QbD/PAT/RTRT application and regulatory interaction in Japan

Transcription

1 QbD/PAT/RTRT application and regulatory interaction in Japan QbD Track 2: Real Time Release Testing/New Technologies IFPAC-2016 Hiroshi Nakagawa, Ph.D Formulation Technology Research Laboratories, Daiichi Sankyo Co., Ltd. January 26, 2016

2 Edoxaban Tablets (Lixiana : Japan) RTRT approval in April 2011 (DVT: Deep vein thrombosis) Appearance, ID, UDU, Dissolution and Assay 15-mg tablets 30-mg tablets RTRT approval in September 2014 (AF: Atrial Fibrillation/VTE: Venous thromboembolism) Dose addition and Dissolution design space change 60-mg tablets 2

3 Enhanced QbD Approach in Edoxaban Tablets CQA p-c p-c Fish bone, PHA, FMEA p-c p-c RTRT FMEA C C Attribute based design space PP PP PP PP PP PP PP PP PP PP PP PAT p-cpp p-cpp Fish bone, PHA, FMEA p-cpp p-cpp FMEA CPP CPP C (Critical Material Attribute) Based Approach 3

4 Advantage of C Based Approach Ex. In case that two Cs of API particle size and tablet hardness affect CQA of dissolution (General example) Milling CPP Tableting Tableting Pressure Rotational Speed Feed Rate Turn Table Rotational Speed CPP PAT API particle size C Tablet Hardness PAT Design space Dissolution Equipment A Equipment B Pros Design space portability for manufacturing site/equipment change Scale independent Cons Finding C with comprehensive risk assessment C monitoring method development

5 Control Strategy in Edoxaban Tablets Manufacturing Drug Substance Granulation Blending Tableting RTRT has been applied to all spec. items for Edoxaban Tablets in Japan market. RTRT is applied when Cs are controlled within design spaces. Assay Uniformity of Dosage Units (UDU) Dissolution Film-Coating Packaging Drug Product RTRT is applied when the test items are assured in the manufacturing area. Appearance Identification 5

6 Risk Assessment for Assay and UDU Risk Assessment of Cs affecting CQAs of Assay and UDU No need of individual tablet assay approach (e.g. Large N) First principle model Low risk p-c Granule segregation p-c Tablet weight Variation p-c Blend uniformity Blending Tableting p-c API content in granules p-c Tablet weight CQA UDU/Assay CQA of UDU = f (C of blend uniformity, API content in granules, tablet weight/variation) CQA of Assay = f (C of API content, tablet weight) NIR Reference: 2.3 Quality Overall Summary; Sakura Tablet (2008) 6

7 Control strategy for CQA of assay X = XX WW 111 X: Assay Xa: API content in granules for tableting Wa: (% of the theoretical concentration) Uncoated tablet weight (% of the theoretical weight) Design space inside the two walls the assay meets the specification *Theoretical uncoated tablet weight for each strength is established as 100%. 7

8 Control strategy for CQA of UDU (1/2) AV (%) = M- X + ks <JP/USP/Ph.Eur.> = M XX WW k s w2 ( XX 100 )2 + s x2 ( WW 100 )2 X < 98.5% 98.5% X % X> % M 98.5% X % AV: Xa: Wa: s x : s w : Acceptance value of UDU API content in granules for tableting (% of the theoretical concentration) Uncoated tablet weight (% of the theoretical weight) Blend uniformity (RSD, %) Uncoated tablet weight variation (RSD, %) N. Katori; N. Aoyagi; S. Kojima; Chem. Pharm. Bull., 49 (11) 2001,

9 Control strategy for CQA of UDU (2/2) 6% Design space inside the tunnel the UDU meets the specification (AV 15.0) 5% 4% 3% 2% 1% Uncoated tablet weight variation 90% 95% 100% 105% Uncoated tablet weight* 110% 0% 105% 104% 103% 102% 101% 100% 99% 98% 97% 96% 95% API content in granules for tableting * Theoretical uncoated tablet weight for each dose is established as 100%. Design space plotted at s x = 5.0% (RSD) blend uniformity Applied worst case value of blend uniformity to realize the tight quality control T. Garcia et al., J. Pharm. Innov., (10) 2015,

10 Integrated Design Space for Assay and UDU Integrated Design Space *1 Overlay of design spaces for Assay and UDU (Inside of the tunnel meets the specification for Assay and UDU) *1: Design space plotted at s x =5.0% (RSD) blend uniformity *2: Theoretical uncoated tablet weight for each strength is established as 100%. Assay and UDU can be assured when the combination of Cs is within the tunnel. 10

11 Validation of Design Space for Assay and UDU (1/2) 6% % 4% 3% 2% 90% 95% 100% 1 105% % % 0% 105% 104% 103% 102% 101% 100% 99% 98% 97% 96% 95% Comparison between conventional and RTRT methods 11

12 Validation of Design Space for Assay and UDU (2/2) Fig. Validation Results for Assay Fig. Validation Results for UDU Values calculated by conventional and RTRT methods were well correlated in both Assay and UDU (BU: actual). 12

13 Pros of 1 st Principle Assay and UDU Control Strategy No need of dose dependent NIR model One NIR model predicting API content is commonly used for all strengths to calculate Assay and UDU. Key Quality Control with Design Space Flexibility Ex. Assay Change of target tablet weight depending on the API content in granules API content in granules IPC/Spec. Tablet assay Spec. Assay can be controlled close to the target value by changing the uncoated tablet weight within design space. 13

14 API Content in Granules Prediction by NIR NIR model development 1. Data Acquisition NIR spectra Calibration set X API content 2. Parameter Selection Y 99.8% 100.5% 70.2% 115.7% 80.0% 108.1% Pretreatment, Wavenumber, Spectral analysis (e.g. PLS), Number of latent variables 3. Model Evaluation Correlate coefficient, RMSECV, Loading, Regression coefficient, Score, etc. 4. Analytical Validation Specificity, Linearity, Range Accuracy, Precision, Robustness Routine measurement Unknown sample Developed calibration model 100.0% 97.9% H. Nakagawa; M. Kano; S. Hasebe; T. Miyano; T. Watanabe; N. Wakiyama; Int. J. Pharm., 471 (2014)

15 Establishment of Calibration Set Concept of Calibration Set (NIR spectra) Samples with purposeful variations of API content (e.g. 70% to 130%) + Samples with variations affecting NIR spectra (e.g. variations of particle size) + Samples manufactured with commercial equipment (e.g. Scale up study samples) Key for robust model development Selection of samples affecting NIR spectra in routine manufacturing 15

16 Model Evaluation / Analytical Validation Prediction Value by NIR [%] Measurement Value by HPLC [%] Specificity Linearity Accuracy Precision Range Robustness API peak Correlation coefficient SEP RSD of repeated measurements Validated range Variations of environment Reference Guidance for Industry, Development and Submission of Near Infrared Analytical Procedures (2015, FDA) Guideline on the use of near infrared spectroscopy by the pharmaceutical industry and the data requirements for new submissions and variations (2014, E) Standard Practices for Infrared Multivariate Quantitative Analysis (E1655, ASTM), etc. 16

17 Dialogue with PMDA (1/3) Regulatory Flexibility in C Based Approach Low C based Approach CPP based approach Potential to have an adverse effect on CQA CPP Critical Process Parameters that ensure Cs C Critical Material Attributes that assure CQA Regulatory flexibility for change control CPP Design Space Critical Process Parameters that ensure CQA High CQA Critical Quality Attributes (ex. assay, CU, dissolution) CQA Critical Quality Attributes (ex. assay, CU, dissolution) C based approach was applied to new QbD mock sponsored by MHLW: Sakura bloom Tablets (2015) 17

18 Dialogue with PMDA (2/3) Model Maintenance Max CQA Std Min Trend change Prediction error Model check (and updated) Actual Predict Quality Assurance in RTRT Appropriateness of prediction models Daily check Periodical check Event check Model maintenance trigger SST, etc Trend analysis Comparison with conventional testing Change control, QA judgment Countermeasures for unexpected variations in model development stage 18

19 Dialogue with PMDA (3/3) Quality Management System for NIR Model Laboratory - Development Development of NIR model Validation of NIR model Transfer of NIR model (Laboratory to Plant) Plant - Operation Model update and Re-validation In case that model update according to SOP is difficult Model Maintenance Unacceptable error Verification of prediction error (Comparison with conventional method) Acceptable error Routine Trigger of measurement model maintenance Close cooperation between laboratory and plant 19

20 Queries from PMDA 1 st submission (April 2011) Queries from PMDA 2 nd submission (September 2014) Total General QbD/PAT/RTRT related Total Significant decrease of queries in 2 nd submission based on the close-dialogue with PMDA in 1 st submission All the queries had been successfully solved by close-dialogue with PMDA. 20

21 Conclusion Edoxaban tablets 15 mg, 30 mg, and 60 mg were approved as RTRT products in Japan. C based enhanced QbD approach, in which a design space is constructed only using Cs, was applied to Edoxaban tablets. e.g., Assay and UDU design space was constructed using four Cs of 1 st principle equation based on compendial requirements. A lot of queries were inquired by PMDA, but all had been successfully solved by close-dialogue. 21

22 Acknowledgment Formulation Tomoyuki Watanabe Naoki Wakiyama Tatsuya Suzuki Masazumi Kojima Hidehiro Imai Koichi Ishidoh Kazuko Maeda Makoto Kamada Jin Hisazumi Jin Maeda Masatomo Ito Hideaki Tanabe Takuya Miyano Kazuhiro Matsuura Yoshito Kikkawa Shuichi Tanabe Takafumi Kato Rie Saguchi CMC Takeshi Hamaura Shuichi Ando Motonori Kidokoro Hiromi Morita Kenichi Enokita Hiroyuki Nakata Kyoko Nakanishi Analytical Motomu Ohara Tadanobu Takatani Hiroki Hifumi Other Company Hiroaki Nakagami Takashi Maehara Plant Jun Hishida Katsuhiko Nagao Tsuyoshi Tanaka Satoshi Osaki Hiroaki Inoue Eiji Kawai Shigenari Sugaya Takahiko Nuga Yasuhiro Maniwa Tomohiro Yoshida Ryota Suzuki Masaaki Koyama Masanobu Kabasawa Headquarters Chiaki Matsumoto Kenji Ochiai Hisashi Takeuchi Ayako Fujitani Kyoto University Manabu Kano Shinji Hasebe 22

23 Thank you for your attention. Any questions? 23