Prediction of Caco-2 cell permeability using a combination of MO-calculation and neural network

Fujiwara, Sayaka; Yamashita, Fumiyoshi; Hashida, Mitsuru

doi:10.1016/s0378-5173(02)00045-5

Cited by 58 publications

(45 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The best linear model proposed by Fujiwara et al has a regression coefficient of 0.79. 14 The data set used by Fujiwara et al includes 87 compounds, which is much larger than those used by other researches. We think that the model developed by Fujiwara et al may be more reliable than some other models, but the predictive ability of the Fujiwara's model was not validated by external tested molecules.…”

Section: Resultsmentioning

confidence: 99%

ADME Evaluation in Drug Discovery. 5. Correlation of Caco-2 Permeation with Simple Molecular Properties

Hou

Zhang

Xia

et al. 2004

J. Chem. Inf. Comput. Sci.

174

153

View full text Add to dashboard Cite

The correlations between Caco-2 permeability (logP app ) and molecular properties have been investigated. A training set of 77 structurally diverse organic molecules was used to construct significant QSAR models for Caco-2 cell permeation. Cellular permeation was found to depend primarily upon experimental distribution coefficient (logD) at pH ) 7.4, high charged polar surface area (HCPSA), and radius of gyration (rgyr). Among these three descriptors, logD may have the largest impact on diffusion through Caco-2 cell because logD shows obvious linear correlation with logP app (r)0.703) when logD is smaller than 2.0. High polar surface area will be unfavorable to achieve good Caco-2 permeability because higher polar surface area will introduce stronger H-bonding interactions between Caco-2 cells and drugs. The comparison among HCPSA, PSA (polar surface area), and TPSA (topological polar surface area) implies that high-charged atoms may be more important to the interactions between Caco-2 cell and drugs. Besides logD and HCPSA, rgyr is also closely connected with Caco-2 permeabilities. The molecules with larger rgyr are more difficult to cross Caco-2 monolayers than those with smaller rgyr. The descriptors included in the prediction models permit the interpretation in structural terms of the passive permeability process, evidencing the main role of lipholiphicity, H-bonding, and bulk properties. Besides these three molecular descriptors, the influence of other molecular descriptors was also investigated. From the calculated results, it can be found that introducing descriptors concerned with molecular flexibility can improve the linear correlation. The resulting model with four descriptors bears good statistical significance, n ) 77, r ) 0.82, q ) 0.79, s ) 0.45, F ) 35.7. The actual predictive abilities of the QSAR model were validated through an external validation test set of 23 diverse compounds. The predictions for the tested compounds are as the same accuracy as the compounds of the training set and significantly better than those predicted by using the model reported. The good predictive ability suggests that the proposed model may be a good tool for fast screening of logP app for compound libraries or large sets of new chemical entities via combinatorial chemistry synthesis.

show abstract

Section: Resultsmentioning

confidence: 99%

ADME Evaluation in Drug Discovery. 5. Correlation of Caco-2 Permeation with Simple Molecular Properties

Hou

Zhang

Xia

et al. 2004

J. Chem. Inf. Comput. Sci.

174

153

View full text Add to dashboard Cite

show abstract

“…98 The data set had 87 compounds. They used dipole moment, polarizability, sum of charges on nitrogen and oxygen atoms, and so on.…”

Section: Modeling Of Intestinal Permeabilitymentioning

confidence: 99%

Recent progress in the computational prediction of aqueous solubility and absorption

Johnson

Zheng

2006

AAPS J

View full text Add to dashboard Cite

The computational prediction of aqueous solubility and/or human absorption has been the goal of many researchers in recent years. Such an in silico counterpart to the biopharmaceutical classifi cation system (BCS) would have great utility. This review focuses on recent developments in the computational prediction of aqueous solubility, P-glycoprotein transport, and passive absorption. We fi nd that, while great progress has been achieved, models that can reliably affect chemistry and development are still lacking. We briefl y discuss aspects of emerging scientifi c understanding that may lead to breakthroughs in the computational modeling of these properties.

show abstract

“…Further, a cross-validation technique such as a ''leaveone-out (LOO) method'' may be applied to ensure the optimality of an ANN structure [22][23][24]. One data pair is systematically removed from the training data set, and the ANN is then trained by using the reduced data set.…”

Section: Artificial Neural Network Modelingmentioning

confidence: 99%

Artificial Neural Network in Drug Delivery and Pharmaceutical Research

Sutariya¹,

Groshev²,

Sadana³

et al. 2013

TOBIOIJ

View full text Add to dashboard Cite

Artificial neural networks (ANNs) technology models the pattern recognition capabilities of the neural networks of the brain. Similarly to a single neuron in the brain, artificial neuron unit receives inputs from many external sources, processes them, and makes decisions. Interestingly, ANN simulates the biological nervous system and draws on analogues of adaptive biological neurons. ANNs do not require rigidly structured experimental designs and can map functions using historical or incomplete data, which makes them a powerful tool for simulation of various non-linear systems.ANNs have many applications in various fields, including engineering, psychology, medicinal chemistry and pharmaceutical research. Because of their capacity for making predictions, pattern recognition, and modeling, ANNs have been very useful in many aspects of pharmaceutical research including modeling of the brain neural network, analytical data analysis, drug modeling, protein structure and function, dosage optimization and manufacturing, pharmacokinetics and pharmacodynamics modeling, and in vitro in vivo correlations. This review discusses the applications of ANNs in drug delivery and pharmacological research.

show abstract

Prediction of Caco-2 cell permeability using a combination of MO-calculation and neural network

Cited by 58 publications

References 29 publications

ADME Evaluation in Drug Discovery. 5. Correlation of Caco-2 Permeation with Simple Molecular Properties

ADME Evaluation in Drug Discovery. 5. Correlation of Caco-2 Permeation with Simple Molecular Properties

Recent progress in the computational prediction of aqueous solubility and absorption

Artificial Neural Network in Drug Delivery and Pharmaceutical Research

Contact Info

Product

Resources

About