human intestinal absorption
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P122 - Combination of data-driven and mechanism based approach for prediction of human intestinal absorption

Drug Metabolism and Pharmacokinetics ◽

10.1016/j.dmpk.2020.04.123 ◽

2020 ◽

Vol 35 (1) ◽

pp. S59-S60

Author(s):

Koichi Handa ◽

Seishiro Sakamoto ◽

Shin Umeda

Keyword(s):

Intestinal Absorption ◽

Data Driven ◽

Human Intestinal Absorption

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Constructing an In Silico Three-Class Predictor of Human Intestinal Absorption With Caco-2 Permeability and Dried-DMSO Solubility

Journal of Pharmaceutical Sciences ◽

10.1016/j.xphs.2019.07.014 ◽

2019 ◽

Vol 108 (11) ◽

pp. 3630-3639 ◽

Author(s):

Tsuyoshi Esaki ◽

Rikiya Ohashi ◽

Reiko Watanabe ◽

Yayoi Natsume-Kitatani ◽

Hitoshi Kawashima ◽

...

Keyword(s):

Intestinal Absorption ◽

In Silico ◽

Human Intestinal Absorption

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Prediction of human intestinal absorption using micellar liquid chromatography with an aminopropyl stationary phase

Biomedical Chromatography ◽

10.1002/bmc.4515 ◽

2019 ◽

Vol 33 (7) ◽

pp. e4515

Author(s):

Dina S. Shokry ◽

Laura J. Waters ◽

Gareth M.B. Parkes ◽

John C. Mitchell

Keyword(s):

Liquid Chromatography ◽

Stationary Phase ◽

Intestinal Absorption ◽

Micellar Liquid Chromatography ◽

Human Intestinal Absorption

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Determination of the bioaccessible fraction of cupric oxide nanoparticles in soils using an in vitro human digestibility simulation

Environmental Science Nano ◽

10.1039/c8en00687c ◽

2019 ◽

Vol 6 (2) ◽

pp. 432-443

Author(s):

J. Vassallo ◽

K. Tatsi ◽

R. Boden ◽

R. D. Handy

Keyword(s):

Intestinal Absorption ◽

Cupric Oxide ◽

Oxide Nanoparticles ◽

Human Intestinal Absorption ◽

Bioaccessible Fraction ◽

Cupric Oxide Nanoparticles

This study looks at the potential for ingested nanomaterials dosed in soil, to become bioaccessible for human intestinal absorption.

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Formation of a Bile Salt-Drug Hydrogel to Predict Human Intestinal Absorption

Journal of Pharmaceutical Sciences ◽

10.1016/j.xphs.2018.10.005 ◽

2019 ◽

Vol 108 (1) ◽

pp. 279-287 ◽

Author(s):

Dina S. Shokry ◽

Laura J. Waters ◽

Gareth M.B. Parkes ◽

John C. Mitchell ◽

Martin J. Snowden

Keyword(s):

Bile Salt ◽

Intestinal Absorption ◽

Human Intestinal Absorption

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pH-permeability profiles for drug substances: Experimental detection, comparison with human intestinal absorption and modelling

European Journal of Pharmaceutical Sciences ◽

10.1016/j.ejps.2018.07.014 ◽

2018 ◽

Vol 123 ◽

pp. 429-440 ◽

Author(s):

Mare Oja ◽

Uko Maran

Keyword(s):

Intestinal Absorption ◽

Human Intestinal Absorption ◽

Drug Substances ◽

Experimental Detection

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Incorporating physiologically relevant mobile phases in micellar liquid chromatography for the prediction of human intestinal absorption

Biomedical Chromatography ◽

10.1002/bmc.4351 ◽

2018 ◽

Vol 32 (12) ◽

pp. e4351 ◽

Author(s):

Dina S. Shokry ◽

Laura J. Waters ◽

Gareth M. B. Parkes ◽

John C. Mitchell

Keyword(s):

Liquid Chromatography ◽

Intestinal Absorption ◽

Micellar Liquid Chromatography ◽

Human Intestinal Absorption ◽

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A rule of unity for human intestinal absorption 3: Application to pharmaceuticals

Biopharmaceutics & Drug Disposition ◽

10.1002/bdd.2113 ◽

2018 ◽

Vol 39 (2) ◽

pp. 67-74

Author(s):

Raj B. Patel ◽

Samuel H. Yalkowsky

Keyword(s):

Intestinal Absorption ◽

Human Intestinal Absorption

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Prediction of Human Intestinal Absorption of Compounds Using Artificial Intelligence Techniques

Current Drug Discovery Technologies ◽

10.2174/1570163814666170404160911 ◽

2017 ◽

Vol 14 (4) ◽

Author(s):

Rajnish Kumar ◽

Anju Sharma ◽

Mohammed Haris Siddiqui ◽

Rajesh Kumar Tiwari

Keyword(s):

Artificial Intelligence ◽

Intestinal Absorption ◽

Artificial Intelligence Techniques ◽

Human Intestinal Absorption

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Predicting human intestinal absorption with modified random forest approach: a comprehensive evaluation of molecular representation, unbalanced data, and applicability domain issues

RSC Advances ◽

10.1039/c6ra28442f ◽

2017 ◽

Vol 7 (31) ◽

pp. 19007-19018 ◽

Author(s):

Ning-Ning Wang ◽

Chen Huang ◽

Jie Dong ◽

Zhi-Jiang Yao ◽

Min-Feng Zhu ◽

...

Keyword(s):

Random Forest ◽

Intestinal Absorption ◽

Model Validation ◽

Comprehensive Evaluation ◽

Applicability Domain ◽

Unbalanced Data ◽

Human Intestinal Absorption ◽

A relatively larger dataset consisting of 970 compounds was collected. Classification RF models were established based on different training sets and different descriptors. model validation and evaluation.

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