QSAR Modeling: Where Have You Been? Where Are You Going To?

Quantitative Structure-Activity Relationship modeling is one of the major computational tools employed in medicinal chemistry. However, throughout its entire history it has drawn both praise and criticism concerning its reliability, limitations, successes, and failures. In this paper, we discuss: (i) the development and evolution of QSAR; (ii) the current trends, unsolved problems, and pressing challenges; and (iii) several novel and emerging applications of QSAR modeling. Throughout this discussion, we provide guidelines for QSAR development, validation, and application, which are summarized in best practices for building rigorously validated and externally predictive QSAR models. We hope that this Perspective will help communications between computational and experimental chemists towards collaborative development and use of QSAR models. We also believe that the guidelines presented here will help journal editors and reviewers apply more stringent scientific standards to manuscripts reporting new QSAR studies, as well as encourage the use of high quality, validated QSARs for regulatory decision making

Prioritization of chemicals based on chemoinformatic analysis

Several different chemical properties/activities must be contemporaneously taken into account to prioritize compounds for their hazardous behaviour. Examples of application of chemoinformatic methods, such as Principal Component Analysis for obtaining ranking indexes and Hierarchical Cluster Analysis for grouping chemicals with similar properties, are summarized for various classes of compounds of environmental concern. These cumulative end-points are then modelled by validated Quantitative Structure-Activity Relationships, based on theoretical molecular descriptors, to predict the potential hazard of new chemicals

Introduction to the need of Alternative Methods in REACH

In vitro and in silico methods are foreseen in the EU regulation REACH, to prioritize more dangerous compounds, to focus expensive experiments, by reducing animal tests and to fill the data gaps. A brief historical introduction of QSAR modelling and the importance of model validation according to the OECD principles for reliable predictions is presented

Chemometric Methods and Theoretical Molecular Descriptors in Predictive QSAR Modeling of the Environmental Behaviour of Organic Pollutants

This chapter surveys the QSAR modeling approaches (developed by the author\u2019s research unit) for the prediction of environmental properties of organic pollutants. Different chemometric methods, based on different theoretical molecular descriptors, have been applied: explorative techniques (such as PCA for ranking, SOM for similarity analysis, etc.), modeling approaches by Multiple-Linear Regression (MLR, in particular Ordinary Least Squares: OLS) and classification methods (mainly k-NN, CART, CP-ANN). The focus of this review is on the main topics of environmental chemistry and ecotoxicology, related to the physico-chemical properties, the reactivity and biological activity of chemicals of high environmental concern; thus the review deals with atmospheric degradation reactions of VOC with tropospheric oxidants, persistence and Long Range Transport of POPs, sorption behavior of pesticides (Koc and leaching), bioconcentration, toxicity (acute aquatic toxicity, mutagenicity of PAHs, estrogen binding activity for Endocrine Disruptors Compounds (EDCs)), and finally PBT (Persistent Bioaccumulative and Toxic) behavior for the screening and prioritization of organic pollutants. Common to all the proposed models is the careful attention paid to model validation for predictive ability (not only internal but also, and mainly, external validation for chemicals not participating in the model development) and the checking of the chemical domain of applicability. Adherence to such a policy, requested also by the OECD principles, ensures the production of reliable predicted data

QSAR e la nuova normativa europea REACH

The predictive QSAR (Quantitative Structure-Activity Relationships), whose basic principles are here illustrated, is a valid tool for the prediction of physico-chemical properties and biological activities (toxicities, etc.) of chemicals that lack of experimental data. This modelling approach is here presented highlighting the potential applications to fill the data gaps and for hazard assessment in the new European legislation Reach

External Evaluation of QSAR Models, in Addition to Cross-Validation: Verification of Predictive Capability on Totally New Chemicals

The necessity to externally validate QSAR models is highlighted with example of models stable by cross-validation but not predictive for new chemical

CAse studies on the Development and Application of in-Silico Techniques for Environmental hazard and Risk assessment (CADASTER)

EU-FP7 CADASTER Grant agreement no.: 212668 Implementation of REACH requires demonstration of the safe manufacture and use of chemicals. REACH aims to achieve a proper balance between societal, economic and environmental objectives, and attempts to efficiently use the scarce and scattered information available on the majority of substances. Thereupon REACH aims to reduce animal testing by optimized use of in silico and in vitro information on related compounds. The REACH regulation advocates the use of non-animal testing methods, but guidance is needed on how these methods should be used. The procedures include alternative methods such as chemical and biological read-across, in vitro results, in vivo information on analogues, (Q)SARs, and exposure-based waiving. The concept of Intelligent Testing Strategies for regulatory endpoints has been outlined to facilitate the assessments. Intensive efforts are needed to translate the concept into a workable, consensually acceptable, and scientifically sound strategy. CADASTER aims at providing the practical guidance to integrated risk assessment by carrying out a full hazard and risk assessment for chemicals belonging to four compound classes. A Decision Support System (DSS) will be developed that will be updated on a regular basis in order to accommodate and integrate the alternative methods mentioned above. Operational procedures will be developed, tested, and disseminated that guide a transparent evaluation of four classes of emerging chemicals, explicitly taking account of variability and uncertainty in data and in models. The main goal is to exemplify the integration of information, models and strategies for carrying out safety-, hazard- and risk assessments for large numbers of substances. Real risk estimates will be delivered according to the basic philosophy of REACH of minimizing animal testing, costs, and time. CADASTER will show how to increase the use of non-testing information for regulatory decision whilst meeting the main challenge of quantifying and reducing uncertainty