The Product Quality Research Institute (PQRI) recently published on their recommendations for safety thresholds and best practices for extractables and leachables (E&L) in Parenteral Drug products1. The publication describes, and addresses, issues related to the definition and procedures for defining safety thresholds. Within the document, areas of application for in silico methods are also defined. We see three main categories for the in silico approaches.
- Established procedures
Such methods have well defined and documented procedures and could be accepted for regulatory submissions. The in silico assessment of mutagenicity falls within this category and it is important to examine the factors/characteristics of this assessment type as we look forward to developing other areas. There is scientific consensus on the use and application of in silico approaches to assess mutagenicity based on the publication of a detailed guideline, ICH M72. Further, the availability of data3, and growth of SAR principles4 and application of the methods5 have allowed for advanced interpretation of results.
- Evolving procedures
The areas which are considered evolving have foundational elements laid out and the application structure is being defined. In these areas, models are available, SAR understanding is acceptable and various application methods are being considered6. Skin sensitization and irritation/corrosion models appear to fall into this category. In these areas there is a need for documented procedures and questions remain as how to interpret and utilize potency predictions in the context of safety thresholds. The ELSIE consortium is working on advancing this area and others.
- Emerging applications
Within the publication by PQRI, special consideration is given to biological products where the potential for a reactive E&L compound to react with therapeutic proteins is mentioned. Here the proof of concept for the application of theoretically relevant models is needed. As such, efforts are ongoing with the in silico protocol initiative to examine the performance of relevant models against empirical data and examine the scope of potentially reactive chemicals within the E&L chemical space.
At the Extractables and Leachables 2022 conference, I presented on these topics with focus on emerging applications. Here we demonstrated the conservative nature of a consensus result based on a prediction of peptide reactivity and reaction domain profiling, while detailing a decision workflow for the application of in silico models to assess the reactivity of E&L molecules with biomolecules. Noted from the conference is the ongoing work in the application of in silico methods in the context of E&L evaluations; and additional details to be provided in the ISO-10993 guideline and various upcoming publications.
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- Product Quality Research Institute (PQRI). 2021. Safety Thresholds and Best Demonstrated Practices for Extractables and Leachables in Parenteral Drug Products (Intravenous, Subcutaneous, and Intramuscular). https://www.pda.org/bookstore/product-detail/6576-pqri-pdp.
- ICH, 2017. M7 (R1) Assessment and control of DNA reactive (mutagenic) impurities in pharmaceuticals to limit potential carcinogenic risk (No. EMA/CHMP/ICH/83812/2013), ICH Harmonised Guideline. European Medicines Agency. https://database.ich.org/sites/default/files/M7_R1_Guideline.pdf
- Landry, Curran et al. 2019. “Transitioning to Composite Bacterial Mutagenicity Models in ICH M7 (Q)SAR Analyses.” Regulatory Toxicology and Pharmacology 109: 104488. https://www.sciencedirect.com/science/article/pii/S0273230019302521.
- Ahlberg, Ernst et al. 2016. “Extending (Q)SARs to Incorporate Proprietary Knowledge for Regulatory Purposes: A Case Study Using Aromatic Amine Mutagenicity.” Regulatory toxicology and pharmacology : RTP 77: 1–12.
- Amberg, Alexander et al. 2019. “Principles and Procedures for Handling Out-of-Domain and Indeterminate Results as Part of ICH M7 Recommended (Q)SAR Analyses.” Regulatory Toxicology and Pharmacology.
- Johnson, Candice et al. 2022. “Evaluating Confidence in Toxicity Assessments Based on Experimental Data and in Silico Predictions.” Computational Toxicology 21: 100204. https://www.sciencedirect.com/science/article/pii/S2468111321000505.