Operational Opportunities in Continued Process Validation

Transcription

1 Operational Opportunities in Continued Process Validation IFPAC 2015 Tamar Ben-Avi Director, Head of Pharmaceutical Technology Taro Pharmaceuticals, Haifa, Israel

2 Content Introduction Continued Process Validation Concept Method Case study 1 Surprise! Case study 2 Urban legends Case study 3 Show me the money Case study 4- Question Vision Continued Process Validation closure Acknowledgments

3 Introduction Process validation : Collection and evaluation of data, from the process design stage through commercial production, which establishes scientific evidence that a process is capable of consistently delivering quality product. My Boss: After How many batches you know your product/process well? variation, understanding, detecting, responding, and controlling from input through output

4 Continued Process Verification = continual assurance that the process remains in a state of control A system or systems for detecting unplanned departures from the process as designed. detection of undesired process variability

5 Continued Process Validation= continual assurance that the process remains in a state of control How? Adherence to the CGMP requirements, specifically, the collection and evaluation of information and data about the performance of the process, will allow detection of undesired process variability. What tools? Who?..The data should be statistically trended and reviewed by trained personnel What to analyze?..product and process data.relevant process trends and quality of incoming materials or components, in-process material, and finished products.... process parameters and quality attributes

6 Continued Process Verification = continual assurance that the process remains in a state of control What will be gained? If properly carried out, these efforts can identify variability in the process and/or signal potential process improvements. How to implement? Team approach When? Periodically timely manner product life

7 Continued Process Verification=basic cgmp requirements (a) Sampling and testing of in-process materials and drug products, requires that control procedures... be established to monitor the output and to validate the performance of those manufacturing processes that may be responsible for causing variability in the characteristics of in-process material and the drug product (e) requires that information and data about product quality and manufacturing experience be periodically reviewed to determine whether any changes to the established process are warranted. Ongoing feedback about product quality and process performance is an essential feature of process maintenance.

8 Product and Process Knowledge Continued Process Verification = Knowledge Continuum ISPE Guide Series: PQLI from Concept to Continual Improvement, Part 4 Process Performance and Product Quality Monitoring System

9 Continued Process Verification = Culture of excellence Integrated team approach. Understanding what drives process variation and stability is the core of Lean. Value-added activities. Increase business performance through more stable and shorter cycle time, more consistent and reduced inventory and greater flexibility to respond to market opportunities (competitiveness)

10 Concept Need to create value (Value+Quality=Money) Need to leverage process knowledge. Adherence to requirements - timely response to regulatory requirements is needed. For legacy products - not all CPPs and CMAs are identified. Where development data is available from development - CPP and CQA might have been changed with time (due to post approval changes) Capturing data by going into the lab? - High variability during stage 3 (can it be simulated?) Trials - cost time and money Integrated approach: Commercial production as the ultimate research facility. CQA trending (control charts) in the APR.

11 Method Product prioritization CQA & CPP & CMA & COP & more - identification Analysis methods (Protocol) Database 4 Data collection 5 Statistical analysis 6 Recommendation implementation (report)

12 Method - Products prioritization Priority New Products All Old /Legacy Products Relatively high risk(*) Old/legacy product with major change Number of batches/yr MT 30 (*) Currently based on incidents reports, investigations, complaints. Under implementation - based on incidents reports, investigations, complaints and CQA process capability indexes (from the APR)

13 Case study 1 - Decreasing trend in stability. CL already in use for release evaluation. Risk assessment Prior Knowledge

14 Case study 1 Handling of continuous process data. The more parameters you have the more batches you need.

15 Case study 1 Data analysis P value < indicates that the model is significant. The model explains about 60% of the variation in AVG Diss. The factors : d(0.5), Mean screen delta P (mmwc),mean inlet dewpoint ( C) and AVG Hardness( Kp), were found significant.

16 Case study 2 - When time increase, dissolution decrease.

17 Case study 2 When time increase, dissolution decreases.

18 Case study 3 100% weight sorting. Tight unit weight acceptance limits (tighter than approved). Yield after sorting 90% (~20 batches a year going to waste)

19 Case study 3 No correlation was observed between capsule weight and dissolution rate in the NOR. Weight ranges can be loosen (Gain: 1% ~ 3-5 batches/year)

20 Case study 4 Question the statistical results. Process understanding is a must. * Surface area*fill-o-matic speed - the combination of Fill-o-matic speed at the High level (72) and Surface area at the Low level (6.4) produces low level of dissolution. * PSD*Fill-o-matic speed - the combination of Fill-o-matic speed at the High level (72) and PSD at the Low level (46) produce low level of DISS.

21 Individual Value Individual Value Moving Range Moving Range Process Capability of Assay % - 200mg LSL USL Process Data O v erall C apability LSL 90 Pp 2.56 Target * USL 110 PPL 2.26 PPU 2.86 Sample Mean Sample N 49 Ppk 2.26 C pm * StDev (O v erall) I-MR Chart of Moisture Content of unmilled granulate % LOD - 100,150,200mg NMT - 1 UC L= _ X= LC L= BN O bserv ed Performance Exp. O v erall Performance UC L= % < LSL 0.00 % > USL 0.00 % Total 0.00 % < LSL 0.00 % > USL 0.00 % Total MR= LC L= BN Process Capability of Dissolution 100mg Johnson Transformation I-MR Chart of Mixer Power densification stage - 100mg BN UC L= USL - 18 _ X=17.35 LC L= LSL - 15 Process Data LSL 85 Target * USL * Sample Mean Sample N 300 StDev (O v erall) A fter Transformation LSL* Target* * USL* * Sample Mean* StDev (O v erall)* LSL* transformed data O v erall C apability Pp * PPL 1.27 PPU * Ppk 1.27 C pm * 3 2 UC L= BN MR=0.704 LC L=0 O bserv ed Performance % < LSL 0.00 % > USL * % Total Exp. O v erall Performance % < LSL* 0.01 % > USL* * % Total

22 Continued Process Verification Continual assurance that the process remains in a state of control Culture of excellence cgmp requirements Lean Knowledge Continuum Not - although we are generics. But - because we are generics!

23 Thanks To. Marta Lichtig, Scientific adviser Sharon Levi, Statistician The Technology Champions Yair Satt Ella Katzir Raz Menashe Leonid Polsky Amnon Blumethal Haya Birenbaum Dikla Eyal Ronen Avraham Adi Sasson Our management