Confidence Beyond Compliance

PharmaLex provides a full range of training either for scientists or for applied statisticians. Most of our training courses are delivered on site and can be adapted depending on the very needs of the customers. Contact us so we can help you build the table of content you are looking for.

Our services include:

Qualification and validation of bioassays (2 days)

Extensive training on validation, including regulatory requirements, introduction in statistics, computational details and all-in-one designs of experiments to maximize chance of successful validation and improve analytical knowledge as far as possible.

Transfer of analytical method and bioassay with applications (2 days)

Objective of Validation and Objective of Transfer, Regulatory expectations and guidelines in Transfer, Analytical method transfer steps, Evaluation of the method transfer, The statistics to perform: Descriptive, equivalence, Total error, Assay performance vs Results, Mean results vs Individual results, Pros and cons, Defining Acceptance limits: performances vs results, Experimental design required for a successful transfer, Examples

Stability (2 days)

Why study stability, An introduction to kinetics, The effects of temperature, Basic statistical principles, Regression analysis, Guidance documents, Multiple lots, Performing an ICH analysis, Fundamentals of specifications, Clinical support of specifications, Release models, Accelerated and stressed studies, Comparison after process change, Ongoing stability studies, Maintaining specifications post licensure, Evaluating Product after a Temperature Excursion

LifeCycle Process Validation (2 days)

  • Introduction
  • What is Lifecycle Process Validation
  • Stage 1 Process Design
    • QbD
    • Criticality Analysis
    • DOE, Historical Data
    • Stages of Experimentation
  • Stage 2 PPQ
    • Sampling Uncertainty
    • Evaluating PPQ Results
    • Designing a Sampling Plan
    • Number of Batches
    • Intra and Inter batch Sampling
    • Development of Stage 3A Plan
  • Stage 3 CPV
    • Control charts and “the State of Control”
    • Capability
    • Risk based Approach to CPV
    • Baseline Performance and Transition to Stage 3B

Bayesian statistics (2-4 days, for statisticians)

  • Present the main Bayesian concepts and some theoretical results
  • Illustrate Bayesian methodology applied to a wide range of pharmaceutical projects (non-clinical and clinical)
  • Show how to move from a « p-value »-based decision making process to a prediction-based one
  • Show the standard Bayesian softwares (Convergence checks, Improve convergence, Model selection: DIC, Missing/Censored/BLQ values, Reparametrization)

From Target ID to Phase 1 study

  • A typical drug development
  • Growing role of Model-Based Drug Development
  • Introduction to PKPD modeling
  • Why Bayesian statistics
  • Regulatory documents
  • Phase 0 or microdosing studies
  • SAD and MAD studies
  • ADME concepts
  • Hepatic and renal impairment studies
  • Comparative bioavailability
  • Food effect, QT studies

Introduction to Adaptive Design (4 hours)

  • Difference with fixed design
  • Decision making vs p-values and alpha-burning
  • Types of adaptive designs
  • Classical adaptive design (play-the-winner, …)
  • Advanced adaptive designs
  • Challenges and opportunities
  • When adaptive or not
  • Strategies to conduct simulations

Introduction to ODE (Ordinary Differential Equations) modeling (4 hours)

  • Introduction to differential equations
  • Some examples
  • Solving ODE
  • Difference with Partial derivative equations
  • Solving ODE with SAS
  • Introduction to PK compartmental models
  • Introduction to PK/PD compartmental models
  • Direct and indirect effects
  • Using PROC MODEL
  • Using PROC FSMP
  • Using PROC MCMC
  • Bayesian ODE models

Intro to basic statistics using JMP (1 day)

  • Import data within JMP
  • Recognize different types of statistical variables
  • Perform basic data visualization and analysis using JMP
  • Perform simple statistical tests
  • Edit lines/rows/observations of a data table
  • Edit columns/variables of data a data table
  • Manipulate data to obtain proper format for further analysis
  • Have an overview of the capabilities of JMP
  • Refresh basics on data observations and statistics

Linear models, response surface models using JMP (1 day)

  • Fit linear models
  • Make diagnostic plots
  • Make calibration
  • Perform multiple linear regression models
  • Feel the value of DoE
  • Understand what it means
  • Estimate variance components

Design of experiments (also using JMP) (1 day)

  • Understand Doe concepts
  • Plan an experiment
  • Organize your work and anticipate
  • Find the appropriate design
  • Use the JMP catalog of DoE

Statistical Process Control (SPC) using JMP (1 day)

  • Aim of SPC
  • Role of control charts
  • Different types of control charts
  • Capability analysis

Validation of analytical method / Validation of bioassays (1day)

  • Training is adapted for either analytical method or bio-analytical method
  • Review of ICH Q2 criteria
  • Computation of ICH Q2 criteria in JMP
  • Total error approach
  • Implementation of the total error approach in JMP

HPLC optimization with a Quality by Design approach, using JMP (1 day)

  • Analytical Quality by Design (aQbD)
  • Design Space
  • Important chromatographic criteria and why not to model them
  • Apply DoE methodology
  • Model retention times (Begin, Apex, End) using JMP
  • Use Prediction Profiler and Simulator of JMP
  • Assess Design Space using Gaussian process.

JMP – Clinical (2 days)

  • Main Window
  • Studies Folder
  • How to import a study
  • Profile Subject
  • AE Narratives
  • Reviews Folder
  • How to generate a review
  • List of Review
  • Medical Monitoring
    • Demographic Distribution – Adverse Event Distribution
    • How to create a review template and share it
    • Adverse Events Narrative
    • Findings Domains: Finding Trends, Findings Time to event, Findings Distributions
  • AE Domains: AE Bayesian Hierarchical Model, AE incidence screen
  • Patient Recruitment
  • Data Integrity and Risk Based Monitoring
    • What’s the RBM
    • Study Risk Data set and Risk Threshold Data set
    • RBM with JMP Clinical v6.0
  • Data Integrity (Birthdays and Initials, Duplicate Records report, Digit Preference Report)
  • Keep the history of the data’s update
  • JMP and JMP Clinical

Introduction to Statistical process control in JMP (4h)

  • In-control, out of control
  • Charts concepts
  • Different types of charts for different objectives
  • From Shewart to multivariate charts
  • Xbar, IR, EWMA, CUSUM, ….
  • How to define control limits

Introduction to Qualification and validation of bioassays in JMP (4h)

  • Training is adapted for either analytical method or bio-analytical method
  • Review of ICH Q2 criteria
  • Computation of ICH Q2 criteria in JMP
  • Total error approach
  • Implementation of the total error approach in JMP

Gage R&R studies in JMP (4h)

  • Some theory
  • How to evaluate a measurement system?
  • What is a Gage R&R study?
  • How to design/analyse Gage R&R studies?
  • Gage R&R using JMP
  • Exercises
  • Advanced examples

Method comparison JMP (4h)

  • Why comparing two measurement methods
  • Present the basic approaches
  • Error-in-variables models approach
  • Bland-Altman approach
  • Discuss their qualities and limitations and propose improvements
  • Illustrate them on a case study and on simulations

Programming in R (2 days)

  • Installing R-Studio, installing packages
  • Introduction to basic programing elements of R
  • Reading/writing data files
  • Defining and handling vectors/matrices/data frames
  • Defining, calling functions and retrieving results of functions
  • Making graphics with R
  • Performing simulations in R
  • Searching/selecting/sorting objects/vectors/matrix
  • basic statistics
  • linear models, including mixed effect models
  • non-linear models
  • easy Bayesian models (MCMCpack family)
  • Introduction to JAGS
  • Introduction to STAN
  • Defining priors, using posteriors, generating predictive distribution

JMP – JSL development (1 day)

  • Automate regularly scheduled analysis in production settings
  • Save traceability of analysis
  • JSL is used to perform many actions:
  • Implements columns formulas
  • Launches and modifies platforms
  • Creates graphics
  • Shares results