Permeability: Caco‐2/MDCK

doi:10.1002/9781118286067.ch8

Cited by 6 publications

(2 citation statements)

References 155 publications

(162 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To only investigate passive permeability, special cell lines with low expression of endogenous canine transporters such as MDCKII-low-efflux cells can be used (Di et al 2011). The apparent permeability observed using cultured cell lines must be normalized according to accessible intestinal surface area, paracellular permeability, pH dependence, resistance of the aqueous boundary layer and transcellular permeability to predict the effective permeability in vivo (Avdeef 2012).…”

Section: Permeabilitymentioning

confidence: 99%

Drug Transporters

Talevi¹,

Bellera²,

Pesce³

2018

ADME Processes in Pharmaceutical Sciences

View full text Add to dashboard Cite

show abstract

Section: Permeabilitymentioning

confidence: 99%

Drug Transporters

Talevi¹,

Bellera²,

Pesce³

2018

ADME Processes in Pharmaceutical Sciences

View full text Add to dashboard Cite

show abstract

“…Over the years, various experimental techniques have been exploited to estimate permeability across lipid bilayers. , Currently, the most common in vitro experiments to access the membrane permeability rely on planar bilayers, , cell monolayers − (Caco-2 or MDCK cells), parallel artificial membrane permeability assay (PAMPA), − and liposomes. − Since these experimental methods are cost- and time-intensive, there has been considerable interest in developing in silico methods for predicting drug permeability. Initially, methods based on the physical properties of compounds − were used to rapidly evaluate the permeability of molecules.…”

Section: Introductionmentioning

confidence: 99%

Combining SILCS and Artificial Intelligence for High-Throughput Prediction of the Passive Permeability of Drug Molecules

Pandey,

MacKerell

2023

J. Chem. Inf. Model.

View full text Add to dashboard Cite

Membrane permeability of drug molecules plays a significant role in the development of new therapeutic agents. Accordingly, methods to predict the passive permeability of drug candidates during a medicinal chemistry campaign offer the potential to accelerate the drug design process. In this work, we combine the physics-based site identification by ligand competitive saturation (SILCS) method and data-driven artificial intelligence (AI) to create a high-throughput predictive model for the passive permeability of druglike molecules. In this study, we present a comparative analysis of four regression models to predict membrane permeabilities of small druglike molecules; of the tested models, Random Forest was the most predictive yielding an R 2 of 0.81 for the independent data set. The input feature vector used to train the developed prediction model includes absolute free energy profiles of ligands through a POPC-cholesterol bilayer based on ligand grid free energy (LGFE) profiles obtained from the SILCS approach. The use of the membrane free energy profiles from SILCS offers information on the physical forces contributing to ligand permeability, while the use of AI yields a more predictive model trained on experimental PAMPA permeability data for a collection of 229 molecules. This combination allows for rapid estimations of ligand permeability at a level of accuracy beyond currently available predictive models while offering insights into the contributions of the functional groups in the ligands to the permeability barrier, thereby offering quantitative information to facilitate rational ligand design.

show abstract

Retention behavior of flavonoids on immobilized artificial membrane chromatography and correlation with cell‐based permeability

Tsopelas

Tsagkrasouli

Poursanidis

et al. 2017

Biomedical Chromatography

View full text Add to dashboard Cite

The aim of the study was to investigate the immobilized artificial membrane (IAM) retention mechanism for a set of flavonoids and to evaluate the potential of IAM chromatography to model Caco-2 permeability. For this purpose, the retention behavior of 41 flavonoid analogs on two IAM stationary phases, IAM.PC.MG and IAM.PC.DD2, was investigated. Correlations between retention factors, logk and octanol-water partitioning (logP) were established and the role of hydroxyl groups of flavonoids to the underlying retention mechanism was explored. IAM retention and logP values were used to establish sound linear models with Caco-2 permeability (logP ) taken from the literature. Both stepwise regression and multivariate analysis confirmed the contribution of hydrogen bond descriptors, as additional parameters in the either logk or logP models. Retention factors on both IAM stationary phases showed comparable performance with n-octanol-water partitioning towards Caco-2 permeability.

show abstract

Permeability: Caco‐2/MDCK

Cited by 6 publications

References 155 publications

Drug Transporters

Drug Transporters

Combining SILCS and Artificial Intelligence for High-Throughput Prediction of the Passive Permeability of Drug Molecules

Retention behavior of flavonoids on immobilized artificial membrane chromatography and correlation with cell‐based permeability

Contact Info

Product

Resources

About