Dataset Information

An investigation into pharmaceutically relevant mutagenicity data and the influence on Ames predictive potential.

ABSTRACT:

Background

In drug discovery, a positive Ames test for bacterial mutation presents a significant hurdle to advancing a drug to clinical trials. In a previous paper, we discussed success in predicting the genotoxicity of reagent-sized aryl-amines (ArNH2), a structure frequently found in marketed drugs and in drug discovery, using quantum mechanics calculations of the energy required to generate the DNA-reactive nitrenium intermediate (ArNH:+). In this paper we approach the question of what molecular descriptors could improve these predictions and whether external data sets are appropriate for further training.

Results

In trying to extend and improve this model beyond this quantum mechanical reaction energy, we faced considerable difficulty, which was surprising considering the long history and success of QSAR model development for this test. Other quantum mechanics descriptors were compared to this reaction energy including AM1 semi-empirical orbital energies, nitrenium formation with alternative leaving groups, nitrenium charge, and aryl-amine anion formation energy. Nitrenium formation energy, regardless of the starting species, was found to be the most useful single descriptor. External sets used in other QSAR investigations did not present the same difficulty using the same methods and descriptors. When considering all substructures rather than just aryl-amines, we also noted a significantly lower performance for the Novartis set. The performance gap between Novartis and external sets persists across different descriptors and learning methods. The profiles of the Novartis and external data are significantly different both in aryl-amines and considering all substructures. The Novartis and external data sets are easily separated in an unsupervised clustering using chemical fingerprints. The chemical differences are discussed and visualized using Kohonen Self-Organizing Maps trained on chemical fingerprints, mutagenic substructure prevalence, and molecular weight.

Conclusions

Despite extensive work in the area of predicting this particular toxicity, work in designing and publishing more relevant test sets for compounds relevant to drug discovery is still necessary. This work also shows that great care must be taken in using QSAR models to replace experimental evidence. When considering all substructures, a random forest model, which can inherently cover distinct neighborhoods, built on Novartis data and previously reported external data provided a suitable model.

SUBMITTER: McCarren P

PROVIDER: S-EPMC3277490 | biostudies-literature | 2011 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

An investigation into pharmaceutically relevant mutagenicity data and the influence on Ames predictive potential.

McCarren Patrick P Springer Clayton C Whitehead Lewis L

Journal of cheminformatics 20111122

<h4>Background</h4>In drug discovery, a positive Ames test for bacterial mutation presents a significant hurdle to advancing a drug to clinical trials. In a previous paper, we discussed success in predicting the genotoxicity of reagent-sized aryl-amines (ArNH2), a structure frequently found in marketed drugs and in drug discovery, using quantum mechanics calculations of the energy required to generate the DNA-reactive nitrenium intermediate (ArNH:+). In this paper we approach the question of wha ...[more]

PMID: 22107807

Similar Datasets

Project description:Atropisomerism is a conformational chirality that occurs when there is hindered rotation about a σ-bond. While atropisomerism is exemplified by biaryls, it is observed in many other pharmaceutically relevant scaffolds including heterobiaryls, benzamides, diarylamines, and anilides. As bond rotation leads to racemization, atropisomers span the gamut of stereochemical stability. LaPlante has classified atropisomers based on their half-life of racemization at 37 °C: class 1 (t1/2 < 60 s), class 2 (60 s < t1/2 < 4.5 years), and class 3 (t1/2 > 4.5 years). In general, class-3 atropisomers are considered to be suitable for drug development. There are currently four FDA-approved drugs that exist as stable atropisomers, and many others are in clinical trials or have recently appeared in the drug discovery literature. Class-1 atropisomers are more prevalent, with ∼30% of recent FDA-approved small molecules possessing at least one class-1 axis. While class-1 atropisomers do not possess the requisite stereochemical stability to meet the classical definition of atropisomerism, they often bind a given target in a specific set of chiral conformations.Over the past decade, our laboratory has embarked on a research program aimed at leveraging atropisomerism as a design feature to improve the target selectivity of promiscuous lead compounds. Our studies initially focused on introducing class-3 atropisomerism into promiscuous kinase inhibitors, resulting in a proof of principle in which the different atropisomers of a compound can have different selectivity profiles with potentially improved target selectivity. This inspired a careful analysis of the binding conformations of diverse ligands bound to different target proteins, resulting in the realization that the sampled dihedral conformations about a prospective atropisomeric axis played a key role in target binding and that preorganizing the prospective atropisomeric axis into a desired target's preferred conformational range can lead to large gains in target selectivity.As atropisomerism is becoming more prevalent in modern drug discovery, there is an increasing need for strategies for atropisomerically pure samples of pharmaceutical compounds. This has led us and other groups to develop catalytic atroposelective methodologies toward pharmaceutically privileged scaffolds. Our laboratory has contributed examples of atroposelective methodologies toward heterobiaryl systems while also exploring the chirality of less-studied atropisomers such as diarylamines and related scaffolds.This Account will detail recent encounters with atropisomerism in medicinal chemistry and how atropisomerism has transitioned from a "lurking menace" into a leverageable design strategy in order to modulate various properties of biologically active small molecules. This Account will also discuss recent advances in atroposelective synthesis, with a focus on methodologies toward pharmaceutically privileged scaffolds. We predict that a better understanding of the effects of conformational restriction about a prospective atropisomeric axis on target binding will empower chemists to rapidly "program" the selectivity of a lead molecule toward a desired target.

Project description:The International Conference on Harmonization (ICH) M7 guideline allows the use of in silico approaches for predicting Ames mutagenicity for the initial assessment of impurities in pharmaceuticals. This is the first international guideline that addresses the use of quantitative structure-activity relationship (QSAR) models in lieu of actual toxicological studies for human health assessment. Therefore, QSAR models for Ames mutagenicity now require higher predictive power for identifying mutagenic chemicals. To increase the predictive power of QSAR models, larger experimental datasets from reliable sources are required. The Division of Genetics and Mutagenesis, National Institute of Health Sciences (DGM/NIHS) of Japan recently established a unique proprietary Ames mutagenicity database containing 12140 new chemicals that have not been previously used for developing QSAR models. The DGM/NIHS provided this Ames database to QSAR vendors to validate and improve their QSAR tools. The Ames/QSAR International Challenge Project was initiated in 2014 with 12 QSAR vendors testing 17 QSAR tools against these compounds in three phases. We now present the final results. All tools were considerably improved by participation in this project. Most tools achieved >50% sensitivity (positive prediction among all Ames positives) and predictive power (accuracy) was as high as 80%, almost equivalent to the inter-laboratory reproducibility of Ames tests. To further increase the predictive power of QSAR tools, accumulation of additional Ames test data is required as well as re-evaluation of some previous Ames test results. Indeed, some Ames-positive or Ames-negative chemicals may have previously been incorrectly classified because of methodological weakness, resulting in false-positive or false-negative predictions by QSAR tools. These incorrect data hamper prediction and are a source of noise in the development of QSAR models. It is thus essential to establish a large benchmark database consisting only of well-validated Ames test results to build more accurate QSAR models.

Project description:UnlabelledBackgroundExperimental screening of chemical compounds for biological activity is a time consuming and expensive practice. In silico predictive models permit inexpensive, rapid "virtual screening" to prioritize selection of compounds for experimental testing. Both experimental and in silico screening can be used to test compounds for desirable or undesirable properties. Prior work on prediction of mutagenicity has primarily involved identification of toxicophores rather than whole-molecule predictive models. In this work, we examined a range of in silico predictive classification models for prediction of mutagenic properties of compounds, including methods such as J48 and SMO which have not previously been widely applied in cheminformatics.ResultsThe Bursi mutagenicity data set containing 4337 compounds (Set 1) and a Benchmark data set of 6512 compounds (Set 2) were taken as input data set in this work. A third data set (Set 3) was prepared by joining up the previous two sets. Classification algorithms including Naïve Bayes, Random Forest, J48 and SMO with 10 fold cross-validation and default parameters were used for model generation on these data sets. Models built using the combined performed better than those developed from the Benchmark data set. Significantly, Random Forest outperformed other classifiers for all the data sets, especially for Set 3 with 89.27% accuracy, 89% precision and ROC of 95.3%. To validate the developed models two external data sets, AID1189 and AID1194, with mutagenicity data were tested showing 62% accuracy with 67% precision and 65% ROC area and 91% accuracy, 91% precision with 96.3% ROC area respectively. A Random Forest model was used on approved drugs from DrugBank and metabolites from the Zinc Database with True Positives rate almost 85% showing the robustness of the model.ConclusionWe have created a new mutagenicity benchmark data set with around 8,000 compounds. Our work shows that highly accurate predictive mutagenicity models can be built using machine learning methods based on chemical descriptors and trained using this set, and these models provide a complement to toxicophores based methods. Further, our work supports other recent literature in showing that Random Forest models generally outperform other comparable machine learning methods for this kind of application.

Dataset Information

An investigation into pharmaceutically relevant mutagenicity data and the influence on Ames predictive potential.

Background

Results

Conclusions

Publications

An investigation into pharmaceutically relevant mutagenicity data and the influence on Ames predictive potential.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets