Decision trees to characterise the roles of permeability and solubility on the prediction of oral absorption

Newby, Danielle; Freitas, Alex A.; Ghafourian, Taravat

doi:10.1016/j.ejmech.2014.12.006

Cited by 77 publications

(63 citation statements)

References 74 publications

(45 reference statements)

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“…We curated recent human intestinal absorption (HIA) data4 by literature, patent, and database cross‐checks (refer to Methods S1 in the Supporting Information) to gather 660 small molecules (567 well‐ and 93 poorly absorbed) with cleansed structures and reliable measurements of the fraction absorbed by human (FA), excluding actively transported compounds. This HIA dataset is given in Table S1 in the Supporting Information.…”

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confidence: 99%

A BOILED‐Egg To Predict Gastrointestinal Absorption and Brain Penetration of Small Molecules

2016

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Apart from efficacy and toxicity, many drug development failures are imputable to poor pharmacokinetics and bioavailability. Gastrointestinal absorption and brain access are two pharmacokinetic behaviors crucial to estimate at various stages of the drug discovery processes. To this end, the Brain Or IntestinaL EstimateD permeation method (BOILED‐Egg) is proposed as an accurate predictive model that works by computing the lipophilicity and polarity of small molecules. Concomitant predictions for both brain and intestinal permeation are obtained from the same two physicochemical descriptors and straightforwardly translated into molecular design, owing to the speed, accuracy, conceptual simplicity and clear graphical output of the model. The BOILED‐Egg can be applied in a variety of settings, from the filtering of chemical libraries at the early steps of drug discovery, to the evaluation of drug candidates for development.

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confidence: 99%

A BOILED‐Egg To Predict Gastrointestinal Absorption and Brain Penetration of Small Molecules

2016

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“…Solubility. Experimental and qualitative aqueous solubility data were obtained from the previously published data set 29 and combined to give a final total of 750 solubility From this initial data set of 750 compounds, 127 compounds whose permeability was known were used for the BCS validation set. This resulted in a smaller data set of 623 compounds used to build and validate the resulting solubility models.…”

Section: Methodsmentioning

confidence: 99%

Comparing Multilabel Classification Methods for Provisional Biopharmaceutics Class Prediction

2014

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The biopharmaceutical classification system (BCS) is now well established and utilized for the development and biowaivers of immediate oral dosage forms. The prediction of BCS class can be carried out using multilabel classification. Unlike single label classification, multilabel classification methods predict more than one class label at the same time. This paper compares two multilabel methods, binary relevance and classifier chain, for provisional BCS class prediction. Large data sets of permeability and solubility of drug and drug-like compounds were obtained from the literature and were used to build models using decision trees. The separate permeability and solubility models were validated, and a BCS validation set of 127 compounds where both permeability and solubility were known was used to compare the two aforementioned multilabel classification methods for provisional BCS class prediction. Overall, the results indicate that the classifier chain method, which takes into account label interactions, performed better compared to the binary relevance method. This work offers a comparison of multilabel methods and shows the potential of the classifier chain multilabel method for improved biological property predictions for use in drug discovery and development.

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“…6,17,18 Especially, some studies have shown that there is a good sigmoidal relationship between human oral drug absorption and Caco-2 permeability but this relationship seems not so clear. [19][20][21][22] Hou et al have conrmed that Caco-2 cell lines can be used as a predictive tool to estimate oral absorption just for compounds with good intestinal absorption. While for compounds with low or medium absorption, the Caco-2 cell lines may not give a very good rank for estimating oral absorption.…”

Section: 11mentioning

confidence: 99%

“…While for compounds with low or medium absorption, the Caco-2 cell lines may not give a very good rank for estimating oral absorption. 12,20 So even if the Caco-2 permeability predictive models have developed a lot, a high-quality and practical HIA predictive model is still in demand. To achieve this goal, major efforts have been made to build a satisfactory HIA predictive model.…”

Section: 11mentioning

confidence: 99%

“…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%

See 1 more Smart Citation

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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Decision trees to characterise the roles of permeability and solubility on the prediction of oral absorption

Cited by 77 publications

References 74 publications

A BOILED‐Egg To Predict Gastrointestinal Absorption and Brain Penetration of Small Molecules

A BOILED‐Egg To Predict Gastrointestinal Absorption and Brain Penetration of Small Molecules

Comparing Multilabel Classification Methods for Provisional Biopharmaceutics Class Prediction

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

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