The cost of drug development has skyrocketed to an estimated $2.6B for every FDA approved drug primarily due to failures from lack of efficacy or safety. This suggests that our current preclinical validation process has been insufficient in predicting therapeutic potential and toxicity in humans. While genetically engineered mice have become the gold standard for dissecting cancer mechanisms and evaluating novel drug targets in vivo, the rat has historically been the major model species in many biomedical fields, notably toxicology and carcinogenicity testing; and for many scientists, the rat still remains the preferred rodent due to their larger size for surgical manipulation, repeat blood sampling, and their cognitive and physiological characteristics that more closely resemble humans than their mouse counterparts. Moreover, many hormone-dependent tumors cannot be engineered in mouse models and are better modeled in rats. Here, we take advantage of our two-step engineering approach and exploit the efficiency of CRISPR-based targeting to develop RNAi rat models that enable inducible and reversible gene silencing to simulate therapeutic regimes. We demonstrate that our approach allows us to rapidly generate RNAi rat models and mimic the function of the targeted small molecule inhibitors, such as BET inhibitors targeting Brd4. These results demonstrate that our high-throughput system currently used to generate RNAi mice is also applicable to the rat system and, by extension, other mammalian models. Inducible RNAi rat models will undoubtedly be powerful tools that can be used to model human cancers, to mimic the action of putative drugs, and to assess the potential of therapeutic targeting strategies in vivo prior to the costly drug development, ultimately guiding the development of safer and more effective drugs.

Citation Format: Chia-Lin Wang, Yu-ting Yang, Ana Vasileva, Allison Maurice, Lukas Dow, Johannes Zuber, Scott Lowe, Prem K. Premsrirut. RNAi rat models for drug discovery [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 6119.