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Assessing Combination Therapies in Syngeneic Tumor Models

by Jody Barbeau PhD, June 28, 2018 at 12:00 PM | Tags

Cancer drugs, immunotherapy combination assessment, syngeneic model combination efficacy testing

Cancer drugs, immunotherapy combination assessment, syngeneic model combination efficacy testingCombination immunotherapy could enhance the clinical benefits of monotherapies and overcome patient lack of response to these novel agents. This means assessing a wide range of potential combinations, and syngeneic tumor models provide an ideal evaluation platform.

Syngeneic Models: Workhorse of Immunotherapy Assessment

Syngeneic models have been the mainstay immunocompetent platform in immunotherapy assessment, particularly for checkpoint inhibitors. This is due to syngeneics being easy to set up, allowing synchronized growth for efficacy studies, and inducing a large immune response upon engraftment. This results in immune checkpoint inhibitors working well against a selection of these models.

Panels of syngeneics have been characterized for high throughput screening studies, with common checkpoint inhibitor benchmarking data (e.g. anti-PD-1, PD-L1, CTLA-4) and immunophenotyping pre- and post-treatment available.

This provides an ideal platform to move into combination assessment to study efficacy and mechanisms of action. Most preclinical studies of checkpoint blockers combined with other immunomodulatory agents have so far been performed in syngeneic models.

Testing Combination Therapies with Syngeneic Models

Syngeneic models can be used to trial a range of combination regimens, including immunotherapy with standard treatments (e.g. chemotherapy, radiotherapy, targeted agents) and pairs of immune checkpoint inhibitors (ICIs). The most common combinations currently being trialled involve at least one drug targeting the PD-1 pathway.

ICIs can also be combined with immunogenic cell death (ICD) inducers. Inducers of ICD are a varied group of chemotherapy and radiotherapy regimens which increase the presentation of cell-associated antigens to CD4+ and CD8+ T lymphocytes by dendritic cells. The list of known agents includes oxaliplatin, doxorubicin, bortezomib, and mitoxantrone. Repurposing these agents into combination regimens with immunotherapy could harness the immune system to extend survival, even for the most heavily pretreated and metastatic patients.

Combining an inducer of ICD with immunotherapy may also increase the efficacy of immunotherapy in cancer types with low immunogenic status.

Selecting a Partial Responder Model

Large panels of benchmarked models make choosing a syngeneic model for combination assessment relatively simple. A partial responder needs to be chosen to allow a combination readout; for example, if assessing a combination based around anti-PD-1, choose a model showing around 50% response to single agent anti-PD-1.

Choosing a model with any higher response makes it unlikely that further combination effects would be seen. Picking a partial responder for combination studies allows additive and synergistic effects to be evaluated, and helps ensure no interference occurs between the two agents being tested.

Understand the Mechanism of Action of I/O Agents

Syngeneic models can also be used to understand how immunotherapeutics are functioning, either as combination regimens or as single agents, to decide which therapy moves forward into combination treatment. For example, if anti-CTLA-4 and anti-PD-1 treatment both elicit a similar efficacy response upon treating a certain model, there may still be a difference in how they affect tumor infiltrating lymphocytes (TILs). This could help you to select one agent in preference over the other.

Immunoprofiling of models before and after treatment helps researchers understand exactly how treatment affects TILs, and immune cell population representations.

Move with Confidence to More Complex Humanized Systems

Syngeneics are often just the first step in a comprehensive preclinical assessment program. The robust efficacy and mechanism of action data they provide can validate which agents to carry forward into more complex, humanized systems and, eventually, to clinical trials.


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