Increasing the efficiency of batch manufacturing is an important goal for the pharmaceutical industry, as is the development of successful continuous processes. A more sophisticated approach to processing has the potential to reduce the amount of time and material wasted in development and routine manufacturing, but relies on the timely, relevant and reliable monitoring of critical quality parameters. PAT that delivers on these requirements holds considerable value for the industry.
This article will provide an introduction to the technique of dynamic light scattering (DLS), highlighting its capabilities as a PAT. Online DLS systems offer rapid, automatic particle size measurement in the nano size range, for both suspensions and emulsions.
A Shrinking Target
As the nature of active pharmaceutical ingredients changes, nanoemulsions are emerging as useful vehicles for drug delivery. Consisting of dispersed colloidal droplets, typically around 50-600 nm in size, within an immiscible continuous phase, nanoemulsions are used routinely to encapsulate drugs that are poorly soluble or susceptible to hydrolysis and/or enzymatic breakdown. For these drugs nanosized micelles act as protective delivery vesicles in vivo, ensuring successful drug delivery to the target site.
In pharmaceutical nanoemulsions, droplet size typically has a defining influence on in vivo behavior, thereby impacting the pharmacological profile of a drug and its clinical performance. Droplet size often influences factors such as absorbance, bioavailability, diffusion rate and stability, and as result tends to be a critical to quality parameter for the drug product. Understanding how formulation composition and processing parameters impact droplet size is therefore a critical step in the development of a robust and well-controlled nanoemulsification process.
The use of active ingredients that are only sparingly soluble in solid dosage forms also creates a need to disperse particles to a very fine size. Increasingly, the optimal particle size for an active ingredient is in the nanometer, rather than the micrometer range.
Nanomilling or micronization processes are applied to grind the particles, in suspension, and achieve the necessary comminution. Controlling particle size during the milling process is essential to ensure a safe and efficacious drug.
DLS is a technique used routinely to measure all types of nanoparticles across the size range of 0.3 nm to 10 µm. The principle that underpins it is a relatively simple one – small particles in a dispersion or solution are subject to Brownian motion, which is driven by collisions with the solvent molecules.
A DLS instrument determines the rate of diffusion of particles moving under Brownian motion, from measurements of fluctuations in scattered light intensity. The resulting diffusion coefficient is used to calculate particle size through application of the Stokes-Einstein equation where D = diffusion coefficient, k = Boltzmann’s constant, T = absolute temperature, h = viscosity, and DH = hydrodynamic radius.
This relationship shows clearly how size can be determined from diffusion speed, provided that the temperature and continuous phase viscosity of the sample are known. However, there are additional features of modern instrumentation that further enhance the industrial usefulness of the technique.
For example, Non-Invasive Back Scattering technology (NIBS) makes it possible to achieve high sensitivity across a broad size range and at high measurement concentrations. It is also possible to apply DLS to measure a sample in flow, providing that the flow rate does not exceed 1 mL/min.
The Value of On-Line DLS Measurement
The conventional way to use DLS in process development or monitoring is to manually extract a sample and carry out a measurement in the laboratory to determine particle size. The endpoint of the process is marked by particle size reaching a target value and this can be confirmed by offline sampling and analysis. However this approach introduces a significant lag between an action and any assessment of the impact, with the results often obtained only once the batch is complete. This does not allow for an assessment of the trajectory of the process, as it progresses, or provide an opportunity to tune processing parameters to achieve a better result.
For example, in the production of a pharmaceutical nanoemulsion, the endpoint of processing is marked by particle size reaching a stable value and the top end of the droplet population dropping below an upper limit. This limit is often guided by the need to achieve certain performance characteristics in subsequent processing steps, such as sterile filtration using a 0.2 µm filter, for example.
Ensuring that the droplet size has been sufficiently reduced while minimizing over-processing requires multiple manual analyses over the course of a batch, or during continuous production. This is a time and labor-intensive approach, both at the pilot scale, when the focus is to scope and optimize processing conditions, and during commercial production, where precise monitoring is linked directly with confidence in quality and variable cost management. The need to submit in-process samples to an analytical lab for analysis can result in the loss of valuable time and challenges the ability to respond to process deviations.
The use of on-line particle characterization tools, in contrast, enables rapid, time relevant process monitoring. Real-time data makes it possible to quickly see the impact of changing a process parameter and efficiently establish conditions that will deliver a successful outcome. Online measurement therefore brings clarity and reduces development efforts during the scoping and scale-up studies associated with Quality by Design (QbD), as well as enabling efficient process monitoring and control at the commercial scale.
The following case studies illustrate the potential benefits of online DLS measurements in the production of nanoemulsions and in nanomilling.
Case Study: Using Online DLS to Monitor Nanoemulsion Production Processes
Forming a stable nanoemulsion requires intensive energy input and also, in most cases, the addition of a suitable emulsifying agent. A critical aspect of the manufacturing process is detection of the endpoint, i.e. the point at which the dispersed phase droplets reach the required size and size distribution. Because emulsification is an energy intensive process, over-processing incurs a substantial cost penalty in the form of excessive energy consumption. It can also have a negative impact on product quality. Measuring droplet size in a timely way is essential to achieving the necessary process control during process development and scale-up, in order to accelerate progress, or in commercial manufacturing.
In an experimental program to support process scale-up, an on-line DLS system was used to monitor a 1L batch of an oil-in-water emulsion produced using a bench top high-shear mixer. For the purpose of drawing a sample from the process vessel into the analyzer, a tube is attached to the static part of the mixer shaft.
The sample is drawn automatically by the DLS system, analyzed and then pumped into a waste container at a frequency of one measurement every two minutes. Although not as direct as in in-situ analysis, this sampling method offers an opportunity to condition the sample before analysis to achieve higher quality results.
With many emulsification and homogenization processes running for tens of minutes or even multiple hours, this data frequency delivers sufficient information to enable effective process trending and end point determination. Data collected during this emulsification run shows that early in the process the droplet size distribution is approximately 240 nm. This measure trends down until reaching a plateau after 20 minutes of processing. Continued homogenization for one further hour produces no further reduction in emulsion droplet size.
The process interface used with this online system simplifies scale up of the PAT solution towards pilot and commercial scale production as the following study demonstrates.
Efficient Size Reduction
In further work, the online DLS system was used to monitor the production of a pharmaceutical, oil-in-water based nanoemulsion at the 100L batch size. Here, a similar system set-up is used as in the first study but the sample is drawn automatically from the recirculation loop of a high shear homogenization system.
The discontinuous phase is added gradually into the recirculating stream at a relatively low pump speed until the phase addition is complete, at which point the emulsion is homogenized at a pump speed of approximately 5,000 RPM.
Results are produced every two-and-a-half minutes and show a gradual reduction in Z-average mean size until the process reaches a plateau at 55-60 nm. Figure 3 shows the progress of the Z-average parameter (red) over time and indicates that the process reaches an end point after approximately 30 minutes of homogenization. This suggests that mixing time could be reduced significantly from the fixed mixing time of two hours specified in the original time-based recipe.
Converting the intensity-based DLS results into a volumetric particle size distribution using Mie theory highlights a further point about the value of using DLS measurements. Although conversion of an intensity distribution into a volume-based analogue will inherently introduce additional errors, the trend information produced by calculated Dv10, Dv50 and Dv90 (green, blue and black) emphasizes the advantage of monitoring size reduction by Z-average shown in figure 3.
While volumetric PSD no longer senses further changes to the sample after five to ten minutes of processing, the intensity based parameter (Z-average) indicates that size reduction continues for another 20 minutes. The higher sensitivity of Z-average to small amounts of large particles explains this observation. After five to ten minutes the small overall volume of larger particles is no longer significant enough to affect the volumetric PSD, but continues to be detectable by DLS.
These results demonstrate the feasibility of using online DLS as PAT for process monitoring and suggest that the Z average diameter is an appropriately stable and relevant metric for tracking changes in droplet size in a nanoemulsion. The use of real-time monitoring has the potential to substantially reduce processing times by enabling timely endpoint detection and also opens the option for automatic process control through feedback of size results to manipulate upstream process parameters.
Case Study: Monitoring a Nanomilling Process
Figure 4 shows data measured using an online DLS system over a six hour period. In this trial a 500mL batch of active pharmaceutical ingredient (API) suspension was processed using a Delta Vita 15- 300 milling system (Netzsch GmbH). Analysis began around 30 minutes after the start of processing, at the point at which particle size became measurable by DLS.
The starting size of the raw material was ~50µm, which is beyond the upper size of the DLS measurement range. High intensity milling rapidly reduces the particle size distribution of the material to below a micron, so DLS is highly effective in monitoring the crucial latter stage of the process. Once particles became measurable by DLS, a particle size distribution is reported every five minutes. Less than 0.5 mL of sample was consumed for each measurement.
As in the preceding study this data demonstrates the ability of the DLS system to efficiently track the size reduction process and identify the point at which the target particle size is met. Again, this is useful in commercial manufacturing because of the potential to reduce energy consumption costs and provide a tighter grip on production quality.
DLS is cited as a well-established particle sizing technique, as detailed in ISO 13321 and ISO 22412, with the underlying simplicity needed for successful online implementation. The introduction of online DLS systems could be an important advancement for those developing and manufacturing nanoscaled pharmaceutical and bio-pharmaceutical products because measuring particle size in a timely way enables faster, more robust process development and efficient endpoint detection.
Such PAT has the potential to accelerate the application of QbD and support efficient, well-controlled manufacturing processes, whether batch or continuous.
This article can also be found in the October 2015 edition.
