Elucidation of bioconcentration characteristics of ionic organic organic chemicals and development of a prediction method

2022 Fiscal Year Final Research Report

• Project/Area Number: 20H04356
• Research Category: Grant-in-Aid for Scientific Research (B)
• Allocation Type: Single-year Grants
• Section: 一般
• Review Section: Basic Section 64010:Environmental load and risk assessment-related
• Research Institution: Prefectural University of Kumamoto
• Principal Investigator: Kobayashi Jun 熊本県立大学, 環境共生学部, 教授 (00414368)
• Co-Investigator(Kenkyū-buntansha): 櫻井 健郎 国立研究開発法人国立環境研究所, 環境リスク・健康領域, 室長 (90311323)
• Project Period (FY): 2020-04-01 – 2023-03-31
• Keywords: 生物濃縮 / PFAS / 医薬品 / 結合自由エネルギー

Outline of Final Research Achievements

The objective of this study was to elucidate the bioconcentration characteristics of ionic organic compounds such as per- and polyfluoroalkyl substances (PFAS) and pharmaceuticals in fish and to develop a method for predicting bioconcentration factors (BCF). Bioconcentration experiment of pharmaceuticals was conducted on rainbow trout to clarify BCF, half-life, and efficiency of uptake via the respiratory surface. The binding constants were determined by protein binding experiments with albumin of the target substance, and the binding free energy (ΔG) was further estimated by simulation. The log BCF of PFAS and pharmaceuticals could not be explained or predicted only by the ΔG values. We applied machine learning using the ΔG and other variables and developed the method to predict the log BCF of PFAS and pharmaceuticals in an integrated manner.

Free Research Field

環境化学

Academic Significance and Societal Importance of the Research Achievements

ポリ塩化ビフェニルのような疎水性の有機化合物はオクタノール/水分配係数の対数値（log Kow）を用いて生物濃縮係数（BCF）を予測する手法が確立されているが、特に界面活性剤であるPFASはlog Kowを得ることが困難なため、適切な予測手法の確立が課題であった。本研究ではPFAS、医薬品を対象とし、アルブミン等のタンパクとの結合自由エネルギーに着目して各種実験やシミュレーション、機械学習を用い、生物濃縮係数の予測手法を構築した。本手法を用いることで4700種以上あるPFASや医薬品といったイオン性有機化合物のBCFの予測が可能となり、魚類に対するPFASの暴露評価やリスク評価に貢献できる。