Intestinal Perfusion Methods for Oral Drug Absorptions

Zhu, Wei; Jeong, Eun Ju

doi:10.1002/9781118067598.ch29

Cited by 1 publication

(1 citation statement)

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“…Of various in vitro assay systems to measure intestinal permeability, human colorectal adenocarcinoma cells (Caco-2), Madin−Darby canine kidney cells (MDCK), and parallel artificial membrane permeability assay (PAMPA) are commonly used [9], and they can be affected by factors such as cell line types and cultured conditions. The in situ single-pass intestinal perfusion (SPIP) model is the most prevalent assay [17] that normally measures effective permeability (P eff ) of the gastrointestinal (GI) tract segments, namely duodenum, jejunum, ileum, and colon, in human, rat, and mouse [18]. The parameter P eff , which is expressed as cm/s, can be calculated by…”

Section: Introductionmentioning

confidence: 99%

In Silico Prediction of Intestinal Permeability by Hierarchical Support Vector Regression

Lee

Weng³

et al. 2020

IJMS

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The vast majority of marketed drugs are orally administrated. As such, drug absorption is one of the important drug metabolism and pharmacokinetics parameters that should be assessed in the process of drug discovery and development. A nonlinear quantitative structure–activity relationship (QSAR) model was constructed in this investigation using the novel machine learning-based hierarchical support vector regression (HSVR) scheme to render the extremely complicated relationships between descriptors and intestinal permeability that can take place through various passive diffusion and carrier-mediated active transport routes. The predictions by HSVR were found to be in good agreement with the observed values for the molecules in the training set (n = 53, r2 = 0.93, q CV 2 = 0.84, RMSE = 0.17, s = 0.08), test set (n = 13, q2 = 0.75–0.89, RMSE = 0.26, s = 0.14), and even outlier set (n = 8, q2 = 0.78–0.92, RMSE = 0.19, s = 0.09). The built HSVR model consistently met the most stringent criteria when subjected to various statistical assessments. A mock test also assured the predictivity of HSVR. Consequently, this HSVR model can be adopted to facilitate drug discovery and development.

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Section: Introductionmentioning

confidence: 99%