Carbamates and ICH M7 classification: Making use of expert knowledge

Migration of styrene oligomers from food contact materials: in silico prediction of possible genotoxicity

Styrene oligomers (SO) are well-known side products formed during styrene polymerization. They consist mainly of dimers (SD) and trimers (ST) that have been shown to be still residual in polystyrene (PS) materials. In this study migration of SO from PS into sunflower oil at temperatures between 5 and 70 °C and contact times between 0.5 h and 10 days was investigated. In addition, the contents of SD and ST in the fatty foodstuffs créme fraiche and coffee cream, which are typically enwrapped in PS, were measured and the amounts detected (of up to 0.123 mg/kg food) were compared to literature data. From this comparison, it became evident, that the levels of SO migrating from PS packaging into real food call for a comprehensive risk assessment. As a first step towards this direction, possible genotoxicity has to be addressed. Due to technical and experimental limitations, however, the few existing in vitro tests available are unsuited to provide a clear picture. In order to reduce uncertainty of these in vitro tests, four different knowledge and statistics-based in silico tools were applied to such SO that are known to migrate into food. Except for SD4 all evaluated SD and ST showed no alert for genotoxicity. For SD4, either the predictions were inconclusive or the substance was assigned as being out of the chemical space (out of domain) of the respective in silico tool. Therefore, the absence of genotoxicity of SD4 requires additional experimental proof. Apart from SD4, in silico studies supported the limited in vitro data that indicated the absence of genotoxicity of SO. In conclusion, the overall migration of all SO together into food of up to 50 µg/kg does not raise any health concerns, given the currently available in silico and in vitro data.

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.

Industrial approaches and consideration of clinical relevance in setting impurity level specification for drug substances and drug products

The safety and efficacy of drug substances or products do not solely depend on its active(s). The quantity of impurities present in the product has a significant role in its safety profile. Pharmaceutical impurities are one of the primary reasons for the withdrawal of many approved products from the market. Therefore, the level of impurities in the pharmaceuticals needs to be controlled within a specified safe limit. Nowadays, setting impurity level specification remains a great challenge for pharmaceutical manufacturers. Regulatory guidelines recommend to control the impurity based on the concentration level criteria and provides limits of allowable impurities in pharmaceuticals. However, a single set of impurity limits cannot work for all the drug substances. There are numerous reasons which demand to set the impurity level specification based on safety dominated critical quality attribute principle. In this review, we have discussed the need for the consideration of both concentration based and patient safety-related approaches for setting the impurity level specification. To achieve this goal, it is required to identify the safety limits of the impurities during clinical development and provide a specification for the finished pharmaceutical products before entering the market. However, tremendous challenges faced by pharmaceutical companies to have an appropriate balance amongst the critical factors like safety, efficacy, analytical variability, process knowledge and regulatory requirement. Finally, the specification for API and finished drug product should be established considering both quality and patient safety. Considering all such factors, we have included a systematic and scientific approach that can guide to establish the safe and flexible impurity limit specification for pharmaceuticals.

A practice of expert review by read-across using QSAR Toolbox

The International Council for Harmonisation of Technical Requirement for Pharmaceuticals for Human Use (ICH) M7 guideline on 'Assessment and Control of DNA Reactive (Mutagenic) Impurities in Pharmaceuticals to Limit Potential Carcinogenic Risk' provides the application of two types of quantitative structure-activity relationship (QSAR) systems (rule- and statistics-based) as an alternative to the Ames test for evaluating the mutagenicity of impurities in pharmaceuticals. M7 guideline also states that the expert reviews can be applied when the outcomes of the two QSAR analyses show any conflicting or inconclusive prediction. However, the guideline does not provide any information of how to conduct expert reviews. Therefore, a conservative approach was chosen in this study, which is based on the intention to capture any mutagenic chemical substances. The 36 chemical substances, which are the model chemical substances in which positive mutagenicity was not observed according to the two types of QSAR analyses (i.e. the results are either conflicting or both negative), were selected from the list of chemical substances with strong mutagenicity known as the reported chemicals under the Industrial Safety and Health Act in Japan. The QSAR Toolbox was used in this study to rationally determine the positive mutagenicity of the 36 model chemical substances by applying a read-across method, a technique to evaluate the endpoint of the model chemical substances using the endpoint information of chemicals that are structurally similar to the model chemical substances. Resulting from the expert review by the read-across method, the 23 model chemical substances (63.8%) were rationally concluded as positive. In addition, 9 out of 11 model chemical substances that were assessed as negative for mutagenicity by both of the QSAR systems had positive analogues, supporting their mutagenicity. These results suggested that the read-across is a useful method, when conducting a conservative approach intended to capture any mutagenic chemical substances.