock-GPU (AD4-GPU) to enhance the throughput of virtual screening campaigns for drug discovery. By implementing PyRMD2Dock, we demonstrate that it is possible to rapidly screen massive chemical databases and identify those with the highest predicted binding affinity to a target protein. Our benchmarking and screening experiments illustrate the predictive power and speed of PyRMD2Dock and highlight its potential to accelerate the discovery of novel drug candidates. Overall, this study showcases the value of combining AI-powered LBVS tools with docking software to enable effective and high-throughput virtual screening of ultralarge molecular databases in drug discovery. PyRMD and the PyRMD2Dock protocol are freely available on GitHub (https://github.com/cosconatilab/PyRMD) as an open-source tool.

A cyanine-based NIR fluorescent Vemurafenib analog to probe BRAFV600E in cancer cells.

BRAF represents one of the most frequently mutated protein kinase genes and BRAFV600E mutation may be found in many types of cancer, including hairy cell leukemia (HCL), anaplastic thyroid cancer (ATC), colorectal cancer and melanoma. Herein, a fluorescent probe, based on the structure of the highly specific BRAFV600E inhibitor Vemurafenib (Vem, 1) and featuring the NIR fluorophore cyanine-5 (Cy5), was straightforwardly synthesized and characterized (Vem-L-Cy5, 3), showing promising spectroscopic properties. Biological validation in BRAFV600E-mutated cancer cells evidenced the ability of 3 to penetrate inside the cells, specifically binding to its elective target BRAFV600E with high affinity, and inhibiting MEK phosphorylation and cell growth with a potency comparable to that of native Vem 1. Taken together, these data highlight Vem-L-Cy5 3 as a useful tool to probe BRAFV600E mutation in cancer cells, and suitable to acquire precious insights for future developments of more informed BRAF inhibitors-centered therapeutic strategies.