> Leveraging Development & Commercial Knowledge for NIR Method Performance
This webinar explores how high-volume pharmaceutical manufacturing environments are addressing the growing complexity of Near-Infrared (NIR) spectroscopy in release testing. Featuring Brendan Lyons of Bristol Myers Squibb, alongside Joe Andrews of Optimal Industrial Technologies, the discussion focuses on how organizations can better understand and troubleshoot NIR method performance at scale.
The Challenge of NIR at Scale
In high-throughput environments, NIR methods are increasingly used for assay and content uniformity testing. However, as testing volume grows, so do challenges around system suitability, statistical limits, and comparability to reference methods. These challenges often trigger investigations, but unlike traditional techniques such as HPLC, the root causes behind NIR performance issues are rarely tied to simple laboratory errors.
Instead, NIR methods rely on multivariate calibration models, which introduce a layer of mathematical abstraction that can be difficult to interpret. This makes it harder to connect performance deviations to specific, actionable causes without deeper contextual understanding.
Rethinking Root Cause Analysis
What sets NIR apart is that meaningful root cause analysis depends less on identifying isolated errors and more on understanding patterns within complex data. The webinar highlights how these multivariate models, while abstract, contain valuable diagnostic insights when viewed in the right context.
Rather than treating deviations as isolated events, organizations are encouraged to look at broader trends and relationships within their data. This requires a shift in how teams approach investigations, moving from reactive troubleshooting to more data-informed analysis.
The Role of Historical and Development Context
A key takeaway is the importance of context. Historical knowledge—whether from Quality by Design (QbD) strategies during method development or from long-term performance trends in commercial operations—provides the foundation needed to interpret NIR data effectively.
By connecting current performance issues to this historical context, teams can better understand variability, identify underlying risk factors, and make more informed decisions. This approach transforms abstract model outputs into actionable insights.
From Complexity to Insight
Ultimately, the session reinforces that while NIR introduces additional complexity, it also offers a richer set of data for those who know how to use it. When supported by the right context and analytical approach, these methods can provide deeper visibility into process performance and product quality.
As laboratories continue to adopt advanced analytical techniques, the ability to connect data, context, and interpretation will become increasingly critical.
Final Thought
NIR doesn’t simplify the problem—it changes how the problem is understood. The organizations that succeed will be those that can turn complex data into clear, contextualized insight.
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