Homology modelling and molecular docking of MDR1 with chemotherapeutic agents in non-small cell lung cancer

• Human MDR1 model was generated based on Mouse MDR1 template.
• Paclitaxel was found to have best binding affinity among the docked drugs.
• Cisplatin was found to have least affinity among the 5 docked drugs.
• Gln 195 and Gln 946 residues were found to be involved the most in drug binding to MDR1.
• Paclitaxel + Gemcitabine showed best combination drug binding affinity.

MDR1, a protein commonly involved in drug transport, has been linked to multi drug resistance and disease progression in cancers such as non-small cell lung cancer. Hence, targeting this protein is essential for improving drug design and preventing adverse drug-drug interactions. The aim of the study was to examine chemotherapeutic drug binding to MDR1 and the interactions therein. We have used Schrödinger suite 2014, to perform homology modelling of human MDR1 based on Mouse MDR1, followed by Induced Fit Docking with Paclitaxel, Docetaxel, Gemcitabine, Carboplatin and Cisplatin drugs. Finally, we evaluated drug binding affinities using Prime/MMGBSA and using these scores we compared the affinities of combination therapies against MDR1. Analysis of the docking results showed Paclitaxel > Docetaxel > Gemcitabine > Carboplatin > Cisplatin as the order of binding affinities, with Paclitaxel having the best docking score. The combination drug binding affinity analysis showed Paclitaxel + Gemcitabine to have the best docking score and hence, efficacy. Through our investigation we have identified the residues Gln 195 and Gln 946 to be more frequently involved in drug binding interactions with MDR1. Our results suggest that, Paclitaxel or combination of Paclitaxel + Gemcitabine could serve as a suitable therapy against MDR1 in NSCLC patients. Thus, our study provides new insight into the possible repurposing of chemotherapeutic drugs in targeting elevated MDR1 levels in NSCLC patients, thereby ensuring better overall outcome. Further our study highlights the use of in silico methodologies in understanding drug binding to protein targets and its relevance to advancing lung cancer therapy.

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