In vitro test methods for metabolite identification: A review

Gummadi, Sowjanya; Ganapaty, S; Sharma, Rupakshi

doi:10.31024/ajpp.2019.5.3.2

Cited by 6 publications

(2 citation statements)

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“…In some cases, S9 fractions produce metabolites that are not formed by either microsomes or cytosol alone. However, the disadvantage of S9 fraction is overall lower enzyme activity compared with the microsomes and cytosol, thus it may leave some metabolites unnoticed [69,70].…”

Section: S9 Fractionsmentioning

confidence: 99%

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In vitro Metabolic Stability of Drugs and Applications of LC-MS in Metabolite Profiling

Krishna¹,

Padmalatha²,

Madhavi³

2021

Drug Metabolism

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Metabolic stability of a compound is an important factor to be considered during the early stages of drug discovery. If the compound has poor metabolic stability, it never becomes a drug even though it has promising pharmacological characteristics. For example, a drug is quickly metabolized in the body; it does not have sufficient in vivo exposure levels and leads to the production of toxic, non-active or active metabolites. A drug is slowly metabolized in the body it could remain longer periods in the body and lead to unwanted adverse reactions, toxicity or may cause drug interactions. Metabolic stability assay is performed to understand the susceptibility of the compound to undergo biotransformation in the body. Intrinsic clearance of the compound is measured by metabolic stability assays. Different in vitro test systems including liver microsomes, hepatocytes, S9 fractions, cytosol, recombinant expressed enzymes, and cell lines are used to investigate the metabolic stability of drugs. Metabolite profiling is a vital part of the drug discovery process and LC–MS plays a vital role. The development of high-resolution (HR) MS technologies with improved mass accuracy, in conjunction with novel data processing techniques, has significantly improved the metabolite detection and identification process. HR-MS based data acquisition (ion intensity-dependent acquisition, accurate-mass inclusion list-dependent acquisition, isotope pattern-dependent acquisition, pseudo neutral loss-dependent acquisition, and mass defect-dependent acquisition) and data mining techniques (extracted ion chromatogram, product ion filter, mass defect filter, isotope pattern filter, neutral loss filter, background subtraction, and control sample comparison) facilitate the drug metabolite identification process.

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Section: S9 Fractionsmentioning

confidence: 99%

“…Conversely, the lack or low expression of most important phase I and phase II drug metabolizing enzymes limits the application of this in vitro model. In addition, it is difficult to investigate individual enzymes due to their low expression level [70].…”

Section: Cell Linesmentioning

confidence: 99%