Literature DB >> 11274898

Present and future in vitro approaches for drug metabolism.

S Ekins1, B J Ring, J Grace, D J McRobie-Belle, S A Wrighton.   

Abstract

The 1980s through 1990s witnessed the widespread incorporation of in vitro absorption, distribution, metabolism, and excretion (ADME) approaches into drug development by drug companies. This has been exemplified by the integration of the basic science of cytochrome P450s (CYPs) into most drug metabolism departments so that information on the metabolic pathways of drugs and drug-drug interactions (DDIs) is no longer an academic exercise, but essential for regulatory submission. This has come about due to the application of a variety of new technologies and in vitro models. For example, subcellular fractions have been widely used in metabolism studies since the 1960s. The last two decades has seen the increased use of hepatocytes as the reproducibility of cell isolations improved. The 1990s saw the rejuvenation of liver slices (as new slicers were developed) and the utilization of cDNA expressed enzymes as these technologies matured. In addition, there has been considerable interest in extrapolating in vitro data to in vivo for parameters such as absorption, clearance and DDIs. The current philosophy of drug development is moving to a 'fail early--fail cheaply' paradigm. Therefore, in vitro ADME approaches are being applied to drug candidates earlier in development since they are essential for identifying compounds likely to present ADME challenges in the latter stages of drug development. These in vitro tools are also being used earlier in lead optimization biology, in parallel with approaches for optimizing target structure activity relationships, as well as identification of DDI and the involvement of metabolic pathways that demonstrate genetic polymorphisms. This would suggest that the line between discovery and development drug metabolism has blurred. In vitro approaches to ADME are increasingly being linked with high-throughput automation and analysis. Further, if we think of perhaps the fastest available way to screen for successful drugs with optimal ADME characteristics, then we arrive at predictive computational algorithms, which are only now being generated and validated in parallel with in vitro and in vivo methods. In addition, as we increase the number of ADME parameters determined early, the overall amount of data generated for both discovery and development will increase. This will present challenges for the efficient and fast interpretation of such data, as well as incorporation and communication to chemistry, biology, and clinical colleagues. This review will focus on and assess the nature of present in vitro metabolism approaches and indicate how they are likely to develop in the future.

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Year:  2000        PMID: 11274898     DOI: 10.1016/s1056-8719(00)00110-6

Source DB:  PubMed          Journal:  J Pharmacol Toxicol Methods        ISSN: 1056-8719            Impact factor:   1.950


  25 in total

1.  Use of high-throughput enzyme-based assay with xenobiotic metabolic capability to evaluate the inhibition of acetylcholinesterase activity by organophosphorous pesticides.

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Journal:  Toxicol In Vitro       Date:  2019-01-06       Impact factor: 3.500

2.  Towards personalized medicine with a three-dimensional micro-scale perfusion-based two-chamber tissue model system.

Authors:  Liang Ma; Jeremy Barker; Changchun Zhou; Wei Li; Jing Zhang; Biaoyang Lin; Gregory Foltz; Jenni Küblbeck; Paavo Honkakoski
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3.  Selection of suitable prodrug candidates for in vivo studies via in vitro studies; the correlation of prodrug stability in between cell culture homogenates and human tissue homogenates.

Authors:  Yasuhiro Tsume; Gordon L Amidon
Journal:  J Pharm Pharm Sci       Date:  2012       Impact factor: 2.327

4.  Comparative Proteomics Analysis of Human Liver Microsomes and S9 Fractions.

Authors:  Xinwen Wang; Bing He; Jian Shi; Qian Li; Hao-Jie Zhu
Journal:  Drug Metab Dispos       Date:  2019-11-07       Impact factor: 3.922

5.  Antitumor activity of DMAKO-05, a novel shikonin derivative, and its metabolism in rat liver microsome.

Authors:  Xu Zhang; Ru-Bing Wang; Wen Zhou; Sui Xiao; Qing-Qing Meng; Shao-Shun Li
Journal:  AAPS PharmSciTech       Date:  2014-10-02       Impact factor: 3.246

6.  Data Mining and Computational Modeling of High-Throughput Screening Datasets.

Authors:  Sean Ekins; Alex M Clark; Krishna Dole; Kellan Gregory; Andrew M Mcnutt; Anna Coulon Spektor; Charlie Weatherall; Nadia K Litterman; Barry A Bunin
Journal:  Methods Mol Biol       Date:  2018

7.  Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets.

Authors:  Alexandru Korotcov; Valery Tkachenko; Daniel P Russo; Sean Ekins
Journal:  Mol Pharm       Date:  2017-11-13       Impact factor: 4.939

8.  Effect of benzo(a)pyrene exposure on fluoranthene metabolism by mouse adipose tissue microsomes.

Authors:  Ashley C Huderson; Deacqunita L Harris; Mohammad S Niaz; Aramandla Ramesh
Journal:  Toxicol Mech Methods       Date:  2010-02       Impact factor: 2.987

Review 9.  Drug Metabolism in Preclinical Drug Development: A Survey of the Discovery Process, Toxicology, and Computational Tools.

Authors:  Naiem T Issa; Henri Wathieu; Abiola Ojo; Stephen W Byers; Sivanesan Dakshanamurthy
Journal:  Curr Drug Metab       Date:  2017       Impact factor: 3.731

10.  Metabolism of benzo(a)pyrene by subcellular fractions of gastrointestinal (GI) tract and liver in Apc(Min) mouse model of colon cancer.

Authors:  Jane A Mantey; Perumalla V Rekhadevi; Deacqunita L Diggs; Aramandla Ramesh
Journal:  Tumour Biol       Date:  2014-01-30
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