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2 minute read
Preclinical oncology research is a critical component in the development of new cancer therapeutics, and it is essential that drug candidates are carefully screened before they are tested in human clinical trials. One valuable tool in preclinical oncology research is the use of cell line-derived xenograft (CDX) models. In this blog post, we will explore the benefits of CDX models for preclinical oncology screening and how they can be used to improve our understanding of cancer and the development of new treatments.
CDX models are created by implanting human cancer cells into immunocompromised mice. These cells develop into tumors that resemble their human counterparts and can be used to study the biology of cancer and test new treatments. One significant advantage of CDX models is that they are relatively easy and fast to establish, making them a cost-effective alternative to other preclinical screening models.[1]
CDX models allow researchers to study the response of tumors to treatments in vivo, replicating the conditions of human cancer. Additionally, they can be used to evaluate the pharmacodynamics and pharmacokinetics (PD/PK) of candidate drugs. These models allow researchers to evaluate how drugs impact tumor growth over time, and how the drug is distributed throughout the body. This information is critical in determining the optimal dosing regimen for cancer patients.[2-4]
CDX models have been widely used in preclinical research for several decades and can provide valuable insights into the underlying biology of cancer. Tumors grown in CDX models can be analyzed in depth to identify biological markers that can inform the development of targeted therapies. The use of CDX models can help researchers better understand the complexity of cancer and identify new therapeutic targets.[5-6]
In conclusion, CDX models offer several advantages for preclinical oncology screening, including their ease of establishment, ability to replicate human conditions, and ability to provide insights into the biology of cancer. By incorporating CDX models into their preclinical screening strategies, researchers can more accurately predict the efficacy of cancer drugs and improve the development of new cancer therapies.
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2. Qin L, Wang L, Zhang J, Zhou H, Yang Z, Wang Y, Cai W, Wen F, Jiang X, Zhang T, Ye H, Long B, Qin J, Shi W, Guan X, Yu Z, Yang J, Wang Q, Jiao Z. Therapeutic strategies targeting uPAR potentiate anti-PD-1 efficacy in diffuse-type gastric cancer. Sci Adv. 2022 May 27;8(21):eabn3774. doi: 10.1126/sciadv.abn3774. Epub 2022 May 25. PMID: 35613265; PMCID: PMC9132454.
3. Chen Y, Chen HN, Wang K, Zhang L, Huang Z, Liu J, Zhang Z, Luo M, Lei Y, Peng Y, Zhou ZG, Wei Y, Huang C. Ketoconazole exacerbates mitophagy to induce apoptosis by downregulating cyclooxygenase-2 in hepatocellular carcinoma. J Hepatol. 2019 Jan;70(1):66-77. doi: 10.1016/j.jhep.2018.09.022. Epub 2018 Oct 1. PMID: 30287340.
4. Kendsersky NM, Lindsay J, Kolb EA, Smith MA, Teicher BA, Erickson SW, Earley EJ, Mosse YP, Martinez D, Pogoriler J, Krytska K, Patel K, Groff D, Tsang M, Ghilu S, Wang Y, Seaman S, Feng Y, Croix BS, Gorlick R, Kurmasheva R, Houghton PJ, Maris JM. The B7-H3-Targeting Antibody-Drug Conjugate m276-SL-PBD Is Potently Effective Against Pediatric Cancer Preclinical Solid Tumor Models. Clin Cancer Res. 2021 May 15;27(10):2938-2946. doi: 10.1158/1078-0432.CCR-20-4221. Epub 2021 Feb 22. PMID: 33619171; PMCID: PMC8127361.
5. Oduwole OO, Poliandri A, Okolo A, Rawson P, Doroszko M, Chrusciel M, Rahman NA, Serrano de Almeida G, Bevan CL, Koechling W, Huhtaniemi IT. Follicle-stimulating hormone promotes growth of human prostate cancer cell line-derived tumor xenografts. FASEB J. 2021 Apr;35(4):e21464. doi: 10.1096/fj.202002168RR. PMID: 33724574.
6. Tang N, Cheng C, Zhang X, Qiao M, Li N, Mu W, Wei XF, Han W, Wang H. TGF-β inhibition via CRISPR promotes the long-term efficacy of CAR T cells against solid tumors. JCI Insight. 2020 Feb 27;5(4):e133977. doi: 10.1172/jci.insight.133977. PMID: 31999649; PMCID: PMC7101140.
