- Medical Writer at DynaMedex/EBSCO and Biomedical Researcher
Model-informed drug development (MIDD) is transforming how therapies are discovered, evaluated, and brought to patients. UJ’s Graduate Certificate in Model-Informed Drug Development prepares professionals to apply quantitative modeling, simulation, and data-driven decision-making throughout the drug development process.
Through interdisciplinary coursework in pharmacology, computational modeling, clinical trial simulation, and regulatory science, you’ll gain the knowledge and practical skills to support more efficient, evidence-based drug development.
12 Credit Hours | 100% Online | Complete in as little as a year
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Modern drug development increasingly relies on computational modeling and simulation to improve decision-making, optimize clinical trials, and accelerate regulatory review. Organizations across the pharmaceutical, biotechnology, and regulatory sectors are seeking professionals who understand how quantitative approaches can improve the efficiency and success of drug development.
This graduate certificate provides a focused foundation in pharmacometrics, clinical trial modeling, and regulatory strategy while exploring emerging technologies such as artificial intelligence, digital biomarkers, and multi-modal data integration.
Whether you are expanding your expertise or preparing for advanced roles in clinical development, this certificate equips you with practical skills that translate directly to today’s evolving pharmaceutical landscape.
Throughout the program, you will learn to:
Graduates will be prepared to strengthen their expertise in areas including:
Introduces core principles of model-informed drug development (MIDD), including pharmacokinetics (PK), pharmacodynamics (PD), modeling paradigms, translational science, and regulatory frameworks that guide quantitative decision-making in drug development.
Learning Objectives:
Develops computational skills required for quantitative pharmacology, including modeling workflows, simulation techniques, and statistical analysis used in pharmacometrics.
Learning Objectives:
Explores the role of modeling in clinical trial design, adaptive studies, precision medicine, and regulatory strategy, emphasizing evidence-based decision frameworks.
Learning Objectives:
Focuses on regulatory science, AI applications, digital biomarkers, and emerging technologies shaping the future of MIDD.
Learning Objectives:
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