# 生体を模倣したモデルを構築し、化合物評価の精度を向上します。

マイクロ流路デバイスを用いて、化合物評価に適したモデル、生体を模倣した異種細胞結合モデルを構築し、ハイスループットに化合物を評価します。計測データ(画像)のAI解析や多変量解析によって、化合物の毒性、効果、作用機序を予測します。必要に応じて、Biomarker assayも組み合わせて実施します。


 K. Matsuda, X Han, N. Matsuda, M. Yamanaka, I Suzuki*,  Development of an In Vitro Assessment Method for Chemotherapy-Induced Peripheral Neuropathy (CIPN) by Integrating a Microphysiological System (MPS) with Morphological Deep Learning of Soma and Axonal Images, Toxics 11(10) 848, 2023 DOI

X Han, N Matsuda, K Matsuda, M Yamanaka, I Suzuki*, “An in vitro microfluidic culture device for peripheral neurotoxicity prediction at low concentrations based on deep learning” Fundam. Toxicol Sci., Vol. 9 No. 7 pp. 203-209, 2022 DOI

Odawara, A., Gotoh, M., and Suzuki, I.* “A three-dimensional neuronal culture technique that controls the direction of neurite elongation and the position of soma to mimic the layered structure of the brain” RSC Advances 3(45) (2013) pp. 23620-23630. DOI

Odawara, A., Gotoh, M., and Suzuki, I.* “Control of neural network patterning using collagen gel photothermal etching,”Lab on a chip 13 (2013), pp.2040-2046. DOI

Suzuki, I.*, Nakamura, K., Odawara, A., Alhebshi, A., Gotoh, M., “A simplified micropatterning method for straight-line neurite extension of cultured hippocampal neurons,” Analytical Sciences, 29(2), (2013),263-266 DOI