How to improve particle size measurements for active pharmaceutical ingredients

Particle sizing for APIs: More of your questions answered 

When scaling your active pharmaceutical ingredient (API) for manufacturing, it's critical to control the particle size distribution within your tight specifications. In fact, particle size affects the processability and bioavailability of the API – in other words, how well it is absorbed and processed by the body.  

Because precision and reliability are critical, researchers benefit greatly from support throughout the analytical process, including method development, measurement, and data interpretation.   

In our recent webinar, Paul Senior, Product Manager for the Mastersizer 3000+, explained how the instrument's SOP Architect, Adaptive Diffraction and Size Sure technology, and Data Quality Guidance features provide researchers with this critical support – including real-world examples. You can now watch it on-demand here – and read on for some extra content…

Answers to the questions we missed 

The final Q&A session is always a highlight of our webinars, giving us the opportunity to answer questions from experts and professionals in the field. And this time, you had so many questions that we couldn’t answer them all! Here are Paul Senior’s answers to a few of the ones we missed: 

• What test do you recommend for accuracy in method validation?
  There are usually two tests to verify accuracy. The first involves measuring reference materials of known size, such as glass beads or latex. The reported data is then compared to certified reference values to verify that the instrument is performing as expected. Accuracy can then be further assured by using orthogonal techniques such as microscopy and imaging to verify that the results are consistent with those obtained by laser diffraction.  
  
  
• What parameters or tests do you recommend to validate particle size methods?
  When it comes to validation, both dry and wet dispersions have specific parameters that need to be controlled and validated.
  For dry dispersion, validation focuses on parameters such as repeatability, dispersion pressure, feed rate, and sample volume. For wet dispersion, additional parameters include sonication, stirring speed, and sample stability to ensure that the dispersion is consistent and accurate.
  Tools such as Ishikawa plots are useful to cover all critical parameters, especially in dry methods where factors such as dispersion pressure and feed rate are critical. 
  
  
• Can I use Size Sure for routine QC testing?
  Yes, provided that validated protocols are followed to ensure accurate results. For example, if you are testing a product that requires the use of a surfactant for wetting purposes and bubble formation is affecting your PSD, Size Sure can help. It will provide a repeatable and reliable steady-state sample PSD without the presence of additional peaks from bubbles. However, you must demonstrate that Size Sure produces accurate steady-state PSDs for your sample over the expected variability of the material, such as differences in agglomeration or aggregation. If this is a hurdle, you should consider whether your method can be modified to eliminate the need for Size Sure.

More about how the Mastersizer 3000+’s features can support your API scale-up 

SOP Architect

When working on the same project, it’s important that each person's measurements to be as reliable as everyone else's. That's why method development is critical to helping you avoid errors and inconsistencies. SOP Architect guides you through a standardized workflow that supports novice users and validates the decisions of experts. Its specialized tests and intelligent algorithms give you the best possible starting point for your research method. Clear guidance and a step-by-step process act as a mini training session, making future development even easier. 

Adaptive Diffraction 

Adaptive Diffraction is a cutting-edge laser diffraction technology that processes data at a rate of 10,000 data points per second. This speed allows for a clearer distinction between ‘steady state’ and ‘transient state’ data: 

• Steady-state: Constant and reliable data from the sample. 
• Transient state: Rare or irrelevant interferences, like contamination, bubbles, or dust, which can skew results if not identified properly. 

By distinguishing between these two states, Adaptive Diffraction simplifies method development and helps eliminate unwanted noise, leading to better data clarity and troubleshooting accuracy. 

Size Sure measurement mode 

The Size Sure measurement mode complements Adaptive Diffraction by isolating transient interferences from steady-state measurements, ensuring that contamination or other extraneous factors are correctly classified and excluded from the final particle size distribution (PSD) measurement. 

Data Quality Guidance 

Data Quality Guidance is designed to take the guesswork out of your experiments.  It continuously scans for a wide range of potential data quality issues, flags them, and provides a list of causes and solutions ranked from most likely to least likely. This saves valuable time and effort that would otherwise be spent identifying the source of the problem and trying different solutions without guidance.  

By providing live feedback and identifying problems such as contamination or bubbles during the measurement process itself, Data Quality Guidance ensures that you're immediately aware of potential problems. This allows you to fix them immediately and reduce the waste associated with failed experiments.