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

Abstract

BackgroundIn 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.ResultsIn 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.ConclusionsDespite 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.

Ellagic Acid, Kaempferol, and Quercetin from Acacia nilotica: Promising Combined Drug With Multiple Mechanisms of Action

“…Furthermore, the predicted AMES toxicity of Epicatechin will lead to genotoxicity and mutagenicity [81], the predicted hERG II potassium channel inhibitory effect of Acacetin Bprolongs the QT interval in ECG that increases the risk for potentially fatal ventricular arrhythmias^ [82]; subsequently, such drugs will not be considered as drug-likeness candidate (Table 9).…”

In silico predictions of genotoxicity for aromatic amines

“…The nitrenium hypothesis is likely best suited to predict the mutagenicity for a series of congeneric compounds as pointed out above when we discussed Borosky's work (49). The usefulness of the nitrenium hypothesis to predict the Ames activity was demonstrated by yet another group at Novartis (59)(60). McCarren et al found the same two equations, Equation 8 and 9, to give the best discrimination between Ames positive and Ames negative compounds.…”

“…McCarren et al identified that besides the nitrenium ion formation energy, the AM1 calculated HOMO energy shows a good separation between Ames positive and Ames negative compounds (59). The nitrenium natural charge and the anion formation energy lead to much lower separation between the Ames positive and Ames negative classes.…”

“…In this paper the authors also compare the performance of the nitrenium ion formation to PLS and Random Forest models. They conclude that the bestvalidated and best-performing prediction available for low molecular weight aryl amines is the quantum-mechanics calculated reaction energy representing the formation of the nitrenium ion (59).…”

Predicting mammalian metabolism and toxicity of pesticides in silico

“…Despite the questions that have been raised about their relevance to pesticide registration, Ames mutagenicity tests remain a major regulatory hurdle that must be passed early in agrochemical pesticide development. Although hundreds of thousands of these assays have been performed, the majority of the results obtained are not publicly available.…”