Identity, Content, Potency, Impurity assays according to ICH Q2(R2)

This insight categorizes analytical procedures as proposed by the International Council for Harmonisation (ICH Q2(R2)) and how to tackle them with PLA 3.0.
May 13 / Dr. Andre Rölke-Wellmann

Introduction

Biological assays play a critical role in ensuring the quality, safety, and efficacy of a pharmaceutical product throughout its lifecycle. More precisely, we may question whether the product is what it should be, how much of it is present, how well it works, and how free it is from unwanted material. This leads into the four groups of identity assays, content assays, potency assays, and aspect of product quality. Together, these categories support batch release and stability by confirming the molecule’s authenticity, controlling its strength and activity, and managing risks to safety and performance. Methods in each category are expected to be fit for purpose and validated according to the International Council for Harmonisation (ICH).
In the following, we provide a brief summary to the four assay types and how to can tackle them with PLA 3.0.

Identity assays

Identity assays make sure that the biological material in a sample is the intended product, thus are of qualitative nature. Because tests must be highly specific to the molecular structure, identification can be achieved through techniques like infrared spectroscopy or antibody binding. Additional analysis by biostatistics software like PLA 3.0, which focuses on quantitative methods, is usually not necessary.

Content assays

Content assays establish how much of the intended analyte or defined active component is present in a product in relation to the produced biological effect. Thus, typically referred to as dose-response analysis. In this category fall several analytical applications also covered by the Dose-Response Analysis Package of PLA 3.0: 
Single curve analysis allows for assessing how a biological system responds to varying doses, effectively ensuring curve fit quality. It can be used during routine batch testing where the assay response is stable and the curve model is established. PLA 3.0 provides linear and logistic models (2PL, 3PL, 4PL, 5PL) for curve fitting.
A coordinate system of the regression curve of the Standard sample
The effective concentration (EC) determines the amount of a substance required for a specific level of effect, usually corrected for dilution factors and sample handling. This allows the comparison of lots or steps when samples are diluted differently, for example. In PLA 3.0, we can define the percentiles of interest, such as the EC50 for calculating the effective concentration at 50% of the maximum possible effect.
A table showing effective concentration for percentiles 10, 50 and 90
Another option are calibration curves to estimate the concentration of an unknown sample relative to a known dose-response curve. This enables the conversion of a sample response into a concentration value with traceability to a reference standard. Dose-response analysis in PLA 3.0 automatically interpolates single-dose samples against the defined dose-response curve.
A coordinate system showing the interpolation plot for one Test sample
Spike and recovery analysis adds a known amount of analyte into the sample matrix and measures how much is recovered. This allows for assessing matrix effects by calculating the recovery rate. A near 100% recovery rate means that the assay accurately measures the substance. Too low or high recovery rates point towards assay interferences that should be dealt with. PLA 3.0 reports the recovery rate if the known concentration is provided.
A table showing the interpolation result of one test sample
Linearity of dilution tests whether the measured concentration is proportional across a dilution series of a sample. This tells us that the assay behaves predictably across dilutions and that samples can be diluted into range without bias. Any deviations may reveal hook effects or matrix interferences that diminish with dilution. PLA 3.0 backcalculates sample concentration from the provided predilution.
A graphic showing the interpolation result on the dashboard
We can also compare curves to assess equivalence or meaningful differences. For example, to confirm if samples are consistent across experimental conditions like the analyst, instrument, or SOP. Notably, PLA 3.0 allows us to define sample groups for combined reporting.
A graphic showing the regression result for a group of multi-dose samples

Potency assays

Potency assays are used to determine the biological activity of a pharmaceutical product assuring consistent performance throughout its lifecycle. They are a critical element of quality control, particularly for biological and biotechnological products where structure alone may not fully predict activity. Potency is typically measured against a reference standard and expressed as relative potency. Similarity of the two curves therefore play an important role, requiring a well-defined test system. PLA 3.0 provides methods for quantitative response assays, dichotomous assays, and microbial assays to determine the potency of a product.
A coordinate system showing a 4-parameter logistic fit on quantitative responses
Quantitative response assays measure a graded, continuous biological response to increasing concentrations or doses of the active component. They are commonly used during routine testing, stability trending, and bridging after method changes. In PLA 3.0, we can choose between linear parallel-line, logistic fit (3PL, 4PL, 5PL), and slope ratio models.
A coordinate system showing the concentration estimation of a Test sample in a cylinder-plate assay as recommended by USP 81
Microbial assays determine the antimicrobial activity of a substance by assessing its ability to inhibit the growth rate of microorganisms. Typical approaches are cylinder-plate and turbidimetric assays. PLA 3.0 covers both by following recommendations from USP <81> and JP 4.02.
A coordinate system showing a restricted model fit of the probability distribution
Dichotomous assays classify outcomes into two distinct categories, usually indicating the presence or absence of a biological effect. They measure the endpoint as probability of affected versus tested specimen. Such assays are important for products where the biology is inherently ‘all-or-none’ at the measurement level. PLA 3.0 provides probit and logit approaches to calculate potency.

Impurity assays

Impurity assays detect and quantify unwanted materials in a biological product that could impact safety, efficacy, or stability. The major difference to other approaches is an expected ‘no signal’ as response. Meaning that the product is free from contaminations. Such assays typically utilize product controls to eliminate false-negative or false-positive results. Results are used for release and stability, and are trended to identify process drift or emerging degradation. PLA 3.0 currently supports rFC-based endotoxin detection assays, following the methodology suggested by bioMérieux.
rFC-based endotoxin detection assays are an animal-free variant to detect lipopolysaccharide endotoxin from Gram-negative bacteria via a fluorometric readout. The analytical method of PLA 3.0 includes the linear and 4-parameter logistic models for endotoxin concentration estimation. It also contains tools to control the measurement system, for example, gain optimization, morning test, and uniformity test.
The standard curve of a 4-PL model and a table of a Sample which passed the endotoxin limit

Continue exploring

Shorts

Data analysis

On-demand Course

PLA 3.0 for Method Developers

The PLA 3.0 Academy is part of the learning and support landscape for the PLA 3.0 software product, alongside the PLA 3.0 Knowledge Center and the PLA 3.0 Support Portal.
By using this product, you agree to the Privacy Policy of Stegmann Systems.