Scale-up verification using Multivariate Analysis Christian Airiau, PhD.

Transcription

1 Scale-up verification using Multivariate Analysis Christian Airiau, PhD. GlaxoSmithKline, R&D, USA. 22JAN14

2 Scope 1. Introduction 1. Scale-up requirements 2. Project background 2. Proposed approach 3. Results and Conclusions 2

3 Introduction Scale-up verification API process development activities are typically conducted at lab scale Process specific approach each chemistry route lead to specific CPPs Experimental design used for control strategy development at lab scale 2L CLR Scale-up verification step required Mechanistic / empirical - risk based, supported by thorough scientific rational In this application Lab scale: Sequential experimental design applied on crystallization step Identification of CPP - Proposed control strategy Plant scale: Some process stretching to verify the control strategy Not all parameters stretched to the registered limit Stretched conditions carefully defined to test multiple CQA MVA: confirm data structure (parameter/attribute) comparable at both scales 200L plant 3

4 Empirical Mechanistic Risk Based Scale-up verification scenario Mechanistic / empirical Risk Assessment support the lack of scale-up effect on all CPP identified at small scale Require strong argument documented in the Risk Assessment Mechanistic model available describing the CPP and taking into account the scale parameters Often difficult to establish such comprehensive mechanistic model based on 1 st principles for all CPP in a process Define the control strategy using scale independent parameters E.g. Agitation converted to tip speed Re-run robustness study - stretched process conditions - at plant scale Costly but may sometime be the only option Run specific process conditions to the limit of the CPP ranges most forcing for each CQA Considered risky to run at most forcing conditions Run batches between centre points and extremes, demonstrate expected shift in CQA Mathematical structure (parameters/attribute) is similar at small & large scale 4

5 Temperature ( C) Project Background Seeded crystallization step with Ostwald ripening and cooling precipitation Temperature profile a c c High temperature during cycle b b b c c Low temperature during cycle d Time parameters a Hold time post seeding b Time to heat/cool during cycling c Hold time during cycling d Final Cool time Time (Hours) CQA identified as Chemical properties (impurities) and Physical properties (size) CPP defined at lab scale using a sequential Experimental Design approach Stretched experiments run at plant scale during development campaigns Objectives: Can we demonstrate that the Parameter / Attribute relationship defined at lab scale still holds at plant scale? 5

6 CPP derived from lab scale experimentation Sequential Experimental Design Risk assessment:14 parameters likely to impact the crystallisation output Scoping / screening studies: 14 parameters, 22 experiments Objective: Identify the main effects and likely interactions Robustness study: 9 factors, 16 experiments Focus on the most influential parameters Run at 2L scale; significant engineering input to align lab set-up with Plant configuration Use scale independent parameters (power volume), mimic heat / mass transfer Objective: Confirm parameter ranges delivering acceptable quality output 6

7 Rational for plant scale verification Methodology Development activities provide in-depth process understanding from lab scale experimentation Understanding of CPP influencing CQA Main effects / interactions Understand the ranges that can be safely apply Specific iteration of Risk Assessment to understand scale-up risk Are CPP identify from lab scale the same at plant scale? Are the proposed lab scale ranges the same at plant scale? Define direction & magnitude of effects on all CQA E.g. decreasing Param#5: favourable to CQA1 but detrimental to CQA2 Param Range CQA1 CQA Param Range CQA1 CQA

8 Constraints of Empirical plant scale verification With 9 CPP and 5 CQA: very complex picture of process stretching impact Effect direction and magnitude will be different between CQA Un-realistic to run at plant scale the number of batches required to generate such understanding API batches cost >$10M Costly increase of inventory High risk to run at the limit of the registered ranges Sometimes necessary if no reasonable scientific rational to justify the range on scale up Can be required to generate stretched API-CQA to be tested in Drug Product 8

9 Potential solution for plant scale verification Use intermediate approach to stretch gradually towards range limits - Risk assess parameters to stretched at plant scale e.g. Scale dependent - Define the number of stretched batches to be run at plant scale cover gaps in scale-up process understanding Param Range BX1 Range BX For each batch expected outcome should be predictable: Run the batches at plant scale: Verify the CQA levels against expected outcome CQA 1 CQA 2 BX1 Significant increase No impact BX2 Minor increase Significant increase The Proof is... 9

10 Rational to use Multivariate Analysis Running stretch batches a plant scale provides the verification step CQA are / are not within registered limits Does not provide mathematical solution about the parameter / attribute relationship Can not use Experimental Design solver as limited number of stretched batches MVA specificity 1. Ability to handle large amount of data 2. Understanding of the correlation structure of complex datasets (multiple parameters / attributes) 3. Simplified representation of the information Qualitative / semi-quantitative / quantitative 1

11 6+17 Batches Results Plant data Dataset 17 batches run under Standard process conditions (6 campaigns, 2 sites) 6 batches run under Stretched process conditions Plant process stretching aimed at testing working hypothesis from lab scale Some process data obtained from Time series (IP21) dataset across multiple cycles in Ostwald Ripening 18 process parameters representing the 9 CPP Response (Y): Full Particle Size Distribution relates to API-CQA 18 parameters 49 variables - PSD X Y 1

12 Results Proposed Methodology O2PLS O2PLS identifies correlation between parameters and PSD Correlation from 3 different sources: X-Y: joint variability between parameters and PSD Specific variation in parameters NOT impacting the PSD Structured variation in PSD NOT originating from parameters Variation in X not related to Y O2PLS Model used on Plant stretched batches: PLS components describing the joint X-Y variability - R 2 X cum : 0.74, R 2 Y cum : 0.86, Q 2 cum: % of the parameter variability explains 86% of the PSD variability Poor Q 2 due to only 6 stretched batches and 14 consistent standard batches 1 PLS components orthogonal in X - R 2 X cum : 0.09: <10% of structured variability in parameters not impacting PSD 0 PLS component orthogonal in Y: not structured variability in PSD outside the joint X-Y No further factor generating variability in PSD T Yo P T Yo X X-Y correlated variation T U Y Variation in Y not related to X U XoPT W T Symmetric W T PCA comp Trygg, J., J. Chemometrics 2002, 16, (6), Xo 1

13 Results O2PLS model Standard and Stretch Batches Scores plot of the joint X-Y variability indicate good clustering/consistency of the 14 standard batches The stretch batches are distributed as expected: Batch 14 and Batches 12/15 are designed to produce opposite extreme characteristics (Coarse to Fine) Driven by stretched conditions around seeding and Temperature profiles Batches 16/17 are expected to produce intermediate material size milder process conditions Batch 1 designed to produce coarser batch Driven by stretched conditions around seeding only t1: Increase in Particle size 13

14 Results O2PLS model Loadings of the X-Y joint variability Parameters with positive loadings: drive finer material d, e, b, c Parameters with negative loadings: drive coarser material a, j, f, g, h Provides info on magnitude of parameter impact d is the main effect a, e are the least impactful (Note: depends on stretch conditions defined at plant scale)

15 Results O2PLS model Variable Importance to the Projection (VIP) The VIP plot provides a ranking order of the impact of the process parameters Provides scientific rational align with DoE results Most impactful parameters are the same Seeding conditions Temperatures in Ostwald Ripening All parameters are significant Not expected to match exactly the DoE output Only 6 batches to evaluate 9 parameters not enough Degrees of Freedom to explain all variation Targeted stretching conditions to test working hypothesis 15

16 Conclusions Comparison between Lab scale and Plant scale experiments All 6 stretch plant scale batches led to CQA within specifications Plant stretch demonstrated robustness of the process The ranking of CQA (size of particles) was as expected based on scientific understanding from lab scale Verifies the working hypothesis that were evaluated for the 6 batches Use of Multivariate Analysis Applied on existing data no requirement for specific batch: Maximise the information available O2PLS very well suited to understand the Parameter / Attribute relationship Thorough description of the X-Y relationship Y-orthogonal: Demonstrated that all variability in PSD is captured by 9 proposed CQA Confirm the expected mathematical relation between Parameters / Attribute at plant scale Direction & magnitude of the impact of parameters 16

17 Potential solution for plant scale verification Running full API Robustness study at scale is not financially realistic Running most forcing conditions at extreme of the registration ranges is not the preferred option Defining (protocol) a set of plant scale conditions to address key scale-up concerns Document the rational to stretch specific parameters Document the expected impact on each CQA (qualitative, quantitative if sufficient process understanding) Verify the CQA and use MVA to document the parameter/attribute relationship Risk Assessment + Process Understanding Define: - Stretching objectives - Stretching conditions By protocol defines the expected outcome: Param Range BX1 Range BX Run the batches at plant scale: - Verify the CQA levels against the protocol - Verify the parameter / attribute relationship with MVA CQA 1 CQA 2 BX1 Increased by 50-70% No impact BX2 No impact Increase 2-3 fold 17

18 Acknowledgement Jeegna Patel-Jones Matt Popkin Inderjit Mann Acureomics Johan Trygg Jon Gabrielsson 18

19 Thank you