Utility of published DNA reactivity alerts

A rapid and sensitive UPLC-MS/MS method for simultaneous determination of four potential mutagenic impurities at trace levels in ripretinib drug substance

In the synthesis of ripretinib, a new oral tyrosine kinase inhibitor, impurities could arise directly from starting materials, reagents and intermediates. Among these process impurities, four specific intermediate impurities were found to contain the structural alerts of primary aromatic amine and aldehyde groups, triggering the concern of potential mutagenic impurities (PMIs). Two complementary (quantitative) structure-activity relationship [(Q)SAR] evaluation systems (expert rule-based and statistics-based) were subsequently employed to assess and classify the mutagenic risk of the four known impurities. The Sarah prediction results of these four impurities were all positive and they were categorized as class 3, where the threshold of toxicological concern (TTC) of 1.5 μg d-1 would apply. Hereby, a rapid and sensitive UPLC-MS/MS method was developed for the simultaneous and trace level quantification of the four PMIs in ripretinib drug substance. The separation was achieved on a C18 column under the optimized gradient elution program consuming only nine minutes and the four PMIs were all well separated from ripretinib so that they could be easily diverted to waste via a switch valve. The time-segmented multiple reaction monitoring (MRM) mode further improved the sensitivity and allowed for the quantification of the four PMIs as low as 10% of the acceptable limit. The method was fully validated, and proved sufficient in terms of selectivity, sensitivity, linearity, precision and accuracy. The factors involved in the method development and pathways for fragment ions of the four PMIs were also discussed and the study will contribute to risk management of PMIs present in ripretinib.

Assessing the impact of expert knowledge on ICH M7 (Q)SAR predictions. Is expert review still needed?

The ICH M7 (R1) guideline recommends the use of complementary (Q)SAR models to assess the mutagenic potential of drug impurities as a state-of-the-art, high-throughput alternative to empirical testing. Additionally, it includes a provision for the application of expert knowledge to increase prediction confidence and resolve conflicting calls. Expert knowledge, which considers structural analogs and mechanisms of activity, has been valuable when models return an indeterminate (equivocal) result or no prediction (out-of-domain). A retrospective analysis of 1002 impurities evaluated in drug regulatory applications between April 2017 and March 2019 assessed the impact of expert review on (Q)SAR predictions. Expert knowledge overturned the default predictions for 26% of the impurities and resolved 91% of equivocal predictions and 75% of out-of-domain calls. Of the 261 overturned default predictions, 15% were upgraded to equivocal or positive and 79% were downgraded to equivocal or negative. Chemical classes with the most overturns were primary aromatic amines (46%), aldehydes (45%), Michael-reactive acceptors (37%), and non-primary alkyl halides (33%). Additionally, low confidence predictions were the most often overturned. Collectively, the results suggest that expert knowledge continues to play an important role in an ICH M7 (Q)SAR prediction workflow and triaging predictions based on chemical class and probability can improve (Q)SAR review efficiency.

In silico toxicology: From structure–activity relationships towards deep learning and adverse outcome pathways

In silico toxicology is an emerging field. It gains increasing importance as research is aiming to decrease the use of animal experiments as suggested in the 3R principles by Russell and Burch. In silico toxicology is a means to identify hazards of compounds before synthesis, and thus in very early stages of drug development. For chemical industries, as well as regulatory agencies it can aid in gap‐filling and guide risk minimization strategies. Techniques such as structural alerts, read‐across, quantitative structure–activity relationship, machine learning, and deep learning allow to use in silico toxicology in many cases, some even when data is scarce. Especially the concept of adverse outcome pathways puts all techniques into a broader context and can elucidate predictions by mechanistic insights.

This article is categorized under:

Structure and Mechanism > Computational Biochemistry and Biophysics

Data Science > Chemoinformatics

Construction and application of (Q)SAR models to predict chemical-induced in vitro chromosome aberrations

In drug development, genetic toxicology studies are conducted using in vitro and in vivo assays to identify potential mutagenic and clastogenic effects, as outlined in the International Council for Harmonisation (ICH) S2 regulatory guideline. (Quantitative) structure-activity relationship ((Q)SAR) models that predict assay outcomes can be used as an early screen to prioritize pharmaceutical candidates, or later during product development to evaluate safety when experimental data are unavailable or inconclusive. In the current study, two commercial QSAR platforms were used to build models for in vitro chromosomal aberrations in Chinese hamster lung (CHL) and Chinese hamster ovary (CHO) cells. Cross-validated CHL model predictive performance showed sensitivity of 80 and 82%, and negative predictivity of 75 and 76% based on 875 training set compounds. For CHO, sensitivity of 61 and 67% and negative predictivity of 68 and 74% was achieved based on 817 training set compounds. The predictive performance of structural alerts in a commercial expert rule-based SAR software was also investigated and showed positive predictivity of 48-100% for selected alerts. Case studies examining incorrectly-predicted compounds, non-DNA-reactive clastogens, and recently-approved pharmaceuticals are presented, exploring how an investigational approach using similarity searching and expert knowledge can improve upon individual (Q)SAR predictions of the clastogenicity of drugs.