The UNC MP1U was established in 2006 to facilitate the pre-clinical development of novel anti-cancer therapeutics in highly faithful genetically engineered murine models of human cancer. Our collaborations at UNC involve scientists from 10 different academic departments and a majority of our experiments include; evaluating novel therapies, innovative drug delivery systems and a deeper exploration of the genetics behind our mouse models.
The Mouse Phase 1 Unit has developed considerable infrastructure to facilitate pre-clinical research. Animal housing, funding, animal welfare and support services such as imaging and PK have been solved in the MP1U. Additionally, the facility is supported by state-of-the-art laboratories for pharmacology, pharmacodynamics and other ADME studies (including a Good Laboratory Practice facility at UNC); small animal imaging; medicinal chemistry facilities; and a committed Office of Technology Development to help negotiate requisite material transfer agreements.
The Mouse Phase 1 Unit is particularly valuable for optimizing a treatment regimen and schedules, which has proven to be a very difficult task in human systems. We have several ongoing studies using novel candidate therapies developed in academia or industry. These translational efforts are having an ongoing influence on the clinical development of human anti-cancer therapies.
The MP1U has extensive capabilities for:
Administering treatments (IP, IV, subQ injections; medicated chow; etc)
Serial imaging (Ultrasound, IVIS, PET)
Pharmacology and pharmacokinetics
Molecular target validation
The MP1U is not a fee-for-service core, but we are always seeking collaborations that will lead to funding opportunities. We are generously supported in part by the University Cancer Research Fund.
Validating the GEMM for Therapeutic Research
The anti-cancer drug development pipeline has historically relied on xenograft models as the primary in-vivo screening method. These models have had too few predictable achievements and many notable failures. Genetically engineered mouse models (GEMMs) of cancer have begun to gain popularity as an alternative to xenograft models for biological and therapeutic investigations. Before GEMMs can be routinely used for preclinical testing of novel therapeutics, they must first be validated as predictive preclinical platforms. The major criticism of traditional xenograft models is their high false positive rate, meaning they often incorrectly predict that a compound will be effective in humans. To validate GEMMs will more accurately predict clinical efficacy of novel compounds, they must first be credentialed against existing treatments with known response rates and survival data in patients. If these models recapitulate the clinical data of existing therapeutics, then there will be confidence in their ability to predict clinical efficacy of novel therapeutics.
- Credential the GEMM
Most of our models have had extensive molecular analysis including, RNA seq, RNA Microarray Expression analysis and exome sequencing.
- “Success” of a therapy requires tumor regression and survival extension.
We prefer to avoid tumor growth inhibition experiments as we feel they are less informative and rarely translate to efficacious drugs in the clinical setting.
- Pharmacokinetics, Pharmacodynamics and Dose Escalations
This is imperative in novel or tool compounds when toxicity, biodistribution and molecular targets are not known or poorly defined. Through Dr. William Zamboni, who runs the UNC Analytical Pharmacology Core, the MP1U is routinely able to perform PK analysis of novel therapeutics to monitor drug exposure.
- Use large cohorts (n>15)
In common with human cancers and in contrast to xenograft models, we generally observe heterogeneity of response in GEMMs, and therefore typically study larger treatment cohorts.
- Test old drugs
In vivo experiments using novel anti-cancer agents often exclude a comparison against known standard of care treatment. We feel a more informative approach is to directly compare new compounds against the clinical standard of care.