Inconsistent biomarker data between preclinical and clinical phases presents a significant risk to therapeutic development programs. A robust cross-validation strategy ensures data continuity, directly comparing assay performance between nonclinical species and human matrices. By systematically evaluating parameters like parallelism, selectivity, and matrix effects on platforms such as ELISA and Meso Scale Discovery (MSD), we de-risk the transition to first-in-human studies. This approach generates a coherent, GxP-compliant dataset that strengthens regulatory submissions and supports an accelerated IND timeline. At our >100,000 sq ft facility, this rigorous bioanalytical framework has supported a 100% IND success rate for our clients since 2019 (noting the Franklin Biolabs brand launched in 2024) and contributes to an average 18-24 month IND timeline.
What is the primary goal of biomarker assay cross-validation?
The objective is to establish a clear correlation between preclinical and clinical biomarker data. This ensures that the insights gained from in vivo models are directly translatable to human trials by confirming that the assay measures the same analyte with comparable performance across different species and biological matrices.
Which platforms are typically used for this cross-validation?
Immunoassays like ELISA and multiplex electrochemiluminescence platforms from partners like Meso Scale Discovery (MSD) are standard. The selection depends on the required sensitivity, dynamic range, throughput, and sample volume constraints. MSD platforms, for instance, offer advantages for multiplexing and low sample volume requirements.
When should cross-validation be initiated?
Planning should begin during late-stage preclinical development, well before initiating first-in-human studies. This proactive timing ensures the clinical assay is fully qualified and ready for sample analysis, preventing delays in the clinical timeline.
What are key parameters assessed during cross-validation?
The core assessments include parallelism, selectivity, specificity, and relative accuracy. Parallelism is particularly important, as it confirms that the dose-response curves of the endogenous analyte and the reference standard are similar in both preclinical and clinical matrices, indicating the assay is reacting consistently to the biomarker in both contexts.
