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ORIGINAL ARTICLE
Year : 2021  |  Volume : 13  |  Issue : 4  |  Page : 387-393

Development of Hif1α pharmacogenomic mutation models to study individual variations in drug action for tumor hypoxia: An in silico approach


Computational Chemistry Group (CCG), AMMAS Research Lab, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, Tamil Nadu, India

Correspondence Address:
Dr. P K Krishnan Namboori
AMMAS Research Lab, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Ettimadai, Coimbatore - 641 112, Tamil Nadu
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jpbs.jpbs_766_21

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Objective: Tumor hypoxia, a predominant feature of solid tumor produces drug resistance that significantly impacts a patient's clinical outcomes. Hypoxia-inducible factor 1-alpha (HIF1α) is the major mutation involved in establishing the microenvironment. As a consequence of its involvement in pathways that enable rapid tumor growth, it creates resistance to chemotherapeutic treatments. The propensity of medications to demonstrate drug action often diverges according to the genetic composition. The aim of this study is therefore to examine the effect of population-dependent drug response variations using mutation models. Methods: Genetic variations distinctive to major super-populations were identified, and the mutated gene was acquired as a result of incorporating the variants. The mutated gene sequence was transcribed and translated to obtain the target amino acid sequence. To investigate the effects of mutations, protein models were developed using homology modeling. The target templates for the backbone structure were identified by characterization of primary and secondary protein structures. The modeled proteins were then validated for structural confirmation and flexibility. Potential models were used for interaction studies with hypoxia-specific molecules (tirapazamine, apaziquone, and ENMD) using docking analysis. To verify their stability under pre-defined dynamic conditions, the complexes were subjected to molecular dynamics simulation. Results: The current research models demonstrate with the pharmacogenomic-based mutation of HIF1α the impact of individual variants in altering the person-specific drug response under tumor hypoxic conditions. It also elucidates that the therapeutic effect is altered concerning population-dependent genetic changes in the individual. Conclusion: The study, therefore, asserts the need to set up a personalized drug design approach to enhance tumor hypoxia treatment efficacy.


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