Transgenic Rodent (TGR) Gene Mutation Assay: A Short Commentary for Deriving AI Limits and Exploring Better Alternatives for Carcinogenicity Testing

Introduction to TGR In Vivo Assays

Transgenic Rodent (TGR) in vivo assays are essential in investigating the mutagenic potential of chemicals, and in recent times nitrosamine impurities in pharmaceuticals. These assays involve transgenic rodents that have specific reporter genes, permitting the identification of mutations in various tissues (OECD 488, 2013). They are proved to be an effective method to determine whether a chemical is likely to cause DNA alterations which is a crucial step in determining carcinogenicity. TGR assays have been used to evaluate chemicals to gain a deeper understanding of mutagenic processes (Lambert et al., 2005). TGR assays go beyond simple genotoxicity testing. As global concerns rise over nitrosamine impurities in drug products (DPs) and active pharmaceutical ingredients (APIs), acceptable intake (AI) limits for carcinogenic and mutagenic impurities are being determined using TGR assays. Although, regulatory agencies are yet to accept AI limits derived through this approach. This blog explores the use of TGR assays in determining AI limits and also if there are better alternatives for predicting carcinogenicity. 

TGR assays employ rodents in which foreign DNA sequences have been inserted (Provost et al., 1993). These sequences (often bacterial genes such as lacZ or gpt) function as reporters, allowing any changes generated by chemical exposure to be easily recognized within these genes. The TGR models are exposed to the impurity, and tissue samples from multiple organs (including the liver, lung, and kidney) are obtained. The DNA is then extracted and examined for mutations in the reporter genes (OECD 488, 2013). The strength of TGR assays is its capacity to detect mutations across several tissues, as well as their applicability to human risk assessment. This is especially critical for nitrosamines, which are known to cause mutations in various organs and are considered probable human carcinogens (Gorelick, 1995). 

Using TGR Assays to Derive AI Limits for Nitrosamine Impurities

One of the most important applications of TGR assays in recent times has been to determine AI limits for nitrosamine impurities. Nitrosamines are potent mutagenic chemicals (not all) that  may form during the production or storage of APIs and DPs (U.S FDA, 2020). Regulatory bodies such as the FDA and EMA have established stringent AI limits for nitrosamine impurities in pharmaceutical products (EMA, 2020). For new nitrosamines especially nitrosamine drug substance-related impurities (NDSRIs) apart from the carcinogenic potency categorization approach (CPCA), TGR assays are critical in deriving AI limits. Quantitative information about mutation frequences induced by the chemical or impurity are the endpoint which can be used to derive AI limits. Higher mutation frequencies indicate more mutagenic potential (Doak et al., 2007). TGR assays can also estimate the No observed adverse effect level (NOAEL) which can be used as Point of Departure (PoD) to calculate the permitted daily exposure (PDE) or AI limits (Johnson et al., 2021). However, it advisable to derive a benchmark dose lower confidence (BMDL) from the mutation frequency data in order to derive the PDE or AI limit, BMDL is considered a better PoD than NOAEL since the NOAEL is limited to the number of doses and does not take it into account the dose-response relationship (Kodell RL, 2009). 

The Correlation Between TGR Assays and Carcinogenicity

TGR assays are excellent in detecting mutations, however, it is critical to understand whether these mutations can be correlated to carcinogenesis. The answer to this question is not always straightforward. Even though mutagenic chemicals can induce cancer other factors such as genetic variations, oxidative stress, cell proliferation and epigenetic changes also play a part in carcinogenesis (Ravegnini, 2015). Hence, a positive result in a TGR assay does not always mean that the chemical will cause cancer. In vivo rodent carcinogenicity studies continue to be the gold standard for assessing carcinogenicity. 

Are There Better Alternatives to TGR Assays for Predicting Carcinogenicity?

Given the limitations of TGR assays in directly correlating to carcinogenicity, regulatory agencies have not yet accepted AI limits for nitrosamines derived through TGR analysis data. More data is being generated by various groups to evaluate whether TGR mutation data correlates to carcinogenicity.

Next-Generation Sequencing (NGS): Error-corrected Next Generation Sequencing (eNGS), can evaluate mutations at a much deeper level than standard TGR assays. Whole genome sequencing enables the detection of even low-frequency mutations that would be missed by other methods. eNGS also yields mutation spectra, which are helpful in deciphering the fundamental mechanisms behind carcinogenesis. NGS methods have transformed molecular biology and genomics. They provide high throughput screening for the sequencing of DNA or RNA, providing an abundance of information on genetic variants, mutations, and gene expression. It is theoretically possible to use NGS data to create prediction models to find putative nitrosamine-forming pathways or mutagenesis events by examining genomic sequences. NGS can also provide insights into gene expression patterns. If specific genes are associated with nitrosamine metabolism or detoxification, their expression levels could inform safety assessments. 

The Future of Carcinogenicity Testing

Even though TGR assays are important in genotoxicity testing, limitations exists to whether the data can be correlated to carcinogenicity. This highlights the requirement for complementary methods. NGS can provide better understanding of how mutagenesis leads to cancer, although deriving an AI limit remains a challenge. For now, from a regulatory perspective TGR assays will continue to play a key role in ensuring product safety. As science advances, a combination of TGR assays and newer technologies should bring more comprehensive and accurate assessment of carcinogenic risks.

Disclaimer: The views and opinions expressed in this blog are solely my own and do not necessarily reflect the official policy or position of my employer. The content provided is for informational purposes only and is based on my personal experiences and research and not intended to represent the views of my employer. 

References

Doak SH, Jenkins GJS, Johnson GE, et al. Mechanistic influences for mutation induction curves after exposure to DNA-reactive carcinogens. Cancer Res. 2007;67(8). 

EMA. "Questions and answers for marketing authorisation holders/applicants on the CHMP opinion for the presence of nitrosamine impurities in human medicinal products." European Medicines Agency, 2020. 

Ravegnini G, Sammarini G, Hrelia P, Angelini S. Key genetic and epigenetic mechanisms in chemical carcinogenesis. Toxicol Sci. 2015;148(1):2–13. doi: 10.1093/toxsci/kfv165. 

Organization for Economic Co-operation and Development (OECD). "Test No. 488: Transgenic Rodent Somatic and Germ Cell Gene Mutation Assays." OECD Guidelines for the Testing of Chemicals, Section 4: Health Effects, 2013. 

Lambert IB, Singer TM, Boucher SE, Douglas GR. Detailed review paper on transgenic rodent mutation assays. Mutagenesis. 2005;20(2):83-97. 

Provost GS, Kretz PL, Hamner RT, Matthews CD, Rogers BJ, Lundberg KS, Dycaico MJ, Short JM. Transgenic systems for in vivo mutation analysis. Mutation Research. 1993;288(1):133-149. doi: 10.1016/0027-5107(93)90215-290215-2). 

Gorelick NJ. Overview of mutation assays in transgenic mice for routine testing. Environmental and Molecular Mutagenesis. 1995: 25(3):218-230. doi: 10.1002/EM.2850250309 

U.S FDA. "Control of Nitrosamine Impurities in Human Drugs." Guidance for Industry, 2020. 

Johnson GE, Dobo K, Gollapudi B et al. Permitted daily exposure limits for noteworthy N-nitrosamines. Environ Mol Mutagen. 2021;62(5):293-305. doi: 10.1002/em.22446. 

Kodell RL. Replace the NOAEL and LOAEL with the BMDL01 and BMDL10 . Environ Ecol Stat. 2009;16:3–12. doi.org/10.1007/s10651-007-0075-3