
Analytical Method Validation Course
Master every stage of analytical method validation, from regulatory foundations to lifecycle management. This course equips analytical chemists, QC scientists, and regulatory professionals with the technical depth and practical tools needed to design, execute, and document validation studies that satisfy global regulatory expectations.
What you will learn:
You will gain a full understanding of core validation parameters—diagnostic specificity, linearity, accuracy, precision, and detection limits—and the statistical methods used to evaluate them. You will learn to design robustness studies with Plackett‑Burman designs and to derive scientifically justified system suitability criteria. The course also covers bioanalytical validation, measurement uncertainty via the GUM framework, and data‑integrity requirements aligned with ALCOA+ principles. You will acquire practical skills for compiling regulatory‑ready validation reports and managing method changes through a structured lifecycle model. By course end, you will be ready to lead validation projects and confidently defend your data before regulatory reviewers.
How you study in practice Analytical Method Validation Course
How you practise Analytical Method Validation Course
For companies looking to train their teams
With Dedika for Businesses, the course includes exercises and examples tailored to your own business and the specific needs of your company.
Course content
8 Chapters • 39 LessonsDuration between 4 and 360 hours (you decide)
Chapter 1HideHide detailsSee detailsFoundations of Analytical Method Validation
Foundations of Analytical Method Validation
Lesson 1 • Purpose and Scope of Validation
Defines what validation proves and why it is mandatory. Connects regulatory expectations to laboratory practice as the conceptual anchor for the entire course.
Lesson 2 • Key Validation Parameters Overview
Introduces all core performance characteristics without deep calculation. Sets expectations for the parameter-by-parameter study covered in later chapters.
Lesson 3 • Types of Analytical Methods
Classifies methods by purpose and matrix to determine which validation parameters apply. Provides the decision logic used throughout subsequent chapters.
Lesson 4 • Validation Planning and Documentation
Covers the validation protocol structure and required pre-study documentation. Ensures students can draft a compliant plan before any laboratory work begins.
Lesson 5 • Regulatory Frameworks and Guidance Documents
Surveys internationally recognized guidance documents governing validation. Equips students to identify applicable requirements before designing a validation study.
Chapter 2HideHide detailsSee detailsDiagnostic Specificity and Selectivity Assessment
Diagnostic Specificity and Selectivity Assessment
Lesson 1 • Interference and Placebo Studies
Teaches construction of placebo and stressed samples to challenge method selectivity. Directly supports the experimental section of a specificity protocol.
Lesson 2 • Concepts of Diagnostic Specificity and Selectivity
Clarifies the distinction between specificity and selectivity as used in different guidance documents. Grounds subsequent experimental design in precise terminology.
Lesson 3 • Chromatographic Resolution and Peak Purity
Applies resolution calculations and diode-array or mass-spectrometric peak purity tools. Provides quantitative criteria for declaring a method specific.
Lesson 4 • Forced Degradation Studies
Covers stress-testing conditions used to generate degradation products for specificity evaluation. Links degradation chemistry to chromatographic resolution requirements.
Chapter 3HideHide detailsSee detailsLinearity, Range, and Calibration
Linearity, Range, and Calibration
Lesson 1 • Principles of Calibration Modeling
Introduces linear and nonlinear calibration models and their assumptions. Establishes the statistical foundation required for all quantitative validation parameters.
Lesson 2 • Nonlinear and Multi-Segment Calibration
Addresses quadratic, logistic, and segmented calibration for methods with inherent nonlinearity. Extends students' capability beyond simple linear models.
Lesson 3 • Defining and Justifying the Analytical Range
Links linearity results to the validated working range and its regulatory justification. Prepares students to document range decisions in a validation report.
Lesson 4 • Designing a Linearity Study
Specifies concentration levels, replication, and sample preparation for linearity experiments. Translates regulatory guidance into a practical experimental design.
Lesson 5 • Statistical Evaluation of Linearity
Applies regression statistics, residual analysis, and lack-of-fit tests to linearity data. Enables students to accept or reject a calibration model with statistical rigor.
Chapter 4HideHide detailsSee detailsAccuracy and Trueness Determination
Accuracy and Trueness Determination
Lesson 1 • Reference Material and Certified Standard Use
Covers selection and traceability requirements for certified reference materials. Ensures accuracy data are metrologically traceable to recognized standards.
Lesson 2 • Spiked Recovery Study Design
Specifies spike levels, replication, and matrix matching for recovery experiments. Provides a template directly applicable to pharmaceutical and environmental matrices.
Lesson 3 • Accuracy Concepts and Terminology
Distinguishes accuracy, trueness, and bias using metrological definitions. Prevents common terminology errors that cause regulatory deficiencies.
Lesson 4 • Method Comparison and Bias Studies
Uses Bland-Altman analysis and regression-based comparison to assess bias between methods. Supports validation by comparison when reference materials are unavailable.
Lesson 5 • Statistical Analysis of Accuracy Data
Applies t-tests, confidence intervals, and equivalence testing to recovery data. Enables objective pass/fail decisions against pre-set acceptance criteria.
Chapter 5HideHide detailsSee detailsPrecision: Repeatability, Intermediate, and Reproducibility
Precision: Repeatability, Intermediate, and Reproducibility
Lesson 1 • Precision Hierarchy and Definitions
Maps repeatability, intermediate precision, and reproducibility to their experimental conditions. Clarifies which precision level is required for each regulatory submission type.
Lesson 2 • Variance Component Analysis
Applies ANOVA-based variance component estimation to decompose total precision. Enables identification of dominant variation sources for process improvement.
Lesson 3 • Repeatability Study Design and Execution
Specifies sample preparation, number of replicates, and run conditions for repeatability. Provides a ready-to-use experimental template for the laboratory.
Lesson 4 • Intermediate Precision Study Design
Designs multi-factor studies varying analyst, day, instrument, and reagent lot. Teaches nested experimental designs that isolate each variance source.
Lesson 5 • Collaborative Studies and Reproducibility
Covers the design and statistical analysis of interlaboratory studies for reproducibility. Prepares students to participate in or organize proficiency testing programs.
Chapter 6HideHide detailsSee detailsDetection and Quantitation Limits
Detection and Quantitation Limits
Lesson 1 • Theoretical Basis for Detection Limits
Explains signal-to-noise, blank distribution, and decision theory underlying LOD. Provides the conceptual framework for choosing among calculation approaches.
Lesson 2 • Limit Reporting and Regulatory Compliance
Formats LOD and LOQ data for inclusion in regulatory submissions and method documents. Addresses common deficiencies cited by regulatory reviewers.
Lesson 3 • Calibration Curve-Based Limit Estimation
Derives LOD and LOQ from residual standard deviation and calibration slope. Connects limit estimation directly to the linearity study completed in Chapter 3.
Lesson 4 • Signal-to-Noise and Visual Approaches
Applies the 3:1 and 10:1 signal-to-noise criteria for LOD and LOQ estimation. Covers practical chromatographic noise measurement and documentation.
Lesson 5 • Experimental Verification of LOD and LOQ
Designs confirmation experiments at estimated limits using replicate injections. Ensures that calculated limits are achievable under routine laboratory conditions.
Chapter 7HideHide detailsSee detailsRobustness Testing and System Suitability
Robustness Testing and System Suitability
Lesson 1 • Documenting and Implementing Robustness Findings
Translates robustness results into method procedure controls and validation report sections. Prepares students to write defensible robustness conclusions.
Lesson 2 • System Suitability Test Design
Derives system suitability parameters and limits from validation and robustness data. Ensures that routine suitability tests are scientifically justified, not arbitrary.
Lesson 3 • Experimental Designs for Robustness
Applies Plackett-Burman and fractional factorial designs to screen multiple parameters efficiently. Teaches design construction and factor level selection.
Lesson 4 • Robustness Concepts and Risk Assessment
Defines robustness and links it to method risk assessment and control strategy. Establishes which parameters to challenge based on prior knowledge and risk ranking.
Lesson 5 • Statistical Analysis of Robustness Data
Calculates main effects and identifies critical factors from robustness experiment results. Enables evidence-based decisions about method operating ranges.
Chapter 8HideHide detailsSee detailsValidation Report, Lifecycle, and Revalidation
Validation Report, Lifecycle, and Revalidation
Lesson 1 • Continued Method Performance Verification
Designs ongoing monitoring programs using control charts and trend analysis. Ensures the method remains in a validated state throughout its operational life.
Lesson 2 • Change Control and Revalidation Strategy
Classifies method changes by risk level and determines the required revalidation scope. Provides a decision framework applicable to any change scenario.
Lesson 3 • Method Transfer and Cross-Site Validation
Covers comparative testing approaches for transferring validated methods between laboratories. Prepares students to design and evaluate method transfer studies.
Lesson 4 • Compiling the Validation Report
Structures all validation data into a regulatory-compliant report with conclusions. Covers each required section and the logic for presenting pass/fail decisions.
Lesson 5 • Method Lifecycle Management Principles
Applies the three-stage method lifecycle model from development through discontinuation. Connects validation to ongoing performance monitoring and control.
Your valid completion certificate
This course is for you:
QC chemist: ready to move beyond routine testing into formal validation work.
Analytical scientist: seeking structured knowledge to validate methods independently.
Regulatory affairs specialist: needing deeper technical grounding in validation science.
Recent chemistry graduate: entering a regulated industry and building foundational expertise.
Lab manager: responsible for validation oversight but lacking formal training in it.
Method transfer scientist: managing cross-site studies and needing a rigorous framework.
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