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2022 Fiscal Year Final Research Report

Development and formulation of deep eutectic search method using machine learning based on drug and molecular science information

Research Project

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Project/Area Number 20K15976
Research Category

Grant-in-Aid for Early-Career Scientists

Allocation TypeMulti-year Fund
Review Section Basic Section 47020:Pharmaceutical analytical chemistry and physicochemistry-related
Research InstitutionKagoshima University (2022)
Tokyo University of Science (2020-2021)

Principal Investigator

Otsuka Yuta  鹿児島大学, 医歯学域歯学系, 助教 (10822520)

Project Period (FY) 2020-04-01 – 2023-03-31
Keywordsケモメトリックス / 共結晶 / 共アモルファス / 深共晶 / バイオマテリアル
Outline of Final Research Achievements

The purpose of this study was to develop a search method for deep eutectic and co-amorphous structures by combining drug information and molecular chemical information, and to evaluate molecular complexes based on the combinations found. We believe that we have achieved our goal by discovering co-amorphous compounds such as indomethacin/cimetidine and piroxicam/saccharin and reporting their pharmacological evaluation.

Free Research Field

ケモメトリックス

Academic Significance and Societal Importance of the Research Achievements

医薬品創製においては、候補化合物の溶解性の低さ、分配係数、保存安定性などの物理化学特性がしばしば製剤化の断念につながり、このことが医薬品開発の遅れの一因になっている。これらの問題の解決法の1つに、医薬品候補化合物と添加物による複合体形成よる、溶解性の改善がある。我々は共アモルファスの合成に成功し、一定の知見を得た。これらの成果は医薬品開発における基礎的検討として学術的意味と社会的意味を持つ。

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Published: 2024-01-30  

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