Our MSc Model-based Drug Development course provides the knowledge and skills for making evidence-based decisions at various stages of drug development.
It covers the scientific and regulatory aspects of evaluating a drug, with emphasis on the use of modelling and simulation methods. You will learn why these methods are so highly valued by industry and regulatory authorities as effective, cost-saving, decision-making tools. Learning is reinforced via hands-on application of the skills to real data.
The course has been developed with an emphasis on mechanistic approaches to assessing and predicting pharmacokinetics and pharmacodynamics (PKPD), such as physiologically-based pharmacokinetics (PBPK).
As this comes under the general umbrella of systems biology, you will be able to apply your knowledge of modelling and simulation in various areas of research within the pharmaceutical industry.
Full-time students benefit from immersion in the varied biomedical research environment at The University of Manchester, including interaction with research staff at the renowned Centre for Applied Pharmacokinetic Research.
Alternatively, part-time students already working in the pharmaceutical industry can take advantage of the flexible, distance learning mode of the course, which allows you to fit study around other commitments.
The aim of the course is to provide specialist knowledge and skills that are highly relevant for a career linked to drug development and pharmaceutical industry.
It is designed for science, engineering or mathematics graduates who want to acquire:
-Awareness of the commercial and regulatory factors in drug development
-Understanding of the physiological, chemical, and mathematical foundations used to define the safe and effective use of potential medicines
-Training in the use of mathematical modelling and simulation methods to guide drug development
The course aims to:
-Provide background information on the theory and methods for quantitative assessment of drug absorption, distribution and elimination
-Provide an understanding of the role of pharmacometrics in the process of drug development
-Provide background information on in vitro assays used to characterise ADME properties of new drug entities
-Indicate the mathematical framework that is capable of integrating in vitro information with knowledge of the human body to predict pharmacokinetics
-Provide familiarity and experience of using different software platforms related to pharmacometric data analysis including R, Phoenix, NONMEM, MATLAB, Simcyp, WinBUGS and MONOLIX
-Equip you to reflect upon influential research publications in the field, to critically assess recent published literature in a specific area
-Provide awareness of the elements of a convincing research proposal based on modelling and simulation
-Provide the opportunity to undertake a project and carry out original research
Teaching and learning
The course emphasises the development of problem-solving skills. A large portion of the learning involves structured problems requiring the you to apply theory and practical skills to solve typical problems that arise in drug development.
The following teaching and learning methods are used throughout the course:
-Self-directed learning to solve given problems
-Webinars and tutorials by leading scientists in industry/academia
-Mentorship in solving problems and writing the research dissertation
This course was originally developed for scientists working within the pharmaceutical industry who wished to qualify as modellers with hands-on experience. The qualification will enhance your abilities within your current role or provide you with skills to progress into new posts.
The course is also appropriate for science and engineering graduates who wish to enter the industry. The role of modelling and simulation or pharmacometrics is assuming greater and greater importance in the pharmaceutical industry. Pharmaceutical companies and government regulatory agencies are recognising its value in making best use of laboratory and clinical data, guiding and expediting development, saving time and costs and a range of well paid jobs exist in this area across the globe. Scientific and industry publications often discuss the current shortage and growing need for modellers.