Unveiling Novel ERCC1-XPF Complex Inhibitors: Bridging the Gap from In Silico Exploration to Experimental Design.

Modifications in DNA repair pathways are recognized as prognostic markers and potential therapeutic targets in various cancers, including non-small cell lung cancer (NSCLC). Overexpression of ERCC1 correlates with poorer prognosis and response to platinum-based chemotherapy. As a result, there is a pressing need to discover new inhibitors of the ERCC1-XPF complex that can potentiate the efficacy of cisplatin in NSCLC. In this study, we developed a structure-based virtual screening strategy targeting the inhibition of ERCC1 and XPF interaction. Analysis of crystal structures and a library of small molecules known to act against the complex highlighted the pivotal role of Phe293 (ERCC1) in maintaining complex stability. This residue was chosen as the primary binding site for virtual screening. Using an optimized docking protocol, we screened compounds from various databases, ultimately identifying more than one hundred potential inhibitors. Their capability to amplify cisplatin-induced cytotoxicity was assessed in NSCLC H1299 cells, which exhibited the highest ERCC1 expression of all the cell lines tested. Of these, 22 compounds emerged as promising enhancers of cisplatin efficacy. Our results underscore the value of pinpointing crucial molecular characteristics in the pursuit of novel modulators of the ERCC1-XPF interaction, which could be combined with cisplatin to treat NSCLC more effectively.

Harnessing Protein-Ligand Interaction Fingerprints to Predict New Scaffolds of RIPK1 Inhibitors.

Necroptosis has emerged as an exciting target in oncological, inflammatory, neurodegenerative, and autoimmune diseases, in addition to acute ischemic injuries. It is known to play a role in innate immune response, as well as in antiviral cellular response. Here we devised a concerted in silico and experimental framework to identify novel RIPK1 inhibitors, a key necroptosis factor. We propose the first in silico model for the prediction of new RIPK1 inhibitor scaffolds by combining docking and machine learning methodologies. Through the data analysis of patterns in docking results, we derived two rules, where rule #1 consisted of a four-residue signature filter, and rule #2 consisted of a six-residue similarity filter based on docking calculations. These were used in consensus with a machine learning QSAR model from data collated from ChEMBL, the literature, in patents, and from PubChem data. The models allowed for good prediction of actives of >90, 92, and 96.4% precision, respectively. As a proof-of-concept, we selected 50 compounds from the ChemBridge database, using a consensus of both molecular docking and machine learning methods, and tested them in a phenotypic necroptosis assay and a biochemical RIPK1 inhibition assay. A total of 7 of the 47 tested compounds demonstrated around 20−25% inhibition of RIPK1's kinase activity but, more importantly, these compounds were discovered to occupy new areas of chemical space. Although no strong actives were found, they could be candidates for further optimization, particularly because they have new scaffolds. In conclusion, this screening method may prove valuable for future screening efforts as it allows for the exploration of new areas of the chemical space in a very fast and inexpensive manner, therefore providing efficient starting points amenable to further hit-optimization campaigns.

Computational Modulation of the V3 Region of Glycoprotein gp125 of HIV-2.

HIV-2 infection is frequently neglected in HIV/AIDS campaigns. However, a special emphasis must be given to HIV-2 as an untreated infection that also leads to AIDS and death, and for which the efficacy of most available drugs is limited against HIV-2. HIV envelope glycoproteins mediate binding to the receptor CD4 and co-receptors at the surface of the target cell, enabling fusion with the cell membrane and viral entry. Here, we developed and optimized a computer-assisted drug design approach of an important HIV-2 glycoprotein that allows us to explore and gain further insights at the molecular level into protein structures and interactions crucial for the inhibition of HIV-2 cell entry. The 3D structure of a key HIV-2ROD gp125 region was generated by a homology modeling campaign. To disclose the importance of the main structural features and compare them with experimental results, 3D-models of six mutants were also generated. These mutations revealed the selective impact on the behavior of the protein. Furthermore, molecular dynamics simulations were performed to optimize the models, and the dynamic behavior was tackled to account for structure flexibility and interactions network formation. Structurally, the mutations studied lead to a loss of aromatic features, which is very important for the establishment of π-π interactions and could induce a structural preference by a specific coreceptor. These new insights into the structure-function relationship of HIV-2 gp125 V3 and surrounding regions will help in the design of better models and the design of new small molecules capable to inhibit the attachment and binding of HIV with host cells.

Cyclodextrin solubilization and complexation of antiretroviral drug lopinavir: In silico prediction; Effects of derivatization, molar ratio and preparation method.

Lopinavir (LPV) is currently used in combination with ritonavir for the clinical management of HIV infections due to its limited oral bioavailability. Herein, we report the application of an in silico method to study cyclodextrin (CyD) host-guest molecular interaction with LPV for the rational selection of the best CyD for developing a CyD based LPV delivery system. The predicted CyD, a (2-hydroxy)propyl-gamma derivative with high degree of substitution (HP17-γ-CyD) was synthesized and comparatively evaluated with γ-CyD and the commercially available HP-γ-CyD. All complexes were prepared by supercritical assisted spray drying (SASD) and co-evaporation (CoEva) at molar ratios (1:1 and 1:2); and afterwards fully characterized. Results indicate a higher LPV amorphization and solubilization ability of HP17-γ-CyD. The SASD processing technology also enhanced LPV solubilization and release from complexes. The application of in silico methodologies is a feasible approach for the rational and/or deductive development of CyD drug delivery systems.