MSc Model-based Drug Development / Course details
Year of entry: 2021
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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 mathematical 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 application of modelling and simulation skills to real pharmacokinetic and pharmacodynamic 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, 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.
Distance learning option
Our distance learning option is ideal for scientists linked to the pharmaceutical industry who wish to expand their expertise while working in the industry.
The full-time mode allows suitably trained mathematics, science or engineering graduates to focus on obtaining the advanced skills needed for a career in this area. We utilise a blended learning approach in which online learning content is supported by regular face-to-face contact with tutors.
Your learning will be reinforced over the duration of the course via application of acquired skills to real pharmacokinetic and pharmacodynamic data using mathematical modelling and simulation techniques.
Additional course information
The course focuses on the following topics.
- Pharmacokinetics: addressing how a drug dose is administered to the body and the fate of drug molecules that enter the body.
- Pharmacodynamics: addressing the chemical and physiological response of the body to drug.
- Pharmacometrics: the science that quantifies drug, disease and trial information to aid efficient drug development and/or regulatory decisions (definition used by the US FDA).
- Systems pharmacology: analysis of interactions between drug and a biological system, using mathematical models.
- In vitro: in vivo extrapolation using physiologically based pharmacokinetic models (IVIVE-PBPK).
Teaching and learning
The course emphasises the development of problem-solving skills. A large portion of the learning involves structured problems requiring 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:
- taught lectures;
- computational modelling and simulation workshops;
- self-directed learning to solve given problems;
- webinars and tutorials by leading scientists in industry/academia;
- supervised research;
- mentorship in solving problems and writing the research dissertation;
- independent study.
Coursework and assessment
We assess your achievement of the learning outcomes for this course through:
- unit assignments (submitted electronically);
- unit examinations;
- research project dissertation and oral presentation.
Course unit details
Year 1 (full-time)
Term 1 (September to January)
- Introduction to clinical trials (15 credits). This online unit provides an overview of the drug development process followed by in-depth coverage of scientific, statistical and regulatory considerations in the design and interpretation of clinical trials.
- Basic Pharmacokinetics and Pharmacodynamics (15 credits). This unit provides information on how drugs get into the body, how they get around the body and how they are removed from the body
- In Vitro - In Vivo Extrapolation in ADME (15 credits). The applications of modelling and simulation have been limited to the so-called phase I-III of drug development. This unit provides information on modelling and simulation as applied to extrapolating data from in vitro systems to the in vivo conditions (IVIVE) prior to phase I.
- Biostatistics in Clinical Trials (15 credits). This unit is designed to give the student an understanding of fundamental statistical concepts that are routinely used in designing clinical trials or interpreting trial results.
Term 2 (February to May)
- Data Analysis in PKPD (30 credits). This addresses the specialised area of modelling that combines mathematical and statistical knowledge in data fitting with the sound knowledge of the human body as a system. The unit emphasises the value and implementation of mechanistic models. You will be introduced to different types of modelling packages and learn how to choose which package is best for a given application.
- Advanced topics in modelling and simulation (15 credits). This unit focuses on the elements that make up a successful modelling and simulation project. We consider the scientific and commercial rationale for undertaking a modelling project, and how these rationales then guide the project design. After reviewing case studies which illustrate the characteristics of a successful project, you will create a research proposal on a topic of current importance in the general area of modelling, simulation and data analysis. The primary aim should be to identify a gap in knowledge and investigate this area by proposing a detailed research project.
- Advanced topics in Physiologically-based PK models (15 credits). This unit provides further training on the use of mechanistic pharmacokinetic models, which use mathematical descriptions of physiological processes to predict the fate of drug molecules within the human body. The unit addresses two key aspects of IVIVE: 1) developing mathematical representation of key chemical and physiological processes that affect drug molecules in the body, and 2) defining the relationships that link these processes.
Term 3 (May to September)
- Supervised research project/dissertation (60 credits). This unit follows on directly from the research proposal in modelling and simulation (PHAR69924).
Course content for part-time mode
If you are planning to complete the course in 29 months, the content will be delivered in the following sequence:
- Basic Pharmacokinetics and Pharmacodynamics (PKPD)
- Biostatistics in Clinical Trials
- Data Analysis in PKPD
September - June
- Introduction to Clinical trials
- In Vitro - In Vivo Extrapolation in ADME
- Advanced topics in modelling
- Advanced topics in Physiologically-based PK models
June - January
Supervised research project/dissertation
Course unit list
The course unit details given below are subject to change, and are the latest example of the curriculum available on this course of study.
|Supervised Research Project on M&S - Dissertation||PHAR69920||60||Mandatory|
|Basic Pharmacokinetics and Pharmacodynamics||PHAR69921||15||Mandatory|
|PBPK and In Vitro In Vivo Extrapolation (IVIVE) (1) Fundamental Concepts||PHAR69922||15||Mandatory|
|Data Analysis in Pharmacokinetics and Pharmacodynamics||PHAR69923||30||Mandatory|
|Advanced Research Topics and Project Development in Modelling and Simulation||PHAR69924||15||Mandatory|
|Biostatistical concepts in clinical trials||PHAR69931||15||Mandatory|
|PBPK and IVIVE 2 Advanced Concepts and Applications||PHAR69932||15||Mandatory|
|Introduction to Clinical Trials (CT1)||PHAR72010||15||Mandatory|
What our students say
'The course has been an incredibly stimulating and rewarding experience for me. The content has increased and broadened, and in some cases refreshed, my knowledge of the principles and the modelling element. The course provided the hands-on experience I was looking for and fulfilled all my expectations. Balancing the course alongside family and work was certainly a challenge but rewarding, nonetheless. From my perspective the content remains highly relevant to industry and beneficial for those with a variety of experience, either perfect as a career platform or for a change of direction into modelling.'
Simon Taylor, Pharmaron, UK (Part-time student, Sept 2017 -Sept 2019)
Full-time students in residence will have access to general university facilities, including language and library support, computing, and sports facilities.
In addition to regular face-to-face contact with course tutors, residential students are encouraged to take advantage of the academic and cultural environment at Manchester. You will be able to access a range of facilities throughout the University.
Distance learning students interact with staff and students and are supported via an online virtual learning environment that includes webinars, discussion forums, and access to recorded lectures and other learning resources.
You will have access to the most commonly used data analysis platforms such as R, Phoenix, NONMEM, MATLAB, Simcyp and MONOLIX.
Practical support and advice for current students and applicants is available from the Disability Advisory and Support Service .