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Exploring different strategies for imbalanced ADME data problem: case study on Caco-2 permeability modeling

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Abstract

In many absorption, distribution, metabolism, and excretion (ADME) modeling problems, imbalanced data could negatively affect classification performance of machine learning algorithms. Solutions for handling imbalanced dataset have been proposed, but their application for ADME modeling tasks is underexplored. In this paper, various strategies including cost-sensitive learning and resampling methods were studied to tackle the moderate imbalance problem of a large Caco-2 cell permeability database. Simple physicochemical molecular descriptors were utilized for data modeling. Support vector machine classifiers were constructed and compared using multiple comparison tests. Results showed that the models developed on the basis of resampling strategies displayed better performance than the cost-sensitive classification models, especially in the case of oversampling data where misclassification rates for minority class have values of 0.11 and 0.14 for training and test set, respectively. A consensus model with enhanced applicability domain was subsequently constructed and showed improved performance. This model was used to predict a set of randomly selected high-permeability reference drugs according to the biopharmaceutics classification system. Overall, this study provides a comparison of numerous rebalancing strategies and displays the effectiveness of oversampling methods to deal with imbalanced permeability data problems.

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Abbreviations

AD:

Applicability domain

ADME:

Absorption, distribution, metabolism, and excretion

AURC:

Area under the ROC

BCS:

Biopharmaceutics classification system

BE:

Bioequivalence

C :

Penalty parameter

CD:

Critical distance

EMA:

European medicines agency

F:

Bioavailability

FN:

False negative

FDA:

US Food and Drug Administration

FP:

False positive

H class:

High-permeability class

HIA:

Human intestinal absorption

IVIVC:

In vitro–In vivo correlation

MD:

Molecular descriptor

M-P class:

Moderate-to-poor permeability class

Papp:

Apparent permeability coefficient

RBF:

Radial basis function

ROC:

Receiver operator curve

SMOTE:

Synthetic minority oversampling technique

SVM:

Support vector machine

SVs:

Support vectors

WHO:

World health organization

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Acknowledgments

H.L-T-T is supported by Vietnam National University. H.P-T, M.B, I.G-A, T.G, and M.A.C-P acknowledge financial support of AECID (Grant No. 1- D/031152/10 and DCI-ALA/19.09.01/10/21526/245-297/ALFA 111(2010)29). We greatly appreciate Mr. Aaron Burns from Oxford English UK Vietnam for his careful review and helpful editing of this manuscript.

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Authors and Affiliations

  1. Hanoi University of Pharmacy, 13-15 Le Thanh Tong, Hanoi, Vietnam

    Hai Pham-The & Nam Nguyen-Hai

  2. Departament de Bioquímica i Biologia Molecular, Universitat de València, Burjassot, 46100, Valencia, Spain

    Gerardo Casañola-Martin

  3. Unidad de Investigación de Diseño de Fármacos y Conectividad Molecular, Departamento de Química Física, Facultad de Farmacia, Universitat de València, Valencia, Spain

    Gerardo Casañola-Martin

  4. Facultad de Ingeniería Ambiental, Universidad Estatal Amazónica, Paso lateral km 2 1/2 via Napo, Puyo, Ecuador

    Gerardo Casañola-Martin

  5. Department of Pharmacy and Pharmaceutical Technology, University of Valencia, Burjassot, 46100, Valencia, Spain

    Teresa Garrigues & Miguel Ángel Cabrera-Pérez

  6. Department of Engineering, Area of Pharmacy and Pharmaceutical Technology, Miguel Hernández University, 03550 Sant Joan d’Alacant, Alicante, Spain

    Marival Bermejo, Isabel González-Álvarez & Miguel Ángel Cabrera-Pérez

  7. Unit of Modeling and Experimental Biopharmaceutics, Chemical Bioactive Center, Central University of Las Villas, 54830, Santa Clara, Villa Clara, Cuba

    Miguel Ángel Cabrera-Pérez

  8. School of Medicine and Pharmacy, Vietnam National University, 144 Xuan Thuy, Hanoi, Vietnam

    Huong Le-Thi-Thu

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  1. Hai Pham-The
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  2. Gerardo Casañola-Martin
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Correspondence to Huong Le-Thi-Thu.

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Pham-The, H., Casañola-Martin, G., Garrigues, T. et al. Exploring different strategies for imbalanced ADME data problem: case study on Caco-2 permeability modeling. Mol Divers 20, 93–109 (2016). https://doi.org/10.1007/s11030-015-9649-4

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