Screening of phytochemicals as potential anti-breast cancer agents targeting HER2: an in-silico approach

Abstract

Breast cancer is the most common cancer among women around the world. Human Epidermal growth factor Receptor-2 (HER2) is a membrane tyrosine kinase overexpressed in 30% of human breast cancers; thus, it serves as an important drug target. Currently available HER2 inhibitor lapatinib targets the ATP binding site of the cytoplasmic kinase domain, blocking autophosphorylation and activation of HER-2. However, it causes side effects like diarrhea, nausea, rash and possible liver toxicity. As phytochemicals have fewer side effects and are relatively affordable, they offer an effective alternative. Hence, we aimed to identify potential phytochemicals that could act as HER2 inhibitors employing computational methods such as molecular docking, molecular dynamic simulation, and ADMET prediction. Out of 1500 phytochemicals docked to the ATP binding site of the HER2 kinase domain, luxenchalcone, rhinacanthin Q, subtrifloralacton D, and 7,7″-dimethyllanaraflavone exhibited higher binding affinity than the reference inhibitor and satisfied the Lipinski’s rule of five. Analysis of molecular dynamics simulation trajectory showed that Rhinacanthin Q, subtrifloralacton D, and 7,7″-dimethyllanaraflavone formed a stable and compact complex without vast conformational fluctuations. MM/PBSA binding free energy analysis revealed that Rhinacanthin Q, subtrifloralacton D, and 7,7″-dimethyllanaraflavone have high binding affinity to HER2. Therefore, Rhinacanthin Q, subtrifloralacton D, and 7,7″-dimethyllanaraflavone could be potential bioactive molecules to act as inhibitor of HER2 protein. Eventually, experimental studies are needed to evaluate the potentials of these phytochemicals further. The development of drug for HER2 positive breast cancer could be accelerated with the findings of our research.

Communicated by Ramaswamy H. Sarma

Keywords:

• Breast cancer
• HER2
• molecular docking
• phytochemicals
• molecular dynamics simulation

Acknowledgement

This research was supported in part through computational resources provided by the Supercomputer Center, Kathmandu University, which was established with equipment donated by CERN. We would also like to acknowledge the GPU Grant from NVIDIA in which all the MD simulations were performed. We are thankful to Sarun Jonchhe and Apurba Bhattarai for their guidance and support

Disclosure statement

No potential conflict of interest was reported by the authors.