Top Five Reasons Mouse Models Fail: Mistakes to Avoid

September 11, 2024
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Introduction

Preclinical studies in mouse models have been invaluable in advancing biomedical research, but they come with challenges[^1]. Poorly designed studies result in unreproducible results that contribute to both delays and costs of therapeutic drug development. With growing pressure and scrutiny on preclinical biomedical research[^2], strategic planning and addressing pitfalls that can be overlooked, especially before the research plan starts, is not only crucial for obtaining reliable and reproducible results but it could determine whether a research award or investment gets granted[^3]. Here’s a look at some of the most frequent issues researchers face and how to overcome them:

1: Improper Selection of the Appropriate Model

The foundation of any successful research study lies in choosing a suitable mouse model. A critical mistake researchers make is using inappropriate or obsolete tools that do not align with their study’s objectives [^4]. For example, a particular mouse model might be known not to reproduce human disease to provide meaningful information sufficiently, resulting in findings that do not apply to human conditions. Researchers usually perceive that not using the most advanced models will save time and money despite the known limitations in translating findings to human conditions. One significant factor is the historical data and extensive research foundation already established with a particular model, making them a convenient baseline for comparison and experiment continuity. Notable examples include models of chronic inflammatory diseases, where early mouse models, while initially providing mechanistic insights, failed to replicate the human disease, particularly regarding chronicity and gene responses[^5]. Since then, enhanced humanized immune system mouse models have been developed to understand human immunology better and develop immunotherapies[^6], [^7].

The recent advancement of genome editing tools, including CRISPR/Cas9, has significantly accelerated the generation of certain custom mouse models for research with extreme precision, ie \- conventional knockouts and point mutation knock-in models. CRISPR technologies can allow the routine humanization of individual codons. By creating strains with human clinical variants (∼50% of which are point mutations), these genetically engineered humanized mouse models offer significant research advantages: they can represent human diseases, providing insights into pathophysiology and treatment responses, while the time and cost of model generation have decreased[^8]. Custom humanized models enable the study of human-specific pathogens, immune responses, and disease mechanisms, leading to more effective therapies and drugs \- consequently reducing clinical trial failures and accelerating new treatment development[^9].

Tips:

  • Utilize Catalogs: Research available models thoroughly to determine if they are adequate for your specific study aims. A well-crafted catalog must provide extensive strain datasheets, including how each was created and updated information on their usage.
  • Detailed Planning and Expert Consultation: Seek advice from experts to ensure that a model aligns with your research goals, such as collaborating with a laboratory with an established track record on mouse model research on your specific field or disease of interest or a shared resource/core facility. Biotechnology companies specializing in genetically modified mouse models for scientific research are also invaluable sources of expertise in current mouse models and genetic engineering and have a broad and up-to-date awareness of the latest trends in the industry and preclinical research.
  • Consider tailored humanized models designed to meet your study’s unique requirements. Custom animal model creation services offer improved gene editing techniques, including enhanced CRISPR technologies, enabling the more precise and efficient creation of relevant humanized mouse models.

Specialized biotechnology companies and CROs with state-of-the-art expertise in genetic engineering and comprehensive services can help speed up your research while supporting consistent and relevant outcomes.

2: Inadequate Genetic Background and Variability

Genetic variability among mouse strains can significantly influence research outcomes. Different mouse strains carry various genetic variants that might impact research results. If not adequately controlled, experimental or control groups in mouse studies can have a biased representation of these variants, affecting the phenotype as much as the experimental variable being studied[^10]. Ignoring these differences can lead to inconsistent and non-reproducible results.

One dramatic example of how genetic background can mislead research results is the overexpression of the Alzheimer’s amyloid precursor protein (Tg(APP695) transgene). This results in significantly different phenotypes depending on the mouse strain, from merely producing amyloid plaques in outbred strains to a lethal phenotype in FVB/N or C57BL/6J mice[^11]. Using mice with inadequate or unrepresentative genetic backgrounds can compromise the study's validity.

Tips:

  • Source your animals from a trusted vendor that carefully controls the genetics of inbred strains by refreshing from cryopreserved stock and performing extensive genetic testing.
  • Research genetic backgrounds: pay close attention to the genetic composition of your models to avoid unexpected variability that can affect your results.
  • Carefully plan cohort development and colony maintenance to prevent genetic drift and genetic contamination with substrains. Again, consultation with experts is critical to avoid costly mistakes. 

3: Uncontrolled Environmental Factors

Environmental conditions play a crucial role in the behavior and physiology of mouse models. Housing, diet, and handling can introduce significant variability if not adequately controlled. Preclinical mouse models show that housing temperature significantly affects the gut microbiome, with cooler temperatures (22°-23 °C) inducing chronic adrenergic stress and altering microbial composition, particularly enriching Lachnospiraceae. This modulation impacts experimental outcomes, including tumor growth, highlighting the importance of considering housing temperature in experimental reproducibility[^12]. Overlooking these factors can lead to biased and irreproducible outcomes, undermining the reliability of the research.

Tips:

  • Standardize Conditions: Regularly monitor and standardize the microbiome, diet, and handling practices to ensure uniformity across experiments.
  • Outsource to Expert Housing Services: Utilize facilities with consistent and controlled environmental conditions to minimize variability.

4: Flawed Experimental Design and Statistical Analysis

The reliability of experimental findings depends on the rigor of experimental design. A robust experimental design and proper statistical analysis are essential for generating valid and reliable research findings. Unfortunately, many academic studies fail to sufficiently detail key measures essential in in-vivo research, making it challenging to detect biases and misinterpretations[^13]. Pitfalls that hinder the quality of in vivo research are:

  • Not stating Inclusion and Exclusion Criteria: Pre-determining and reporting rules for including or excluding subjects and data are vital in maintaining transparency and consistency.
  • Lack of Randomization: Assigning subjects randomly to different groups to assure that each group is statistically comparable.
  • Poor Blinding: To prevent bias in interpretation, the researchers assessing the outcomes must be unaware of which group each subject belongs to.
  • Inadequate Sample Size: Calculating the number of subjects needed to detect a meaningful effect ensures that the study is adequately powered.

All of this can introduce bias and affect the integrity of the results. Notably, the National Institutes of Health (NIH) mandate to consider sex as a biological variable to be controlled in preclinical research discourages bias toward using a specific sex[^14].

Tips:

  • Implement Rigorous Planning: Ensure adequate sample sizes, randomization, and blinding are in place to produce credible and reproducible outcomes.
  • Always Consult with Biostatisticians: Work with biostatisticians to design robust experiments that minimize bias and maximize the validity of the results.

5: Poor Reporting Standards

A 2015 report estimated that the prevalence of irreproducible preclinical research exceeded 50%, resulting in approximately $28 billion annually spent on irreproducible research in the United States alone[^15].  While controlling the technical misalignments described above is necessary to ensure reproducibility, most studies would still be flawed if the reported research does not reflect the actual research. Poor reporting practices are a major contributor to irreproducible research[^16], jeopardizing the credibility of the main findings from animal research[^17].  In 2019, it was broadly estimated that US biomedical and agricultural R\&D waste due to flawed preclinical animal research ranged from $5 billion to $9 billion in taxpayer-funded studies and $9 billion to $15 billion in industry resources per year[^18]. Transparent reporting is a fundamental principle of scientific research. Incomplete reporting and a lack of transparency can severely diminish a study’s impact or invalidate completely its conclusions. Improving the conduct and reporting of in vivo research is a priority for many funders and publishers.

Tips:

  • If budget allows, outsource to a reputable contract research organization or preclinical services. To ensure the integrity of your research, consider companies with a proven record of prioritizing reproducibility and comprehensive reporting.
  • Adhere to Established Guidelines: Follow established guidelines for reporting research to enhance transparency and reproducibility, such as ARRIVE 2\.0[^19], even if your funding agency does not enforce them.

Conclusion

In summary, addressing common pitfalls is crucial for improving the reliability and impact of mouse model research. Researchers can achieve more consistent and translatable outcomes by collaborating with experts, which helps select the appropriate model, carefully consider genetic variability, control environmental factors, design robust experiments, and adhere to strict reporting standards.  
Biomedical research institutions that maintain core facilities and shared resource units for mouse model research can improve the quality of their animal model research by implementing standard operating procedures (SOPs) and transparency protocols, which are strict requirements in industry suppliers of mouse models[^20].

About Ingenious Targeting Laboratory, Inc.

Since 1998, ingenious targeting laboratory has been a leading provider of custom genetically modified mouse models. As one of the pioneering mouse gene targeting companies, we tailor each model to meet the exact needs of our clients in fields such as immunology, neuroscience, cancer research, and more. Utilizing classic and cutting-edge technologies, including CRISPR and ES cells, we produce advanced animal models that enhance the reproducibility and reliability of research. Our trusted models are highly validated and featured in hundreds of high-impact journals. We have recently expanded our service offerings to include catalog mouse models and preclinical solutions. By choosing ingenious targeting laboratory, our academic and industry clients benefit from our extensive expertise, ensuring their mouse models are relevant to human health, addressing key criticisms in the field, and safeguard their research investments.

[^1]:  “[Using the mouse to model human disease: increasing validity and reproducibility](https://journals.biologists.com/dmm/article/9/2/101/20086/Using-the-mouse-to-model-human-disease-increasing).” *Disease models & mechanisms*vol. 9,2 (2016): 101-3. doi:10.1242/dmm.024547
[^2]:  “[Troublesome variability in mouse studies](https://doi.org/10.1038/nn0909-1075).” Nat Neurosci 12, 1075 (2009). doi:10.1038/nn0909-1075
[^3]:   “[Enhancing Reproducibility through Rigor and Transparency](http://grants.nih.gov/policy/reproducibility/index.htm.).”Grants.nih.gov.” *Nih.gov*, 2019,
[^4]:  “[Are Outdated Research Tools Holding Academic Researchers Back?](http://www.sheridan.com/journals-on-topic/outdated-research-tools-holding-academic-researchers/.)” Sheridan, 22 Mar. 2018,
[^5]:   “[Mice Fall Short as Test Subjects for Some of Humans’ Deadly Ills](http://www.nytimes.com/2013/02/12/science/testing-of-some-deadly-diseases-on-mice-mislead-report-says.html).” *The New York Times*, 11 Feb. 2013, by Kolata, Gina
[^6]:  “[Generating mouse models for biomedical research: technological advances](https://journals.biologists.com/dmm/article/12/1/dmm029462/3039/Generating-mouse-models-for-biomedical-research).” Disease models & mechanisms vol. 12,1 dmm029462. 8 Jan. 2019, doi:10.1242/dmm.029462
[^7]:  “[Humanized mice: A brief overview on their diverse applications in biomedical research](https://onlinelibrary.wiley.com/doi/10.1002/jcp.26022)”. J Cell Physiol. 2018 Apr;233(4):2889-2901. doi: 10.1002/jcp.26022.
[^8]:  “[Genome rewriting generates mouse models of human diseases](https://www.nature.com/articles/d41586-023-03079-2).” *Nature*, 10.1038/d41586-023-03079-2. 1 Nov. 2023, doi:10.1038/d41586-023-03079-2
[^9]:  “[Humanized mice: A brief overview on their diverse applications in biomedical research](https://onlinelibrary.wiley.com/doi/10.1002/jcp.26022)”. J Cell Physiol. 2018 Apr;233(4):2889-2901. doi: 10.1002/jcp.26022.
[^10]:  “[Troublesome variability in mouse studies](https://doi.org/10.1038/nn0909-1075)”. Nat Neurosci 12, 1075 (2009). doi:10.1038/nn0909-1075
[^11]:  “[Genetic modification of the phenotypes produced by amyloid precursor protein overexpression](https://academic.oup.com/hmg/article-abstract/6/11/1951/687649?redirectedFrom=PDF)” *Human molecular genetics* vol. 6,11 (1997): 1951-9. doi:10.1093/hmg/6.11.1951
[^12]:  “[Housing temperature plays a critical role in determining gut microbiome composition in research mice: Implications for experimental reproducibility](https://www.sciencedirect.com/science/article/pii/S0300908423000160#:~:text=The%20gut%20microbiome%20of%20mice,by%20an%20increase%20in%20Lachnospiraceae.).” Biochimie vol. 210 (2023): 71-81. doi:10.1016/j.biochi.2023.01.016
[^13]:  “[Risk of Bias in Reports of In Vivo Research: A Focus for Improvement.](https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1002273)” *PLoS biology* vol. 13,10 e1002273. 13 Oct. 2015, doi:10.1371/journal.pbio.1002273
[^14]:  “[NOT-OD-15-102: Consideration of Sex as a Biological Variable in NIH-Funded Research](http://grants.nih.gov/grants/guide/notice-files/NOT-OD-15-102.html).” Department of Health and Human Services (HHS). *Grants.nih.gov*, 9 June 2015
[^15]:  “[The Economics of Reproducibility in Preclinical Research](https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1002165).” *PLoS biology* vol. 13,6 e1002165. 9 Jun. 2015, doi:10.1371/journal.pbio.1002165
[^16]:  "[Poor transparency and reporting jeopardize the reproducibility of science](http://www.sciencedaily.com/releases/2016/01/160104163155.htm)." PLOS. ScienceDaily, 4 January 2016\.
[^17]:   “[Risk of Bias in Reports of In Vivo Research: A Focus for Improvement.](https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1002273)” *PLoS biology* vol. 13,10 e1002273. 13 Oct. 2015, doi:10.1371/journal.pbio.1002273
[^18]:  “[Chapter 10: Wasted Money in United States Biomedical and Agricultural Animal Research](http://brill.com/display/book/edcoll/9789004391192/BP000013.xml?language=en&body=fullhtml-60832).” in *Animal Experimentation: Working towards a Paradigm Change Series: Human-Animal Studies, Volume: 22*, Brill, 17 Apr. 2019, pp. 244–272
[^19]:  “[The ARRIVE guidelines 2.0: Updated guidelines for reporting animal research](https://bmcvetres.biomedcentral.com/articles/10.1186/s12917-020-02451-y).” *Experimental physiology* vol. 105,9 (2020): 1459-1466. doi:10.1113/EP088870
[^20]:  “[Rigor, Reproducibility, and Transparency in Shared Research Resources: Follow-Up Survey and Recommendations for Improvements](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10001929/pdf/jbt-33-3-5rv3b2jr.pdf).” *Journal of biomolecular techniques : JBT* vol. 33,3 3fc1f5fe.fa789303. 16 Aug. 2022, doi:10.7171/3fc1f5fe.fa789303

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