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How important is matrix effect in analyzing bioprocess samples?

Meet the Expert: Petra, Analytical Scientist

The matrix effect is the effect on an analytical assay caused by all other components of the sample except the specific compound (analyte) to be analyzed. Matrix effects can be observed either as a loss in response – resulting in an underestimation of the amount of analyte – or as an increase in response – producing an overestimated result. Matrix effects have long been associated with bioanalytical techniques and their evaluation for each assay and each sample matrix can be very time consuming and brings additional costs. So, how important is evaluation of the matrix effects when analyzing bioprocess samples? What are the best approaches to evaluate the matrix effect? And in what situations is the matrix effect acceptable?

Importance of matrix effect evaluation

In order release final product for administration to patients, the product undergoes different quantitative or quantitative analytical procedures and based on the obtained data/results decision for approval or rejection of the product batch for use in patients is made. For that reason it is necessary to show that generated analytical data are true and no matrix effect is involved. During the assay development and validation impact of sample matrix is investigated in the assay specific way and each assay is developed in such a way to monitor matrix effect and if possible  completely eliminate it.

Methods to evaluate and eliminate

There are a few quick, qualitative options to determine whether a matrix effect is present such as the dilution-based method, but lets focus on some quantitative methods often used for bioanalytical techniques.

  • Signal-based method – This method allows for quantification of the matrix effect for one specific concentration. At this concentration, the analyte is measured in the matrix and subsequently measured in a solvent known not to induce any matrix effect. The analyte signal in the matrix is then divided by the analyte signal in the solvent and multiplied by hundred, resulting in the percentage of matrix effect. If the percentage is below hundred, the matrix effect results in a suppression of the result. When it is above hundred, the matrix effect causes enhancement of the result. This method is useful when only this concentration is relevant. It doesn’t necessarily provide any indication of the matrix effect for other analyte concentrations.
  • Concentration-based method – In this method, the matrix effect is measured with the signal-based method, but for a range of analyte concentrations. The concentration-based method is used to show that the matrix effect is not analyte concentration dependent.
  • Calibration-based method – This method is particularly relevant when blank matrix is not available. Different analyte concentrations are measured in solvent as well as the matrix and obtained data are plotted in a graph and linear regression model is used to generate a slope value. The slope of calibration analyzed in the matrix is then divided by the slope of calibration curve prepared in the solvent and the ratio is multiplied by hundred to generate %ME. For %ME >100% matrix results in overestimation and for %ME < 100% tested matrix leads to signal suppression.

When a matrix effect turns out to be present, the effect causing component needs to be removed from the sample before analysis. Unfortunately, this is not always an option. In those cases, matrix minimization (dilution) will provide a way forward. Matrix minimization is especially useful when the analytical technique has sensitivity to spare. Elimination of matrix effect for particular matrix is proved during the assay development or validation.

When the matrix effect can be ignored

Theoretically samples consisting a pure compound could be ignored for matrix testing. However in some cases even a supposedly pure compounds may contain other elements, such as reaction impurities or by-products that may lead to matrix effects. Especially during process development it is challenging to remove the matrix effect, due to the large number of matrices generated at each step of upstream or downstream process development. Analysis of PD samples usually serve to monitor process and provide process developers with an indication whether changes in certain process parameters have beneficial or undesirable consequences. As the absolute value generation during analysis is less important during the process development compared to batch release analysis, matrix effects are often not completely removed but only monitored using spike recovery approach. This approach enables sample analysis with sufficient information on a potential matrix effect while saving time and resources.

Batavia Biosciences offers a broad range of process development and manufacturing services for all major classes of biopharmaceuticals, i.e., viral vaccines, viral vectors, recombinant proteins and antibodies. As a company dedicated to help bringing biopharmaceuticals to the market at higher speed, with reduced costs, and with a higher success rate, Batavia Biosciences has vast experience in developing product-specific assays for viral vectors, viral vaccines and protein-based biopharmaceuticals. With our team of experienced scientists and technicians, we are well equipped to take on any challenge associated with biopharmaceutical development.


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