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Immunotherapy, particularly targeting the PD-1/PD-L1 pathway, holds promise in cancer treatment by regulating the immune response and preventing cancer cells from evading immune destruction. Nonetheless, this approach poses a risk of unwanted immune system activation against healthy cells. To minimize this risk, our study proposes a strategy based on selective targeting of the PD-L1 pathway within the acidic microenvironment of tumors. We employed in silico methods, such as virtual screening, molecular mechanics, and molecular dynamics simulations, analyzing approximately 10,000 natural compounds from the MolPort database to find potential hits with the desired properties. The simulations were conducted under two pH conditions (pH = 7.4 and 5.5) to mimic the environments of healthy and cancerous cells. The compound MolPort-001-742-690 emerged as a promising pH-selective inhibitor, showing a significant affinity for PD-L1 in acidic conditions and lower toxicity compared to known inhibitors like BMS-202 and LP23. A detailed 1000 ns molecular dynamics simulation confirmed the stability of the inhibitor-PD-L1 complex under acidic conditions. This research highlights the potential of using in silico techniques to discover novel pH-selective inhibitors, which, after experimental validation, may enhance the precision and reduce the toxicity of immunotherapies, offering a transformative approach to cancer treatment. 

Background:SARS-CoV-2's remarkable capacity for genetic mutation enables it toswiftly adapt to environmental changes, influencing critical attributes, such as antigenicity andtransmissibility. Thus, multi-target inhibitors capable of effectively combating various viral mutants concurrently are of great interest. Objective:This study aimed to investigate natural compounds that could unitedly inhibit spikeglycoproteins of various Omicron mutants. Implementation of various in silico approaches allows us to scan a library of compounds against a variety of mutants in order to find the ones thatwould inhibit the viral entry disregard of occurred mutations. Methods:An extensive analysis of relevant literature was conducted to compile a libraryof chemical compounds sourced from citrus essential oils. Ten homology models representingmutants of the Omicron variant were generated, including the latest 23F clade (EG.5.1),and the compound library was screened against them. Subsequently, employing comprehensivemolecular docking and molecular dynamics simulations, we successfully identifiedpromising compounds that exhibited sufficient binding efficacy towards the receptorbinding domains (RBDs) of the mutant viral strains. The scoring of ligands was based ontheir average potency against all models generated herein, in addition to a reference OmicronRBD structure. Furthermore, the toxicity profile of the highest-scoring compounds waspredicted. Results:Out of ten built homology models, seven were successfully validated and showed to bereliable for In Silico studies. Three models of clades 22C, 22D, and 22E had major deviations intheir secondary structure and needed further refinement. Notably, through a 100 nanosecondmolecular dynamics simulation, terpinen-4-ol emerged as a potent inhibitor of the OmicronSARS-CoV-2 RBD from the 21K clade (BA.1); however, it did not show high stability in complexes with other mutants. This suggests the need for the utilization of a larger library of chemical compounds as potential inhibitors. Conclusion:The outcomes of this investigation hold significant potential for the utilization of ahomology modeling approach for the prediction of RBD’s secondary structure based on its sequence when the 3D structure of a mutated protein is not available. This opens the opportunitiesfor further advancing the drug discovery process, offering novel avenues for the development ofmultifunctional, non-toxic natural medications.