Challenges of Statistical Analysis/Control in a Continuous Process

Transcription

1 PQRI workshop on Sample Sizes for Decision Making in New Manufacturing Paradigms Challenges of Statistical Analysis/Control in a Continuous Process Fernando Muzzio, Professor II Director, ERC-SOPS Rutgers University ENGINEERING RESEARCH CENTER FOR STRUCTURED ORGANIC PARTICULATE SYSTEMS RUTGERS UNIVERSITY PURDUE UNIVERSITY NEW JERSEY INSTITUTE OF TECHNOLOGY UNIVERSITY OF PUERTO RICO AT MAYAGÜEZ 1/11/5 1

2 Why Continuous Manufacturing? Smaller equipment No scale up No wasted batches Better quality control Meaningful PAT More uniform processing Controllable segregation? Faster development?

3 Raw material properties Feeding Feeder size, Screw type, feed rate Roller compaction (Design, speed, flow rate, Gap between rolls, compaction pressure) API Blending Blender size, design, Blender speed, flow rate Lubricant Blending Direct Compression σ i Blender size, Blender speed, design, flow rate σ i Design, Feeding Speed, Tabletting speed PSD Blend homogeneity (RSD) Feed frame Tabletting (Speed, compression force, thickness) PSD, Blend homogeneity, Hydrophobicity Flow properties Tabletting Ribbon density, Composition PSD PSD, Hydrophobicity, Flow properties σ i Weight variability, Dissolution, Hardness Content Uniformity Throughput? Milling (Mill size, Mill type, Speed, flow rate) Lubricant Mixing (Blender speed, design, flow rate, humidity, shear) Roller Compaction Tablet size Compression speed Composition 3

4 PAT for continuous secondary pharmaceutical manufacturing Pneumatic transfer Gravimetric feeding Flow rate (Feeder Load sensor) Wet granulation and drying Granule size distribution (Laser Diffraction) Moisture content (NIR) WG DC DG Roller Compaction Ribbon density (NIR, X-Ray, Ultrasound) Milling Granule Size Distribution (Laser diffraction) Blending Blend uniformity (NIR) Blender hold-up (Load sensor) Tabletting Content Uniformity (CU) (NIR) Hardness Ultrasound, X-ray Density 4

5 PAT for continuous secondary pharmaceutical manufacturing (cont.) Feeding o o Blending o o Feed rate variability Feeder refill Blend uniformity (NIR) Blender hold-up (Load sensor) Roller compaction o o Ribbon density profile (NIR, Ultrasound, X-ray) Ribbon thickness (Gap between rollers) Milling o Particle size distribution (Laser diffraction) Wet Granulation and Drying o o Tabletting o o Granule Size distribution (Laser diffraction) Moisture content (NIR) Weight (Load sensor) Hardness, density (Ultrasound) (Load sensor) 5

6 Challenges Real time automated control is REQUIRED Underdeveloped sensors Lack of models No experience regarding performance of pharma materials in these systems Lack of a regulatory framework 6

7 Requisites of Continuous process More flexibility in continuous processing in terms of throughput and control Development of compact process models for individual unit operations Identification of manipulated and controlled variables Model based control to ensure efficient operation under closed loop conditions Production throughput (Capacity of Tablet press, size of tablet) Equal flow rate through all the units 7

8 Feeders Multi-point NIR Malvern Insitec Blender Delta V Control System ENGINEERING RESEARCH CENTER FOR STRUCTURED ORGANIC PARTICULATE SYSTEMS RUTGERS UNIVERSITY PURDUE UNIVERSITY NEW JERSEY INSTITUTE OF TECHNOLOGY UNIVERSITY OF PUERTO RICO AT MAYAGÜEZ Tablet Press Optical Tablet thickness Measurement 1/11/5 8

9 PAT Approach Probe Mixer outlet Gravity influenced flow of powder on metal chute placed right after the blender to make the NIR measurement possible A remote NIR probe with 5 measurement spots was used Tablet press inlet Chute 9 9

10 Measurement configuration - VTT Mixer outlet Probes Chute The probes measured from a distance of about 15 cm 6 µm fibers were used in both illumination and collection Illumination spot size ~5 mm Collection spot size ~ 8 mm 1

11 Measurement equipment Mixer Chute Powder Schematic of the 3x5 probe measurement system Illumination fiber bundles Light source Spectral camera Probe 5 measurement spots Collection fiber bundles Real-time calculation module Multipoint NIR measurement system was used 5-point probe, measurement spot Ø 3 mm Fiber-optic light source To process control NIR spectral camera 11 11

12 Results: 1 % APAP concentration 35 Smoothed APAP concentration (ref. 1. %) 3 Predicted concentration [%] Time [s] Mixer was operating at 1 % APAP Again some peaks of high APAP concentration visible 1 1

13 Results: % APAP concentration 35 Smoothed APAP concentration (ref.. %) 3 Predicted concentration [%] Time [s] Mixer was operating at % APAP Nice ramp from 1 % concentration to % No peaks of high APAP concentration visible 13 13

14 SBC calibration results for caffeine b vector Response and regression vectors g b Predicted concentration Prediction vs. reference RMSEC :.995 cc :.9339 CV : R :.8497 #of smpl : Wavelength [nm] Response spectrum (blue, scaled), shown for reference Regression vector (green) picks up caffeine features Reference concentration Prediction scatter plot The slope had to be adjusted (.897) since the scattering properties of pure caffeine and the 8 % blend are different 14 14

15 Results from the continuous blending trials Predicted concentration Caffeine concentration vs. time Average Time [s] Concentration measurement 15 15

16 Impulse responses Continuous mixing of pure CaHPO 4 Add 7g caffeine in blender inlet Measure the time response after blender Predicted concentration Caffeine concentration vs. time Average Predicted concentration Caffeine concentration vs. time Average Time [s] Blender speed 3 % Time [s] Blender speed 8 % 16 16

17 Overall scheme for DC (In-line NIR/Raman/PSD Sensing) API & excipient characterization Multipoint NIR / Raman / Partice size Process control Ultimate goals 1 % inspection Closed-loop feedback control Methodology for design and construction of continuous manufacturing lines Measurements Multipoint NIR Particle size Multipoint Raman Methodology for measurements needs Robust but easy calibration Sampling Process and measurement understanding Real-time computing & process connectivity 17 17

18 Approach for optimizing PAT method Experimentally measured Linearization: Methodology to assess the relationship between blend uniformity (variance in concentration) and sample size RSD RSD total total Ln( RSD x = Sample _ Size σ x = x + total ( ) σ mixing ( ) σ σ σ Total = f ( x) = α. x σ ) β = βln( x) Power law relationship between the normalized variance and sample size + Ln( a) Unknown parameter Determined by optimizing R (result of regression) 18

19 Dataset-1 (3% Gran APAP UV Spec) RSD vs. sample size RSD Sample size(g) Confidence intervals are for the Std deviation which were normalized by the mean 19

20 Dataset-1 (3% Gran APAP UV Spec) (Cont.) Ln (RSD ) Ln (RSD ) vs Ln (Sample size) R vs. σ y = -.66x R² = Ln (Sample size) Series1 Linear (Series1) R Method Error (σ ) σ = (Best case)

21 Dataset- (3% Gran APAP NIR) RSD vs. sample size.5. RSD Sample size(g) 1

22 Dataset- (3% Gran APAP NIR) (Cont.) Ln (RSD ) vs Ln (sample size) R vs. σ Ln (RSD ) y = -.663x R² = Ln (Sample size) Ln(Rsd^) Linear (Ln(Rsd^)) R Error (σ ) σ = (Best case)

23 Comparison between In-line NIR and UV Spectroscopy.1.1 Unit dose (1 Tablet) RSD Sample size(g) 3% NIR This methodology provides the number of NIR measurements to be averaged to measure blend uniformity at the desired sample size (unit dose). 3

24 Conclusions To minimize the required experiments for identifying the optimum plan for the production of a new product: Characterize basic material properties Use all the existing knowledge (experimental data and modeling techniques) that connect material properties to unit operation performance to identify appropriate equipment designs and operating conditions 4

25 Take Home In continuous processing, every component needs to operate simultaneously and at the same rate One bottleneck is the need to feed small rates of cohesive powders Solution pre-conditioning PAT challenges Slide 5 5