Estimation of ADME Properties in Drug Discovery: Predicting Caco-2 Cell Permeability Using Atom-Based Stochastic and Non-stochastic Linear Indices

Castillo-Garit, Juan A.; Marrero-Ponce, Yovani; Torrens, Francisco; García-Domènech, Ramón

doi:10.1002/jps.21122

Cited by 77 publications

(31 citation statements)

References 87 publications

(116 reference statements)

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“…Table 4 presents a comparison among these classification models and our approach. For example, models proposed by Marrero et al [57,18] and Castillo-Garit et al [58] have shown relative good statistical significance. However, their predictive capacity still remained questionable due to the small dataset used to obtain the models and the lack of a complete theoretical and experimental validation.…”

Section: Comparison With Other In Silico Classification Modelsmentioning

confidence: 97%

In Silico Prediction of Caco‐2 Cell Permeability by a Classification QSAR Approach

González‐Álvarez

Bermejo

Mangas‐Sanjuán

et al. 2011

Molecular Informatics

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In the present study, 21 validated QSAR models that discriminate compounds with high Caco-2 permeability (Papp ≥8×10(-6) cm/s) from those with moderate-poor permeability (Papp <8×10(-6) cm/s) were developed on a novel large dataset of 674 compounds. 20 DRAGON descriptor families were used. The global accuracies of obtained models were ranking between 78-82 %. A general model combining all types of molecular descriptors was developed and it classified correctly 81.56 % and 83.94 % for training and test sets, respectively. An external set of 10 compounds was predicted and 80 % was correctly assessed by in vitro Caco-2 assays. The potential use of the final model was evaluated by a virtual screening of a human intestinal absorption database of 269 compounds. The model predicted 121 compounds with high Caco-2 permeability and the 90 % of them had high values of human intestinal absorption (HIA≥80). This study provides the most comprehensive database of Caco-2 permeability and evidenced the utility of the combined methodology (in silico+in vitro) in the prediction of Caco-2 permeability. It suggests that the present methodology can be used in the design of large libraries of compounds with appropriate values of permeability and to perform virtual screening in the early stages of drug development.

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Section: Comparison With Other In Silico Classification Modelsmentioning

confidence: 97%

In Silico Prediction of Caco‐2 Cell Permeability by a Classification QSAR Approach

González‐Álvarez

Bermejo

Mangas‐Sanjuán

et al. 2011

Molecular Informatics

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“…A subset of the available papers dealing with predictive models of published Caco‐2 cell permeability will be used here 6–10,12,21,31. Each of these papers cites several literature sources for experimental Caco‐2 data; the reader is referred to the papers cited above and references therein for details on the experimental derivation of tabulated values.…”

Section: Methodsmentioning

confidence: 99%

“…Linear discriminant analysis (LDA) was applied to a set of 157 compounds, with reported Caco‐2 permeabilities, by Castillo‐Garit and co‐workers 6. The authors point out a large limitation in the interpretation of previous models involves limited data sets, and results not fully validated by external test sets.…”

Section: Introductionmentioning

confidence: 99%

Insulin therapy: a pocket guide

Begg¹

2013

European Diabetes Nursing

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“…These MDs codify molecular information based on the bilinear, quadratic and linear algebraic forms and the graph - theoretical electronic - density and edge - adjacency matrices in order to consider atom- and bond-based relations, respectively. The ToMoCOMD-CARDD MDs have been successfully applied in the screening of chemical compounds of different therapeutic applications ranging from antimalarials [14], trichomonacidals [15, 16], antitrypanosomals [17], paramphistomicides [18], antibacterials [19], tyrosinase inhibitors [20, 21] and others [22, 23]. To compute these descriptors, a computational program with the same name was developed.…”

Section: Introductionmentioning

confidence: 99%

QuBiLS-MAS, open source multi-platform software for atom- and bond-based topological (2D) and chiral (2.5D) algebraic molecular descriptors computations

et al. 2017

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Background In previous reports, Marrero-Ponce et al. proposed algebraic formalisms for characterizing topological (2D) and chiral (2.5D) molecular features through atom- and bond-based ToMoCoMD-CARDD (acronym for Topological Molecular Computational Design-Computer Aided Rational Drug Design) molecular descriptors. These MDs codify molecular information based on the bilinear, quadratic and linear algebraic forms and the graph-theoretical electronic-density and edge-adjacency matrices in order to consider atom- and bond-based relations, respectively. These MDs have been successfully applied in the screening of chemical compounds of different therapeutic applications ranging from antimalarials, antibacterials, tyrosinase inhibitors and so on. To compute these MDs, a computational program with the same name was initially developed. However, this in house software barely offered the functionalities required in contemporary molecular modeling tasks, in addition to the inherent limitations that made its usability impractical. Therefore, the present manuscript introduces the QuBiLS-MAS (acronym for Quadratic, Bilinear and N-Linear mapS based on graph-theoretic electronic-density Matrices and Atomic weightingS) software designed to compute topological (0–2.5D) molecular descriptors based on bilinear, quadratic and linear algebraic forms for atom- and bond-based relations.ResultsThe QuBiLS-MAS module was designed as standalone software, in which extensions and generalizations of the former ToMoCoMD-CARDD 2D-algebraic indices are implemented, considering the following aspects: (a) two new matrix normalization approaches based on double-stochastic and mutual probability formalisms; (b) topological constraints (cut-offs) to take into account particular inter-atomic relations; (c) six additional atomic properties to be used as weighting schemes in the calculation of the molecular vectors; (d) four new local-fragments to consider molecular regions of interest; (e) number of lone-pair electrons in chemical structure defined by diagonal coefficients in matrix representations; and (f) several aggregation operators (invariants) applied over atom/bond-level descriptors in order to compute global indices. This software permits the parallel computation of the indices, contains a batch processing module and data curation functionalities. This program was developed in Java v1.7 using the Chemistry Development Kit library (version 1.4.19). The QuBiLS-MAS software consists of two components: a desktop interface (GUI) and an API library allowing for the easy integration of the latter in chemoinformatics applications. The relevance of the novel extensions and generalizations implemented in this software is demonstrated through three studies. Firstly, a comparative Shannon’s entropy based variability study for the proposed QuBiLS-MAS and the DRAGON indices demonstrates superior performance for the former. A principal component analysis reveals that the QuBiLS-MAS approach captures chemical information orthogonal to that codified by the DRAGO...

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Estimation of ADME Properties in Drug Discovery: Predicting Caco-2 Cell Permeability Using Atom-Based Stochastic and Non-stochastic Linear Indices

Cited by 77 publications

References 87 publications

In Silico Prediction of Caco‐2 Cell Permeability by a Classification QSAR Approach

In Silico Prediction of Caco‐2 Cell Permeability by a Classification QSAR Approach

Insulin therapy: a pocket guide

QuBiLS-MAS, open source multi-platform software for atom- and bond-based topological (2D) and chiral (2.5D) algebraic molecular descriptors computations

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