Globally, cancer is a major burden of disease threatening human health. In previous decades, the most common types of cancer treatments were surgery, radiotherapy, and chemotherapy. Recently, immune checkpoint inhibitors, which are monoclonal antibodies used as monotherapy or in combination, have revealed remarkable clinical success in a wide range of solid tumors and hematologic malignancies. Immune checkpoint inhibitors in current clinical settings target programmed death-1 (PD1), programmed death-ligand 1 (PD-L1), or cytotoxic T lymphocyte antigen 4 (CTLA-4) and are regarded as breakthroughs in cancer immunotherapy. Given the limitations of antibody therapies, orally bioavailable small-molecule inhibitors present a viable alternative.
In the Silico Computational study, 30 hit compounds were initially
retrieved by pharmacophore-based virtual screening. Thereafter, 5 compounds
with the lowest Gibb's free energy (ΔG) values, namely ZINC85867378,
ZINC16267039, ZINC64219346, ZINC68604154 and ZINC20576138, have been chosen for
further evaluation. This study establishes the workflow combining pharmacophore
virtual screening, molecular docking, and absorption, distribution, metabolism,
and excretion-toxicity (ADMET) prediction to identify possible small molecules
that can interact with PD-1. The identified compounds might serve as starting
points to design potential safe and efficacious molecules in cancer
Immunotherapy. Further evaluation is necessary to optimize drug properties.
The discovery of new therapeutic drugs is complex, costly, and
time-consuming. Leveraging a combination
of computational methods can significantly accelerate the drug discovery
process, enabling the identification of promising drug candidates from large
compound libraries.
Author
(s) Details
Hubert Chen
Vanderbilt University, United States.
Please see the book here:- https://doi.org/10.9734/bpi/dhrd/v6/4679
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