Understanding How Pre-Clinical Models Impact Immuno-Oncology Therapeutics
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Understanding How Pre-Clinical Models Impact Immuno-Oncology Therapeutics

July 16, 2020

Understanding How Pre-Clinical Models Impact Immuno-Oncology Therapeutics

Finding a best-fit or combination of pre-clinical models in immuno-oncology drug development poses a unique series of challenges. Foundational knowledge of the tumor microenvironment and its evasion of the immune system continues to evolve based on emerging data from areas including regulation, academia, and industry.

When functioning under homeostatic conditions, the immune system exhibits immunosurveillance mechanisms which function to prevent carcinogenesis and tumor formation. Cancer cells exhibit an array of classic hallmarks including, proliferation, replicative immortality, angiogenesis, growth suppressor evasion, metastases, and apoptotic resistance, differentiating them from normal cells. If left unchallenged, disease progression may evolve to include immune system evasion and the ability to reprogram cell metabolism.

The complexity of cellular processes involved in the immuno-oncology landscape offers significant opportunity for the development of cancer therapeutics with not just with high specificity for tumor cells, but as a means of using the body’s homeostatic anti-carcinogenic mechanisms to promote remission and elimination of disease.

Foresight and planning required

Choosing a pre-clinical model that accommodates the complexity of the immune-oncologic intersection requires a thoughtful balance, with a primary focus on developing safe and efficacious drug candidates for oncology patients.

Different pre-clinical species and study focuses will offer varying types of data, all of which is submitted for regulatory approval prior to beginning evaluation in a clinical setting. Investigation at the molecular level can be captured in pre-clinical in vitro studies, which provide key insights into the mechanistic interactions of the drug candidate in its target environment. Utilizing small animal models to evaluate systemic or localized toxicity offers both broad and specific safety data.

However, retrospective evaluation of compounds with clinical gastrointestinal (GI) toxicity revealed that GIT in rodents had only about 46% clinical concordance. Large animal models are beneficial in mimicking the complexity of body system and immunological signaling pathways, and therefore allow for a higher predictability of of primary and secondary clinical endpoint success. Regulatory approval prior to human testing may require characterization of safety and efficacy, mechanism of action, associated signaling pathways, potential downstream effects, pharmacokinetics, pharmacodynamics, and immunogenicity.

Balancing time and financial investment with robust yet specific study design involves careful foresight and planning. The heightened complexity of the immuno-oncology market makes attaining balance increasingly difficult.

Utilizing clinically relevant data

The drug development life cycle for immuno-oncology is lengthy, upwards of twelve years in total. Efficiency and relevancy planning in all phases of development is therefore essential to the success of a candidate. Generating relevant pre-clinical data to characterize local and systemic toxicity, immunogenicity, and efficacy in physiologically relevant models allows for more predictive, applicable, and meaningful clinical outcomes measure.

In scenarios where molecules fail in the clinic for toxicity or tolerability parameters, additional pre-clinical data is generated to further investigate and characterize clinical observations. With a heightened granularity into the pharmacokinetics, pharmacodynamics, and immunogenicity, additional data may support future clinical evaluations through adjustments to dosing and recovery periods.

The pre-clinical model represents an area where clinically relevant data utilization can help to maximize the design and efficiency of clinical and potential follow-up preclinical assessments.

For a more in-depth discussion of this subject, please see our whitepaper.

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