Pat Monitoring of Particles in API Manufacture and Formulation

Transcription

1 Pat Monitoring of Particles in API Manufacture and Formulation By: Brian O Sullivan Ph.D PAT Seminar Singapore Sept 11 th 2007 brian.osullivan@mt.com

2 What do these have in common? Space shuttle Lipitor Sugar Cocoa Oil rig 2007 METTLER TOLEDO

3 Particle Size Matters! Space shuttle - Solid rocket boosters contain propellant particles - If the particles are too large, insufficient thrust is generated, too small and the propellant becomes an explosive Pharmaceuticals - Particle size is a critical process parameter for assurance of dosage consistency and product efficacy - In formulations, particle size of active ingredients (API) and excipients will impact product performance Sugar - Particles that are too fine will create product separation and handling problems - Crystals that are too coarse will not dissolve quickly enough and can negatively impact mouth feel in final products 2007 METTLER TOLEDO

4 Why does Particle Size Matter? Particle and droplet size is a critical property of many products. - it may directly relate to product quality and performance Bioavailability Taste and mouthfeel Suspension stability - it may directly relate to processing efficiency Filterability and separation efficiency Reactivity Retention and drainage In-process measurement of particle and droplet size: - can significantly reduce wasted time and wasted money by speeding up process development and ensuring consistent quality in production METTLER TOLEDO

5 Presentation Outline A. Introduction to Lasentec Technology FBRM & PVM B. Case Studies - Crystallization A B C METTLER TOLEDO

6 6 Mettler Toledo FBRM in-situ Probe-Based Instruments Mettler Toledo FBRM is a quantitative measurement enabling chemists or engineers to quickly link particle system dynamics to processing conditions. Track the rate and degree of change to particles and droplets as they actually exist in process FBRM measurement tracks changes to particle dimension, shape, & count FBRM is controlled by a new state of the art software which integrates with most Mettler Toledo tools - No Sampling Required - At process concentrations to 70% solids - In lab or plant environments - In opaque or translucent slurries - Submicron to 3mm C to 150 C * * Standard (other options available) Sapphire Window Probe installed in process stream Mettler Toledo FBRM measures a chord length distribution

7 The typical FBRM system Mettler-Toledo AutoChem, Inc.

8 The FBRM Method of Measurement Cutaway view of FBRM In-process Probe PVM image illustrating the view from the FBRM Probe Window Laser source fiber Detection fiber Beam splitter Rotating optics FBRM Probe Tube Focused beam Sapphire Window Probe installed in process stream 2007 METTLER TOLEDO

9 What is Lasentec Technology? 9 Enlarged view Image illustrating the view from the Lasentec Probe Window Path of Focused Beam Probe detects pulses of Backscattered light And records measured Chord Lengths This core patented technology is called Focused Beam Reflectance Measurement [FBRM ]

10 What is FBRM Technology? Enlarged view Path of focused beam Thousands of Chord Lengths are measured each second to produce the FBRM Chord Length Distribution: METTLER TOLEDO

11 The FBRM chord length distribution Distributions T=0 T=4 T=8 T=9 T=10 T=13 Dimension (micron) METTLER TOLEDO

12 The FBRM chord length distribution Distributions T=0 Statistics are calculated from the distributions and trended over time Trended Statistics T=4 Fines T=8 T=9 T=10 Mean T=13 Dimension (micron) Time METTLER TOLEDO

13 General goals of FBRM applications Improve process efficiency - Optimize production process - Improve yield and throughput - Reduce downstream processing problems Improve product quality - Fingerprint product particle and/or droplet system for QC - Ensure batch-to-batch consistency - Provide early detection of disturbances 2006 Mettler-Toledo AutoChem, Inc.

14 14 Mettler Toledo PVM in-situ Probe-Based Instruments Mettler Toledo PVM is a probe based vision tool which gives an instantaneous, information rich, insight into particle system behavior. Light weight with quick setup, PVM is easily moved from one vessel to another - At process concentrations to 50% solids - No sampling, sample preparation, or dilution required - No calibration needed - 2μm to 1mm - 10 images per second - In opaque or translucent slurries * Standard (other options available)

15 New PVM : V819 Lactose

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17 Polymorph Visualization 17

18 New PVM technology: droplets with solids IMAGE 1 NEW

19 Presentation Outline A. Introduction to Lasentec Technology FBRM & PVM B. Case Studies - Crystallization A B C METTLER TOLEDO

20 Crystallization Scale Up 20 Where do Particles Play a Role? Compoun d D Solvent S1 Slow throughput poor filtration rate Excessive washing of retained solvent Inconsistent Batch Endpoint Particle Distribution Particle Distribution out of Spec Nucleation causing mixing problems Inconsistent Polymorph Distribution Filter Dryer Mill Formulation: Dissolution Flow Properties Tablet properties Long Drying Times Crystals Breaking in Dryer Impurities/solvents included in crystal Multiple Passes required Batch to Batch Variability

21 Solid Liquid Separation 21 Improving Filtration, Centrifugation, Bulk Density Predicting downstream filtration rate with in-situ particle dimension, shape, and count Optimizing Filtration and Scale-up to Manufacturing Improving throughput and reducing cycle time in the manufacturing plant

22 22 Understanding Filtration: Solid/Liquid Separation Filtration and centrifugation worsens as: Particles tend smaller The distribution of particles widens The particle surface area increases The particle concentration increases

23 Specific Cake Resistance Optimizing Filtration and scale-up 1) A correlation is made between specific cake resistance (filterability) and both the dimension and number of particles 2) One can measuring the in-situ particle dimension and count with Mettler Toledo FBRM and predict downstream filtration rates. 3) Mettler Toledo FBRM is highly successful in predicting filtration because of its high sensitivity to changes in the number of fine particles Specific Cake Resistance 1.2 x 10^ Specific Cake Resistance vs Mean Square Wt 1.8 x 10^9 3.8 x 10^9 3.9 x 10^ y = x R 2 = Mean Chord, square-weighted (µm) 23

24 Crystallization optimization using Mettler Toledo FBRM The Application of FBRM to Resolve Batch Processing Problems in Drug Manufacture, A Case History Kaz Wood-Kaczmar, GlaxoSmithKline, Real Time Analytics User s Conference 2001

25 Temperature ºC 10:06:46 10:12:10 10:17:34 10:22:58 10:28:22 10:33:48 10:39:12 10:44:36 10:50:00 10:55:22 11:00:48 11:06:12 11:11:36 11:17: :22:24 11:27:46 11:33:10 11:38:34 11:43:58 11:49:24 11:54:48 12:00:12 12:05:34 12:11:00 12:16:22 Counts Comparison of seeded and unseeded batches Huge increase in # of fine temperature (no seed) Nucleation fines (no seed) coarse (no seed) Time

26 Concentration Temperature 26 Unseeded Crystallization Supersaturation Generation by Cooling cooling Concentration Starting Point Solubility Curve

27 27 Problems with Unseeded Crystallizations Point of nucleation not the same batch-to-batch Process often does not scale up well High numbers of fine particles = Poor Filtration Potentially Polymorph issues

28 Solubility Seeding studies to optimize crystallization Three individual experiments 3 Seed Point Temperatures MSZW supersaturated 19 C 27 C 33 C Cool metastable undersaturated (stable) Solubility curve Temperature By Seeding, concentration is maintained within the MSZW. This helps avoid excessive fines generation or secondary nucleation But what temperature and at what concentration should the seed be added The following study examines Nucleation? 28 Growth? Combination of nucleation METTLER and TOLEDO growth

29 #/s (1-10 microns) Mettler Toledo FBRM Data for Three Experiments FBRM trends the number of fine counts/sec in the range 1-10 microns 60 mins 90 mins > 120 mins Seeding temp = 19 C Seeding temp = 27 C 0.25g seed added Seeding temp = 33 C Nucleation rate increases as the seeding temperature decreases (supersaturation increases) Batch time increases as supersaturation decreases 29

30 #/s ( microns) Mettler Toledo FBRM Data for Three Experiments FBRM trends the number of coarse counts/sec in the range microns Seeding temp = 19 C Seeding temp = 27 C Seeding temp = 33 C Growth rate increases as seeing temp increases & supersaturation decreases Low supersaturation results in a low nucleation rate and a high growth rate High supersaturation results in a high nucleation rate and a low growth rate 30

31 Instead of nucleation seeds added Seeded process has much lower # of fines

32 32 Seeded process produces bigger crystals = Better filtration

33 33 Increasing throughput in production using Mettler Toledo FBRM The Application of FBRM to Resolve Batch Processing Problems in Drug Manufacture, A Case History Kaz Wood-Kaczmar, GlaxoSmithKline, Mettler Toledo FBRM User s Conference 2001 Seeding was used to optimize crystal growth over nucleation

34 34 Problems with Unseeded Crystallizations Point of nucleation not the same batch-to-batch Process often does not scale up well High numbers of fine particles = Poor Filtration Potentially Polymorph issues

35 Crystallization optimization using Mettler Toledo FBRM Crystallization Processes in a Manufacturing Environment Dave Sharp, Pfizer Ltd, Real Time Analytics User s Conference 2005

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41 Batch 1 had the Lowest Number of Coarse Particles 41

42 Good Batch Failed Specification 42

43 Crystallization optimization using Mettler Toledo FBRM Crystallization Processes in a Manufacturing Environment Dave Sharp, Pfizer Ltd, Real Time Analytics User s Conference 2005 Inconsistencies batchto-batch due to different nucleation temperatures..

44 Lasentec Probe Installed in 2000 Gallon Crystallizer Dip Tube FBRM Probe Probe is in the area of good mixing Probe window constantly swept with the batch 44

45 Counts per second chords 1-5 um Comparison of Batch A, B and C Results- Fines 1-5 mm Batch A Batch B Batch C Batch C has significantly higher population of fines. Time 45

46 46 Increasing throughput in production using Mettler Toledo FBRM Crystallization Process Monitoring in Pharmaceutical Manufacturing Chi-Shi Chen & Joep Timmermans, Pfizer Ltd, Mettler Toledo FBRM User s Conference 2006 Sometimes the batch nucleated before seed was introduced causing inconsistencies.

47 Monitoring the Finishing Steps of a Pharmaceutical API Process using the Lasentec FBRM Kevin Girard, Mario Alvarez, Tu Lee, Terry Moore, Carlos Escobar, San Kiang Lasentec Users Conference February 24-27, 2002

48 % of Particles Sized x What Do You Do With A Perfect Scaleup? L Batch L Batch L Batch 1 10L Batch Size (um) 2006 Mettler-Toledo AutoChem, Inc.

49 % of Particles Sized x Ruin it in the Dryer! Original Slurry SQM = Filter Dried SQM = Tumble Dried SQM = Pin Milled SQM = Size (um) 2006 Mettler-Toledo AutoChem, Inc.

50 Frequency Frequency Frequency T=0 min High Shear Drying T=120 min T=200 min size (microns) Median= size (microns) Median= size (microns) Median= Mettler-Toledo AutoChem, Inc.

51 Frequency Frequency Frequency T=0 min Low Shear Drying T=200 min T=700 min size (microns) size (microns) size (microns) Median=61.27 Median=60.70 Median= Mettler-Toledo AutoChem, Inc.

52 2006 Mettler-Toledo AutoChem, Inc.

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55 Automated Lab Reactor ReactIR FBRM Monitor liquid phase concentration and use this to control supersaturation Utilized to monitor particle behavior ie growth or nucleation Mettler-Toledo,

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57 Concentration Temp Curve =ΔC MSZW Seeding Supersaturation Set point = 2 Temperature & MSZW and Calculated FTIR Data Seed half way between solubilty curve Solubility Curve Temperature ( o C) Decrease in Counts Growth Nucleation Mettler-Toledo, 2005 Lasentec FBRM Data Mean at Endpoint = 95μm 57

58 =ΔC Temp Curve Supersaturation Set point = 1.5 Temperature & Calculated FTIR Data Lasentec FBRM Data Growth Mean at Endpoint = 107μm Mettler-Toledo,

59 =ΔC =ΔC Lower Supersaturation yielded larger Crystals Mettler-Toledo,

60 Concentration Controlled cooling: keeps concentration within the MSZ MSZW Solubility Curve Cooled in such a manner to stay within the MSZ Temperature ( o C) Mettler-Toledo AutoChem, Inc.

61 Optimum cooling profile Temperature Profile Isothermal age Optimum Cooling Profile Supersaturation consumption during isothermal age C Constant Supersaturation Controlled cooling: Supersaturation constant Time Mettler-Toledo AutoChem, Inc.

62 Mettler Toledo Process Analytical Technology Particle System Characterization Fingerprint & Optimize Tech Transfer Reduced Cycle Times Product Quality & Purity Control Upstream/Downstream Process Increased Yields Reduce Human Error Improved Safety Reduced Analytical Cost 62