Optimizing Preclinical Pharmacology Models for Oncology New Molecular Entities (NMEs): A Closer Look

Developing effective new molecular entities (NMEs) for oncology indications necessitates robust preclinical pharmacology models. These models serve as a critical bridge between in vitro discovery and human clinical trials; and they offer critical insights into a drug's efficacy, safety profile, and potential mechanisms of action. Here, we explore three prominent models: cell line-derived xenograft (CDX), patient-derived xenograft (PDX), and immune-competent animal models.

Cell Line-Derived Xenograft (CDX) Models:

• Advantages: CDX models have become the workhorse of initial NME evaluation due to their standardization and cost-effectiveness (Sharpe et al., 2016). Established human cancer cell lines are readily available, allowing for rapid assessment of a drug's ability to inhibit tumor growth and facilitating dose optimization studies (Sharpe et al., 2016). The controlled environment minimizes confounding variables, enabling researchers to focus on the drug's direct effect on the cancer cells and explore its mechanism of action (Pagano et al., 2021).
• Limitations: A major drawback of CDX models is their lack of tumor heterogeneity. Established cell lines often undergo genetic and phenotypic changes during their prolonged culture, resulting in a poor representation of the complex tumor microenvironment observed in patients (Zhu, 2018). Additionally, the absence of a functional immune system restricts their utility for evaluating immuno-oncology drugs, which rely on the body's immune response to combat cancer cells (Zhu, 2018).

Patient-Derived Xenograft (PDX) Models:

• Advantages: PDX models bridge the gap between CDX models and clinical trials by utilizing tumor tissue directly obtained from cancer patients. This approach preserves the original tumor's genetic and phenotypic diversity, resulting in models with a higher degree of clinical relevance (Perez et al., 2016). PDX models are valuable tools for personalized medicine approaches, allowing researchers to evaluate the efficacy of targeted therapies against a patient's specific tumor biology and potentially predict their response to treatment (Perez et al., 2016).
• Limitations: Establishing PDX models is a time-consuming and expensive endeavor. Obtaining high-quality patient tumor tissue can be challenging, and the engraftment process itself can be variable (Perez et al., 2018). Similar to CDX models, PDX models lack a functional immune system, limiting their application for studying immunotherapies.

Immune-Competent Animal Models:

• Advantages: These models represent the next frontier in preclinical oncology research. Genetically engineered mice harboring human tumor mutations develop cancers within a functional immune system (Day et al., 2015). This enables the evaluation of immunotherapies and the intricate interplay between the drug, the tumor, and the immune response in a more physiologically relevant setting (Day et al., 2015). Immune-competent models are crucial for assessing potential immune-related adverse events associated with immunotherapies. If the NME is targeting immune-related targets, then models with immune-competent animals are preferred. Such immune-competent animal models assess not only the involvement of the immune system, but also the complexities of the tumor microenvironment (Day et al., 2015)
• Limitations: Development and maintenance of these models are resource-intensive (Day et al., 2015). Currently available models may not encompass the full spectrum of human tumor types, limiting their generalizability (Day et al., 2015).

Comparing Your Options

Table 1 summarizes some of the critical features of each model, including the key differentiating features, the advantages and the limitations.

Table 1: Comparison of Preclinical Pharmacology Models for Oncology NMEs

Optimizing the Model Choice

No single preclinical model perfectly replicates the complexities of human cancer. A strategic combination of these models, alongside in vitro assays, provides a comprehensive picture of an NME's potential. CDX models offer a cost-effective starting point for initial efficacy studies and dose optimization. PDX models bridge the gap to clinical relevance, allowing for exploration of personalized medicine approaches. Immune-competent models are essential for evaluating the efficacy and safety of immunotherapies.

Looking Forward

The continuous development of novel preclinical models holds immense promise for accelerating the discovery of effective cancer therapies. Advancements in 3D tumor organoid models and microfluidics technologies offer the potential to create even more complex and patient-specific microenvironments for drug testing (Meijer et al., 2017). Ultimately, by leveraging the strengths and weaknesses of each model system, researchers can make informed decisions about the most promising NMEs for further development, bringing us closer to a future where cancer is a treatable disease.

References:

1. Day CP, Merlino G, Van Dyke T. Preclinical mouse cancer models: a maze of opportunities and challenges. Cell. 2015 Sep 24;163(1):39-53. doi: 10.1016/j.cell.2015.08.068. PMID: 26406370; PMCID: PMC4583714. [https://www.cell.com/action/showPdf?pii=S0092-8674%2815%2901120-4].
2. Meijer TG, Naipal KA, Jager A, van Gent DC. Ex vivo tumor culture systems for functional drug testing and therapy response prediction. Future Sci OA. 2017 Mar 27;3(2):FSO190. doi: 10.4155/fsoa-2017-0003. PMID: 28670477; PMCID: PMC5481868. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5481868/
3. Pagano, E., Bergamo, A., Carpi, S., Donnini, S., Notarbartolo di Villarosa, M., Serpe, L., & Lisi, L. (2021). Preclinical models in oncological pharmacology: Limits and advantages. Pharmadvances, 3(online first), 402-420. [https://iris.unito.it/bitstream/2318/1805879/1/2021%20PA.pdf].
4. Perez M, Navas L. Carnero A. Patient-derived xenografts as models for personalized medicine research in cancer. 2016;2(6):197–202. [https://media.proquest.com/media/hms/OBJ/auBGS?_s=E2Kwbfr4MWvGz2F8Evirrb9QP1w%3D]
5. Sharpe et al. (2016). Preclinical Cancer Models and Biomarkers for Drug Development: New Technologies and Emerging Tools. (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5743226/)
6. Zhu, A. Z. (2018). Quantitative translational modeling to facilitate preclinical to clinical efficacy & toxicity translation in oncology. Future science OA, 4(5), FSO306. [https://www.future-science.com/doi/pdf/10.4155/fsoa-2017-0152]

**