Lifecycle Management of Process Analytical Technology Procedures

Transcription

1 Lifecycle Management of Process Analytical Technology Procedures IFPAC 2015 Marta Lichtig Senior Scientist in New Testing Technologies, ACS Member

2 Contents General Comparison : PV guide to NIR model development Basics Stage 1 Stage 2 Stage 3 Analytical methodology

3 General Many articles were published on process and product lifecycle management, analytical method lifecycle management nothing about PAT methods lifecycle management. WHY?

4 Because you can t really and trustingly use a NIR method if its lifecycle is not properly managed!

5 Comparison PV guide The basic principle of quality assurance is that a drug should be produced that is fit for its intended use. NIR The basic principle of quality assurance of a NIR model is that is fit for its intended use Example: Determining the end of API blending by NIR monitoring: Blending 19:26 19:33 19:40 19:48 19:55 20:02 20: API

6 Comparison PV guide NIR Quality, safety, and efficacy are designed or built into the product. Quality cannot be adequately assured merely by in-process and finished-product inspection or testing. Specificity, accuracy, precision, linearity and robustness are designed or built into the model. Quality cannot be adequately assured merely by once-in-a lifetime method development and validation.

7 Comparison PV guide NIR Each step of a manufacturing process is controlled to assure that the finished product meets all quality attributes including specifications Each step of a measurement is controlled to assure that the results meet all requirements (accuracy and precision, detection of unusual samples). Each NIR model needs to have, as first step, the identification of the sample population to be the same as that used for the library building

8 Stage 1 Process Design Model Design The goal of this stage is to design a process suitable for routine commercial manufacturing that can consistently deliver a product that meets its quality attributes The goal of this stage is to design a model suitable for consistent routine testing of the product/controlling the process at a high confidence level. To define the purpose of the model, it s scope an limitations

9 Predicted LOD [%] Stage 1 Process Design Model Design Building and Capturing Process Knowledge and Understanding Building and Capturing Process/Product/Method Knowledge and Understanding Extended release product granulation LOD curve Consecutive reading []

10 ED Stage 1 Process Design Model Design Establishing a Strategy for Process Control Establishing a plan (protocol) for feasibility study Example R&D batch Blending MgSt addition :0011:0511:1011:1511:2011:2511:3011:3511:4011:4511:50 Green- API Time

11 Stage 2 Process Qualification Model Qualification Process Qualification- the process design is evaluated to determine if it is capable of reproducible commercial manufacture Process Qualification- the model design is evaluated to determine if it is capable of providing specific, reproducible, accurate and precise results, at a high confidence level. (Model validation)

12 Stage 2 Process Qualification Model Qualification Design of a Facility and Qualification of Utilities and Equipment Establishing: the optimal position for measurement Performing IQ/OQ/PQ of the instrument (is the calibration possible in the place of measurement? The instrument needs to be removed for calibration?...)

13 Stage 2 Process Performance Qualification (PPQ) Model Performance Qualification (Model Validation) PPQ protocol (how many batches?) Tests to be performed (in-process, release, characterization) and acceptance criteria for each significant processing step Validation protocol (how many batches? Library? External?) Tests to be performed USP <1119> (all?) and acceptance criteria for each parameter

14 Stage 2 In line quantitative model (e.g LOD) USP<1225> Analytical Performance Characteristics Accuracy Category I Yes Precision Yes (?) Specificity Detection Limit Yes No The precision of an analytical procedure is determined by assaying a sufficient number of aliquots of a homogeneous sample to be able to calculate statistically valid estimates of standard deviation or relative standard deviation (coefficient of variation). Quantitation Limit Linearity Range No Yes Yes

15 Stage 2 PPQ Performance Model Validation Routine production conditions (target values) Challenging conditions Criteria and process performance indicators that allow for a science- and riskbased decision about the ability of the process to consistently produce quality products. The criteria should include: Data acquisition (routine process) Extreme values Criteria and performance indicators that allow for a science- and risk-based decision about the ability of the model to consistently produce quality results. The criteria should include:

16 Stage 2 PPQ Performance Model Validation A description of the statistical methods to be used in analyzing all collected data (e.g., statistical metrics defining both intra-batch and inter-batch variability). A description of the statistical methods to be used in analyzing all collected data (pretreatment(s), PCA, MLR, etc)

17 Stage 2 PPQ Performance Model Validation Provision for addressing deviations from expected conditions and handling of nonconforming data. Data should not be excluded from further consideration in terms of PPQ without a documented, sciencebased justification. Provision for addressing deviations from expected conditions and handling of nonconforming data (handling of outliers). Outliers should not be excluded from the model without a documented, sciencebased justification.

18 Stage 2 PPQ Report Model Validation Report Review and approval of the protocol/report by appropriate departments and the quality unit. Discuss and cross-reference all aspects of the protocol. Summarize data collected and analyze the data, as specified by the protocol. Review and approval of the protocol/report by appropriate departments and the quality unit. Discuss and cross-reference all aspects of the protocol. Summarize data collected and analyze the data, as specified by the protocol.

19 Data Stage 3 - Continued Process Verification Process The goal of the third validation stage is continual assurance that the process remains in a state of control (the validated state) during commercial manufacture Model The goal of the third validation stage is continual assurance that the model remains in a state of control (the validated state) during routine use Time Series Plot of Calculated LOD (%), Reference Method LOD(%) Etodolac Tablets USP 400mg & 500mg External Validation Set+ Ongoning Evaluation Samples Variable Calculated LOD (%) Reference Method LOD(%) USL=3 UTL=2.5 Average =1.5 LTL= Index

20 Stage 3 - Continued Process Verification Process Evaluating the performance of the process identifies problems and determines whether action must be taken to correct, anticipate, and prevent problems so that the process remains in control Model Evaluating the performance of the model identifies problems and determines whether action must be taken to correct, anticipate, and prevent problems so that the model remains in control

21 Stage 3 - Continued Process Verification Process Procedures should describe how trending and calculations are to be performed and should guard against overreaction to individual events as well as against failure to detect unintended process variability. Model Procedures should describe how trending and calculations are to be performed and should guard against overreaction to individual events as well as against failure to detect unintended process variability or model drift.

22 Stage 3 - Continued Process Verification Process A process is likely to encounter sources of variation that were not previously detected or to which the process was not previously exposed Model A model is likely to encounter sources of variation that were not previously detected or to which the model was not previously exposed

23 Stage 3 - Continued Process Verification Process Data gathered during this stage might suggest ways to improve and/or optimize the process by altering some aspects of the process or product, such as the operating conditions (ranges and setpoints), process controls, component, or in-process material characteristics. Model Data gathered during this stage might suggest ways to improve and/or optimize the model by altering some aspects of the model.

24 Stage 3 - Continued Process Verification Model Data gathered during this stage might suggest ways to improve and/or optimize the model by altering some aspects of the model.

25 Stage 3 - Continued Process Verification Process change A description of the planned change, a well-justified rationale for the change, an implementation plan, and quality unit approval before implementation must be documented Model update A description of the planned change a well-justified rationale for the change, an implementation plan (revalidation protocol), and quality unit approval before implementation must be documented

26 ANALYTICAL METHODOLOGY Process Analytical methods supporting commercial batch release must follow CGMPs in parts 210 and 211 Model Analytical methods supporting model building must follow CGMPs in parts 210 and 211 (be validated)

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