Literature DB >> 33006800

Metabolic stability studies of lead compounds supported by separation techniques and chemometrics analysis.

Szymon Ulenberg1, Tomasz Bączek1.   

Abstract

With metabolism being one of the main routes of drug elimination from the body (accounting for removal of around 75% of known drugs), it is crucial to understand and study metabolic stability of drug candidates. Metabolically unstable compounds are uncomfortable to administer (requiring repetitive dosage during therapy), while overly stable drugs increase risk of adverse drug reactions. Additionally, biotransformation reactions can lead to formation of toxic or pharmacologically active metabolites (either less-active than parent drug, or even with different action). There were numerous approaches in estimating metabolic stability, including in vitro, in vivo, in silico, and high-throughput screening to name a few. This review aims at describing separation techniques used in in vitro metabolic stability estimation, as well as chemometric techniques allowing for creation of predictive models which enable high-throughput screening approach for estimation of metabolic stability. With a very low rate of drug approval, it is important to understand in silico methods that aim at supporting classical in vitro approach. Predictive models that allow assessment of certain biological properties of drug candidates allow for cutting not only cost, but also time required to synthesize compounds predicted to be unstable or inactive by in silico models.
© 2020 Wiley-VCH GmbH.

Keywords:  chemometrics; drug design; machine learning; metabolic stability; regression models

Year:  2020        PMID: 33006800     DOI: 10.1002/jssc.202000831

Source DB:  PubMed          Journal:  J Sep Sci        ISSN: 1615-9306            Impact factor:   3.645


  1 in total

1.  A Validated LC-MS/MS Assay for the Simultaneous Quantification of the FDA-Approved Anticancer Mixture (Encorafenib and Binimetinib): Metabolic Stability Estimation.

Authors:  Mohamed W Attwa; Hany W Darwish; Nasser S Al-Shakliah; Adnan A Kadi
Journal:  Molecules       Date:  2021-05-05       Impact factor: 4.411

  1 in total

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