Host-cell proteins (HCPs) must be assessed early in biopharmaceutical product development to establish the efficiency of a purification process. Liquid chromatography coupled with mass spectrometry (LC-MS) is a strong orthogonal method for HCP identification and quantitation. During an 18 March 2021 “Ask the Expert” presentation, Christina Morris (senior scientist at BioPharmaSpec) explained how MS analyses can enhance process development (PD), then presented considerations for generating spectral libraries, selecting samples for quantitation, and reviewing data.

Morris’s Presentation
Spectral Libraries: A typical MS workflow comprises spectral-library creation, sample analysis, and data review. Libraries are constructed using information-/data-dependent acquisition (IDA/DDA), which collects HCP data with high confidence because fragment ions observed during a run can be attributed to defined precursor signals.

Typically, IDA/DDA analysis is performed on host-cell lysate. Doing so generates a library that is specific to a product’s purification process, which is important because even small changes in a manufacturing process can influence a drug product’s HCP population. However, generating lysate can be time- and resource-intensive. The molecular complexity of lysate also can increase chromatography costs.

Alternatively, in-process samples or drug substance/product (DS/DP) samples can be used when host-cell lysate is difficult to generate or when an overview is preferred, with less focus on low-abundance HCPs. These strategies streamline sample preparation and significantly reduce experimental time and costs. But compared with a lysate workflow, sample-based libraries yield lower-confidence data and increase the risk of missing low-abundance HCPs.

A third option is to base spectral libraries on host cell line standards. Doing so generates a broad HCP library and reduces timelines and costs for library setup. Unlike libraries derived from host-cell extracts, those based on cell-line standards are not process specific.

Sample Selection: Quantitation involves data-independent acquisition (DIA) of mass spectra. Such testing can provide highly reproducible results and scan for weak signals from low-intensity HCPs, raising several possibilities for HCP quantitation strategy. Thus, analysts should consider different sample-selection rationale.

One option is to conduct an overview analysis based on samples (sometimes from multiple batches) representing one or two process stages. That approach is ideal for monitoring improvements to a purification step and performing relative quantitation of DS/DP for orthogonal confirmation of other findings.

Full assessment requires samples that span a DP’s entire purification process. Such evaluation helps when PD teams must rely on orthogonal testing in lieu of reliable enzyme-linked immunosorbent assays (ELISAs) and when process changes upstream necessitates quick evaluation of HCP clearance. A complete workup also can help establish HCP clearance efficiency for regulatory submissions.

Analysts can apply heavy-isotope labels to final DP samples in cases requiring absolute quantitation. Such analyses can help to establish HCP clearance when relative quantitation using a label-free method finds that HCP quantities have approached a process’s clearance threshold.

Data Limitations: Analysts use proteomics software to match observed signals with those in a spectral library and to estimate HCP concentrations. MS for HCP analysis generates large data sets with thousands of possible HCP hits, which is why bioinformatics tools are needed to filter results. But because such tools have limitations, analysts should be aware of HCP misassignment. Weak library data can prompt software to underestimate peptide-confidence scores and exclude HCPs. Concerns also arise with retention-time alignment. Sometimes areas with the same signal are assigned wrongly to different HCPs; selection of the wrong area within a retention-time window can lead to high method error rates across replicates. Manual review of selected HCPs — e.g., those of the highest intensity — helps to weed out false identifications.

Data quality depends on method quality and suitability. Because different DS/DPs and processes raise distinct concerns, analysts must consider process- and project-specific variables.

Questions and Answers
Can the same spectral library be used for multiple DPs? Although a lysate-derived library might be suitable for multiple DP lots, differences in growth and stress conditions can influence how a cell line expresses a protein product. A cell line should be characterized as it is used in a particular process for a specific DS.

Will regulators accept data from a generic ELISA kit if orthogonal MS data are available? That depends on a kit’s HCP coverage. High-coverage kits accompanied by MS data might suffice, but low-coverage kits are unlikely to instill confidence, even with MS data.

Watch the full presentation now.