Foundations of Pharmacometrics (Theory Track)

A conceptual workflow for thinking in pharmacometrics: observe → explain → evaluate → decide

A theory-first introduction to pharmacometrics: build the mental models behind PK, variability, estimation, evaluation, and decision-making before touching code.

Welcome

Foundations of Pharmacometrics (Theory Track) is a concept-first course for clinical pharmacologists, pharmacometricians, quantitative scientists, and learners entering the field who want to understand how pharmacometrics works before implementing anything in software.

The goal is not just to memorize terms like clearance, volume, or AUC. It is to build the underlying mental model of the field:

  • what pharmacometric data represents
  • how models simplify biology
  • why variability matters
  • how models are estimated and evaluated
  • how quantitative reasoning supports real decisions
  • where different modeling approaches fit across the PMx landscape

Get Access

Enroll — Foundations of Pharmacometrics Try Free Previews First

Free introductory modules are available with a free AEAcademy Member account — no credit card required.

Who this course is for

  • Analysts and scientists who want a clear conceptual foundation before learning PMx tools
  • Learners who understand some PK terms but want to see how the pieces fit together
  • Quantitative scientists transitioning into pharmacometrics from adjacent disciplines
  • Anyone who has asked: “I can run an analysis, but do I really understand what it means?”
  • Teams who want a common language for discussing PK, models, variability, estimation, evaluation, and decisions

Learning Objectives

By the end of this course, you will be able to:

  • Explain what pharmacometrics is and what kinds of problems it helps solve
  • Describe the data → model → decision pipeline that structures PMx thinking
  • Interpret PK data conceptually and understand what a profile is telling you
  • Explain the meaning of core PK parameters such as CL, V, and half-life
  • Understand what NCA can and cannot tell you
  • Recognize the role of variability, covariates, and hierarchical thinking in population models
  • Explain how population models are estimated and why diagnostics and model evaluation matter
  • Understand how PMx supports dose selection, trial design, simulation, and Model-Informed Drug Development
  • Navigate the broader PMx landscape, including where PD, time-to-event models, disease progression, PBPK, TMDD, QSP, and Bayesian methods fit

Course Structure

This course is organized as a conceptual learning path you can reuse whenever you encounter a new PMx problem:

  1. Orient → define what pharmacometrics is, what questions it answers, and where it fits in development
  2. Interpret → understand PK data, profile shapes, compartments, parameter meaning, and exposure summaries
  3. Reason → think about variability, assumptions, model limitations, estimation, and evaluation
  4. Decide → connect models to exposure–response, simulation, Model-Informed Drug Development, and the broader modeling landscape

After the final lesson, you’ll reach a Course Completion page where you can confirm completion and generate your certificate.

Lessons and Modules

This course is organized into the following modules:

  1. What is Pharmacometrics?
  2. Understanding PK Data
  3. The Shape of PK Profiles
  4. Compartments and Simplification
  5. Parameters as Meaning
  6. Noncompartmental Analysis (Theory)
  7. Variability and Populations
  8. Population Modeling and Estimation
  9. Diagnostics and Model Evaluation
  10. From Models to Decisions
  11. The Pharmacometrics Landscape
  12. Where to Go Next

How This Course Fits Into Your Learning Path

A recommended sequence is:

  1. Foundations of Pharmacometrics (Theory Track)
  2. Computational Foundations for Pharmacometrics (R Track)
  3. Noncompartmental Analysis (R Track)
  4. Future advanced tracks in population modeling, PK/PD, PBPK, Bayesian methods, and related PMx topics
Tip

This course is intentionally theory-first.
You do not need prior programming experience, and you do not need to know R before starting.

What You’ll Be Ready For After This Course

After this course, you should be able to:

  • read PMx material with much stronger intuition
  • understand why a model is being used, not just what it outputs
  • distinguish between descriptive summaries, structural models, population models, and decision-support models
  • interpret estimation, evaluation, and uncertainty at a conceptual level
  • transition into implementation courses with a clearer sense of purpose and meaning

This course gives you the conceptual backbone that makes the computational courses easier and more useful.