Hello, this is GCCL (Global Clinical Central Lab)! 👋
The 5th edition of this year’s [Statistical Series for Bioanalysis] is here!
Throughout this series, In Kyungmin, Unit Head of the Research Division at GCCL,
has covered topics such as linear and nonlinear regression, Digital PCR (ddPCR) quantification, and TOST in biosimilar development,
introducing the key statistical concepts used in bioanalytical processes for clinical trials.
In this 5th chapter, we will discuss the Low Positive Control (LPC) setup in ADA assays and
the statistical meaning behind the ‘1% failure rate’ guideline.
Based on the Immunogenicity Testing of Therapeutic Protein Products (2019) guidance by the U.S. FDA,
we will examine how LPC should be designed for ADA (anti-drug antibody) testing,
and why the ‘1%’ criterion represents a probabilistic design principle rather than a simple QC target.
Before diving in, you can check out the full original article at the link below 👇
🎯 Understanding the “1% Rule” in ADA Assays: The 1% failure rate = not a target, but a statistical design principle.
According to the FDA, the LPC should be designed as follows:
“For the low-positive QC sample, we recommend that a concentration be selected that, upon statistical analysis, would lead to the rejection of an assay run 1% of the time.”
– U.S. FDA, Immunogenicity Testing of Therapeutic Protein Products (2019)
This means that the Low Positive QC (LPC) should be set so that approximately 1% of runs fall below the Cut Point. In other words, this 1% rate serves as a statistical benchmark verifying that the analytical system maintains its expected precision and sensitivity. Rather than a performance goal, it reflects the probabilistic consistency and stability of the assay system itself.
In individual runs, variations of around 0–3% are normal due to statistical randomness. However, a stable system should converge toward an average failure rate of about 1% over time.
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📊 Combined Variance Model and Statistical Stability
Some practices convert the Screening Cut Point (SCP) into concentration units or calculate LPC simply as t0.99 multiples. However, these methods may not fully reflect the variance structure (σ²) of the real analytical system, which can lead to deviations from the theoretical 1% rate.
To prevent such deviations, GCCL applies a Combined Variance Design Principle, taking into account the variance of both SCP and LPC.
μLPC = CP + z0.99 × √(σ²SCP + σ²LPC)
This equation determines how much higher the mean LPC should be than the Cut Point to maintain an actual 1% rejection probability. Therefore, the 1% failure rate reflects the joint statistical behavior of the SCP–LPC system, not the property of LPC alone.
Even when temporary deviations occur, GCCL does not simply adjust LPC concentration upward or downward. Instead, we perform a statistical root-cause analysis—tracking potential sources such as variance shift, SCP drift, or precision changes. This approach ensures Statistical Stability and strengthens both the reliability and reproducibility of clinical bioanalysis.
📘 Read MoreThe original article explores in detail topics such as the statistical limitations of signal-to-concentration transformation, bias introduced when ignoring LPC variance, and interpreting the 1% failure rate as a validation indicator for assay performance.
👉 Read Full Article
📢 GCCL’s Principles for Ensuring Analytical Reliability!
GCCL interprets the
‘1% failure rate’ as a result of system reliability—not as something created by analysts, but as a statistical property that naturally emerges when the system operates stably. To achieve this, GCCL adheres to the following core principles. 😊❤
📌 Quality Control by Design
Focus on long-term stability and statistical consistency rather than short-term variation.
📌 Data-driven Monitoring
Regularly review SCP/LPC signal distributions to detect anomalies at an early stage.
📌 Traceability & Reproducibility
Document every adjustment and rationale statistically to enhance regulatory compliance.
Thank you for your continued interest in the 2025 Statistical Series for Bioanalysis.
The final series will be released in December, so stay tuned!
GCCL will continue to share insights and research efforts to enhance analytical quality and reliability.
📌 For inquiries about analytical services or sample testing, please contact us through the quotation request link below. Our team will respond promptly and accurately.
📢 GCCL will continue to deliver trusted analytical insights and professional perspectives. Stay tuned!
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