Molecular dynamics simulation-guided drug sensitivity prediction for lung cancer with rare EGFR mutations
Abstract
A variety of rare mutations account for 10–20% of EGFR mutations in nonsmall cell lung cancer. However, due to high diversity, proper medication for patients with such mutations is impossible in daily clinic. To appropriately treat lung cancer patients harboring such rare EGFR mutations, a robust prediction model to predict sensitivities of rare EGFR mutants to existing drugs is strongly needed. Using molecular dynamics simulation-based model, we successfully predicted diverse sensitivities of EGFR exon 20 insertion mutants to existing inhibitors. The findings suggest the usefulness of in silico simulation to overcome mutation diversity at a clinically relevant level. The present in silico model will help in selecting effective drugs for these patients.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- May 01, 2019
- Source ID
- 10.1073/pnas.1819430116
Entities
People
- Biao Ma
- Daisuke Arai
- Fumie Ono
- Hiroyuki Yasuda
- Ho Namkoong
- Ichiro Kawada
- Ichiro Nakachi
- Junko Hamamoto
- Katsuhiko Naoki
- Katsuya Tsuchihara
- Keigo Kobayashi
- Keita Masuzawa
- Kenzo Soejima
- Kiyotaka Yoh
- Koichi Goto
- Kota Ishioka
- Mayumi Kamada
- Mitsugu Araki
- Morio Nakamura
- Ryo Kanada
- Sachiyo Mimaki
- Shingo Matsumoto
- Shinnosuke Ikemura
- Susumu S Kobayashi
- Tadashi Manabe
- Takashi Kohno
- Yasushi Okuno
- Yuichiro Hayashi
Organizations
- Congressionally Directed Medical Research Programs
- Harvard Medical School
- Japan Agency for Medical Research and Development
- Japan Society for the Promotion of Science
- Keio University
- Kyoto University
- National Cancer Center
- National Cancer Center Hospital East
- National Institutes of Health
- RIKEN
- Saiseikai Central Hospital