Development of chemokine CXCL12-dependent immunotoxin against small cell lung cancer using in silico approaches
Small cell lung cancer (SCLC) is characterized by accelerated growth and early metastasis, making it a vigorous and deadly cancer. The majority of the patients relapse quickly and show a bad prognosis even after responding to earlier chemotherapy. Because of their high selectivity and effectiveness,...
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Published in: | Informatics in medicine unlocked Vol. 25; p. 100676 |
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Main Author: | |
Format: | Journal Article |
Language: | English |
Published: |
Elsevier Ltd
2021
Elsevier |
Subjects: | |
Online Access: | Get full text |
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Summary: | Small cell lung cancer (SCLC) is characterized by accelerated growth and early metastasis, making it a vigorous and deadly cancer. The majority of the patients relapse quickly and show a bad prognosis even after responding to earlier chemotherapy. Because of their high selectivity and effectiveness, immunotoxin is a promising anticancer therapy. In recent years, the progressions in immunology based knowledge, data and techniques have made it possible to construct immunotoxins in silico with favorable efficiency. The CXCR4 receptor is utilized as the target for SCLC. CXCL12 chemokine is coupled with Pseudomonas aeruginosa exotoxin A (PE) which is expected to show cytotoxicity at the target SCLC cells. The physiochemical properties of the developed IT showed promising results. The predicted 3D structure of the immunotoxin was evaluated with Ramachandran plot analysis and Z-score. A total of 99.6% amino acid residues of IT belonged to the favored region. Additionally, Molecular docking between IT and CXCR4 was estimated to bind with a higher affinity and a lower binding energy of −15.21 kcal/mol. Furthermore, a molecular dynamic simulation was conducted to determine RMSD, RMSF, Radius of Gyration (Rg), B-factor for evaluating the stability of IT-CXCR4 complex. Finally, further assessments through in vitro and in vivo methods should be performed to validate the IT construct.
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ISSN: | 2352-9148 2352-9148 |
DOI: | 10.1016/j.imu.2021.100676 |