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Julian Ashby: Deep preclinical characterization is essential to increase the chances of success in clinical trials

Photo taken from Julian Ashby/LinkedIn

Jun 11, 2024, 15:17

Julian Ashby: Deep preclinical characterization is essential to increase the chances of success in clinical trials

Julian Ashby, Product Marketing Manager at LUMICKS, shared on LinkedIn:

“Each year, companies use tens of thousands of animals for immuno-oncology drug testing.

Yet, more than nine in ten drugs that enter human clinical trials fail because they are unsafe or ineffective.

This has led to the FDA no longer requiring animal tests before human drug trials, paving the way for deep characterization using preclinical in vitro assays.

However, these assays alone provide insufficient detail for evaluating cell therapies.

To fully understand your T-cell therapy, deep preclinical characterization is essential to increase the chances of success in clinical trials.

This involves a comprehensive data package with multiple assays to gain a complete picture of the mechanism of action and to de-risk as many parameters as possible.

Each stage of the characterization process should be thoroughly assessed and involved in the decision-making process.

Here’s why each stage should be measured:

1. Molecular Binding: Identifying the specificity and affinity of the constructs on the engineered T cells to a tumor antigen helps quickly identify suboptimal binding receptors.

This step is crucial for optimizing receptor design.

2. Cell-to-Cell Binding (or Cell Avidity): These measurements provide the bridge between molecular binding to functional outcomes.

Cell binding confirms that the engineered T cells can accurately find and bind to the cancer cells in a complex cellular system. This is necessary for the T cells to exert their therapeutic effect by engaging with the cancer cells directly and to minimize the risk of T cells binding and attacking healthy cells, which is crucial for both efficacy and safety.

3. Functional Outcomes: This stage provides direct evidence of the engineered T cells’ ability to eliminate cancer cells. It helps assess the therapeutic potential, ensuring that the T cells can effectively destroy cancer cells while sparing healthy cells.

Incorporating with single-cell approaches enables the identification and characterization of rare cell types or subpopulations. Crucially, the data can inform the optimal dosing and safety profiles of the therapy, making them essential for optimizing T cell therapies preclinically.”