Matches in SemOpenAlex for { <https://semopenalex.org/work/W2022463947> ?p ?o ?g. }
- W2022463947 endingPage "659" @default.
- W2022463947 startingPage "649" @default.
- W2022463947 abstract "The aim of this study was to assess a physiologically based modeling approach for predicting drug metabolism, tissue distribution, and bioavailability in rat for a structurally diverse set of neutral and moderate-to-strong basic compounds (n = 50). Hepatic blood clearance (CL(h)) was projected using microsomal data and shown to be well predicted, irrespective of the type of hepatic extraction model (80% within 2-fold). Best predictions of CL(h) were obtained disregarding both plasma and microsomal protein binding, whereas strong bias was seen using either blood binding only or both plasma and microsomal protein binding. Two mechanistic tissue composition-based equations were evaluated for predicting volume of distribution (V(dss)) and tissue-to-plasma partitioning (P(tp)). A first approach, which accounted for ionic interactions with acidic phospholipids, resulted in accurate predictions of V(dss) (80% within 2-fold). In contrast, a second approach, which disregarded ionic interactions, was a poor predictor of V(dss) (60% within 2-fold). The first approach also yielded accurate predictions of P(tp) in muscle, heart, and kidney (80% within 3-fold), whereas in lung, liver, and brain, predictions ranged from 47% to 62% within 3-fold. Using the second approach, P(tp) prediction accuracy in muscle, heart, and kidney was on average 70% within 3-fold, and ranged from 24% to 54% in all other tissues. Combining all methods for predicting V(dss) and CL(h) resulted in accurate predictions of the in vivo half-life (70% within 2-fold). Oral bioavailability was well predicted using CL(h) data and Gastroplus Software (80% within 2-fold). These results illustrate that physiologically based prediction tools can provide accurate predictions of rat pharmacokinetics." @default.
- W2022463947 created "2016-06-24" @default.
- W2022463947 creator A5020501777 @default.
- W2022463947 creator A5023253524 @default.
- W2022463947 creator A5027891698 @default.
- W2022463947 creator A5038500101 @default.
- W2022463947 creator A5040465278 @default.
- W2022463947 creator A5082751583 @default.
- W2022463947 date "2007-01-31" @default.
- W2022463947 modified "2023-10-14" @default.
- W2022463947 title "The Prediction of Drug Metabolism, Tissue Distribution, and Bioavailability of 50 Structurally Diverse Compounds in Rat Using Mechanism-Based Absorption, Distribution, and Metabolism Prediction Tools" @default.
- W2022463947 cites W152943885 @default.
- W2022463947 cites W1974875661 @default.
- W2022463947 cites W1987380529 @default.
- W2022463947 cites W1987412010 @default.
- W2022463947 cites W1991512646 @default.
- W2022463947 cites W2009858196 @default.
- W2022463947 cites W2011656904 @default.
- W2022463947 cites W2013258146 @default.
- W2022463947 cites W2019306159 @default.
- W2022463947 cites W2032353075 @default.
- W2022463947 cites W2039250788 @default.
- W2022463947 cites W2039539582 @default.
- W2022463947 cites W2042506106 @default.
- W2022463947 cites W2044625004 @default.
- W2022463947 cites W2051674641 @default.
- W2022463947 cites W2064799851 @default.
- W2022463947 cites W2067468321 @default.
- W2022463947 cites W2070163884 @default.
- W2022463947 cites W2077041244 @default.
- W2022463947 cites W2077226523 @default.
- W2022463947 cites W2077984371 @default.
- W2022463947 cites W2113830209 @default.
- W2022463947 cites W2114871267 @default.
- W2022463947 cites W2137442330 @default.
- W2022463947 cites W2140474672 @default.
- W2022463947 cites W2146393360 @default.
- W2022463947 cites W2158185292 @default.
- W2022463947 cites W2158240950 @default.
- W2022463947 cites W2165334931 @default.
- W2022463947 cites W3175318380 @default.
- W2022463947 cites W68169567 @default.
- W2022463947 doi "https://doi.org/10.1124/dmd.106.014027" @default.
- W2022463947 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/17267621" @default.
- W2022463947 hasPublicationYear "2007" @default.
- W2022463947 type Work @default.
- W2022463947 sameAs 2022463947 @default.
- W2022463947 citedByCount "87" @default.
- W2022463947 countsByYear W20224639472012 @default.
- W2022463947 countsByYear W20224639472013 @default.
- W2022463947 countsByYear W20224639472014 @default.
- W2022463947 countsByYear W20224639472015 @default.
- W2022463947 countsByYear W20224639472016 @default.
- W2022463947 countsByYear W20224639472017 @default.
- W2022463947 countsByYear W20224639472018 @default.
- W2022463947 countsByYear W20224639472019 @default.
- W2022463947 countsByYear W20224639472021 @default.
- W2022463947 countsByYear W20224639472022 @default.
- W2022463947 countsByYear W20224639472023 @default.
- W2022463947 crossrefType "journal-article" @default.
- W2022463947 hasAuthorship W2022463947A5020501777 @default.
- W2022463947 hasAuthorship W2022463947A5023253524 @default.
- W2022463947 hasAuthorship W2022463947A5027891698 @default.
- W2022463947 hasAuthorship W2022463947A5038500101 @default.
- W2022463947 hasAuthorship W2022463947A5040465278 @default.
- W2022463947 hasAuthorship W2022463947A5082751583 @default.
- W2022463947 hasConcept C110121322 @default.
- W2022463947 hasConcept C112705442 @default.
- W2022463947 hasConcept C134018914 @default.
- W2022463947 hasConcept C134306372 @default.
- W2022463947 hasConcept C139254425 @default.
- W2022463947 hasConcept C150903083 @default.
- W2022463947 hasConcept C181389837 @default.
- W2022463947 hasConcept C185592680 @default.
- W2022463947 hasConcept C202751555 @default.
- W2022463947 hasConcept C207001950 @default.
- W2022463947 hasConcept C2780091579 @default.
- W2022463947 hasConcept C33923547 @default.
- W2022463947 hasConcept C55493867 @default.
- W2022463947 hasConcept C62231903 @default.
- W2022463947 hasConcept C86803240 @default.
- W2022463947 hasConcept C87644729 @default.
- W2022463947 hasConcept C89311334 @default.
- W2022463947 hasConcept C98274493 @default.
- W2022463947 hasConceptScore W2022463947C110121322 @default.
- W2022463947 hasConceptScore W2022463947C112705442 @default.
- W2022463947 hasConceptScore W2022463947C134018914 @default.
- W2022463947 hasConceptScore W2022463947C134306372 @default.
- W2022463947 hasConceptScore W2022463947C139254425 @default.
- W2022463947 hasConceptScore W2022463947C150903083 @default.
- W2022463947 hasConceptScore W2022463947C181389837 @default.
- W2022463947 hasConceptScore W2022463947C185592680 @default.
- W2022463947 hasConceptScore W2022463947C202751555 @default.
- W2022463947 hasConceptScore W2022463947C207001950 @default.
- W2022463947 hasConceptScore W2022463947C2780091579 @default.
- W2022463947 hasConceptScore W2022463947C33923547 @default.
- W2022463947 hasConceptScore W2022463947C55493867 @default.
- W2022463947 hasConceptScore W2022463947C62231903 @default.