Optimizing Humanized Model Selection for Enhanced Preclinical Immunotherapy Assessment

It’s impossible to miss the ground-breaking impact immunotherapy has had on oncology research and patient outcomes. From the “coming of age” of the treatment modality, through multiple checkpoint inhibitor approvals across a variety of indications, to the recent endorsement of CAR-T therapies, immunotherapy use has brought many reports of unprecedented patient survival.

However, it should always be remembered that this response is usually in subgroups of patients only – as a research community we still don’t understand why some patients and cancer indications fail to respond to immunotherapy treatment.

To fully maximize the potential of immunotherapeutics we need to improve overall response rates, convert non-responders, and speed the approval and uptake of new therapies. All of these factors have led to the frank assessment of current preclinical tools and if they are suitable for the early stages of this complicated job. A new White Paper reviews the need for tumor-bearing humanized models, and discusses optimized utilization strategies which can be directly applied to preclinical programs to accelerate immunotherapy assessment.

Predictive, Immunocompetent Preclinical Models Needed to Progress Immuno-Oncology Research

Regular in vivo preclinical oncology research uses xenografts in immunodeficient mice to test efficacy and other downstream effects. The rapid rise of immunotherapies has now seen many drug developers need to quickly move to the evaluation of immuno-oncology agents. This is complicated by needing preclinical models with an intact immune system, or at a minimum the aspects of the immune system targeted by a given agent.

While syngeneic models have been somewhat of an immunotherapy assessment workhorse, we need to move above and beyond this model when thinking about driving immuno-oncology research forward and answering the open questions around this modality. As we look to develop combination regimens (often touted as the future of immunotherapy) and provide preclinical data which has improved clinical predictivity, assessment will shift to testing more human-specific agents, so they can be fully evaluated prior to clinical trial.

This then adds further caveats to preclinical models – a murine immune system is no longer suitable, with humanized models required to assess human origin therapeutics.

Of all of the available immuno-oncology models, humanized mice are potentially the most complex preclinical system, with a wide variety of study choices and decisions to be correctly made. Initial questions include:

  • Should you use mice humanized via mature lymphoid populations such as PBMC, or generated using human Scid repopulating cells e.g. human CD34+ hemopoietic stem cells?
  • Should you combine your chosen mouse with traditional xenografts or patient-derived xenografts (PDX)?

These questions, therefore, take one model platform and create four sub-platforms before study design has really even begun.

Problem Driven Model Selection Combined with In Depth Model Knowledge is the Key to Success

While these models are complex, with some careful optimization they can be highly useful tools in an immuno-oncology preclinical program, which has been fully detailed in a new White Paper exemplifying humanized model use through complementary case studies.

Model selection should always be based on a combination of factors - what you want to achieve with your study, linked to known model features and limitations, as well as xenograft characteristics.

Study needs can be driven by the type of agent you are assessing (so which immune cells it targets, which lineages need to be reconstituted) or the question you need to answer (is this a first efficacy test, something more mechanistic, or maybe looking at a long term response?). This information should be combined with an in depth knowledge of the different humanized model types, their individual immunological features, and the model limitations which could impact on your specific study.

Different humanized models have varying pros and cons, and as these models have been used much less widely and long term than models such as syngeneics, they still have many features and questions which need to be answered and optimized. This includes factors such as donor-to-donor variability, which immune cells are recapitulated per model, and in some cases overcoming Graft vs Host Disease.

The final factor in model selection and optimization is then an understanding of different xenograft characteristics, and which would complete your ideal study design – do you need a fast growing, historically used traditional xenograft, or a slower growing but more patient relevant PDX model?

While using this platform may seem daunting, breaking study design down and evaluating the best humanized model, tumor model, and study type based on individual scientific questions to answer leads to optimized study designs and immunotherapy assessment.

Taking advantage of these cutting edge, tumor bearing humanized model platforms offers great opportunities for accelerated preclinical development, and the full maximization of new immunotherapy potential. Read the new White Paper to learn more on immediately transferable humanized model strategies for accelerating immunotherapy drug discovery programs.

Crown Bioscience Inc. is a cutting edge translational technology company providing drug discovery and clinical development services in the areas of oncology, inflammation, cardiovascular, and metabolic disease. We bring clarity to drug discovery, and enable clients around the world to accelerate their new drug development programs and deliver superior clinical candidates. We develop world-leading preclinical efficacy models and provide both in vitro and in vivo testing services as well as preclinical research products, combining to provide premier, integrated Translational Technology Platforms. To learn more, visit www.crownbio.com.

This article was created in collaboration with the sponsoring company and our sales and marketing team. The editorial team does not contribute.