Ensuring pharmaceutical and biopharmaceutical product quality with Critical Quality Attributes (CQA) monitoring

The pharmaceutical industry is trying to embrace quality by design (QbD) methodologies provided by the FDA’s process validation (PV) guidance and International Conference on Harmonization (ICH) Q8/Q9/Q10. Many companies are challenged by Monitoring Critical Process Parameters (CPP) and Critical Quality Attributes (CQA).

Critical Quality Attributes (CQA) is defined by the FDA as a physical, chemical, biological, or microbiological property or characteristic that should be within an appropriate limit, range, or distribution to ensure the desired product quality (ICH Q8). This interpretation of CQA is most applicable to in-process and finished-product specification limits, suggesting these limits must be critical given that they were designed to ensure product quality.

Key product and process considerations (including CQA and CPP) are scientifically designed to meet the desired final-quality objectives and meet the US FDA Pharmaceutical Quality Assessment System (PQAS). For a quality attribute to be designated as “not critical,” it has to have no risk to the patient (e.g., yield, process duration). Attributes that are not critical to quality are sometimes named process performance attributes to distinguish from quality attributes.

Examples of risk levels for CQAs (as stated by ICH Q8 (R2) (Pharmaceutical Development)), include:

• High: assay, immunoreactivity, sterility, impurities, closure integrity
• Medium: appearance, friability, particulates
• Low: container scratches, non-functional visual defects.

The FDA recommends that critical formulation attributes and process parameters are generally identified through an assessment of the extent to which their variation can have impact on the quality of the drug product. Diverse techniques such as NMR, FT-NIR, Raman spectroscopy and Mass Spectrometry are suitable for CQA screening, with seamless generation of screen lists from peptide mapping results for quality attributes monitoring across large datasets.