How to identify the right use case for a multi-attribute analytical method

Picture your formulation scientists screening twenty buffer and excipient conditions before a stability study.

Protein concentration is measured on one instrument, secondary structure on another, excipient levels on a third, and pH and aggregation risk on yet others.

Each method has its own sample preparation, queue, operator steps and data file. The readouts may arrive over several days. Only when scientists align the data can they begin to draw conclusions across all twenty conditions.

In this article, ‘multi-attribute analytical method’ is used in the broad workflow sense: an analytical approach that reports several decision-relevant attributes from one sample or measurement. This broader usage is also noted in an FDA-hosted USP presentation; it is not limited to the LC-MS-based multi-attribute method (MAM) commonly used for therapeutic-protein quality attribute monitoring.

The hidden cost of fragmented analytical workflows in biopharma

The biologics analytics toolbox was built one method at a time, when a single technique could answer only one narrow question. Techniques have advanced, but many workflows remain fragmented. Teams often need to examine several attributes together, while traditional methods still deliver them one at a time.

That dependence appears throughout development. A formulation change may affect protein concentration, secondary structure, surfactant level, buffer composition and stability signals at the same time. When those attributes are split across separate instruments, the overall picture can emerge slowly and with gaps.

That leads to a useful design question: how much decision-relevant information can we obtain from each sample and each measurement?

What value can a multi-attribute analytical method bring?

Samples in biopharma have a real cost. High-value biologics material may be limited, expensive, time-sensitive or difficult to prepare. Each additional assay consumes sample, introduces handling steps, extends turnaround time and creates more data to reconcile.

This is not an argument for replacing established methods. Rather, it is a reason to reduce fragmentation where a single, richer measurement can answer more of the question while remaining fit for its intended use. Getting several attributes from one measurement can create value in three connected ways:

1. Experimental design can become more efficient.
   A design-of-experiments (DoE) campaign may compare dozens of formulation conditions across several analytical attributes. When each condition requires multiple methods, the measurement count can rise quickly. A fit-for-purpose multi-attribute method may allow a team to cover more experimental space without increasing laboratory workload at the same rate.
   
   
2. Results can be easier to interpret.
   One attribute on its own may be ambiguous. Viewing a change in protein concentration alongside excipient concentration and structure-related signals can provide more context, support interpretation and reduce the need for some follow-up measurements.
   
   
3. Teams can collaborate more effectively.
   Development, QC and process teams often investigate related questions using different methods and data systems. A multi-attribute approach can give them a more comparable evidence base across stages, provided the method lifecycle, calibration strategy and data governance are handled appropriately.

None of this is an argument against orthogonal analytical methods. For regulated QC use, analytical procedures should be fit for their intended purpose, with appropriate method understanding, specificity/selectivity, robustness, validation and controls. The performance criteria and extent of validation should reflect the method’s intended purpose and risk, consistent with ICH Q14 and ICH Q2(R2).

“Multi-attribute methods need a clearly defined intended use. The performance evidence, validation strategy and controls should match whether a method supports screening, development decisions or release.”

Dominik Sievert, CEO at CLADE

When evaluating deployment, the key question is not, “Can this method replace Method X?” It is, “Which decision will it support, and how much confidence does that decision require?”

In early formulation screening, the priority is usually speed and relative comparison across many conditions. In process development, it may be the ability to detect trends and interactions. For QC or release-related use, the focus shifts towards validated performance, auditability and repeatability. The same analytical platform may support different roles during a programme, but each method must be fit for its intended use.

Where to deploy a multi-attribute analytical method

Start with a simple inventory. List the methods in the workflow, the attribute each measures, the sample volume it requires, its turnaround time, the data system it uses and the decision it supports. Then identify the points where several methods feed the same decision. Those may be strong candidates for a multi-attribute approach.

Consider UF/DF monitoring, for example. One process sample can contain information about protein concentration, buffer exchange, excipient depletion and structural integrity. A multi-attribute readout may help bring some of that information together, while orthogonal methods remain available where additional specificity or confirmation is needed.

The aim is not simply to run fewer assays. It is to obtain more decision-relevant information from each sample, support more useful comparisons and create a dataset that is easier to reuse for modelling, method understanding, method optimisation and downstream development.

For analytical teams in biopharma under pressure to move faster without loosening scientific control, this is the opportunity. Efficient analytics and strong evidence need not be in tension. The goal is to design each measurement so that it carries more of what the next decision will need.

Explore individual biopharma application areas:

• Raw material control
• Buffer QC
• Upstream processing
• UF/DF monitoring
• Release testing