Marco Pintore

CAESAR models for developmental toxicity

BackgroundThe new REACH legislation requires assessment of a large number of chemicals in the European market for several endpoints. Developmental toxicity is one of the most difficult endpoints to assess, on account of the complexity, length and costs of experiments. Following the encouragement of QSAR (in silico) methods provided in the REACH itself, the CAESAR project has developed several models.ResultsTwo QSAR models for developmental toxicity have been developed, using different statistical/mathematical methods. Both models performed well. The first makes a classification based on a random forest algorithm, while the second is based on an adaptive fuzzy partition algorithm. The first model has been implemented and inserted into the CAESAR on-line application, which is java-based software that allows everyone to freely use the models.ConclusionsThe CAESAR QSAR models have been developed with the aim to minimize false negatives in order to make them more usable for REACH. The CAESAR on-line application ensures that both industry and regulators can easily access and use the developmental toxicity model (as well as the models for the other four endpoints).

Global QSAR models of skin sensitisers for regulatory purposes.

BackgroundThe new European Regulation on chemical safety, REACH, (Registration, Evaluation, Authorisation and Restriction of CHemical substances), is in the process of being implemented. Many chemicals used in industry require additional testing to comply with the REACH regulations. At the same time EU member states are attempting to reduce the number of animals used in experiments under the 3 Rs policy, (refining, reducing, and replacing the use of animals in laboratory procedures). Computational techniques such as QSAR have the potential to offer an alternative for generating REACH data. The FP6 project CAESAR was aimed at developing QSAR models for 5 key toxicological endpoints of which skin sensitisation was one.ResultsThis paper reports the development of two global QSAR models using two different computational approaches, which contribute to the hybrid model freely available online.ConclusionsThe QSAR models for assessing skin sensitisation have been developed and tested under stringent quality criteria to fulfil the principles laid down by the OECD. The final models, accessible from CAESAR website, offer a robust and reliable method of assessing skin sensitisation for regulatory use.

Molecular descriptor selection combining genetic algorithms and fuzzy logic: application to database mining procedures

Abstract A new algorithm, devoted to molecular descriptor selection in the context of Data Mining problems, has been developed. This algorithm is based on the concepts of genetic algorithms (GA) for descriptor hyperspace exploration and combined with a stepwise approach to get local convergence. Its selection power was evaluated by a fitness function derived from a fuzzy clustering method. Different training and test sets were randomly generated at each GA generation. The fitness score was derived by combining the scores of the training and test sets. The ability of the proposed algorithm to select relevant subsets of descriptors was tested on two data sets. The first one, an academic example, corresponded to the artificial problem of Bullseye, the second was a real data set including 114 olfactory compounds divided into three odor categories. In both cases, the proposed method allowed to improve the separation between the different data set classes.

Chemomentum - UNICORE 6 based infrastructure for complex applications in science and technology

Chemomentum, Grid Services based Environment to enable Innovative Research, is an end-user focused approach to exploit Grid computing for diverse application domains. Building on top of UNICORE 6, we are designing and implementing a flexible, user-friendly Grid system focussing on high-performance processing of complex application workflows and management of data, metadata and knowledge. This paper outlines Chemomentum vision, application scenarios, technical challenges, software architecture and design of the system.

Prediction of oral bioavailability by adaptive fuzzy partitioning

An adaptive fuzzy partition (AFP) algorithm was applied on two bioavailability data sets subdivided into four ranges of activity. A large set of molecular descriptors was tested and the most relevant parameters were selected with help of a procedure based on genetic algorithm concepts and stepwise method. After building several AFP models on a training set, the best ones were able to predict correctly 75% of the validation set compounds. Furthermore, an improvement of about 15% in the validation results was got, on the same data set, as regard to other prediction methods. The importance to work with data sets including a large molecular diversity, and to use tools able to manage it, was also shown. The prediction power was increased up to 25% employing a data set with a better-optimised molecular diversity.

Binary classification models for endocrine disrupter effects mediated through the estrogen receptor

Endocrine disrupters (EDs) form an interesting field of application attracting great attention in the recent years. They represent a number of exogenous substances interfering with the function of the endocrine system, including the interfering with developmental processes. In particular EDs are mentioned as substances requiring a more detailed control and specific authorization within REACH, the new European legislation on chemicals, together with other groups of chemicals of particular concern. QSAR represents a challenging method to approach data gap which is foreseen by REACH. The aim of this study was to provide an insight into the use of QSAR models to address ED effects mediated through the estrogen receptor (ER). New predictive models were derived to assess estrogenicity for a very large and heterogeneous dataset of chemical compounds. QSAR binary classifiers were developed based on different data mining techniques such as classification trees, decision forest, fuzzy logic, neural networks and support vector machines. The focus was given to multiple endpoints to better characterize the effects of EDs evaluating both binding (RBA) and transcriptional activity (RA). A possible combination of the models was also explored. A very good accuracy was reached for both RA and RBA models (higher than 80%). †Presented at the 13th International Workshop on QSARs in the Environmental Sciences (QSAR 2008), 8–12 June 2008, Syracuse, USA.

A molecular modeling and 3D QSAR study of a large series of indole inhibitors of human non-pancreatic secretory phospholipase A2

Automated docking allowing protein-based alignment was performed for a series of 188 indole inhibitors of the human non-pancreatic secretory phospholipase A2 (hnps-PLA2). All the substituted indoles were docked to the crystal structure of hnps-PLA2 and a three-dimensional QSAR model was then established using the CoMFA method. The set of 188 compounds was divided into two subsets, the first one constituting the training set (126 compounds), while the second constituted the test set (62 compounds). The established CoMFA model derived from the training set was then applied to the test set. A good correlation between predicted and experimental activity data allows to validate the 3D QSAR model. A second and global 3D QSAR including all the compounds was established, allowing the creation of the hnps-PLA2 pharmacophore.

VALIDATION OF THE MODELS

Although many international regulatory bodies recognize the potential benefits of quantitative structure–activity relationship (QSAR) techniques, they are scarcely used in real applications. Some general principles are listed, but the lack of guidelines and standardized protocols accepted and used by all research groups prevents an effective world-wide development of such strategies. The use of appropriate statistical validation tools, such as the training and test set or others, should be adopted for predictive models not only in the case of QSAR based on descriptors but also in the case of models based on rules. In other words, the rules that are defined as appropriate for predictive purposes should also be validated. The statistical tools should prove the capability of the model to be valid in a general way—to be predictive for compounds not used in development of the model.

Development of predictive models by adaptive fuzzy partitioning. Application to compounds active on the central nervous system

Abstract A new data mining method, derived from Fuzzy Logic concepts, was developed in order to classify biochemical databases and to predict the activities of large series of untested compounds. This technique, called Adaptive Fuzzy Partition (AFP), builds relationships between molecular descriptors and biochemical activities by dynamically dividing the descriptor hyperspace into a set of fuzzily partitioned subspaces. These subspaces are described by simple linguistic rules, from which scores ranging between 0 and 1 can be derived. The latter values define, for each compound, the degrees of membership of the different biological properties analyzed. The prediction ability of AFP was evaluated by analyzing a training set of 377 central nervous system (CNS)-active molecules subdivided into eight receptor classes. After selecting the most relevant descriptors by a procedure combining genetic algorithms and stepwise techniques, the best AFP model was selected and validated by a validation set. Furthermore, its robustness was confirmed by predicting a test set of 102 compounds never used to define the AFP models. Encouraging validation ratios of about 80% were obtained in the prediction of the experimental CNS activities. Finally, a comparison between the results obtained by AFP and by other classic techniques showed that AFP improved sensibly the prediction power of the proposed models.

Hybrid genetic algorithm for dual selection

In this paper, a hybrid genetic approach is proposed to solve the problem of designing a subdatabase of the original one with the highest classification performances, the lowest number of features and the highest number of patterns. The method can simultaneously treat the double problem of editing instance patterns and selecting features as a single optimization problem, and therefore aims at providing a better level of information. The search is optimized by dividing the algorithm into self-controlled phases managed by a combination of pure genetic process and dedicated local approaches. Different heuristics such as an adapted chromosome structure and evolutionary memory are introduced to promote diversity and elitism in the genetic population. They particularly facilitate the resolution of real applications in the chemometric field presenting databases with large feature sizes and medium cardinalities. The study focuses on the double objective of enhancing the reliability of results while reducing the time consumed by combining genetic exploration and a local approach in such a way that excessive computational CPU costs are avoided. The usefulness of the method is demonstrated with artificial and real data and its performance is compared to other approaches.