Romualdo Benigni

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 toward 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.

Structure alerts for carcinogenicity, and the Salmonella assay system: a novel insight through the chemical relational databases technology.

In the past decades, chemical carcinogenicity has been the object of mechanistic studies that have been translated into valuable experimental (e.g., the Salmonella assays system) and theoretical (e.g., compilations of structure alerts for chemical carcinogenicity) models. These findings remain the basis of the science and regulation of mutagens and carcinogens. Recent advances in the organization and treatment of large databases consisting of both biological and chemical information nowadays allows for a much easier and more refined view of data. This paper reviews recent analyses on the predictive performance of various lists of structure alerts, including a new compilation of alerts that combines previous work in an optimized form for computer implementation. The revised compilation is part of the Toxtree 1.50 software (freely available from the European Chemicals Bureau website). The use of structural alerts for the chemical biological profiling of a large database of Salmonella mutagenicity results is also reported. Together with being a repository of the science on the chemical biological interactions at the basis of chemical carcinogenicity, the SAs have a crucial role in practical applications for risk assessment, for: (a) description of sets of chemicals; (b) preliminary hazard characterization; (c) formation of categories for e.g., regulatory purposes; (d) generation of subsets of congeneric chemicals to be analyzed subsequently with QSAR methods; (e) priority setting. An important aspect of SAs as predictive toxicity tools is that they derive directly from mechanistic knowledge. The crucial role of mechanistic knowledge in the process of applying (Q)SAR considerations to risk assessment should be strongly emphasized. Mechanistic knowledge provides a ground for interaction and dialogue between model developers, toxicologists and regulators, and permits the integration of the (Q)SAR results into a wider regulatory framework, where different types of evidence and data concur or complement each other as a basis for making decisions and taking actions.

Carcinogenicity of the aromatic amines: from structure-activity relationships to mechanisms of action and risk assessment

Aromatic amines represent one of the most important classes of industrial and environmental chemicals: many of them have been reported to be powerful carcinogens and mutagens, and/or hemotoxicants. Their toxicity has been studied also with quantitative structure-activity relationship (QSAR) methods: these studies are potentially suitable for investigating mechanisms of action and for estimating the toxicity of compounds lacking experimental determinations. In this paper, we first summarized the QSAR models for the rodent carcinogenicity of the aromatic amines. The gradation of potency of the carcinogenic amines depended firstly on their hydrophobicity, and secondly on electronic (reactivity, propensity to be metabolically transformed) and steric properties. On the contrary, the difference between carcinogenic and non-carcinogenic aromatic amines depended mainly on electronic and steric properties. These QSARs can be used directly for estimating the carcinogenicity of aromatic amines. A two-step prediction is possible: (1) estimation of yes/no activity; (2) if the answer from step 1 is yes, then prediction of the degree of potency. The QSARs for rodent carcinogenicity were put in a wider context by comparing them with those for: (a) Salmonella mutagenicity; (b) general toxicity; (c) enzymatic reactions; (d) physical-chemical reactions. This comparative QSAR exercise generated a coherent global picture of the action mechanisms of the aromatic amines. The QSARs for carcinogenicity were similar to those for Salmonella mutagenicity, thus pointing to a similar mechanism of action. On the contrary, the general toxicity QSARs (both in vitro and in vivo systems) were mostly based on hydrophobicity, pointing to an aspecific mechanism of action much simpler than that for carcinogenicity and mutagenicity. The oxidation of the amines (first step in the main metabolic pathway leading to carcinogenic and mutagenic species) had identical QSARs in both enzymatic and physical-chemical systems, thus providing evidence for the link between simple chemical reactions and those in biological systems. The results show that it is possible to generate mechanistically and statistically sound QSAR models for rodent carcinogenicity, and indirectly that the rodent bioassay is a reliable source of good quality data.

Collaborative development of predictive toxicology applications

OpenTox provides an interoperable, standards-based Framework for the support of predictive toxicology data management, algorithms, modelling, validation and reporting. It is relevant to satisfying the chemical safety assessment requirements of the REACH legislation as it supports access to experimental data, (Quantitative) Structure-Activity Relationship models, and toxicological information through an integrating platform that adheres to regulatory requirements and OECD validation principles. Initial research defined the essential components of the Framework including the approach to data access, schema and management, use of controlled vocabularies and ontologies, architecture, web service and communications protocols, and selection and integration of algorithms for predictive modelling. OpenTox provides end-user oriented tools to non-computational specialists, risk assessors, and toxicological experts in addition to Application Programming Interfaces (APIs) for developers of new applications. OpenTox actively supports public standards for data representation, interfaces, vocabularies and ontologies, Open Source approaches to core platform components, and community-based collaboration approaches, so as to progress system interoperability goals.The OpenTox Framework includes APIs and services for compounds, datasets, features, algorithms, models, ontologies, tasks, validation, and reporting which may be combined into multiple applications satisfying a variety of different user needs. OpenTox applications are based on a set of distributed, interoperable OpenTox API-compliant REST web services. The OpenTox approach to ontology allows for efficient mapping of complementary data coming from different datasets into a unifying structure having a shared terminology and representation.Two initial OpenTox applications are presented as an illustration of the potential impact of OpenTox for high-quality and consistent structure-activity relationship modelling of REACH-relevant endpoints: ToxPredict which predicts and reports on toxicities for endpoints for an input chemical structure, and ToxCreate which builds and validates a predictive toxicity model based on an input toxicology dataset. Because of the extensible nature of the standardised Framework design, barriers of interoperability between applications and content are removed, as the user may combine data, models and validation from multiple sources in a dependable and time-effective way.

Predictive models for carcinogenicity and mutagenicity: frameworks, state-of-the-art, and perspectives.

Mutagenicity and carcinogenicity are endpoints of major environmental and regulatory concern. These endpoints are also important targets for development of alternative methods for screening and prediction due to the large number of chemicals of potential concern and the tremendous cost (in time, money, animals) of rodent carcinogenicity bioassays. Both mutagenicity and carcinogenicity involve complex, cellular processes that are only partially understood. Advances in technologies and generation of new data will permit a much deeper understanding. In silico methods for predicting mutagenicity and rodent carcinogenicity based on chemical structural features, along with current mutagenicity and carcinogenicity data sets, have performed well for local prediction (i.e., within specific chemical classes), but are less successful for global prediction (i.e., for a broad range of chemicals). The predictivity of in silico methods can be improved by improving the quality of the data base and endpoints used for modelling. In particular, in vitro assays for clastogenicity need to be improved to reduce false positives (relative to rodent carcinogenicity) and to detect compounds that do not interact directly with DNA or have epigenetic activities. New assays emerging to complement or replace some of the standard assays include Vitotox™, GreenScreenGC, and RadarScreen. The needs of industry and regulators to assess thousands of compounds necessitate the development of high-throughput assays combined with innovative data-mining and in silico methods. Various initiatives in this regard have begun, including CAESAR, OSIRIS, CHEMOMENTUM, CHEMPREDICT, OpenTox, EPAA, and ToxCast™. In silico methods can be used for priority setting, mechanistic studies, and to estimate potency. Ultimately, such efforts should lead to improvements in application of in silico methods for predicting carcinogenicity to assist industry and regulators and to enhance protection of public health.

Predictivity of QSAR.

A range of good quality, local QSARs for mutagenicity and carcinogenicity have been assessed and challenged for their predictivity in respect to real external test sets (i.e., chemicals never considered by the authors while developing their models). The QSARs for potency (applicable only to toxic chemicals) generated predictions 30-70% correct, whereas the QSARs for discriminating between active and inactive chemicals were 70-100% correct in their external predictions: thus the latter can be used with good reliability for applicative purposes. On the other hand internal, statistical validation methods, which are often assumed to be good diagnostics for predictivity, did not correlate well with the predictivity of the QSARs when challenged in external prediction tests. Nonlocal models for noncongeneric chemicals were considered as well, pointing to the critical role of an adequate definition of the applicability domain.

The second National Toxicology Program comparative exercise on the prediction of rodent carcinogenicity: definitive results.

Chemical carcinogenicity has been the target of a large array of attempts to create alternative predictive models, ranging from short-term biological assays (e.g. mutagenicity tests) to theoretical models. Among the theoretical models, the application of the science of structure-activity relationships (SAR) has earned special prominence. A crucial element is the independent evaluation of the predictive ability. In the past decade, there have been two fundamental comparative exercises on the prediction of chemical carcinogenicity, held under the aegis to the US National Toxicology Program (NTP). In both exercises, the predictions were published before the animal data were known, thus using a most stringent criterion of predictivity. We analyzed the results of the first comparative exercise in a previous paper [Mutat. Res. 387 (1997) 35]; here, we present the complete results of the second exercise, and we analyze and compare the prediction sets. The range of accuracy values was quite large: the systems that performed best in this prediction exercise were in the range 60-65% accuracy. They included various human experts approaches (e.g. Oncologic) and biologically based approaches (e.g. the experimental transformation assay in Syrian hamster embryo (SHE) cells). The main difficulty for the structure-activity relationship-based approaches was the discrimination between real carcinogens, and non-carcinogens containing structural alerts (SA) for genotoxic carcinogenicity. It is shown that the use of quantitative structure-activity relationship models, when possible, can contribute to overcome the above problem. Overall, given the uncertainty linked to the predictions, the predictions for the individual chemicals cannot be taken at face value; however, the general level of knowledge available today (especially for genotoxic carcinogens) allows qualified human experts to operate a very efficient priority setting of large sets of chemicals.

Nonlinear Methods in the Analysis of Protein Sequences: A Case Study in Rubredoxins

Two computational methods widely used in time series analysis were applied to protein sequences, and their ability to derive structural information not directly accessible through classical sequence comparisons methods was assessed. The primary structures of 19 rubredoxins of both mesophilic and thermophilic bacteria, coded with hydrophobicity values of amino acid residues, were considered as time series and were analyzed by 1) recurrence quantification analysis and 2) spectral analysis of the sequence major eigenfunctions. The results of the two methods agreed to a large extent and generated a classification consistent with known 3D structural characteristics of the studied proteins. This classification separated in a clearcut manner a thermophilic protein from mesophilic proteins. The classification of primary structures given by the two dynamical methods was demonstrated to be basically different from classification stemming from classical sequence homology metrics. Moreover, on a more detailed scale, the method was able to discriminate between thermophilic and mesophilic proteins from a set of chimeric sequences generated from the mixing of a mesophilic (Rubr Clopa) and a thermophilic (Rubr Pyrfu) protein. Overall, our results point to a new way of looking at protein sequence comparisons.

Alternatives to the carcinogenicity bioassay: in silico methods, and the in vitro and in vivo mutagenicity assays

Importance of the field: Carcinogenicity and mutagenicity are toxicological end points posing considerable concern for human health. Due to the cost in animal lives, time and money, alternative approaches to the rodent bioassay were designed based on: i) identification of mutations and ii) structure–activity relationships. Areas covered in this review: Evidence on i) and ii) is summarized, covering 4 decades (1971 – 2010). What the reader will gain: A comprehensive, state-of-the-art perspective on alternatives to the carcinogenicity bioassay. Take home message: Research to develop mutagenicity-based tests to predict carcinogenicity has generated useful results only for a limited area of the chemical space, that is, for the DNA-reactive chemicals (able to induce cancer, together with a wide spectrum of mutations). The most predictive mutagenicity-based assay is the Ames test. For non-DNA-reactive chemicals, that are Ames-negative and mutagenic in other in vitro assays (e.g., clastogenicity), no correlation with carcinogenicity is apparent. The knowledge on DNA reactivity permits the identification of genotoxic carcinogens with the same efficiency of the Ames test. Thus, a chemical mutagenic in Salmonella and/or with structural alerts should be seriously considered as a potential carcinogen. No reliable mutagenicity-based alternative tools are available to assess the risk of non-DNA-reactive chemicals.

Structural analysis and predictive value of the rodent in vivo micronucleus assay results

In vivo genotoxicity studies-shortly followed by carcinogenicity-are posing high demand for test-related recourses in terms of animal lives and resources. Among those, the micronucleus test in rodents is the most widely used as a follow-up to positive in vitro mutagenicity results; therefore, the development and extensive use of estimation techniques based on the concept of Structure-Activity Relationships-such as (Quantitative) Structure-Activity Relationships, read-across and grouping of chemicals-might have a huge saving potential for this end point. In this paper, we present a newly derived compilation of Structural Alerts for the rodent in vivo micronucleus assay, thus providing a coarse-grain filter for preliminary screening of potentially in vivo mutagens. The compilation has been implemented as computerized rule of the expert system Toxtree and is freely available: http://ecb.jrc.ec.europa.eu/qsar/qsar-tools/index.php?c=TOXTREE. In addition, analyses on the performance of the micronucleus assay as pre-screening tool for carcinogenesis indicate that this assay is prone to give false-negative predictions and point to the need of improving the in vivo component of the present testing schemes.