Jay Russell Niemelä

A Stepwise Approach for Defining the Applicability Domain of SAR and QSAR Models

A stepwise approach for determining the model applicability domain is proposed. Four stages are applied to account for the diversity and complexity of the current SAR/QSAR models, reflecting their mechanistic rationality (including metabolic activation of chemicals) and transparency. General parametric requirements are imposed in the first stage, specifying in the domain only those chemicals that fall in the range of variation of the physicochemical properties of the chemicals in the training set. The second stage defines the structural similarity between chemicals that are correctly predicted by the model. The structural neighborhood of atom-centered fragments is used to determine this similarity. The third stage in defining the domain is based on a mechanistic understanding of the modeled phenomenon. Here, the model domain combines the reliability of specific reactive groups hypothesized to cause the effect and the domain of explanatory variables determining the parametric requirements in order for functional groups to elicit their reactivity. Finally, the reliability of simulated metabolism (metabolites, pathways, and maps) is taken into account in assessing the reliability of predictions, if metabolic activation of chemicals is a part of the (Q)SAR model. Some of the stages of the proposed approach for defining the model domain can be eliminated depending on the availability and quality of the experimental data used to derive the model, the specificity of (Q)SARs, and the goals of their ultimate application. The performance of the proposed definition of the model domain is tested using several examples of (Q)SARs that have been externally validated, including models for predicting acute toxicity, skin sensitization, and biodegradation. The results clearly showed that credibility in predictions of QSAR models for chemicals belonging to their domain is much higher than for chemicals outside this domain.

QSAR models for reproductive toxicity and endocrine disruption in regulatory use – a preliminary investigation

A special challenge in the new European Union chemicals legislation, Registration, Evaluation and Authorisation of Chemicals, will be the toxicological evaluation of chemicals for reproductive toxicity. Use of valid quantitative structure–activity relationships (QSARs) is a possibility under the new legislation. This article focuses on a screening exercise by use of our own and commercial QSAR models for identification of possible reproductive toxicants. Three QSAR models were used for reproductive toxicity for the endpoints teratogenic risk to humans (based on animal tests, clinical data and epidemiological human studies), dominant lethal effect in rodents (in vivo) and Drosophila melanogaster sex-linked recessive lethal effect. A structure set of 57,014 European Inventory of Existing Chemical Substances (EINECS) chemicals was screened. A total of 5240 EINECS chemicals, corresponding to 9.2%, were predicted as reproductive toxicants by one or more of the models. The chemicals predicted positive for reproductive toxicity will be submitted to the Danish Environmental Protection Agency as scientific input for a future updated advisory classification list with advisory classifications for concern for humans owing to possible developmental toxic effects: Xn (Harmful) and R63 (Possible risk of harm to the unborn child). The chemicals were also screened in three models for endocrine disruption. †Presented at the 13th International Workshop on QSARs in the Environmental Sciences (QSAR 2008), 8–12 June 2008, Syracuse, USA.

QSAR model for human pregnane X receptor (PXR) binding: screening of environmental chemicals and correlations with genotoxicity, endocrine disruption and teratogenicity.

The pregnane X receptor (PXR) has a key role in regulating the metabolism and transport of structurally diverse endogenous and exogenous compounds. Activation of PXR has the potential to initiate adverse effects, causing drug-drug interactions, and perturbing normal physiological functions. Therefore, identification of PXR ligands would be valuable information for pharmaceutical and toxicological research. In the present study, we developed a quantitative structure-activity relationship (QSAR) model for the identification of PXR ligands using data based on a human PXR binding assay. A total of 631 molecules, representing a variety of chemical structures, constituted the training set of the model. Cross-validation of the model showed a sensitivity of 82%, a specificity of 85%, and a concordance of 84%. The developed model provided knowledge about molecular descriptors that may influence the binding of molecules to PXR. The model was used to screen a large inventory of environmental chemicals, of which 47% was found to be within domain of the model. Approximately 35% of the chemicals within domain were predicted to be PXR ligands. The predicted PXR ligands were found to be overrepresented among chemicals predicted to cause adverse effects, such as genotoxicity, teratogenicity, estrogen receptor activation and androgen receptor antagonism compared to chemicals not causing these effects. The developed model may be useful as a tool for predicting potential PXR ligands and for providing mechanistic information of toxic effects of chemicals.

QSAR models for anti-androgenic effect--a preliminary study.

Three modelling systems (MultiCase®, LeadScope® and MDL® QSAR) were used for construction of androgenic receptor antagonist models. There were 923–942 chemicals in the training sets. The models were cross-validated (leave-groups-out) with concordances of 77–81%, specificity of 78–91% and sensitivity of 51–76%. The specificity was highest in the MultiCase® model and the sensitivity was highest in the MDL® QSAR model. A complementary use of the models may be a valuable tool when optimizing the prediction of chemicals for androgenic receptor antagonism. When evaluating the fitness of the model for a particular application, balance of training sets, domain definition, and cut-offs for prediction interpretation should also be taken into account. Different descriptors in the modelling systems are illustrated with hydroxyflutamide and dexamethasone as examples (a non-steroid and a steroid anti-androgen, respectively). More research concerning the mechanism of anti-androgens would increase the possibility for further optimization of the QSAR models. Further expansion of the basis for the models is in progress, including the addition of more drugs.

QSAR screening of 70,983 REACH substances for genotoxic carcinogenicity, mutagenicity and developmental toxicity in the ChemScreen project

The ChemScreen project aimed to develop a screening system for reproductive toxicity based on alternative methods. QSARs can, if adequate, contribute to the evaluation of chemical substances under REACH and may in some cases be applied instead of experimental testing to fill data gaps for information requirements. As no testing for reproductive effects should be performed in REACH on known genotoxic carcinogens or germ cell mutagens with appropriate risk management measures implemented, a QSAR pre-screen for 70,983 REACH substances was performed. Sixteen models and three decision algorithms were used to reach overall predictions of substances with potential effects with the following result: 6.5% genotoxic carcinogens, 16.3% mutagens, 11.5% developmental toxicants. These results are similar to findings in earlier QSAR and experimental studies of chemical inventories, and illustrate how QSAR predictions may be used to identify potential genotoxic carcinogens, mutagens and developmental toxicants by high-throughput virtual screening.

QSAR models for P450 (2D6) substrate activity

Human Cytochrome P450 (CYP) is a large group of enzymes that possess an essential function in metabolising different exogenous and endogenous compounds. Humans have more than 50 different genes encoding CYP enzymes, among these a gene encoding for the CYP isoenzyme 2D6, a CYP able to metabolise drugs and other chemicals. A training set of 747 chemicals primarily based on in vivo human data for the CYP isoenzyme 2D6 was collected from the literature. QSAR models focusing on substrate/non-substrate activity were constructed by the use of MultiCASE, Leadscope and MDL quantitative structure–activity relationship (QSAR) modelling systems. They cross validated (leave-groups-out) with concordances of 71%, 81% and 82%, respectively. Discrete organic European Inventory of Existing Commercial Chemical Substances (EINECS) chemicals were screened to predict an approximate percentage of CYP 2D6 substrates. These chemicals are potentially present in the environment. The biological importance of the CYP 2D6 and the use of the software mentioned above were discussed.

Identification of cytochrome P450 2D6 and 2C9 substrates and inhibitors by QSAR analysis.

This paper presents four new QSAR models for CYP2C9 and CYP2D6 substrate recognition and inhibitor identification based on human clinical data. The models were used to screen a large data set of environmental chemicals for CYP activity, and to analyze the frequency of CYP activity among these compounds. A large fraction of these chemicals were found to be CYP active, and thus potentially capable of affecting human physiology. 20% of the compounds within applicability domain of the models were predicted to be CYP2C9 substrates, and 17% to be inhibitors. The corresponding numbers for CYP2D6 were 9% and 21%. Where the majority of CYP2C9 active compounds were predicted to be both a substrate and an inhibitor at the same time, the CYP2D6 active compounds were primarily predicted to be only inhibitors. It was demonstrated that the models could identify compound classes with a high occurrence of specific CYP activity. An overrepresentation was seen for poly-aromatic hydrocarbons (group of procarcinogens) among CYP2C9 active and mutagenic compounds compared to CYP2C9 inactive and mutagenic compounds. The mutagenicity was predicted with a QSAR model based on Ames in vitro test data.

Accessing and Using Chemical Databases

Computer-based representation of chemicals makes it possible to organize data in chemical databases-collections of chemical structures and associated properties. Databases are widely used wherever efficient processing of chemical information is needed, including search, storage, retrieval, and dissemination. Structure and functionality of chemical databases are considered. The typical kinds of information found in a chemical database are considered-identification, structural, and associated data. Functionality of chemical databases is presented, with examples of search and access types. More details are included about the OASIS database and platform and the Danish (Q)SAR Database online. Various types of chemical database resources are discussed, together with a list of examples.

Lack of genotoxic potential of acetylated monoglyceride: An alternative plasticiser to phthalates

phthalates DTU Orbit (09/08/2019) Lack of genotoxic potential of acetylated monoglyceride: An alternative plasticiser to phthalates Purpose: With a yearly polymer production of more than 400 million tons, phthalates based on non sustainable petrochemical materials are the most used group of plasticisers. Their low biodegradability and endocrine activity suspected to affect reproductive ability of animals and humans caused an interest in alternatives. Biodegradable plasticisers produced from sustainable materials, of low toxicity and no endocrine activity offer desirable alternatives to phthalates. The aim of the project was to screen an alternative plasticiser acetoxylated monoglyceride for genotoxic potential. Methods: The ability of acetylated monoglyceride to induce genotoxicity in vitro was investigated in silico by QSAR modelling. The first step was to assure that an obtained prediction falls within the applicability domain of the models – that there was sufficient similarity (in relevant descriptors) between the query substance and the substances in the training set of the model. The (Q)SARs prediction was followed by in vitro testing using Salmonella/microsome assay (Ames test) with strains TA 98 and TA 100, with and without metabolic activation. Results: There were no warnings for genotoxic fragments (Ashby-Tenant rules) and predictions were negative for several assays: Ames test, chromosomal aberration in Chinese hamster lung cells, mouse lymphoma TK cell mutation and unscheduled DNA synthesis in rat hepatocytes. The in vitro Ames test showed that the plasticiser did not induce gene mutations in bacteria. Presently, an in vivo comet assay to investigate the ability of the plasticiser to induce DNA strand breaks after oral exposure in the liver and kidney of rats is under conduction.