Before a biopharmaceutical company releases a new batch of biotherapeutics, it needs to conduct comparability studies with FDA-approved production lots to demonstrate the new batch is indistinguishable from officially approved lots. While such quality assessment processes are well-established for small molecule drugs, the process presents a new challenge for manufacturers of biotherapeutics.
Mass spectrometry seems to be the technology platform of choice when comparing newly produced batches of biotherapeutics with previously approved reference samples. While detailed regulations for the quality assessment of biotherapeutics are still in development, an increasing number of biopharmaceutical companies (including producers of biosimilars) have adopted mass spectrometry-based workflows in their quality control processes for biotherapeutics.
Obviously, the quality assessment process needs to be reliable and efficient. An important success factor is hereby the standardization and automation of the data management and analysis process. It requires a comprehensive and scalable data-analysis platform such as that available with Genedata Expressionist for Mass Spectrometry.
This tutorial will outline a data-analysis process for assessing the quality of biologics drugs using Genedata Expressionist for Mass Spectrometry.
Over the last decade mass spectrometry (MS) has become the preeminent method for the quantitative analysis of proteins. While characterization of intact proteins is possible using modern high-resolution MS instruments, most applications rely on a tryptic digest of the complete protein to smaller peptides followed by a liquid chromatographic separation and subsequent mass spectrometry of the peptide mixtures (LC-MS).
As there is a positive correlation between the abundance of peptide in a given sample and the magnitude of the corresponding MS signals, MS data is very useful in many life science applications ranging from patient stratification, precision medicine, and predictive toxicology in drug development, to microbial strain optimization in industrial biotechnology.
MS data analysis requires the quantitation and identification of peptide peaks across samples followed by multivariate statistical analysis to find significant peptide and protein biomarkers. Including post-translational modifications, the human proteome is estimated to contain more than 100,000 distinct proteins. Typical LC-MS experiments of human plasma and serum can reliably detect and measure about 25,000 peptide fragments from about 5,000 proteins. As such, protein biomarker discovery truly is a search for a needle in a haystack.
Biotherapeutics are produced by genetically modified organisms (expression systems) in industrial-scale fermentors followed by harvesting, purification, and filling. Some of the factors that can negatively impact production and result in product deviations from the original protein (i.e., changes in safety and efficacy profiles of the final protein) include:
- Potential mutations of the expression system
- Known and unknown viral contaminants
- Post-translational modifications
Before releasing a new biotherapeutics batch, manufacturers must verify that the product matches the reference drug originally approved. While this process is straightforward for small molecule drugs, rigorous establishment of protein bioequivalency is significantly more challenging.
Following the successful adoption of label-free MS methods in different protein analytics applications, manufacturers of protein drugs are now utilizing label-free MS methods to establish bioequivalence: post-production samples of purified proteins are obtained from the production line and are subjected to an enzymatic digestion. The resulting mixture of small peptides is injected into a liquid-chromatography column that is coupled to a high-resolution MS instrument (LC-MS). Alternatively, the instrument can be set to acquire peptide fragmentation data for peak identification using protein databases (LC-MS/MS).