Advances in Process Analytical Technology Improve Understanding

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Equipment and Processing Report

Equipment and Processing Report, Equipment and Processing Report-03-18-2015, Issue 3

Real-time, noncontact imaging and spectroscopy techniques provide insight into pharmaceutical processes.

Continued advances in inline process analytical technology (PAT) are making real-time data available for process engineers to better understand and optimize pharmaceutical processes. "In the past, there was a vacuum of real-time information, and process engineers had to wait for and rely on laboratory data that didn't necessarily show what had really happened in the process," says Luke Kiernan, technical services director at Innopharma Labs. "Now, inline PAT provides information that can be used to find the true root causes of batch failure, which allows processors to implement the most effective corrective actions." In addition to troubleshooting, inline PAT data can be used to increase process understanding and as an input to process modeling and control software.

Accuracy and robustness of analytical instrumentation are important, and-particularly in pharmaceutical processing-non-product contact sensors are easier to integrate. "Nondisruptive technologies, which don't require cutting into validated equipment for installation, reduce the change-control burden," explains Ian Jones, founder and CEO of Innopharma Labs.

Particle imaging
Innopharma Labs' Eyecon particle characterizer is a real-time, non-product contact, high-speed machine vision system that takes images of particles in processes such as granulation, coating, spheronization, and milling. The images are analyzed to measure particle size distribution, morphology, and surface roughness. Jones and his colleagues presented case studies demonstrating Eyecon in fluidized-bed granulation and continuous wet granulation at the IFPAC 2015 conference held in Jan. 2015 in Arlington, VA, and an article, "Monitoring Fluid-Bed Granulation and Milling Processes In-Line with Real-Time Imaging," has been published in the March issue of Pharmaceutical Technology.

Inline imaging is more accurate than at-line sieve analysis, which is the simple method long used to measure particle size distribution, notes Jones. Imaging is also a direct measurement that requires little calibration compared to online laser diffraction particle characterization techniques, which extrapolate from measurements to calculate particle size and require product contact. "The beauty of imaging is that it is an eye into a process," explains Jones. "It is helpful to actually see the product, because you can compare what you see with what instrument data is saying about the process. Imaging is powerfully improving our understanding of the process because we can see in real time how particles are changing." 

Near-infrared spectroscopy
Near-infrared spectroscopy (NIR) is another PAT commonly used in solid-dosage manufacturing processes to measure properties such as moisture content and blend uniformity. Multiple presentations at IFPAC discussed use of NIR for applications including API crystallization and tablet blend uniformity.


NIR has typically been a single-point measurement, but last year Innopharma Labs introduced the Multieye, a multipoint NIR spectrometer that can measure up to four points at the same time, which results in a larger sampling area that gives a better representation of the product. From a regulatory perspective, it is easier to demonstrate that multipoint analysis is a representative measurement, says Kiernan.

NIR is complementary to imaging and the two can be used to cross-validate each other, adds Jones. "If you see an unusual spike in NIR, for example, you can look at the imaging data for a possible reason."

Integrating PAT into a process requires customization for each process and equipment setup. For example, the type of probe needs to be selected carefully and integrated into a system to obtain consistent, representative sampling, explains Jones.

Making the most use of PAT data is another challenge. Process monitoring and increasing understanding are typically the first step. Using data for feedback to control the process is the next step that, so far, a few companies have taken using process modeling and control software systems.