Toward a live validation of purity methods. Izydor Apostol Attribute Sciences, Amgen, Thousand Oaks IVT, December 14, 2016

Transcription

1 Toward a live validation of purity methods Izydor postol ttribute Sciences, mgen, Thousand Oaks IVT, December 4, 206

2 bstract The biopharmaceutical industry puts significant effort into the qualification and validation of analytical methods. Purity methods, which are largely based on chromatographic or electrophoretic separation, represent a significant portion of the portfolio of methods used for release and stability of biologics. In the effort to streamline qualification/validation activities, we applied UBCI model Uncertainty Based on Current Information model to estimate the uncertainty and compared those results with conventional measurements of precision and other performance characteristics for numerous analytical methods. Results demonstrated a good agreement between these approaches. Therefore, UBCI can potentially mitigate the challenges associated with laborious conventional method validation, and facilitates the introduction of more advanced analytical technologies during the method lifecycle. The application of this relationship creates an opportunity for streamlining qualification and validation studies, and for the implementation of a real-time assessment of the uncertainty of test results. 2

3 Method precision is a central performance characteristics STM E29 The reporting interval should be derived from the standard deviation. The results of analytical measurements should be rounded to not less than /20 of the determined standard deviation. Six Sigma aims to have processes where the mean is at least 6σ away from the nearest specification limit. Method precision is an important component. ppk Min USL X, X LSL 6* lt Method Precision Horwitz equation, relationship between analyte concentration and inter-laboratory RSD RSD = logc

4 Survey of purity methods - SEC Performance characteristics: Parameter and units Mean Median 90% percentile Smallest Largest n Specificity Carryover % of nominal load % Recovery Linearity R 2 of total peak area vs. conc. load linearity R 2 of dimer peak area vs. relative contentminor peak linearity Repeatability % RSD for main peak % RSD for dimer Intermediate Precision % RSD for main peak % RSD for dimer ccuracy % accuracy for main peak % accuracy for dimer Range The highest Load μg The lowest load μg Quantitation Limit QL for dimer % purity Detection Limit Not reported 4

5 Survey of purity methods CE-SDS Performance characteristics: Parameter and units Mean % Median 90% percentile Smallest Largest n Specificity Carryover Load Linearity R 2 of total peak area vs. conc Linearity of Minor Peak R 2 of NGHC Precision-Repeatability % RSD for HC % RSD for LC % RSD for NGHC Precision-Intermediate Precision % RSD for HC % RSD for LC % RSD for NGHC % accuracy for LC ccuracy-%main Peak % accuracy for HC % accuracy for NGHC The highest conc. mg/ml Range The lowest conc. mg/ml Quantitation Limit QL for NGHC % Detection Limit Not reported 5

6 Intermidaite Precision %RSD Repeatability vs. Intermediate precision SE-main peak SE-dimer CEx-main peak CEX-acidic peak CEx-basic peak rce-sds- HC rce-sds- LC rce-sds- NGHC Repeatablity %RSD 6

7 Verification of Intrinsic variability Sample # of replicates on # of replicates # of analytes cquisition rates day on day 2 peaks Peptide map 3 3 9, 5, and 20 Hz Glycan map Hz The results show that precision of purity measurements for corresponding pairs of experiments were significantly different. In 2% of cases 4 out of 27 for the peptide map and out 6 for the glycan map the ratio of variances exceeded the value of the corresponding F- critical at the 95% confidence level. 2% of the experiment results had the ratio of two variances greater than 0 7

8 Sample vs. Population Variability Methods RSD Intermediate precision from qualifications, n=~20 methods SEC HMW 3.6%.5-7. Main 0.05% % CE-SDS LC.77% % NGHC 5.99% % HC 0.87% % RSD Population Variability Overall Precision n> 000 measur. 7.54% 0.06% 2.44% 7.5%.00%

9 , Inspired by Horwitz and Dolan The best-known relationship between analyte concentration and inter-laboratory RSD is the empirically derived Horwitz equation2,3 RSD = logc C is the concentration of the analyte in mg/ml.. W. Horwitz. Evaluation of nalytical Methods Used for Regulation of Foods and Drugs. nalytical Chemistry. 54: K.. Rubinson and J.F. Rubinson. Contemporary Instrumental nalysis, Prentice-Hall, Inc,, Upper Saddle River, New Jersey, 2000.

10 Precision %RSD S/N and precision.e+02 Dolan s rule of thumb, RSD 50 S / N.E+0.E+00.E-0.E-02.E-03.E-04.E-06.E-05.E-04.E-03.E-02.E-0 S/N - J.W. Dolan. The role of the Signal-to-Noise Ratio in Precision and ccuracy. LCGC North merica 23: postol, I et al,. Uncertainty Estimates of Purity Measurements Based on Current Information: Toward a Live Validation of Purity Methods. Pharmaceutical Research 202, 29,

11 Building a model Lets start with this R P R = The variance of P can be approximated by the following equation: R E R V V V R E R V E P V

12 Precision of purity methods For purity methods with two analytes, the precision for the measurement of analyte is equal the precision for the measurement of analyte B For purity methods monitoring 3 and more analytes, the precision of components is disconnected B E B V V B V E V B E B E B V V V B E B V E

13 Theory - UBCI model Lets assume that samples are analyzed without the introduction of preparation errors e.g. dilution error: 0 E Inj. E Integration E Noise E Scaling Since all the components are independent V V Injection V Integration V Noise V Scaling 3

14 Components of variability. Numerical integration error 2. Noise influenced integration bias 4

15 Small Peak area %Diff Components of variability, Cont. 3. Injection error 4. Injection/scaling error Total area 6.0% 5.0% 4.0% 3.0% 2.0%.0% y = 0.005x R² = % PLn/P

16 Contributing errors to SEC methods HMW Main HMW Main rea [uvsec] Purity 0.78% 99.22% 0.78% 99.22% STD purity-measured 0.04% 0.04% 0.06% 0.06% STD purity-predicted 0.06% 0.06% 0.04% 0.04% RSD purity-measured 5.66% 0.04% 7.54% 0.06% RSD purity-predicted 8.7% 0.06% 5.66% 0.04% Contributions to variability: UPLC n=934 HPLC n=455 Numerical Integration. 0.05% 66.84% 0.02% 2.56% Noise/Baseline 0.00% 0.00%.43% 0.00% Injection error.55% 33.03% 3.8% 97.26% Scaling error 98.40% 0.3% 95.37% 0.8%

17 Contributing errors to CE-SDS method LC NGHC HC rea Purity 25.6% 0.7% 73.8% STD purity-measured 0.6% 0.04% 0.60% STD purity-predicted 0.62% 0.05% 0.83% RSD purity-measured 2.4% 5.4% 0.8% RSD purity-predicted 2.4% 7.9%.% Contributions to variability: N=542 Numerical Integration.5%.4% 6.5% Noise/Baseline 0.5% 7.7% 0.9% Injection error.9%.9% 48.8% Scaling error 86.% 25.0% 43.8%

18 Frequency 5.0% 5.% 5.2% 5.3% 5.4% 5.5% 5.6% 5.7% 5.8% 5.9% 6.0% 6.% 6.2% 6.3% 6.4% 6.5% 6.6% 6.7% 6.8% 6.9% 7.0% more Frequency distribution of the predicted precision is narrow Frequency 0.000% 0.00% 0.020% 0.030% 0.040% 0.050% 0.060% 0.070% 0.080% 0.090% 0.00% 0.0% 0.20% 0.30% 0.40% 0.50% 0.60% 0.70% 0.80% 0.90% 0.200% more HPLC, N= HMW Peak Main Peak RSD [%] RSD [%]

19 Measured and predicted method variability are consistent Predicted RSD 9.0% 8.0% 7.0% y = x R² = % 5.0% 4.0% 3.0% 2.0%.0% 0.0% 0.00%.00% 2.00% 3.00% 4.00% 5.00% 6.00% 7.00% 8.00% Observed RSD

20 RSD % Predicted Method Precision at LOD/LOQ consistent with expectations 40% 35% 30% LOD y = 0.430x R² = % 20% 5% LOQ 0% 5% 0% S/N Baseline Noise for CE-SDS method has been modulated, UBCI model has been applied to calculate corresponding precision for all analytes

21 Dynamic LQ LOQ LOQ QL P 0 S N N ~9*N LOQ ~90*N The analyte of interest can be hidden within the noise or, alternatively, can be significantly above the noise for the same sample analyzed at two different load levels within the range of the method.

22 Peak height [mu] Peak area [mu*sec] UBCI - Linearity and Range Peak Height Peak rea y = x R² = Protein load [ug] 22

23 How to improve precision of purity methods?. Narrow peaks fast chromatography 2. Narrow and consistent integration practices 3. Sufficiently high acquisition rate 4. Low noise of the system 5. Peak height below the upper limit of the detector 6. Good injector

24 Summary Precision of purity measurement is intrinsically variable UBCI model effectively predicts the precision by accounting for contribution from: Numerical integration Noise and bassline float Injection error Scaling error The model opens an opportunity for live assessment of performance characteristics 24

25 cknowledgments Richard Wu Mee Ko Leszek Poppe Wojciech Szpankowski