ACS Publications. Most Trusted. Most Cited. Most Read
My Activity

Combining in Vitro and in Vivo Pharmacokinetic Data for Prediction of Hepatic Drug Clearance in Humans by Artificial Neural Networks and Multivariate Statistical Techniques

View Author Information
F. Hoffmann-La Roche Ltd., Pharmaceuticals Division, CH-4070 Basel, Switzerland
Cite this: J. Med. Chem. 1999, 42, 25, 5072–5076
Publication Date (Web):November 20, 1999
Copyright © 1999 American Chemical Society

    Article Views





    Other access options


    Abstract Image

    Several statistical regression models and artificial neural networks were used to predict the hepatic drug clearance in humans from in vitro (hepatocyte) and in vivo pharmacokinetic data and to identify the most predictive models for this purpose. Cross-validation was performed to assess prediction accuracy. It turned out that human hepatocyte data was the best predictor, followed by rat hepatocyte data. Dog hepatocyte data and dog and rat in vivo data appear to be uncorrelated with human in vivo clearance and did not significantly contribute to the prediction models. Considering the present evaluation, the most cost-effective and most accurate approach to achieve satisfactory predictions in human is a combination of in vitro clearances on human and rat hepatocytes. Such information is of considerable value to speed up drug discovery. This study also showed some of the limitations of the approach related to the size of the database used in the present evaluation.

    Read this article

    To access this article, please review the available access options below.

    Get instant access

    Purchase Access

    Read this article for 48 hours. Check out below using your ACS ID or as a guest.


    Access through Your Institution

    You may have access to this article through your institution.

    Your institution does not have access to this content. You can change your affiliated institution below.


     Corresponding Author:  Dr. Gisbert Schneider, F. Hoffmann-La Roche Ltd., Pharmaceuticals Division, Grenzacherstr. 124, CH-4070 Basel, Switzerland. Tel:  +41-61-68 70696. Fax:  +41-61-68 89041. E-mail:  [email protected].

    Cited By

    This article is cited by 54 publications.

    1. Christopher E. Keefer, George Chang, Li Di, Nathaniel A. Woody, David A. Tess, Sarah M. Osgood, Brendon Kapinos, Jill Racich, Anthony A. Carlo, Amanda Balesano, Nicholas Ferguson, Christine Orozco, Larisa Zueva, Lina Luo. The Comparison of Machine Learning and Mechanistic In Vitro–In Vivo Extrapolation Models for the Prediction of Human Intrinsic Clearance. Molecular Pharmaceutics 2023, 20 (11) , 5616-5630.
    2. Xiaoyu Ding, Rongrong Cui, Jie Yu, Tiantian Liu, Tingfei Zhu, Dingyan Wang, Jie Chang, Zisheng Fan, Xiaomeng Liu, Kaixian Chen, Hualiang Jiang, Xutong Li, Xiaomin Luo, Mingyue Zheng. Active Learning for Drug Design: A Case Study on the Plasma Exposure of Orally Administered Drugs. Journal of Medicinal Chemistry 2021, 64 (22) , 16838-16853.
    3. Yuchen Wang, Haichun Liu, Yuanrong Fan, Xingye Chen, Yan Yang, Lu Zhu, Junnan Zhao, Yadong Chen, Yanmin Zhang. In Silico Prediction of Human Intravenous Pharmacokinetic Parameters with Improved Accuracy. Journal of Chemical Information and Modeling 2019, 59 (9) , 3968-3980.
    4. Dominique Douguet. Data Sets Representative of the Structures and Experimental Properties of FDA-Approved Drugs. ACS Medicinal Chemistry Letters 2018, 9 (3) , 204-209.
    5. Han van de Waterbeemd,, Dennis A. Smith,, Kevin Beaumont, and, Don K. Walker. Property-Based Design:  Optimization of Drug Absorption and Pharmacokinetics. Journal of Medicinal Chemistry 2001, 44 (9) , 1313-1333.
    6. Tanner C. Reese, Anvita Devineni, Tristan Smith, Ismail Lalami, Jung-Mo Ahn, Ganesh V. Raj. Evaluating physiochemical properties of FDA-approved orally administered drugs. Expert Opinion on Drug Discovery 2024, 19 (2) , 225-238.
    7. Siva Nageswara Rao Gajula, Nimisha Nadimpalli, Rajesh Sonti. Drug metabolic stability in early drug discovery to develop potential lead compounds. Drug Metabolism Reviews 2021, 53 (3) , 459-477.
    8. Ester Papa, Jon A. Arnot, Alessandro Sangion, Paola Gramatica. In Silico Approaches for the Prediction of In Vivo Biotransformation Rates. 2017, 425-451.
    9. Ghasem Ghasemi, Mahyar Nirouei, Shahab Shariati, Parviz Abdolmaleki, Zinab Rastgoo. A quantitative structure–activity relationship study on HIV-1 integrase inhibitors using genetic algorithm, artificial neural networks and different statistical methods. Arabian Journal of Chemistry 2016, 9 , S185-S190.
    10. Syeda Saba Kareem, Yashwant Pathak. Clinical Applications of Artificial Neural Networks in Pharmacokinetic Modeling. 2016, 393-405.
    11. Peng Zou, Yanke Yu, Nan Zheng, Yongsheng Yang, Hayley J. Paholak, Lawrence X. Yu, Duxin Sun. Applications of Human Pharmacokinetic Prediction in First-in-Human Dose Estimation. The AAPS Journal 2012, 14 (2) , 262-281.
    12. . ADME Screening. 2012, 229-250.
    13. Kenneth Bachmann, Sean Ekins. The Potential of In Silico and In Vitro Approaches to Predict In Vivo Drug–Drug Interactions and ADMET/TOX Properties. 2012, 307-329.
    14. Shahab Sheikh-Bahaei, C. Anthony Hunt. Enabling Clearance Predictions to Emerge from In Silico Actions of Quasi-Autonomous Hepatocyte Components. Drug Metabolism and Disposition 2011, 39 (10) , 1910-1920.
    15. Ying Qi Ge, Xiao Ping Luo, Qian Ming Mao. Application of ICA-CMAC in the Prediction of Human Hepatic Clearance. Advanced Materials Research 2011, 268-270 , 1759-1762.
    16. Sami Haddad, Patrick Poulin, Christoph Funk. Extrapolating In vitro Metabolic Interactions to Isolated Perfused Liver: Predictions of Metabolic Interactions between R-Bufuralol, Bunitrolol, and Debrisoquine. Journal of Pharmaceutical Sciences 2010, 99 (10) , 4406-4426.
    17. Peter L. Bullock. Prediction of Pharmacokinetics and Drug Safety in Humans. 2010, 89-130.
    18. So-Young Lee, Dong-Sup Kim. Utility of Structural Information to Predict Drug Clearance from in Vitro Data. Interdisciplinary Bio Central 2010, 2 (2) , 3.1-3.4.
    19. Paulo Paixão, Luís F. Gouveia, José A.G. Morais. Prediction of the in vitro intrinsic clearance determined in suspensions of human hepatocytes by using artificial neural networks. European Journal of Pharmaceutical Sciences 2010, 39 (5) , 310-321.
    20. Urban Fagerholm. Prediction of human pharmacokinetics—evaluation of methods for prediction of hepatic metabolic clearance. Journal of Pharmacy and Pharmacology 2010, 59 (6) , 803-828.
    21. Frederique Fenneteau, Patrick Poulin, Fahima Nekka. Physiologically Based Predictions of the Impact of Inhibition of Intestinal and Hepatic Metabolism on Human Pharmacokinetics of CYP3A Substrates. Journal of Pharmaceutical Sciences 2010, 99 (1) , 486-514.
    22. Konstantin V. Balakin, Nikolay P. Savchuk. Application of Data Mining Algorithms in Pharmaceutical Research and Development. 2009, 87-111.
    23. Haiyan Li, Jin Sun, Xiaowen Fan, Xiaofan Sui, Lan Zhang, Yongjun Wang, Zhonggui He. Considerations and recent advances in QSAR models for cytochrome P450-mediated drug metabolism prediction. Journal of Computer-Aided Molecular Design 2008, 22 (11) , 843-855.
    24. S Ekins, J Mestres, B Testa. In silico pharmacology for drug discovery: applications to targets and beyond. British Journal of Pharmacology 2007, 152 (1) , 21-37.
    25. Erdem Buyukbingol, Arzu Sisman, Murat Akyildiz, Ferda Nur Alparslan, Adeboye Adejare. Adaptive neuro-fuzzy inference system (ANFIS): A new approach to predictive modeling in QSAR applications: A study of neuro-fuzzy modeling of PCP-based NMDA receptor antagonists. Bioorganic & Medicinal Chemistry 2007, 15 (12) , 4265-4282.
    26. Soyoung Lee, Dongsup Kim. Equation chapter 1 section 1A new method for predicting human hepatic clearance fromin Vitro experimental data using molecular descriptors. Archives of Pharmacal Research 2007, 30 (2) , 182-190.
    27. Joseph K Ritter. Intestinal UGTs as potential modifiers of pharmacokinetics and biological responses to drugs and xenobiotics. Expert Opinion on Drug Metabolism & Toxicology 2007, 3 (1) , 93-107.
    28. Donald E. Mager. Quantitative structure–pharmacokinetic/pharmacodynamic relationships. Advanced Drug Delivery Reviews 2006, 58 (12-13) , 1326-1356.
    29. Ruth Hyland, Barry Jones, Han van de Waterbeemd. Utility of human/human-derived reagents in drug discovery and development: An industrial perspective. Environmental Toxicology and Pharmacology 2006, 21 (2) , 179-183.
    30. Keith W Ward. Recent advances in pharmacokinetic extrapolation from preclinical data to humans. Expert Opinion on Drug Metabolism & Toxicology 2005, 1 (4) , 583-594.
    32. Sean Ekins, Sergey Andreyev, Andy Ryabov, Eugene Kirillov, Eugene A Rakhmatulin, Andrej Bugrim, Tatiana Nikolskaya. Computational prediction of human drug metabolism. Expert Opinion on Drug Metabolism & Toxicology 2005, 1 (2) , 303-324.
    33. Dermot F. McGinnity, Matthew G. Soars, Richard A. Urbanowicz, Robert J. Riley. EVALUATION OF FRESH AND CRYOPRESERVED HEPATOCYTES AS IN VITRO DRUG METABOLISM TOOLS FOR THE PREDICTION OF METABOLIC CLEARANCE. Drug Metabolism and Disposition 2004, 32 (11) , 1247-1253.
    34. Toshihiro Wajima, Yoshitaka Yano, Kazuya Fukumura, Takayoshi Oguma. Prediction of Human Pharmacokinetic Profile in Animal Scale up Based on Normalizing Time Course Profiles. Journal of Pharmaceutical Sciences 2004, 93 (7) , 1890-1900.
    35. Michael W. Sinz. Drug Metabolism In Preclinical Development. 2004, 75-132.
    36. Thierry Lavé, Olivier Luttringer, Patrick Poulin, Neil Parrott. Interspecies Scaling. 2004, 133-175.
    37. Fumiyoshi Yamashita, Mitsuru Hashida. Mechanistic and empirical modeling of skin permeation of drugs. Advanced Drug Delivery Reviews 2003, 55 (9) , 1185-1199.
    38. Han van de Waterbeemd, Eric Gifford. ADMET in silico modelling: towards prediction paradise?. Nature Reviews Drug Discovery 2003, 2 (3) , 192-204.
    39. Han van de Waterbeemd, Barry C Jones. Predicting Oral Absorption and Bioavailability. 2003, 1-59.
    40. Toshihiro Wajima, Kazuya Fukumura, Yoshitaka Yano, Takayoshi Oguma. Prediction of Human Clearance from Animal Data and Molecular Structural Parameters using Multivariate Regression Analysis. Journal of Pharmaceutical Sciences 2002, 91 (12) , 2489-2499.
    41. Yau Yi Lau, Elpida Sapidou, Xiaoming Cui, Ronald E. White, K.-C. Cheng. Development of a Novel in Vitro Model to Predict Hepatic Clearance Using Fresh, Cryopreserved, and Sandwich-Cultured Hepatocytes. Drug Metabolism and Disposition 2002, 30 (12) , 1446-1454.
    42. Patrick Poulin, Frank-Peter Theil. Prediction of Pharmacokinetics Prior to In Vivo Studies. II. Generic Physiologically Based Pharmacokinetic Models of Drug Disposition. Journal of Pharmaceutical Sciences 2002, 91 (5) , 1358-1370.
    43. M. G. Soars, B. Burchell, R. J. Riley. In Vitro Analysis of Human Drug Glucuronidation and Prediction of in Vivo Metabolic Clearance. Journal of Pharmacology and Experimental Therapeutics 2002, 301 (1) , 382-390.
    44. George M Grass, Patrick J Sinko. Physiologically-based pharmacokinetic simulation modelling. Advanced Drug Delivery Reviews 2002, 54 (3) , 433-451.
    45. T. Lavé, O. Luttringer, J. Zuegge, G. Schneider, P. Coassolo, F.-P. Theil. Prediction of Human Pharmacokinetics Based on Preclinical In Vitro and In Vivo Data. 2002, 81-104.
    46. Bruce C. Baguley, Kevin O. Hicks, William R. Wilson. TUMOR CELL CULTURES IN DRUG DEVELOPMENT. 2002, 269-cp1.
    47. Balaji Agoram, Walter S. Woltosz, Michael B. Bolger. Predicting the impact of physiological and biochemical processes on oral drug bioavailability. Advanced Drug Delivery Reviews 2001, 50 , S41-S67.
    48. Hanlan Liu, Guy T. Carter, Mark Tischler. Immobilized artificial membrane chromatography with mass spectrometric detection: a rapid method for screening drug‐membrane interactions. Rapid Communications in Mass Spectrometry 2001, 15 (17) , 1533-1538.
    49. . References. 2001, 164-210.
    50. S. E. Clarke, P. Jeffrey. Utility of metabolic stability screening: comparison of in vitro and in vivo clearance. Xenobiotica 2001, 31 (8-9) , 591-598.
    51. Jochen Zuegge, Gisbert Schneider, Philippe Coassolo, Thierry Lav??. Prediction of Hepatic Metabolic Clearance. Clinical Pharmacokinetics 2001, 40 (7) , 553-563.
    52. Sean Ekins, Chris L Waller, Peter W Swaan, Gabriele Cruciani, Steven A Wrighton, James H Wikel. Progress in predicting human ADME parameters in silico. Journal of Pharmacological and Toxicological Methods 2000, 44 (1) , 251-272.
    53. Jens Sadowski. Optimization of chemical libraries by neural networks. Current Opinion in Chemical Biology 2000, 4 (3) , 280-282.
    54. Barbra H. Stewart, Yi Wang. Chapter 27. Ex vivo approaches to predicting oral pharmacokinetics in humans. 2000, 299-307.

    Pair your accounts.

    Export articles to Mendeley

    Get article recommendations from ACS based on references in your Mendeley library.

    Pair your accounts.

    Export articles to Mendeley

    Get article recommendations from ACS based on references in your Mendeley library.

    You’ve supercharged your research process with ACS and Mendeley!

    STEP 1:
    Click to create an ACS ID

    Please note: If you switch to a different device, you may be asked to login again with only your ACS ID.

    Please note: If you switch to a different device, you may be asked to login again with only your ACS ID.

    Please note: If you switch to a different device, you may be asked to login again with only your ACS ID.

    Your Mendeley pairing has expired. Please reconnect