In vitro prediction of gastrointestinal absorption of novel β-hydroxy-β-arylalkanoic acids using PAMPA technique

Savić, Jelena; Dobričić, Vladimir; Nikolic, Katarina; Vladimirov, Sote; Dilber, Sanda; Brborić, Jasmina

doi:10.1016/j.ejps.2017.01.005

Cited by 7 publications

(4 citation statements)

References 30 publications

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“…As such, in silico approaches play an increasing role in ADME/Tox profiling [27]. In fact, numerous qualitative structure–activity relationship (QSAR) models have been developed to predict PAMPA permeability [7,23,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47].…”

Section: Introductionmentioning

confidence: 99%

In Silico Prediction of PAMPA Effective Permeability Using a Two-QSAR Approach

Chi

Lee

Weng

et al. 2019

IJMS

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Oral administration is the preferred and predominant route of choice for medication. As such, drug absorption is one of critical drug metabolism and pharmacokinetics (DM/PK) parameters that should be taken into consideration in the process of drug discovery and development. The cell-free in vitro parallel artificial membrane permeability assay (PAMPA) has been adopted as the primary screening to assess the passive diffusion of compounds in the practical applications. A classical quantitative structure–activity relationship (QSAR) model and a machine learning (ML)-based QSAR model were derived using the partial least square (PLS) scheme and hierarchical support vector regression (HSVR) scheme to elucidate the underlying passive diffusion mechanism and to predict the PAMPA effective permeability, respectively, in this study. It was observed that HSVR executed better than PLS as manifested by the predictions of the samples in the training set, test set, and outlier set as well as various statistical assessments. When applied to the mock test, which was designated to mimic real challenges, HSVR also showed better predictive performance. PLS, conversely, cannot cover some mechanistically interpretable relationships between descriptors and permeability. Accordingly, the synergy of predictive HSVR and interpretable PLS models can be greatly useful in facilitating drug discovery and development by predicting passive diffusion.

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Section: Introductionmentioning

confidence: 99%

In Silico Prediction of PAMPA Effective Permeability Using a Two-QSAR Approach

Chi

Lee

Weng

et al. 2019

IJMS

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“…One advantage of QSAR modelling for PAMPA is its ability to guide structural modifications within a series of structural analogues needed for improvement of permeability. This is well illustrated in the study of Savić et al [53] where the permeability of 13 newly synthesised β-hydroxy-β-arylalkanoic acids (HAA) was measured in PAMPA followed by QSAR modelling with the final aim to propose novel HAA structures with improved GI absorption. Despite the limitation in the training set (it being only 8 compounds), the results indicated some significant trends: the introduction of branched side chains, as well as introduction of substituents on one phenyl ring (which disturbs symmetry of the molecule) have positive impact on the permeability and thus on GI absorption.…”

Section: Qsar Models Of Pampa Permeabilitymentioning

confidence: 81%

Advances in the prediction of gastrointestinal absorption: Quantitative Structure-Activity Relationship (QSAR) modelling of PAMPA permeability

Diukendjieva

Tsakovska

Alov

et al. 2019

Computational Toxicology

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QSARs of PAMPA permeability are useful to predict a chemical's passive transport. • QSAR models allow a deeper insight into the mechanisms of the membrane transport. • Hydrophobicity and hydrogen bonding are important for membrane permeability. • Linear or bilinear relationships exist between PAMPA permeability and log P. • For passive transport predicted permeability is related to Caco-2 and in vivo data.

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“…PAMPA is also used to assess membrane retention, which is of particular interest for compounds that target membrane proteins or compounds that are applied locally [12]. PAMPA results could also be used to develop quantitative structurepermeability relationships (QSPR) and quantitative structureretention relationships (QSRR) models, which could be employed for the design of novel derivatives with improved permeability or membrane retention [12,13]. In general, QSPR refers to quantitative structureproperty relationships analyses, but in PAMPA studies permeability is that particular property which is being modelled.…”

Section: J O U R N a L P R E -P R O O Fmentioning

confidence: 99%