Transitioning to composite bacterial mutagenicity models in ICH M7 (Q)SAR analyses

Abstract

The International Council on Harmonisation (ICH) M7(R1) guideline describes the use of complementary (quantitative) structure-activity relationship ((Q)SAR) models to assess the mutagenic potential of drug impurities in new and generic drugs. Historically, the CASE Ultra and Leadscope software platforms used two different statistical-based models to predict mutations at G-C (guanine-cytosine) and A-T (adenine-thymine) sites, to comprehensively assess bacterial mutagenesis. In the present study, composite bacterial mutagenicity models covering multiple mutation types were developed. These new models contain more than double the number of chemicals (n = 9,254 and n = 13,514) than the corresponding non-composite models and show better toxicophore coverage. Additionally, the use of a single composite bacterial mutagenicity model simplifies impurity analysis in an ICH M7 (Q)SAR workflow by reducing the number of model outputs requiring review. An external validation set of 388 drug impurities representing proprietary pharmaceutical chemical space showed performance statistics ranging from of 66-82% in sensitivity, 91-95% in negative predictivity and 96% in coverage. This effort represents a major enhancement to these (Q)SAR models and their use under ICH M7(R1), leading to improved patient safety through greater predictive accuracy, applicability, and efficiency when assessing the bacterial mutagenic potential of drug impurities.

Keywords: Ames; Bacterial mutagenicity; Computational toxicology; Drug; Genotoxicity; ICH M7; In vitro; QSAR; Regulatory review; Structure-activity relationship.

Figure 1.

Changes (Δ) in sensitivity, specificity, positive predictivity, negative predictivity, and coverage between Salmonella/E. coli cumulative predictions and bacterial mutagenicity models in external validation.

Figure 2.

Changes (Δ) in model statistics when a single statistical model is compared against the model combined with Derek Nexus

Figure 3.

Percentage of out-of-domain (OOD) calls for different model combinations in external validation

Figure 4.

Selected primary aromatic amine fragments present in the CU composite bacterial mutagenicity model. Mean activity values are shown adjacent to or below each feature. Values above 0.5 are considered positive or activating features, while features below 0.5 are considered negative or deactivating features. Asterisks represent non-hydrogen atoms.

Figure 5.

Selected primary aromatic amine features present in the LS composite bacterial mutagenicity model. Mean values are presented above activating features and below deactivating features. Mean values above 0.5 are considered positive or activating features, while features below 0.5 are consider negative or deactivating features. (Ak = Alkyl carbon, Ar = Aromatic carbon)

Figure 6.

Model fragments and features representing toxicophores across CASE Ultra and Leadscope, and their mean activity values when transitioning from the Salmonella mutagenicity model to composite bacterial mutagenicity models. Asterisks represent non-hydrogen atoms, X represents any halogen, Ar represents an aromatic ring, and Q represents any atom other than carbon or hydrogen.

Figure 7:

Case Study 1: Software predictions and supporting information for the assessment of solriamfetol

Figure 8:

Case study 2: Software predictions and supporting information for the assessment of amifapridine

Figure 9:

Case Study 3: Software predictions and supporting information for the assessment of triclabendazole