A Computational Selection of Metabolite Biomarkers Using Emerging Pattern Mining: A Case Study in Human Hepatocellular Carcinoma.

The biomarker development in metabolomics aims at discriminating diseased from normal subjects and at creating a predictive model that can be used to diagnose new subjects. From a case study on human hepatocellular carcinoma (HCC), we studied for the first time the potential usefulness of the emerging patterns (EPs) that come from the data mining domain. When applied to a metabolomics data set labeled with two classes (e.g., HCC patients vs healthy subjects), EP mining can capture differentiating combinations of metabolites between the two classes. We observed that the so-called jumping emerging patterns (JEPs), which correspond to the combinations of metabolites that occur in only one of the two classes, achieved better performance than individual biomarkers. Particularly, the implementation of the JEPs in a rules-based diagnostic tool drastically reduced the false positive rate, i.e., the rate of healthy subjects predicted as HCC patients.

Generative topographic mapping-based classification models and their applicability domain: application to the biopharmaceutics Drug Disposition Classification System (BDDCS).

Earlier (Kireeva et al. Mol. Inf. 2012, 31, 301-312), we demonstrated that generative topographic mapping (GTM) can be efficiently used both for data visualization and building of classification models in the initial D-dimensional space of molecular descriptors. Here, we describe the modeling in two-dimensional latent space for the four classes of the BioPharmaceutics Drug Disposition Classification System (BDDCS) involving VolSurf descriptors. Three new definitions of the applicability domain (AD) of models have been suggested: one class-independent AD which considers the GTM likelihood and two class-dependent ADs considering respectively, either the predominant class in a given node of the map or informational entropy. The class entropy AD was found to be the most efficient for the BDDCS modeling. The predominant class AD can be directly visualized on GTM maps, which helps the interpretation of the model.

Interpretation of honeybees contact toxicity associated to acetylcholinesterase inhibitors.

The widespread use of different pesticides generates adverse effects on non target organisms like honeybees. Organophosphorous and carbamates kill honeybees through the inactivation of acetylcholinesterase (AChE), thereby interfering with nerve signaling and function. For this class of pesticides, it is fundamental to understand the relationship between their structures and the contact toxicity for honeybees. A Quantitative Structure-Activity Relationship (QSAR) study was carried out on 45 derivatives by a genetic algorithm approach starting from more than 2500 descriptors. In parallel, a new 3D model of AChE associated to honeybees was defined. Physicochemical properties of the receptor and docking studies of the derivatives allow understanding the meaningful of three descriptors and the implication of several amino acids in the overall toxicity of the pesticides.

Introduction of jumping fragments in combination with QSARs for the assessment of classification in ecotoxicology.

Starting from a random set of structures taken from the European Chemical Bureau (ECB) Web site, an estimation of the classification by acute category in ecotoxicology was carried out. This estimation was based on two approaches. One approach consists in starting with global quantitative structure-activity relationship (QSAR) equations, analyzing the results and defining an interpretation in terms of overall results and mode of action. The other starts with the notion of emerging fragments and more specifically with the introduction of a particular concept: the jumping fragments. This publication studies the scopes and limitations of each approach for the classification of the derivatives. A promising combination of the two methods is proposed for the classification and also for bringing new information about the importance, for the ecotoxicity, of specific chemical fragments considered alone or in association with others.

Prediction of acute toxicity in fish by using QSAR methods and chemical modes of action.

Three quantitative structure-activity relationship (QSAR) models were evaluated for their power to predict the toxicity of chemicals in two datasets: (1) EPAFHM (US Environmental Protection Agency-Fathead Minnow) and (2) derivatives having a high production volume (HPV), as compiled by the European Chemical Bureau. For all three QSAR models, the quality of the predictions was found to be highly dependent on the mode of action of the chemicals. An analysis of outliers from the three models gives some clues for improving the QSAR models. Two classification methods, Toxtree and a Bayesian approach with fingerprints as descriptors, were also analyzed. Predictions following the Toxtree classification for narcosis were good, especially for the HPV set. The learning model (Bayesian approach) produced interesting results for the EPAFHM dataset but gave lower quality predictions for the HPV set.

ADME-Space: a new tool for medicinal chemists to explore ADME properties.

We introduce a new chemical space for drugs and drug-like molecules, exclusively based on their in silico ADME behaviour. This ADME-Space is based on self-organizing map (SOM) applied to 26,000 molecules. Twenty accurate QSPR models, describing important ADME properties, were developed and, successively, used as new molecular descriptors not related to molecular structure. Applications include permeability, active transport, metabolism and bioavailability studies, but the method can be even used to discuss drug-drug interactions (DDIs) or it can be extended to additional ADME properties. Thus, the ADME-Space opens a new framework for the multi-parametric data analysis in drug discovery where all ADME behaviours of molecules are condensed in one map: it allows medicinal chemists to simultaneously monitor several ADME properties, to rapidly select optimal ADME profiles, retrieve warning on potential ADME problems and DDIs or select proper in vitro experiments.

Quantile regression model for a diverse set of chemicals: application to acute toxicity for green algae.

The potential of quantile regression (QR) and quantile support vector machine regression (QSVMR) was analyzed for the definitions of quantitative structure-activity relationship (QSAR) models associated with a diverse set of chemicals toward a particular endpoint. This study focused on a specific sensitive endpoint (acute toxicity to algae) for which even a narcosis QSAR model is not actually clear. An initial dataset including more than 401 ecotoxicological data for one species of algae (Selenastrum capricornutum) was defined. This set corresponds to a large sample of chemicals ranging from classical organic chemicals to pesticides. From this original data set, the selection of the different subsets was made in terms of the notion of toxic ratio (TR), a parameter based on the ratio between predicted and experimental values. The robustness of QR and QSVMR to outliers was clearly observed, thus demonstrating that this approach represents a major interest for QSAR associated with a diverse set of chemicals. We focused particularly on descriptors related to molecular surface properties.

Consensus QSAR Related to Global or MOA Models: Application to Acute Toxicity for Fish.

Under REACH legislation, alternative methods (in silico or in vitro) like QSAR (Quantitative Structure-Activity Relationships) models are expected to play a significant role. QSARs are based on the assumption that substances with similar chemical structures may have the same biological activities. However, identification of chemical classes could be problematic because chemicals often exhibit different chemical moieties, thereby confounding efforts to achieve a meaningful classification. This publication is focus on the notion of global model with the integration of a recent genetic algorithm for the generation of QSAR models. Starting from three datasets (EPAFHM, ECBHPV, AQUIRE), prediction of acute toxicity for fish (Pimephales promelas) with a global consensus model was carried out leading to very interesting statistics. The integration of the notion of Mode of Action was the second point of this study. A Bayesian classification associated to the genetic algorithm for consensus models was created leading to a good estimation of toxicity associated to derivatives with nonspecific MOA.