2018 Quality by Design Curriculum

Transcription

1 2018 Quality by Design Curriculum Comprehensive CMC Training and Consulting Support for Modern Drug Development BioAssay SCIENCES A Division of Thomas A. Little Consulting

2 Thomas A. Little Consulting and BioAssay Sciences Major biotechnology and pharmaceutical companies have selected Thomas A. Little Consulting (TLC) solutions for QbD training and implementation consulting. TLC has two core products for QbD and Modern Drug Development and MFG 1. Consulting services for Risk Assessment, Formulation, Upstream, Downstream, Release Testing Bioassay and Analytical Methods development and validation for CMC team support 2. A comprehensive QbD JMP/SAS based training curriculum Thomas A. Little Consulting (TLC) is an internationally recognized scientific and engineering consulting firm with a proven record for achieving results. TLC has an extensive health authority and QbD curriculum for method, product and process development, data analysis, characterization, optimization and control. TLC is a strategic partner of SAS/JMP. TLC works actively with the drug development team to assure product development and submission study design, data analysis, risk assessments, design of experiments, assay development and validations all meet the high standards of regulatory review and use best in class approaches to product development and report generation. TLC has developed specific courses in analytics, data analysis, design of experiments, performance modeling, statistical process control, assay development and method validation, measurement systems analysis, mixture design of experiments, quality risk management and failure modes and effects analysis. These courses are used by a variety of fortune 500 companies to train their analysts, scientists and engineers. TLC has extensive experience in the biotechnology, pharmaceutical and medical device industries and has trained over 150,000 professionals globally.

3 Who Uses TLC s Consulting and Curriculum? Biologics and Pharmaceuticals Allergan Alnylam Audentes Therapeutics Boehringer Ingelheim Bonti Bristol-Myers Squibb Catalent Ceva Emergent Biosolutions Jounce Therapeutics Genentech Gilead Sciences Glenmark Pharmaceuticals SA Halozyme Therapeutics Novavax Pfizer Portola Pharmaceuticals Regeneron Pharmaceuticals Revance Takeda Pharmaceutical Thermo Fisher Scientific Health Authorities FDA (CBER, CDER and CVM) CFDA KFDA

4 Comprehensive Drug Dev Curriculum and Experience Days 2018 QbD and Modern Drug Development Curriculum Sciences Analytical Sciences Quality Assurance Formulation 1 Introduction to QbD and Critical Quality Attributes 3 Statistical Methods and Data Analysis 2 Design of Experiments 1 Advanced Design of Experiments 2 Analytical Method Development and Validation 2 Quality Risk Management and Risk Assessment 2 Root Cause Analysis and CAPA 2 Robust Optimization, Design Space and Tolerance Design 1 Statistical Methods for Validation 2 Stability Analysis 2 Control Design using SPC/PAT 2 Bioassay Design, Control and Validation 1 Nonlinear Modeling (Dose Response) 23 Total Thomas A. Little Consulting

5 Introduction to QbD and Critical Quality Attributes For: Analytical Quality Formulation Course Purpose: Foundations in Quality by Design, organizational alignment and development objectives alignment. Defines CQAs, product development requirements, line of sight and requirements flow down. (1 day) Detailed Course Outline Section I: Introduction to Quality by Design Clinical TPP/QTTP CQAs CMC Requirements FDA and EU guidance on QbD Purpose and opportunity QbD benefits and impact on FDA submissions Systematic product development Defined es and Materials High Level Risk Assessment Analytical Method Development Section II: Systematic Product Development Market understanding Target Product Profile (TPP) Quality Target Product Profile (QTPP) Critical Quality Attributes (CQAs) CQA flow down and line of sight Section III: QbD Implications to Analytical Method and Development Method development implications development implications QbD and Validation (Lifecycle Approach) Low Level Risk Assessment Formulation, Dose and Stability Char. and Parameter Set Points Design Space and Tolerance Design CPPs and CMAs & Control Plan Validation, Control and Capability Analytical Method Validation & Control Product Knowledge Knowledge Validation and Control

6 Statistical Methods and Data Analysis For: Analytical Quality Formulation Course Purpose: General data analysis skills, hypothesis testing, capability, exploratory data analysis, equivalence testing and model building. (3 days) Detailed Course Outline Section I: Introduction to SAS/JMP Table commands Column commands Row commands Subset commands Saving Scripts, Journals and Projects Section II: Statistics Foundations and Distribution Analysis Measures of center and spread Standard error and central limit theorem Normal distribution t distribution and confidence intervals Test for normality Individuals and tolerance intervals (normal) capability (normal) Nonnormal distribution fitting and process capability Section III: Nominal X, Continuous Y Contour plots, Components of Variance, REML and POV Sample size for the mean and standard deviation t test one sample t test two sample Test for differences in variances t test paired One-way ANOVA and F test N-way ANOVA Nonparametric data analysis (optional) Section IV: Continuous X, Continuous Y Simple linear regression, correlation Orthogonal regression Multiple regression ANCOVA

7 Quality Risk Management and Risk Assessment For: Analytical Quality Formulation Course Purpose: QbD templates, risk Assessments prior to product development and study designs. QTPP, CQAs, high level and low level risk assessment. (2 days) Detailed Course Outline Section I: Quality Risk Management Principles and Risk management principles Risk management process Responsibilities Risk assessment Risk control Risk communication Risk review Section II: Risk Analysis Tools Basic quality tools and risk weighted analysis Cause and effect diagrams flow and risk assessment Pareto and Risk Weighted Pareto analysis Histograms, capability, simulation and Margin Control charts Regression DOE (product and process) and MSA Key DS Characteristics DS Component Product Development Team Leader: Product Design Requirements and Critical Quality Attributes Attribute No. 4 API- Active Pharmaceutical Ingredient Excipients Impurities Product Quality Attribute Name Critical Quality Attribute Purpose Test or Measurement Definition Attribute Section III: Technical Risk Assessment Application areas for High Level risk assessment Factor response, low level risk assessment Section IV: Methods for Reducing Risk Risk assessment action plans and risk reduction Risk communication Target Attribute Upper Limit Attribute Lower Limit

8 Design of Experiments For: Analytical Quality Formulation Course Purpose: Study designs, product, analytical method and process characterization and analysis for development. (2 days) Detailed Course Outline Section I: Introduction to DOE Section II: Experimental Preparation Section III: Full Factorial Designs Section IV: Screening Designs Augment design Section V: Custom Designs Generating custom designs Evaluating custom designs Analysis of custom designs Simulation for full distribution modeling Strategies to minimize experimental size Adding covariate and uncontrolled factors Life or repeated measures experiments Blocking designs Mixtures in custom designs Setting constraints in a DOE

9 Analytical Method Development and Validation For: Analytical Formulation Course Purpose: Study Designs and analysis for robustness, stability, specificity, precision, accuracy, linearity, LOB, LOD/LOQ, intermediate precision and range. (2 days) Detailed Course Outline Section I: Statistical Foundations and Variation Assessment Introduction to assay and test development, validation and MSA Review of basic statistics Variation analysis methods Section II: DOEs for Assay Development and Evaluation Assay characterization experiments DOE for variation reduction DOE for robustness Establishing the linear range of the method Section III: Chemical and Biological Method Validation Robustness Stability Specificity Linearity Precision (Repeatability, Intermediate Precision, Reproducibility) Limit of Blank, Limit of Detection, Limit of Quantitation Suitability Range Intermediate precision

10 Robust Optimization, Design Space and Tolerance Design For: Analytical Formulation Course Purpose: Product and process optimization, design space generation, edge of failure determination, and PAR and NOR ranges and tolerances. (2 days) Detailed Course Outline Section I: Distribution and tolerance design foundations System, parameter and tolerance design Tolerance design methods Section II: DOE review and robust design principles Eight robust design principles Section III: DOE using custom designs Custom designs Strategies to minimize experimental size Adding covariate and uncontrolled factors Special topics for custom designs (optional) Blocking designs Setting constraints in the design Section IV: Robust optimization methods Tighten the tolerance of X Design to the flats Use interactions to tune out sensitivities Use parameter combinations Section V: Tolerance design and margin analysis Tolerance design procedure Tolerance stack up analysis

11 Advanced Design of Experiments For: Analytical Formulation Course Purpose: Extend the Experimenters Knowledge of DOE (2 days) Detailed Course Outline Custom DOE Basics Review and Analysis including Design Space, Edge of Failure and CPP identification Modeling how the influence of CPPs may be reduced/controlled once NOR/PARs have been selected Setting Acceptance Limits for DOE Validation runs Advanced Linear Modeling and Response Parameterization DOE Advanced Nonlinear Modeling and Response Parameterization DOE Mixture DOE Scale Up/Scale Down Model Calibration Supersaturated Experiments Analysis of Incomplete or Partially completed DOEs Analysis of Single and Multiple Uncontrolled Factors in a DOE Setting Constraints and Disallowed Combinations within a DOE Split Plot and Strip plot designs Central Composite Design for Optimization Path of Steepest Assent for Optimization Evolutionary Operations or how to run a DOE within a Defined Design Space

12 Stability Analysis For: Analytical Quality Formulation Course Purpose: Determination of expiry and rates of degradation under ambient, accelerated and storage conditions. (1 day) Detailed Course Outline Section I: Stability Definition and Introduction FDA guidelines Section II: Stability Study Design Sample size Test conditions Section III: Stability Data Analysis and Life Prediction Extendibility and Confidence Intervals Shelf Life Determination All batches pooled All batches with individual fits Common slope Common Intercept Section IV: Stress and Accelerated Stability Testing

13 Control Design using SPC/PAT For: Analytical Quality Course Purpose: control design, monitoring and associated adjustment protocols. (2 days) Detailed Course Outline Section I: Introduction and Basic Statistics SPC a basis for control Basic statistics Normal distribution Standard error of the mean Central limit theorem Section II: Ten Requirements for Designing Effective Controls 1. Clear product specifications 2. Effective metrology 3. characterization 4. Sampling plan 5. Control chart selection (variables and attributes) 6. Alarms, closing the loop and out-of-control action plans (OCAP) 7. documentation 8. Operator and engineering training 9. Database 10. Routine line audits Section III: Capability Determining process stability prior to computation of capability Cp and Cpk, PPM Sigma and z as measures of process capability Tests for normality Distribution fitting for nonnormal parameters Section IV: Control Implementation Roles and Responsibilities

14 Root Cause Analysis and CAPA, Technical Problem Solving For: Analytical Quality Formulation Course Purpose: Determination of root cause and corrective actions. (2 days) Detailed Course Outline Section I: Introduction to root cause analysis Need for improvement Savings associated with root cause analysis Eight+ basic quality tools Section II: Define and contain the problem Define the problem Contain the problem Determine scope, objectives and goals Project leadership and planning Section III: Measure the problem Map the process Determine data collection plan Establish metrics and capability Section IV: Analyze data and determine root cause Analyze and summarize the data Analyze and summarize the process map Determine root causes and summarize all findings Section V: Improve performance Brainstorming solutions and CAPA Benefit, cost, risk and complexity determination Measuring solution effectiveness Section VI: Control and standardize improvements owner Select controls

15 Nonlinear Modeling (Dose Response, Relative Potency) For: Analytical Course Purpose: All nonlinear responses, potency and dose response determination. (1 day) Detailed Course Outline Section I: Basics of Linear Modeling Basics of Linear Modeling Linear Regression Sniper Plots Fit Special Section II: Nonlinear Models Nonlinear platform Model selection Model evaluation Adding X factors Section III: Bio Assay and Relative Potency EC50 Potency and Relative Potency Parallelism Test options Section IV: Nonlinear DOE

16 Statistical Methods for Validation For: Quality Formulation Course Purpose: Statistical basis for lot variation analysis, PPQ study design and analysis. (1 day) Detailed Course Outline Section I: Qualification and Validation Introduction Validation and Drug Quality General Approach to Validation Statutory and Regulatory Requirements for Validation Validation Recommendations Section II: Stage 1: Design Building and Capturing Knowledge and Understanding Establishing a Strategy for Control Section III: Stage 2: Qualification Design of a Facility and Qualification of Utilities and Equipment Performance Qualification PAT during Qualification PPQ Protocol PPQ Protocol Execution and Report Section IV: Stage 3: Continued Verification Establishing a Monitoring Program Data Analysis Trending and ongoing Capability Monitoring Deviations/Investigations and CAPA Change Control Complaints CPV Data Review and Reporting Section V: Analytical Tools for Validation DOE design space DOE, CPP and PAR analysis POV and Sample Size during PV Capability and Design Margin Control Charts during Validation ANOVA and ANOM Equivalence Testing

17 Certified QbD Practitioner Participants may be awarded the Certified QbD Practitioner certification. Participants must complete all coursework and demonstrate application to formulation, analytical methods, processes and or process controls.

18 Curriculum Deployment Strategy QbD Curriculum Session 1 1 Introduction to QbD and Critical Quality Attributes 3 Statistical Methods and Data Analysis 1 Application Consulting Session 2 2 Quality Risk Management and Risk Assessment 2 Design of Experiments 1 Application Consulting Session 3 2 Analytical Method Development and Validation 2 Robust Optimization, Design Space and Tolerance Design 1 Application Consulting Session 4 1 Mixture DOE 1 Stability Analysis 2 Control Design using SPC/PAT 1 Application Consulting Session 5 2 Root Cause Analysis and CAPA 1 Nonlinear Modeling (Dose Response, Relative Potency) 1 Statistical Methods for Validation 1 Application Consulting Curriculum is divided into five (4) day training sessions. It is recommended that each 4 day training session be followed by one day of direct application where the instructor works with individual teams to accomplish their relevant work and apply the instruction received, making a total of 5 days for the deployment of one session. Each one week session should be followed by 6-8 weeks to apply and absorb the training received. Using this method one group of participants would cycle through the curriculum in a year.

19 Deployment Annual Plan QbD Curriculum 2018 Annual Plan QbD Curriculum Year Quarter Month Week Day 2015 Q Q Q Q Q1 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Session 1 1 Introduction to QbD and Critical Quality Attributes Statistical Methods and Data Analysis 1 Application Consulting Session 2 2 Quality Risk Management and Risk Assessment 2 Design of Experiments Application Consulting Session 3 2 Analytical Method Development and Validation Robust Optimization, Design Space and Tolerance Design Application Consulting Session 4 1 Mixture DOE Stability Analysis 2 Control Design using SPC/PAT 1 Application Consulting Session 5 2 Root Cause Analysis and CAPA 1 Nonlinear Modeling (Dose Response, Relative Potency)? 1 Statistical Methods for Validation Application Consulting Using the curriculum, determine how many people you want to train, which location, and then dates are coordinated and secured. Maximum Class size is 20 persons. Additional persons up to 30 may be added at an additional per person cost.

20 Benefits of Full QbD Implementation Organizational alignment to QbD/ICH guidance Cost savings and latitude in manufacturing to adjust controls within design space First time quality one and done, avoids excessive rework and redo s Fewer and less critical audit findings Ease of Tech Transfer Successful submissions Reliably achieved development timelines Alignment to market opportunities Achieve time to revenue forecast Timely phase exit Effective and safe treatment of indication

21 Now is the Time to Act, Next Steps 1. Set the goal. Make 2018 the year QbD moves from an idea to a core competency 2. Organizational alignment, socialization and communication of goals 3. Build an annual training and consulting plan, request quotation 4. Schedule training and consulting 5. Make sure SAS/JMP software is available prior to training 6. Align to phase development and phase review/exit process for deliverables 7. Measure/monitor progress and success 8. Achieve the goal of agency alignment and effective product development and submission

22 Fast Track Means Working With FDA to Minimize Risk and Time to BLA/Market

23 BioAssay Development and Validation Directly Support BioAssay Characterization, Qualification, Validation and Control Robustness Specificity Linearity Precision (Repeatability, Intermediate Precision, Reproducibility) Suitability and Validity Range Intermediate precision SAS/JMP Script Data Automation Report Generation Systems Suitability and Validity Criteria Control plan Binding Assays Cell Based Assays Animal Asssays Immunoassays and Cut Points BioAssay SCIENCES A Division of Thomas A. Little Consulting

24 References Web site for QbD consulting, ICH Q8, ICH Q9, ICH Q10, ICH Q2, Guideline.pdf FDA EMA QbD, Web site for Thomas A. Little Consulting, Web site for SAS/JMP Wildflower Lane Highland UT, USA BioAssay SCIENCES A Division of Thomas A. Little Consulting