Tools for Monitoring and Controlling Uniformity of Solid Dosage Forms

Transcription

1 Tools for Monitoring and Controlling Uniformity of Solid Dosage Forms Martin Warman Scientific Fellow, Vertex Pharmaceuticals, Inc

2 Controlling process variation does not start with measurement technology.. It s an outcome of the application of a risk based approach to Process Design using QbD And Process Design starts with Product Definition Decide what you want to make, before you decide how to make it Pharmaceutical Industry came up with it s own term for Product Definition (Quality) Target Product Profile

3 First step of process development Carry out Risk Assessment Identifies possible Quality Attributes (and the process parameters which control them.) Cause and Effects matrices, establish severity and probability The outcome of these activities give possible CQA If done correctly it should also give a detailed description of the attribute and the boundaries needed to ensure product/process quality This information is then used to select the most appropriate measurement system

4 Why is this critical? The Risk Assessment will indicate Not only the CQA, but the likely causes of variance in CQA But also what that failure would look like on the product Therefore what the PAT system needs to monitor Sample size, sample position, periodicity/frequency Measurement system accuracy and precision The Risk Assessment will establish if the variance is likely to be Within a single dosage or across a batch Whether it is a trend or an outlier Location related Etc, etc, etc But most importantly; the tolerance impacting product quality When selecting a PAT system it needs to capable of measuring to those tolerances When selecting a PAT system, it needs to be capable of seeing those tolerances Critical as the most commonly used technique (NIR) has particularly poor sensitivity (compared to Raman or Fluorescence)

5 Example of applying this when selecting an on-line blend monitoring system What do you expect will change within the blending step? Looking for a process trend or variability at end point or occurrence of hotspots? How fast do you expect that to change? This decides the degree of averaging done between data points Do you expect the change to be inter or intra unit dose This decides the size of the sample we look at.

6 Sample size comparison

7 It is essential that these steps are executed before selecting any measurement system

8 Use of on-line spectroscopy during DoE There are now many systems available for measuring uniformity in real-time Typically using NIR Designed to fit pilot or commercial scale blenders But most development DoE are carried out at less than 100g scale The issue isn t the scale of the NIR, it is the scale of the blender! So we made our own.. Fits LB Bohle LM40 blender Takes the Expo epat range of spectrometers Selectable sample area Available as NIR or LIF Used to support multi-factorial DoE Methods become independent of scale Same method used through scaleup and into commercial

9 Why understanding what you are trying to control is key Data set of overlaid spectra One acquired each rotation Real time prediction model shows end point was around data point 25 (after 25 rotations) But also that very little change in last 5 spectra (at all wavelengths) Identify unique peaks 1515nm = API Control model setup to look for a period of no-change By plot %RSD If no-change occurs for a minute, blend is said to be complete

10 Understanding the limitations of a variance approach Looking at final spectra from several different batches Very little difference between the batches at the API peak Same endpoint for API But there are two distinct classes of endpoint in the OH region It is essential that the method is able to determine not just that the process has reached an end point but that it is reached the same end point

11 Need to address the cause of the variability If we look at the last five data points from each of the six batches See 2 different end points Further investigation showed the difference was a consequence of variability in one of the excipients Blending can not remove batch to batch variance..

12 Summary of considerations when selecting an on-line system? What do you expect will change within the blending step? Is it purely a process trend, or is it variability at end point, or could there be hotspots? How fast do you expect that to change? This decides the degree of averaging done between data points Do you expect the change to be inter- or intra-unit dose This decides the size of the sample we look at. But all of this just enables control at the blending step Need to ensure variability is not then reintroduced downstream

13 Sample size is not only critical for on-line analysis Bruker MPA, at-line NIR Transmission Liquids, solids Diffuse reflectance Solids, slurries Example applications Raw material ID Blend and tablet analysis CU/homogeneity Density Hardness LOD Also the same basic spectrometer as used in the Bruker Tandem Used to verify uniformity postcompression

14 MPA sample configurations 1. Diffuse reflectance Sample sits in holder positioned at a fixed distance from sphere NIR beam is focused (using a lens) on small (6mm) section Diffuse reflected light collected by integration sphere and focused on detector 2. Transmission Sample sits in same holder NIR beam is still focused Light bounces around insider tablet Transmitted light collected

15 Comparing diffuse reflectance and transmission data Often models are built in both and then the accuracy (using tools like RMSEP) used to determine which is best In reality they are measuring two different scales (and so two different attributes) Diffuse reflectance sees approx 4mg Transmission the whole tablet If data from both are the same, it gives a good indication of scale independent uniformity But what if they aren t?

16 Macro to micro scale 1. Transmission whole tablet 2. Macro reflectance 30mg 3. Micro reflectance Moving the sample away from the focused beam reduces the spot size 6mm = 4mg 1mm = 0.1mg

17 How? - Sample Positioning Stage (SPS) Sample sits on a specially designed x-y-z stage When the stage is adjusted, so the tablet is flat on the surface you have macro diffuse reflectance When the stage is adjusted to 13.5mm = standard tablet holder and can be used for transmission When the lens is used and the height adjusted from 0mm - 16mm, you change the spot size Minimum practical spot is around 1mm

18 Why do we need to consider scrutiny of scale Example from a development product CQA - variability API content in final dosage form Couldn t work out why, as traditional BU testing (at three times unit dose) appeared to have less variability Looking at transmission NIR showed unit dose to unit dose variability Looking with small spot diffuse reflectance NIR showed poor uniformity within the tablet Looking with NIR Chemical Imaging to check distribution of API within the blend (and the tablet) Showed clumps of API

19 What is the biggest cause of uniformity issues? Heterogeneity in the blend.. Chemical and physical

20 Sources of heterogeneity Processes designed to formulate API with excipients maybe be focused on achieving homogeneity, but any process aimed generating homogenous mixes actually lead to opportunities for heterogeneity.. Dry blending and direct compression Granulation, wet/dry Melt extrusion Coating particles Spray drying Characterizing and controlling the uniformity of pharmaceutical powders is necessary to ensure quality of the final dosage form The use of characterization tools during development leads to increased process understanding and more robust processes at commercial scale

21 Tools for analyzing heterogeneity in powders Size and shape can be characterized using the Malvern Morphologi G3 Size attributes: D(v,0.5), D(v,0.9), D(n,0.5), D(n,0.1), Shape attributes: length, aspect ratio, convexity, circularity Raman spectroscopy can then be targeted to determine Chemical Identity of individual particles within different populations Malvern have now commercialized this system called the G3-ID

22 Tracing cause of clumped API Analysis of the loose blend powder (and also the API itself) showed the clumps were carried forward from API process These clumps would never be dispersed by blending Heterogeneity would never be reached Control had to be applied to previous unit operations Variability in solid dosage form was direct result of the API process

23 PAT enabled process control Allows potential impact on process to be determined and controlled Control may come in the form of Engineering control (the process is physically constrained) Attributes maybe Critical but the process is designed so that it can never make product in the critical range Feed-back control (the process is measured and controlled) Feed-forward control (input variance is determined and the process changed to cope with this variability)

24 Importance of understanding cause of variance Indicates where we need to apply control E.g. a simple example where tablet hardness is a CQA Within compression step, the press is setup to maintain tablet hardness across a batch BUT the compression force settings are dependent on the compressibility of the input materials And should be changed based on the material properties of those materials That means the static controls in the press try and maintain steady state whilst the dynamic controls are adjusted to cope with the material attributes defined in the process model.

25 Control of tablet hardness of spray dried product During spray drying.. On-line measurement system monitors particle size Automated sampling system used to determine bulk density Both values feed into the process model (Shown on the GUI as a Dashboard) SDD process is controlled by changing the process parameters in real time; feed-back control However the values are also used to change the process parameters on the compression step (using a look-up table) to ensure the correct compression force is used for the input material Feed-forward Control

26 Risk management across the whole process Even if the have good homogeneity in the blending (and/or granulation) steps, you have to consider the whole process Segregation during transfers can still lead to heterogeneity in the final dosage form e.g. variability in Content Uniformity But what if we could measure API concentration in the tablet feed frame and adjust dosage mass? True adaptive process control Courtesy of Daniel Blackwood, Mark Polizzi, Hugh Clarke, and Howard Ward PGRD Groton Laboratories, Pfizer Inc

27 So why have I not included tools like the Bruker Tandem? The tandem is a transmission NIR which can be installed postcompression to verify uniformity Not just of API not most excipients, LOD etc But the tablet is already made Can be used for feed-forward control Impacting future tablets But that is easily achieved with W/T/H testing Most often used for product trending or release But is non-uniformity caused at this step or has it simply been carried forward?

28 Summary All processes have variability that is not a problem.. Need to establish how much variability the product can tolerate before we can develop a process to make it This is vital as there is a direct linkage between the complexity of the product, and the risk caused by any variance in the process Much better to reduce the risk rather than try and control it But if you need to measure and control, make sure your measurement system is capable of seeing the variability and that you have enough process knowledge to respond to the variance seen