Recent advances in computational metabolite structure predictions and altered metabolic pathways assessment to inform drug development processes / Mary Alexandra Schleiff, Deepika Dhaware, Jasleen K. Sodhi

Abstract Many drug candidates fail during preclinical and clinical trials due to variable or unexpected metabolism which may lead to variability in drug efficacy or adverse drug reactions. The drug metabolism field aims to address this important issue from many angles which range from the study of drug–drug interactions, pharmacogenomics, computational metabolic modeling, and others. This manuscript aims to provide brief but comprehensive manuscript summaries highlighting the conclusions and scientific importance of seven exceptional manuscripts published in recent years within the field of drug metabolism. Two main topics within the field are reviewed: novel computational metabolic modeling approaches which provide complex outputs beyond site of metabolism predictions, and experimental approaches designed to discern the impacts of interindividual variability and species differences on drug metabolism. The computational approaches discussed provide novel outputs in metabolite structure and formation likelihood and/or extend beyond the saturated field of drug phase I metabolism, while the experimental metabolic pathways assessments aim to highlight the impacts of genetic polymorphisms and clinical animal model metabolic differences on human metabolism and subsequent health outcomes.

Medienart:

E-Artikel

Erscheinungsjahr:

2021

Erschienen:

2021

Enthalten in:

Zur Gesamtaufnahme - volume:53

Enthalten in:

Drug metabolism reviews - 53(2021), 2, Seite 173-187

Sprache:

Englisch

Beteiligte Personen:

Schleiff, Mary Alexandra [VerfasserIn]
Dhaware, Deepika [VerfasserIn]
Sodhi, Jasleen K. [VerfasserIn]

Links:

FID Access [lizenzpflichtig]

Themen:

Computational modeling
Cytochromes P450
Drug–drug interactions
Genetic polymorphisms
Interindividual variability
Metabolism
Sites of metabolism (SoM)

Umfang:

1 Online-Ressource (15 p)

doi:

10.1080/03602532.2021.1910292

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

KFL011112743