PRECLINICAL ANIMAL MODELS IN CANCER DRUG DISCOVERY

In cancer drug discovery and development, molecules with potent activity on human cancer cell lines and acceptable pharmacokinetics (PK) are selected for evaluation of efficacy in animal models of cancer to demonstrate proof of concept.

The mouse is the workhorse for cancer studies.

Hollow fibre (HF) model: In this model, semi-permeable fibres are filled with cancer cells and implanted into mice sub-cutaneously or intraperitoneally. After three days, they are treated with anti-cancer compounds for five days, the fibres are removed and the cell viabilities are measured. The HF model is used to rapidly screen compounds against many cancer cell lines in a few mice along with simultaneous measurements of PK. The compounds that show acceptable PK and efficacy in this model are selected for confirmatory proof of concept studies in xenograft models. The HF model provides a bridge between in vitro activity and in vivo efficacy for anti-cancer compounds.

The most frequently used is the xenograft model where cancer cells are implanted sub- cutaneously or intraperitoneally into immune deficient mice (Nude, SCID NOD-SCID), and allowed to form tumors. When tumors reach a measurable volume treatment with molecules is initiated and continued for two to three weeks. The primary efficacy measure is tumor growth inhibition or tumor reduction.

In orthotopic models, cancer cells are implanted directly into the organ of interest in mice (liver, kidney, breast, blood, brain) which is representative of the corresponding cancers in humans. Treatment is initiated following implantation and continued till morbidity and mortality is observed. The primary efficacy measures are inhibition of tumor growth, inhibition of metastasis, overall survival and progression free survival.

Xenograft studies performed in immune competent mice where murine cancer cell lines representing the same strain of mice are implanted and tumors are allowed to form. Treatment is initiated when tumor volumes reach measurable size and continued for two to three weeks. The primary efficacy measure is tumor growth inhibition or tumor reduction.

The xenograft and orthotopic models are used in early stages of drug discovery where several molecules are assessed for efficacy. In advanced stages such as the clinical drug candidate selection, genetically engineered mouse models (GEM) and patient-derived xenograft (PDX) models are used to evaluate efficacy.

In the GEM, mice are genetically engineered to develop cancer over their life time. This mimics the gradual development of cancer in humans. An example of GEMM is the Apc min model of colon cancer.

In PDX models, cancer tissues derived from patients are directly implanted in Immuno-compromised mice and treated with clinical candidates for evaluation of tumor growth inhibition. Since the tumors formed are derived from cancer patients, efficacy in the PDX model is predictive of efficacy in the clinic. The PDX is more predictive of efficacy in the clinic as it represents the heterogeneous nature of mutations found in cancer patients than human cancer cell lines.

In all the above models of cancer, additional parameters such as biomarkers, histopathology, and Immunohistochemistry are used to evaluate efficacy based on mechanism of action of anti-cancer molecules. Pharmacokinetic and Pharmacodynamic (PK/PD) relationships are evaluated to estimate the PK/PD targets in humans.

TheraIndx Contract Research Organization, Bangalore offers a wide range of human cancer models – Hollow Fibre, Xengoraft, Orthotopic, Syngeneic, GEMM and PDX models in mice to support drug discovery programs in Cancer.

    References:
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  • 2. Ann Richmond and Yingjun Su. 2008. Mouse xenograft models vs GEM models for human cancer therapeutics. Disease Models & Mechanisms 1, 78-82. doi:10.1242/dmm.000976.

  • 3. Norman E. Sharpless and Ronald A. DePinho. 2006. The mighty mouse: genetically engineered mouse models in cancer drug development. Nature Reviews Drug Discovery | AOP, published online 18 August 2006; doi:10.1038/nrd2110

  • 4. Alexander Honkala, Sanjay V. Malhotra, Shivaani Kummar, and Melissa R. Junttila. 2022. Harnessing the predictive power of preclinical models for oncology drug development. Nature Reviews Drug Discovery 21: 99