A Bayesian approach to the Comparison of NIR vs HPLC analytical methods in Continuous Manufacturing Process Validation Studies Areti Manola1, Jyh-Ming Shoung1, Steven Novick2, Tara Scherder3, Gilfredo Navarro1, Eric Sanchez1, Stan Altan1 1Janssen R&D, 2MedImmune, 3Arlenda NCB2015 Conference Villanova University October 15, 2015

Outline Overview of Continuous Manufacture Quantitative stakeholders: Process Engineers, Chemometricians, Statisticians Process Performance Qualification Verification of HPLC – NIR calibration Study Design Method Comparability/Equivalence Relative Performance Index Comparison of ratios of probabilities Bayesian approach Case Study Summary 

Recent Industry Announcements

Engineering Definition of Continuous Manufacturing vs Batch Manufacturing FDA Perspective on Continuous Manufacturing, Sharmista Chatterjee, Ph.D. IFPAC Annual Meeting Baltimore, January , 2012

Example of Continuous Manufacturing with On-line Monitoring FDA Perspective on Continuous Manufacturing, Sharmista Chatterjee, Ph.D. IFPAC Annual Meeting Baltimore, January , 2012

Ideal Future Vision of CM

Traditional Release Approach Real Time Release Approach Traditional Release Approach

Advantages of Continuous Manufacture Scientific/Engineering Operational/Business Integration of QbD concepts Cohesive development, quality and technical operations Application of new methodologies, technologies and equipment Improved process capability Real time understanding of process integrating process engineering, chemometrics and statistical considerations Reduces costs Streamlined facility lay-out Reduction of raw materials and intermediates inventories Flexibility in supply size Payoff Reduction of overall drug substance and drug product development time Improve time to market Assures supply of high quality product

Process Engineering Aspects Unit Operations Model: Mixer Residence Time Distribution Model Detector (i.e., PAT tools) Pulse of Tracer (i.e., API) RTD models are used to determine: Disturbance dissipation along process Raw material traceability Scale up requirements Critical process parameters (CPPs) Data (DEM predicted particle velocities) Outputs Powder properties Unit responses: Flow Rate Mixing (RTD) Unit: Mixer

Chemometric Aspects Process Analytical Technology (PAT) Near Infra Red (NIR) calibration modeling for PAT during Process Design PAT PLS = assess comparability to HPLC method Gage R&R designs relating HPLC to NIR during development and subsequently during process validation

Statistical Aspects Verification of HPLC – NIR calibration Process Validation Stage 1 Process Design – DoE, data analysis and interpretation Stage 2 Process Performance Qualification Statistical sampling protocols for Large n sampling plans IPC sampling Verification of HPLC – NIR calibration Stability protocol and sampling designs Definition of sampling frequency and sample size Stage 3 Continued Process Verification (PV) - Process Capability

Verification of HPLC – NIR calibration Calibration model is developed during Process Design, Gage R&R to assess equivalence Blocked design is 3 concentrations, at target tablet weight, tablets are the blocks leading to essentially a paired comparison design During the stages of PV, the calibration model will be tested in production Equivalence measures will be calculated Schuirmann’s test We propose a Relative Performance Index using a Bayesian approach to the assessment of an individual analytical determination falling within a prespecified limit of the true value for comparing NIR vs HPLC

Relative Performance Index Assuming the HPLC is the gold standard method, the probability of a single analytical determination y from HPLC (or NIR) falling within some interval  of the true value µ is calculated as follows: where (•) is the CDF of standard normal distribution. The Relative Performance Index is defined as follows:

Method Comparison using the Relative Performance Index Given delta, Bias and Method Variability OC Curves of probability of falling within delta of true value Rel_Pfm across delta of true value Criterion for equivalence Pr(Rel_Pfm≥1)≥PC, where PC is a desired probability level

Case Study - Data Description A single CM batch was sampled as follows: 20 locations chosen equispaced throughout the CM run 3 tablets per location Tested by both NIR and HPLC methods

Statistical Model Variance component model : where Yj(i),k = assay of jth tablet (j=1,2,3) from ith (i=1,2,…,20) location from kth (k=HPLC, NIR) method, Mk = overall mean from kth method, Li = random effect of ith location: ~ N(0, L2) Tj(i) = random effect of jth tablet from ith location: ~ N(0, T2) j(i),k = residual error from kth method: ~ N(0, k2), k = 1, 2 Preliminary analysis showed no location effect, therefore the random effect of location was dropped from final model.

REML Parameter Estimates Effect Parameter Estimate (se) 95% Confidence Interval Lower Upper Fixed HPLC 100.01 (0.10) 99.81 100.32 NIR 100.18 (0.05) 100.08 100.29 Bias* (NIR-HPLC) 0.17 (0.09) -0.02 0.36 Random (SD) Tablet 0.35 0.26 0.53 Residual (HPLC) 0.70 0.58 0.86 Residual (NIR) 0.20 0.11 1.04 *The 90% confidence interval for Bias = (0.01, 0.33)

JAGS – Posterior Samples A Bayesian simulation of the posterior distribution and credible intervals based on the previous model was done using JAGS with vague priors: Mean[HPLC], Mean[NIR] ~ N( Mean=100, SD=10 ) SD_Tablet ~ U(0, 5) SD_HPLC, SD_NIR ~ U(0, 5) 60,000 posterior samples: Number of chains=3 Burn-in = 20000 No. of sample = 20000 thin=25

JAGS – Parameter Estimates and Credible Intervals Effect Parameter Mean (Median) 95% Credible Interval Lower Upper Fixed HPLC 100.02 (100.02) 99.81 100.22 NIR 100.19 (100.19) 100.08 100.29 Bias* (NIR-HPLC) 0.17 (0.17) -0.02 0.36 Random (SD scale) Tablet 0.36 (0.36) 0.21 0.47 Residual (HPLC) 0.72 (0.71) 0.59 0.89 Residual (NIR) 0.19 (0.20) 0.02 0.37 *The 90% credible interval for Bias = (0.01, 0.33)

Normal Density Plots of HPLC and NIR centered on the true mean Given Estimated mean bias and median of sigmas for HPLC and NIR methods

OC Curves of Probability of Falling within delta of True Value Given Estimated mean bias and median of sigmas for HPLC and NIR methods

Relative Performance Index across delta values Given Estimated mean bias and median of sigmas for HPLC and NIR methods

Summary of Posterior Distribution (JAGS) of Relative Performance Index with Various deltas Mean Median Maximum Minimum Pr(Rel_Pfm ≥ 1) 0.05 2.21 2.01 24.06 0.00 0.819 0.10 2.25 2.05 12.13 0.849 0.15 2.29 2.11 8.46 0.890 0.20 2.31 2.15 6.64 0.929 0.25 2.28 2.17 5.78 0.959 0.30 4.91 0.980 0.35 2.10 4.24 0.991 0.40 2.02 3.72 0.996 0.45 1.90 1.91 3.33 0.999 0.50 1.79 1.80 3.01 0.55 1.70 2.75 0.86 >0.999 0.60 1.61 2.54 0.89 0.65 1.53 2.36 0.92 0.70 1.47 1.46 0.95 0.75 1.41 1.40 2.08 0.97 0.80 1.35 1.34 1.97 0.99 0.85 1.31 1.30 1.87 1.01 1.000 0.90 1.26 1.02 1.23 1.22 1.71 1.00 1.20 1.19 1.64

Comparison of Schuirmann’s Test and Relative Performance Index for Method Comparison Criterion delta= 0.25 delta = 0.50 Schuirmann’s 90%Credible Interval of Bias 90%CI = (0.01, 0.33) Fail Pass Relative Performance Index Pr(RPI ≥ 1) ≥ 80% Pr(RPI ≥ 1) = 0.96 Pr(RPI ≥ 1) = 1.0

Summary CM is being actively encouraged by the FDA; companies are now engaged in weighing its costs/benefits CM offers many scientific and business advantages, major quantitative stakeholders are process engineers, chemometrician, statisticians working together to ensure quality Equivalence of NIR to gold standard HPLC can be established through a Relative Performance Index evaluated through Bayesian calculations Made possible because Tablet dispersion can be removed orthogonally given the paired comparison design Provides a natural interpretation of method performance