ADME evaluation

Klopman, Gilles; Stefan, Liliana R.; Saiakhov, Roustem

doi:10.1016/s0928-0987(02)00219-1

Cited by 120 publications

(22 citation statements)

References 32 publications

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“…1 The use of ADME/Tox (Absorption, Distribution, Metabolism, Excretion, and Toxicity) properties is becoming increasingly important in drug discovery and development. [2][3][4][5][6] It has been estimated that about 50% of such failures are caused by ADME/ Tox deficiency. 1 Blood-brain barrier (BBB) partitioning is an example of ADME/Tox property, which is critical to screen potential therapeutic agents and to improve the side effect profile of drugs with peripheral activity.…”

Section: Introductionmentioning

confidence: 99%

Accurate prediction of the blood–brain partitioning of a large set of solutes using ab initio calculations and genetic neural network modeling

et al. 2006

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A genetic algorithm-based artificial neural network model has been developed for the accurate prediction of the blood-brain barrier partitioning (in logBB scale) of chemicals. A data set of 123 logBB (115 old molecules and 8 new molecules) of a diverse set of chemicals was chosen in this study. The optimum 3D geometry of the molecules was estimated by the ab initio calculations at the level of RHF/STO-3G, and consequently, different electronic descriptors were calculated for each molecule. Indeed, logP as a measure of hydrophobicity and different topological indices were also calculated. A three-layered artificial neural network with backpropagation of an error-learning algorithm was employed to process the nonlinear relationship between the calculated descriptors and logBB data. Genetic algorithm was used as a feature selection method to select the most relevant set of descriptors as the input of the network. Modeling of the logBB data by the only quantum descriptors produced a 5:4:1 ANN structure with RMS error of validation and crossvalidation equal to 0.224 and 0.227, respectively. Better nonlinear model (RMS(V) and RMS(CV) equals to 0.097 and 0.099, respectively) was obtained by the incorporation of the logP and the principal components of the topological indices to electronic descriptors. The ultimate performances of the models were obtained by the application of the models to predict the logBB of 23 molecules that did not have contribution in the steps of model development. The best model produced RMS error of prediction 0.140, and could predict about 98% of variances in the logBB data.

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Section: Introductionmentioning

confidence: 99%

Accurate prediction of the blood–brain partitioning of a large set of solutes using ab initio calculations and genetic neural network modeling

et al. 2006

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“…We collected their HIA values from 13 HIA studies. 1,3,13,20,24,26,27,32,[37][38][39][40][41] To improve the quality and reliability of the dataset, we dealt with it as follows. When there were two or more entries for one molecule, if these values did not differ a lot (<20%), the arithmetic mean value was adopted to reduce the random error; otherwise, the corresponding HIA value will be reconrmed again.…”

Section: Data Collectionmentioning

confidence: 99%

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

et al. 2017

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With the increase of complexity and risk in drug discovery processes, human intestinal absorption (HIA) prediction has become more and more important. Up to now, some predictive models have been constructed to estimate HIA of new drug-like compounds with acceptable accuracies, but there are still some issues to be explored including the limited and unbalanced HIA data, the performance of different types of descriptors and the application domain issues of published models. To address these problems, in this study, we collected a relatively large dataset consisting of 970 compounds, and 9 different types of descriptors were calculated for further modeling. For all the modeling processes, a parameter named samplesize in the random forest (RF) method was applied to balance the dataset. And then, classification models were established based on different training sets and different combinations of descriptors. published models, our model exhibits some advantages in data size, model accuracy and model practicability to some extent. This structure-activity relationship model is necessary and useful for HIA prediction and it could be a convenient tool for virtual screening in the early stage of drug development.

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“…In 2002, Klopman and coworkers developed a novel approach to predict human intestinal absorption [29]. Quit different from the other models, the molecular descriptors used in this model is molecular groups, not usually used molecular descriptors.…”

Section: (F) Eletrotopological State Index (E-state)mentioning

confidence: 99%

Recent Advances in Computational Prediction of Drug Absorption and Permeability in Drug Discovery

Hou¹,

Wang²,

Zhang³

et al. 2006

CMC

157

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Approximately 40%-60% of developing drugs failed during the clinical trials because of ADME/Tox deficiencies. Virtual screening should not be restricted to optimize binding affinity and improve selectivity; and the pharmacokinetic properties should also be included as important filters in virtual screening. Here, the current development in theoretical models to predict drug absorption-related properties, such as intestinal absorption, Caco-2 permeability, and blood-brain partitioning are reviewed. The important physicochemical properties used in the prediction of drug absorption, and the relevance of predictive models in the evaluation of passive drug absorption are discussed. Recent developments in the prediction of drug absorption, especially with the application of new machine learning methods and newly developed software are also discussed. Future directions for research are outlined.Keywords: ADME, drug adsorption, permeability, Caco-2 monolayer, blood-brain partitioning (BBB), logBB, QSAR.In the traditional drug design paradigm, the central stage focuses on the activity and the specificity of a drug candidate, while some other properties, especially those related to absorption, distribution, metabolism, excretion (ADME) and even toxicity (Tox), are only considered at a later stage. The in vitro screening using the traditional strategy may usually lead to potent ligands but not necessarily good drug candidates, since lead compounds that have high molecular weight and increased lipophilicity usually tend to have high potency but poor absorption. It has been estimated that about 40%-60% of such failures are caused by ADME/Tox deficiencies [1][2][3]. The significant failure rate of drug candidates in late stage development is driving the need for development of new in vitro, in vivo, and in silico tools that can eliminate inappropriate compounds before substantial resources are wasted. Accordingly, a paradigm shift has occurred in the initial phases of drug discovery. In addition to potency and selectivity towards the biological target of interest, ADME/Tox properties of a drug are now taken into account at an early stage. Beginning in the early-to mid-1990s, many pharmaceutical companies took steps to integrate the functions of discovery and development scientists. Development scientists are involved in the early stage of the drug discovery program and provide input to in vitro and in vivo optimization process [3]. Furthermore, advances in automation technology and experimental ADME/Tox techniques, such as the Caco-2 permeability screening based on the three-day Caco-2 culture system, the metabolic stability screening using microsomes or hepatocytes, and the P450 inhibition assay, have enabled the assaying of much larger * Address correspondence to these authors at the Department of Chemistry and Biochemistry, Center for Theoretical Biological Physics, University of California at San Diego, La Jolla, CA 92093, USA; E-mail: wei-wang@ucsd.edu and College of Chemistry and Molecular Engineering, Peking ...

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ADME evaluation

Cited by 120 publications

References 32 publications

Accurate prediction of the blood–brain partitioning of a large set of solutes using ab initio calculations and genetic neural network modeling

Accurate prediction of the blood–brain partitioning of a large set of solutes using ab initio calculations and genetic neural network modeling

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

Recent Advances in Computational Prediction of Drug Absorption and Permeability in Drug Discovery

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