In a previous blog entry, Dr. Glenn Myatt discussed the impetus for developing a consortium to define best in silico practices around toxicological endpoints, such as, genetic toxicity, skin sensitization, carcinogenicity, neurotoxicity and acute toxicity, to name a few. The aim of these protocols is to reduce the burden on industry and regulators to justify their use, as well as ensure in silico assessments are performed in a consistent and reproducible manner to support good in silico practices. The consortia’s activities support a number of emerging or existing regulatory guidelines such as the ICH M7: DNA reactive (mutagenic) impurities in pharmaceuticals.

Group activities result in either protocols (which define implementable rules and principles), position papers (which describe the current state of science and the extent to which in silico tool use is feasible), case studies, structure-activity relationships, or fit-for-purpose evaluations. To date, the reliability scoring paradigm¹, which serves as a useful extension of the Klimisch scoring of experimental data has been cited by the World Health Organization, EHC240: Principles and Methods for the Risk Assessment of Chemicals in Food, subchapter 4.5. Genotoxicity².  Further, elements of the ‘In silico toxicology protocols’¹ and ‘Principles and procedures for handling out-of-domain and indeterminate results as part of ICH M7 recommended (Q)SAR analyses’³ have been cited by the European Medicines Agency (EMA)’s reflection paper on the qualification of non-genotoxic impurities⁴ and the ICH guideline M7 on assessment and control of DNA reactive (mutagenic) impurities in pharmaceuticals to limit potential carcinogenic risk – questions & answers Step 2b⁵.

Several working groups are in progress, and new working group activities including an expansion of the carcinogenicity position paper⁶ to develop case studies in support of the development of new approaches and protocols using information from target safety assessments⁷, in silico approaches and in vitro/in vivo data are being formed. Additional working groups, including the assessment of biomolecule reactivity and drug/drug interaction are on the horizon. If you are interested in any of these topics, or have a comment, question, or problem that you are facing please contact gmyatt@leadscope.com or cjohnson@leadscope.com.

References

1. Myatt, G.J., Ahlberg, E., Akahori, Y., et al. (2018), In Silico Toxicology Protocols. Regul. Toxicol. Pharmacol. 98, 1-17. doi:10.1016/j.yrtph.2018.04.014. Open  access: https://doi.org/10.1016/j.yrtph.2018.04.014
2. World Health Organization & Food and Agriculture Organization of the United Nations (2020), Principles and methods for the risk assessment of chemicals in food. Subchapter 4.5 Genotoxicity. Environmental health criteria 240
3. Amberg, A., Andaya, R.V., Anger, L.T., et al. (2019) Principles and procedures for handling out-of-domain and indeterminate results as part of ICH M7 recommended (Q)SAR analyses. Regul. Toxicol. Pharmacol. 102, 53–64. 10.1016/j.yrtph.2018.12.007
4. European Medicines Agency (2018) Reflection paper on the qualification of non-genotoxic impurities
5. European Medicines Agency (2020) ICH guideline M7 on assessment and control of DNA reactive (mutagenic) impurities in pharmaceuticals to limit potential carcinogenic risk – questions & answers Step 2b
6. Tice at al., In Silico Approaches In Carcinogenicity Hazard Assessment: Current Status and Future Needs, submitted to Regulatory Toxicology and Pharmacology
7. https://www.instem.com/solutions/knowledgescan/index.php

One of the most common questions we are asked is how an overall toxicity assessment for a given chemical is arrived at, especially when there are conflicting study results available.

To answer this question, it is often helpful to review the process of producing the content in Leadscope’s databases, which is overseen by Leadscope’s Manager of Database Content, Dave Bower.

The starting point for this production process is the original data sources. For the genetic toxicity database, these sources include CCRIS, CDER, CFSAN, CPDB, DSSTox, EPA-Genetox, NTP, publications, donated company information, and many more.

Converting this information into an integrated database initially involves two parallel processes: (1) the chemical structure processing workflow and (2) the content (toxicity study) building process.

To ensure all studies for the same chemical are linked together, each chemical (test article) is compared against our existing database. It is either registered as a new chemical (and given a new Leadscope ID) or it is linked to a previously registered chemical. This process can be difficult when only a chemical name has been reported, particularly when a chemical is historically referred to by different names. In situations when the chemical structure is displayed within the source material, issues related to the depiction of its stereochemistry as well as aromaticity and tautomerism may need to be taken into consideration. Mixtures, salt forms are often linked to SAR-forms of the chemical to ensure the studies are readily accessible and to support computational modelling efforts.

The content building process is also challenging since the underlying information may or may not be an electronic form that is suitable for processing automatically. In certain situations, it is necessary to enter the information by hand. In others, it is possible to develop computational tools to read the content directly into the electronic database. An essential step here is to map the data elements described in the source material onto standardized terms. For example, a species and strain in one study may be reported as “S. typhimurium 100” and in another as “Sal. TA100” yet both need to be mapped onto a standardized species and strain terms (“Salmonella typhimurium” and “TA100”). In generating the content, multiple QA steps are included to ensure the integrity of the information.

Once the chemical structure processing is complete and harmonized study records are linked to these chemicals, a process of grading the chemicals can then take place. For example, an overall call for bacterial mutagenicity can be derived from the multiple data sources. This process involves an examination of the overall study calls and the underlying individual test results. Some factors that are taken into consideration include whether the data source is trusted or authoritative and whether or not the study is compliant with accepted test protocols. Since the overall study calls for different studies may be conflicting, the weight of the evidence needs to be considered into generating an overall grade for an individual chemical. However, the individual studies are always reported alongside any overall calls to support an expert review of the individual calls.

Dave recently put together a slide deck explaining the process in detail, including a series of case studies illustrating the process. Please get in touch with me (gmyatt@leadscope.com) if you would be interested in learning more about this process or would like a copy of Dave’s slide deck.

The ICH M7 guideline¹ presents the types of methodologies that are relevant to assess mutagenic impurities. The guideline goes further than simply mentioning that QSAR methods and analogs should be considered prior to conducting an experimental study, and it details two complementary methodologies (statistical and expert rule-based), and how the results should be considered to derive an assessment of mutagenicity. These principles could be used in the safety assessment of extractables and leachables to assess mutagenicity, if warranted. However, there are additional areas of toxicology which may be relevant for the safety assessment of extractables and leachables which could benefit from such procedural descriptions such as skin sensitization, and dermal and ocular irritation.  Databases and tools are in place for utilizing existing information to make reliable predictions of a leachable’s toxicity.

The procedures described in the ICH M7 guideline¹ for mutagenicity allow the assessor to obtain accurate and consistent predictions while reaping practical benefits. A harmonized guideline which details the strategic use of in silico methods for safety assessments will reduce the burden on assessors and regulators to perform or review an in silico toxicological assessment. Given our experience conducting assessments, here are some aspects of best practice considerations for in silico assessments.

• An important step is assessing the adequacy and reliability of data in the public domain, including conflict resolutions. This may circumvent the need for in silico testing.
• Two complementary methodologies (statistical and expert rule-based) offer an advantage. While statistical based methodologies assess all areas of the molecule and the structural basis for the prediction can be explained, the alerts identify reactive groups that are linked to a mechanism. However, it can be difficult to support negative predictions using alerts alone. Rules should be defined to derive a consensus based on the two methodologies which generate conservative predictions.
• Criteria illustrated with case-studies should be defined for an expert review, such as, how to assess a chemical which shares an alert with a known negative.

There is much anticipation for the ICH Q3E² guideline in the assessment of extractables and leachables.

Please get in touch if you would like to have a copy of a recent poster that was presented at ACT on this topic (cjohnson@leadscope.com).

References

 1. ICH. M7 Assessment and control of DNA reactive (mutagenic) impurities in pharmaceuticals  to limit potential carcinogenic risk (R1). 2017.

2. Final Concept Paper ICH Q3E: Guideline for Extractables and Leachables (E&L). 2020. https://database.ich.org/sites/default/files/ICH_Q3E_ConceptPaper_2020_0710.pdf

The movement of toxicology away from an observational-based paradigm and towards a mechanism-based one is ongoing. One pertinent question is how to combine data across mechanistic pathways to derive an overall assessment of hazard and what level of confidence should be placed in such a result. Further, where data gaps exist, how could in silico tools be used to support an assessment.

The area of skin sensitization is exemplary. Hinging on knowledge of the adverse outcome pathway (AOP) for skin sensitization, key events across the AOP could be assessed and integrated to derive an overall assessment. But what if information is missing? Could I utilize the power of existing knowledge- stored in a database- to analyze reactive features, statistical correlations, chemical and mechanistic similarity to facilitate an assessment? Could I really ‘pull out all the stops’ as they say? And if I did (congrats to you on getting this far!), and managed to standardize reporting for the various assessments, would I be able to reproducibly justify the overall conclusion across hundreds of chemicals and effectively (don’t forget reproducibly) communicate the confidence in the results?

There is so much to unravel here. A major plus is that we have a starting point. Documents such as the OECD’s Guidance Document on the Reporting of Defined Approaches and Individual Information Sources to Be Used within Integrated Approaches to Testing and Assessment (IATA) for Skin Sensitization¹  are an excellent resource. The recently published skin sensitization in silico protocol² is also a good resource for expert review considerations and guidelines on how to assess the reliability and confidence of an in silico assessment. If you would like to talk more about implementing the principles outlined in the skin sensitization in silico protocol, we would like to hear from you. Together, we could explore the solutions to many of the questions above.

1. OECD. Guidance Document on the Reporting of Defined Approaches and Individual Information Sources to Be Used within Integrated Approaches to Testing and Assessment (IATA) for Skin Sensitisation. OECD; 2017. doi:10.1787/9789264279285-en  
2. Johnson C, Ahlberg E, Anger LT, et al. Skin sensitization in silico protocol. Regul Toxicol Pharmacol. 2020;116:104688. doi:https://doi.org/10.1016/j.yrtph.2020.104688