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Research progress of artificial intelligence combined with physiologically based pharmacokinetic models
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Long-jie LI1, Pei-ying JI2, Ao-le ZHENG1, Muyesaier ALIFU1, Xiao-qiang XIANG1, *
Acta Pharmaceutica Sinica | 2024, 59(9) : 2491 - 2498
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Acta Pharmaceutica Sinica | 2024, 59(9): 2491-2498
Research progress of artificial intelligence combined with physiologically based pharmacokinetic models
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Long-jie LI1, Pei-ying JI2, Ao-le ZHENG1, Muyesaier ALIFU1, Xiao-qiang XIANG1, *
Affiliations
  • 1. School of Pharmacy, Fudan University, Shanghai 200120, China
  • 2. Kong Jiang Hospital of Yangpu District, Shanghai 200000, China
Published: 2024-09-12 doi: 10.16438/j.0513-4870.2024-0195
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Physiologically based pharmacokinetic (PBPK) models have been widely used to predict various stages of drug absorption, distribution, metabolism and excretion. Models based on machine learning (ML) and artificial intelligence (AI) can provide better ideas for the construction of PBPK models, which can accelerate the prediction speed and improve the prediction quality of PBPK. ML and AL can complement the advantages of PBPK model to accelerate the progress of drug research and development. This review introduces the application of machine learning and artificial intelligence in pharmacokinetics, summarizes the research progress of physiological pharmacokinetic models based on machine learning and artificial intelligence, and analyzes the limitations of machine learning and artificial intelligence applications and their application prospects and prospects.

physiologically based pharmacokinetic model  /  artificial intelligence  /  machine learning  /  pharmacokinetics  /  pharmaceutical toxicology  /  drug-drug interaction
Long-jie LI, Pei-ying JI, Ao-le ZHENG, Muyesaier ALIFU, Xiao-qiang XIANG. Research progress of artificial intelligence combined with physiologically based pharmacokinetic models[J]. Acta Pharmaceutica Sinica, 2024 , 59 (9) : 2491 -2498 . DOI: 10.16438/j.0513-4870.2024-0195
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Year 2024 volume 59 Issue 9
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Article Info
doi: 10.16438/j.0513-4870.2024-0195
  • Receive Date：2024-03-05
  • Online Date：2025-11-24
  • Published：2024-09-12
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  • Received：2024-03-05
  • Revised：2024-04-19
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Affiliations
    1. School of Pharmacy, Fudan University, Shanghai 200120, China
    2. Kong Jiang Hospital of Yangpu District, Shanghai 200000, China
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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