State of the Art Analytics

Highly-sensitive methods are essential to analytical understanding — the cornerstone of demonstrating biosimilarity.

In innovative drug development, clinical data is paramount.  In biosimilar development, the analytical data is the foundational component to demonstrating the level of similarity a biosimilar has to a reference product, and serves as a surrogate for a larger clinical dataset. Therefore, the analytical methods used to test for differences must be technologically advanced enough to detect any differences in these attributes, and then the clinical data completes the body of evidence.

Product quality is critical to the efficacy and safety of all medicines. It is assessed at the earliest stage of development and continually through the medicine’s commercial production. However, it is not enough to just test the product quality – the methods used for these tests must be sufficiently sensitive and have a high enough resolution to ensure that differences can be detected if they exist. In many cases, this requires a scientist who has a deep knowledge and specialized training in a particular technique to tailor the assessment. In addition, it often requires state-of-the-art equipment, specialized materials, and additional development and testing time. 

Different companies will take different approaches to analytical testing. The detail of information developed about the product is impacted both by how the test is performed and the actual tests used.

Quality of Analytical Testing Matters

Certain medicines work by inducing different properties of cells.  One example is antibody dependent cell-mediated cytoxicity (ADCC). ADCC is a way for the medicine to cause the patient’s own immune system to kill certain harmful cells, such as cancer cells.

ADCC can be measured in assays using immune cells along with cells expressing the protein recognized by the antibody.  The immune cells can be cells from human donors or cloned cells grown in a laboratory. Counter-intuitively, laboratory cells can provide a test which is more sensitive to differences between products than using cells from human donors. Human cells include a mixture of cell types, not all of which can participate in ADCC. In addition, normal genetic variation can cause differences from donor to donor.

Furthermore, multiple uses of less sensitive testing methods can provide more data which might give the impression of more information, but can actually provide less insight. For example, the extent of ADCC that a product induces is dependent on the amount of certain types of carbohydrates (glycans) attached to the product. Therefore, the analysis of the glycans can give important information to scientists about the medicine. This analysis is usually done by removing the glycans from the protein, separating them based on their chemical properties, and quantifying the amount of each. If the glycans which impart this function are not fully separated, they could be missed as they are usually present in small amounts (see below). The full separation of species depends on many factors, including the speed of the analysis. If the analysis is done too quickly, the different types of glycans are not fully separated and important information could be missed.  So if a less sensitive method with human cells and a glycan method with low resolution are used, data has been generated, but differences are not detected. 

Analytical techniques vary in their quality just as a photograph can vary in quality. Two of the measures that scientists often use to describe the quality of a method are resolution and sensitivity. Just as low resolution in a photograph can hide important details, low resolution in an analytical method can mask differences in products. On this page, there are two otherwise identical photographs with different resolutions and two sets of example data representing two attributes in the same proportion but with different resolution. In these examples, information about the succulent plant is less apparent in the photograph with low resolution, just as information about the smaller peak, which represents a product attribute, is lost in the example analytical data with low resolution. This would be particularly important when the smaller peak represents an attribute that impacts how the drug works. Similarly, sensitivity is like contrast: the more sensitive a method is, the better it is capable of detecting a small difference.

Scientists use many different kinds of analytical tests; among the most common are bioassays and separation techniques.



Bioassays are techniques that measure one or more ways in which a product interacts with a cell or a part of a biological system. The measurement is either based on binding or on the relative dose strength, such as relative potency
Separation techniques
Separation techniques, such as chromatography and electrophoresis, take a mixture of components and separate them based on chemical or physical properties. The amount of each component is measured as it moves past a detector, allowing scientists to calculate its amount as a percent of the total mixture.