Program Eligibility
Designed for graduate-level learners and early-career professionals looking to build or strengthen their quantitative and pharmacometrics skill set.
Across life sciences, clinical research and drug development, data-driven modeling and simulation are increasingly essential. Pharmacometrics supports how organizations design studies, understand variability in treatment response and make evidence-based decisions throughout the research and development lifecycle.
This course is designed for:
- MSc and PhD students in pharmacology, pharmaceutical sciences, biomedical sciences, biotechnology, engineering, or health sciences
- Early-career scientists and professionals working in pharma, biotech, CROs, hospitals, or applied research organizations
- Clinical research staff, regulatory or translational teams seeking stronger modeling literacy
- Individuals interested in career pathways in pharmacometrics, clinical pharmacology, quantitative sciences, or drug development
Program Details
Enriching local talent through the development of fundamental biostatistics and pharmacometrics skills.
The program aims to strengthen the life sciences workforce by delivering a comprehensive, applied curriculum spanning three progressive areas:
- Foundational pharmacokinetics and biostatistics,
- Advanced pharmacometrics methods, and
- Modeling and simulation.
Through a combination of on-demand lectures, guided assignments and live instructor Q&A sessions, participants gain hands-on experience with real-world tools and approaches used across research and applied life sciences environments. The program bridges academic knowledge with practical application, equipping participants to better interpret data, collaborate with modeling teams, and apply quantitative thinking in research, clinical, and development settings.
Goals & Objectives
What will you gain?
This course will give you practical modeling and quantitative skills that are increasingly essential across life sciences, clinical research, and applied health science and that are actively used in real research and drug development settings.
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Expertise in PKPD Modeling
Build and evaluate pharmacokinetic-pharmacodynamic (PKPD) models, with a focus on population pharmacokinetics (PK) and physiologically-based pharmacokinetic (PBPK) modeling.
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R Programming Proficiency
Gain a strong foundation in the R statistical programming environment for data analysis and modeling.
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Experience with Advanced Software
Develop proficiency in modeling and simulation tools, particularly Monolix, to support robust data-driven decisions.
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Real-World Application
Apply your knowledge to a practical drug development project, bridging theory with hands-on experience. Build market-relevant skills to be ready for roles in pharmacometrics, clinical research, biostatistics, regulatory science and applied life science.
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Model-Informed Drug Development
Gain practice in model-informed drug development (MIDD), enhancing decision-making through modeling and simulation.
Course Structure
Modules
Module 1
General pharmacokinetics and basics biostatistics
Introduces pharmacokinetics and biostatistics basics, including drug metabolism, probability, hypothesis testing, and bioequivalence analysis.
● General pharmacokinetics
○ General Introduction to PK section
○ IV bolus injection
○ Metabolism, excretion, Distribution Part 1
○ Routes of Administration
● Introduction to biostatistics
● Exploratory data analysis
● Introduction to probability
● Sampling and probability distribution
● Standard error
● Estimation
● Hypothesis testing
● Wald confidence intervals and likelihood ratio tests
● Pharmacokinetics following oral administration
● Metabolism, Excretion and Distribution Part 2
● Biopharmaceuticals
● Preclinical and clinical research
● Non-compartmental analysis
● Bioequivalence study
● Analysis of variance (ANOVA)
Module 2
Advanced pharmacometrics and biostatistics
Covers advanced analytical methods, including compartmental analysis, maximum likelihood estimators, and Monte Carlo EM techniques.
● Compartmental analysis
● Maximum likelihood estimator (MLE)
● Expectation maximization (EM)
● Monte Carlo EM and Stochastic Approximation EM
Module 3
Modelling and Simulation
Focuses on model selection, PK/PD analysis, mixed-effect models, Bayesian inference, and iterative estimation for population pharmacokinetics.
Population pharmacokinetic analysis
● Model selection
● AIC and BIC
● PK/PD Direct and Indirect Models
● Mixed effect models
● Random effects, empirical bayes estimates and shrinkage
● Iterative two-stage estimation method
● Bayesian inference
Stay updated
This program is suited for graduate students in Pharmaceutical Sciences, Biological Sciences, Biostatistics, Applied Mathematics, Epidemiology, or related fields, as well as PharmD or practicing healthcare professionals with an interest in pharmacometrics and drug development. For inquiries or more information about the program, contact pk.fellowship@appliedpharma.ca.
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