Identifying CEACAM1-targeted Drug Candidates for Cancer Immunotherapy
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Guanrong Cheng
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Carcinoembryonic antigen-related cell adhesion molecule 1 (CEACAM1) is a key regulatory protein in immune modulation and tumor progression, making it a promising target for cancer immunotherapy. Immunotherapy is promising because it harnesses the body's own immune system to identify and eliminate cancer cells, often leading to more durable responses compared to traditional therapies like chemotherapy and radiation. By targeting immune checkpoints, such as those regulated by CEACAM1, immunotherapy can reinvigorate exhausted immune cells, enhancing their ability to fight tumors. CEACAM1 is particularly promising as a target because it plays a key role in immune checkpoint pathways that tumors exploit to evade immune detection. By interacting with immune cells, CEACAM1 can inhibit the immune response against tumors, allowing them to grow unchecked. This study uses computational docking methods to evaluate potential interactions between CEACAM1 and a variety of compounds from the ZINC database. The docking process involved multiple steps, including target and ligand selection, docking simulation, and binding affinity calculation. SwissADME and ProTox 3.0 tools were employed to assess the drug-like properties and toxicity profiles of the top candidates. From an initial pool of 20 compounds, five candidates had the most favorable binding energies (ΔG). Further analysis revealed that while ZINC71788521 and ZINC67902861 exhibited good target affinity, they violated Lipinski's rule of five. Conversely, ZINC08820313, ZINC38617077, and ZINC41591046 adhered to Lipinski's rule, demonstrating promising drug-like characteristics. In the end, ZINC08820313 was chosen as a potential drug candidate due to its high ΔG and low toxicity levels compared to the other compounds chosen. The study identifies potential CEACAM1 inhibitors with favorable energetic interactions and acceptable drug-like properties. Future work will involve in vitro and in vivo validation to substantiate these computational predictions.
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Authors
Guanrong Cheng
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References:
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