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Published on: May 4, 2017
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New Quality-Range-Setting Method Based on Between- and Within-Batch Variability for Biosimilarity Assessment.
Alexis Oliva1, Matías Llabrés1
1Departamento de Ingeniería Química y Tecnología Farmacéutica, Facultad de Farmacia, Universidad de La Laguna, 38200 Tenerife, Spain.
Pharmaceuticals (Basel, Switzerland)
|July 2, 2021
Summary
We propose a new method, QRML, for analytical biosimilarity assessment. QRML improves the reliability of biosimilarity testing by using variance components to set quality range bounds, unlike the traditional QR method.
Area of Science:
- Pharmaceutical Sciences
- Biostatistics
- Analytical Chemistry
Background:
- Analytical biosimilarity assessment requires methods to be fit for intended use and reference products to be under statistical quality control.
- The traditional Quality Range (QR) method for biosimilarity can yield highly variable bounds due to small batch sizes and reliance on single samples per batch.
Purpose of the Study:
- To introduce a novel method, QRML, for setting Quality Range (QR) bounds in biosimilarity assessment.
- To enhance the reliability of biosimilarity assessments by accounting for both between- and within-batch variances of the reference drug product.
Main Methods:
- Developed the QRML method using a two-level nested linear model to estimate variance components.
- Applied maximum likelihood estimation to determine between- and within-batch variances.
- Tested the statistical quality control of the manufacturing process and analyzed confidence intervals for QR bounds.
Main Results:
- The QRML method provides a more reliable estimation of standard deviation for setting QR bounds by incorporating both variance components.
- Analysis of bevacizumab drug product batches demonstrated that QRML is more reliable than the traditional QR method.
- The statistical quality control condition of the manufacturing process is tested as a prerequisite for QRML.
Conclusions:
- The QRML method offers a statistically robust approach to biosimilarity assessment.
- Accurate estimation of batch variability is crucial for reliable biosimilarity testing.
- QRML enhances the confidence in biosimilarity conclusions compared to the conventional QR method.
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