MSc MODEL-BASED DRUG DEVELOPMENT
PROGRAMME HANDBOOK
2024-25
Division of Pharmacy and Optometry
School of Health Sciences
Faculty of Biology, Medicine and Health
Welcome to the Programme
MSc Model-based Drug Development
Welcome to the Model-based Drug Development postgraduate programme.
This student handbook provides details of the University of Manchester Programme leading to the MSc in Model-based Drug Development and the Diploma/Certificate (exit awards). Please read this handbook in conjunction with the School of Health Sciences Student Handbook, which contains general information about postgraduate study at this University and within the School. It is important for you to be aware of the general support and resources that the University provides. You also need to be aware of the University regulations (such as those related to “acceptable use of computers”, academic integrity, and policies on late work, sickness, and much more).
In this handbook you will find information about the programme aims and learning outcomes, structure, content, admissions, assessment and programme management.
Please make yourself familiar with the content and keep this handbook for future reference. We hope you will find it a useful resource as you progress through the programme. If anything remains unclear, please don’t hesitate to ask me or my colleagues for more information or advice.
Section A is a summary of how the course is structured.
Section B is a description of each of the course units.
The students enrolled in this course bring a great range of experience into the programme. Some of you may already have a PhD or years of experience in industry or research. Others will have recently completed an undergraduate degree in a relevant area. Some of you will have detailed knowledge of analytical biochemistry or pharmacology; others will be more knowledgeable about statistical methods or data science and mathematical models. Sharing that knowledge and experience with your tutors and other students in person and through the online discussion boards will significantly enhance the learning experience. Above all, we aim to provide a structure that allows you to develop and demonstrate your ability to provide intelligent guidance to the drug development process.
We have made every effort to provide you with the most up-to-date and accurate information. However, some minor details may change during the course of your studies. All changes and additions will be brought to your attention. If there is anything not answered within the handbook please do not hesitate to contact us.
We hope that your time here in Manchester will be enjoyable and successful.
General information about the Division of Pharmacy and Optometry, staff listings and research interests are contained in this handbook, but more information can be obtained from the following web sites:
- The Pharmacy home page: https://www.bmh.manchester.ac.uk/pharmacy/
- The Faculty of Biology, Medicine and Health home page: https://www.bmh.manchester.ac.uk/
- The University of Manchester home page: http://www.manchester.ac.uk/
Programme Director:
Kayode Ogungbenro, PhD
Division of Pharmacy and Optometry
Stopford Building, Room 3.123
Email: kayode.ogungbenro @manchester.ac.uk
School Administrative Teams:
Student Hub: shs.hub@manchester.ac.uk
Wellbeing: shs.wellbeing@manchester.ac.uk
Attendance monitoring: shs.attendance@manchester.ac.uk
Disability support: shs.dc@manchester.ac.uk
Mitigating Circumstances: shs.mitcircs@manchester.ac.uk
Programmes team: shs.programmes@manchester.ac.uk
Assessments team: shs.assessment@manchester.ac.uk
Section A: Programme Structure
Rationale and General Description
This blended learning Masters course was originally designed for scientists already working within the pharmaceutical industry who wish to qualify as modellers with hands-on experience. In response to ever-increasing demand for modellers in the industry, we have developed a full-time option suitable for recent graduates who wish to begin a career related to drug development.
Given the above, we expect two categories of student:
- Type one, the Part-Time, Distance-Learning student. You are currently working for a pharmaceutical company, a regulatory authority, a contract research organisation or another research body. Your employer is supportive of your participation in the course and recognises its value for your further training and development. Ideally you will be able to identify a research project with your employer when time comes to begin your research dissertation. The distance learning aspect of the course means that you can fit your study around other commitments. However, you should also recognise that balancing your employment and academic workloads will require good self-discipline and time-management skills.
- Type two, the Full-time student in attendance at Manchester. You have completed an undergraduate degree in a scientific, mathematics or engineering discipline, and you are sure that you want to follow a career involving drug development. Ideally, you have some research or work experience that allows you to be confident that this is the right choice for you. As a full-time student in residence at Manchester, you will benefit from the resources and academic environment here. Your research dissertation will likely be carried out at the University but may involve placement with a company.
In brief, what you can expect to get out of the course:
- Familiarity with the language and common scientific/technical principles fundamental to the profession. This includes pharmacokinetic concepts (e.g., bioavailability, volume of distribution, clearance), physiological/biochemical mechanistic concepts (e.g. phase 1 metabolism, types of inhibition, Kd), statistical/mathematical concepts (e.g. covariates, goodness of fit) and clinical/legal concepts (e.g., types of trials, regulatory authorities, endpoints and biomarkers).
- Awareness of current research and state of the art of modelling and simulation for drug development, including a sense of where the technology is headed.
- Exposure to common software packages used in the industry, including hands-on experience. The extent of your experience will depend on your eventual choice of dissertation project. Note: If your main aim is to get intensive training in a particular package, such as NONMEM, you might be better advised to take one of the specialist short-courses offered by the software publishers.
Aims of the MSc Programme
The overall aim of the programme is to provide specialist knowledge and skills that are highly relevant for a career linked to drug development and pharmaceutical industry. The programme is designed for science, engineering or mathematics graduates who want to acquire 1) awareness of the commercial and regulatory factors in drug development, and 2) understanding of the physiological, chemical, and mathematical foundations used to define the safe and effective use of potential medicines.
The programme focuses on these key concepts:
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 and aiming to understand the behaviour of the system as a whole”
Modelling and simulation, use mathematical models to guide drug development and learn how models are improved and validated.
Specific aims of the MSc programme:
| 01. | Provide background information on the theory and methods for quantitative assessment of drug absorption, distribution and elimination (ADME) in the human body. |
| 02. | Provide an understanding of the role of pharmacometrics in the process of drug development, including scientific, regulatory and commercial perspectives. |
| 03. | Provide background information on in vitro assays used to characterise ADME properties of new drug entities. |
| 04. | Indicate the mathematical framework (physiologically-based pharmacokinetics) that is capable of integrating in vitro information with knowledge of the human body to predict pharmacokinetics. |
| 05. | Provide familiarity and experience of using different software platforms related to pharmacometric data analysis including R, Phoenix, NONMEM, MATLAB, Simcyp, and MONOLIX. |
| 06. | Equip students to reflect upon influential research publications in the field, to critically assess recent published literature in a specific area. |
| 07 | Provide awareness of the elements of a convincing research proposal based on modelling and simulation, and provide practise in constructing a proposal for a novel modelling project that would add value in a specific area. |
| 08. | Provide students an opportunity to undertake a project and carry out original research in the field of applied pharmacokinetics, pharmacodynamics and modelling and simulation. |
Programme Learning Outcomes
Students will be able to:
- Display knowledge of historical development and rationale for clinical trials; drug development process; basic requirements for conducting clinical trials; ethical and regulatory aspects of the proper conduct of clinical trials
- Describe the detailed mechanisms involved in drug absorption, distribution, metabolism and excretion (ADME).
- Explain the crucial roles of pharmacometric methods in developing and gaining regulatory approval for a medicine.
- Use the biostatistical concepts and language employed in the design and interpretation of clinical data.
- Describe the details of in vitro systems used to characterise drug absorption, distribution, metabolism and excretion.
- Describe the relationships (scaling) between data obtained from in vitro systems and in vivo observations on ADME.
- Give examples of how modelling and simulation methods are currently transforming the processes of drug development and regulation.
- Evaluate the capability of different tools to perform relevant data analysis.
- Critically analyse the observations on plasma drug concentration-time profiles and characterise them quantitatively for the purpose of making inferences between different drugs, different patients, different conditions etc.
- able to design appropriate studies in delineating effects of co-variates on pharmacokinetics (including drug-drug interactions)
- Gain scholarly knowledge in a focused area of pharmacometrics, modelling and simulation.
- Deliver effective oral presentation of research findings and research proposals.
- Critically assess reports in the literature describing use of modelling and simulation to guide drug development. Identify examples of important innovations as well as examples where inconsistencies exist in the methods of analysis or representation of the outcome.
- Design and outline the details of appropriate projects to investigate including the articulation of research aims and methodology.
- Identify the reasons for differences in the time-courses of drug effect and plasma drug concentration.
- Make informed predictions of the behaviour of drugs in body with respect to plasma drug concentration-time profile.
- Make informed choices on using various models to be used for fit data obtained from studies involving Enzyme Kinetics, Pharmacokinetics, Pharmacodynamics
- Demonstrate the effective use of professional writing skills required in preparing the reports relevant to the subject area
- Execute and complete a piece of research which is narrowly focused on specific area of pharmacometric modelling and simulation.
- Debate the outcome of research and argue for validity of the methods used in the study and the conclusions drawn from the results.
A copy of the programme specification is available on the MBDD Virtual Common Room on Blackboard.
Timetable
Please note: exam dates and deadlines for submitted work given below are approximate.
Please check Blackboard for the actual dates.
| Date (week of), | Full-time and Part-time, year 1 | Full-time and Part-time, year 2 |
| 23 September 2024, 6 weeks |
Unit: Basic PK/PD (PHAR69921) | Unit: Introduction to clinical trials (PHAR72010) |
| November | Due: Report & Oral presentation | Due: Clinical Trials Coursework |
| November,
6 weeks |
Unit: Biostatistical concepts (PHAR69931) | Unit: Physiologically-based PK &IVIVE, part 1 (basic concepts) (PHAR69922) |
| January 2025 | Exams: Basic PKPD | Due: PBPK1 report & presentation
Exams: PBPK1 |
| February 6 weeks |
Unit: Data analysis in PK, (PHAR69923) part 1 | Unit: Physiologically-based PK, part 2 (advanced concepts and applications) (PHAR69932) |
| March | Due: PBPK2 report & presentation | |
| March 6 weeks |
Unit Data analysis in PK, part 2 | Unit: Project development (PHAR69924) |
| May | Due: Data analysis project | Due: Project proposal report |
| May/June | Exam: Data analysis | |
| May/June | Exam: PBPK2 | |
| June
14 weeks |
PT only – summer break in year 1 | Project |
| September | FT only: Due – Oral presentation | |
| September | FT only: Due – Dissertation | |
| December 2025 | FT only: Graduation | |
| January 2026 | PT only: Due: Dissertation and oral presentation | |
| July 2026 | PT only: Graduation |
Programme Staff
The core teaching staff on the course are listed below:
Leon Aarons, BSc, MSc, PhD
|
Following a postdoctoral fellowship at the School of Chemistry, University of Leeds, Professor Aarons joined the staff at the University of Manchester in 1976.
His major research interests lie in the area of pharmacokinetics. He has a general interest in the subject as a whole but has a special interest in data analysis and interpretation. He collaborates with members of the pharmacokinetic group in Manchester and also with other scientists in industry and academia throughout Europe. His current activities revolve around population pharmacokinetics. People involved in the drug development process are becoming increasingly aware of the importance of studying the pharmacokinetic and pharmacodynamics of the drug in the population of interest as soon as possible.
He is a member on various committees promoting research including the United Kingdom Pharmacokinetic Discussion Group.
He is Editor Emeritus of the Journal of Pharmacokinetics and Pharmacodynamics and is a member of the editorial board of the Journal of Pharmacy and Pharmacology, the European Journal of Pharmaceutical Sciences and Pharmacology Research & Perspectives.
Kayode Ogungbenro, BPharm, PhD
|
Dr Kayode Ogungbenro is a Senior Lecturer in Cancer Pharmacometrics in the Division of Pharmacy and Optometry. He is also an Honorary Clinical Scientist at the Christie NHS Foundation Trust, Manchester.
Kayode obtained his PhD in 2005 from University of Manchester; he was appointed a Research Associate in 2005 at the Centre for Applied Pharmacokinetic Research (CAPKR), University of Manchester and a Research Fellow in 2009. He was subsequently appointed a Lecturer in 2014 and a Senior Lecturer in 2020. Kayode is a member of CAPKR where his research activity is currently on population pharmacokinetic and pharmacodynamic study design and data analysis, physiologically based pharmacokinetic modelling, optimal design of population pharmacokinetic and pharmacodynamic experiments, population pharmacokinetics in special population (children) and computer aided clinical trial simulation.
Aleksandra Galetin, PhD
|
Professor Galetin is a Professor of Translational Pharmacokinetics in the Centre for Applied Pharmacokinetic Research, School of Health Sciences, Division of Pharmacy & Optometry, University of Manchester, UK. Prof Galetin is the recipient of the 2012 ISSX European New Investigator Award and 2015 AAPS Meritorious Manuscript Award. She is appointed on the expert panels such as International Transporter Consortium (ITC) and AAPS Drug Transport Focus Group Steering Committee.
Prof Galetin has extensive experience in mechanistic in vitro characterisation of hepatic and renal transporters/metabolism, in vitro-in vivo extrapolation and development of physiologically-based pharmacokinetic (PBPK) models for prediction of transporter-mediated pharmacokinetics/drug-drug interactions in different patient population groups. She led the preparation of the ITC ‘white’ paper on the best practices in transporter in vitro kinetic studies and translational modelling. In 2016, Dr Galetin completed a sabbatical in the US FDA Office of Clinical Pharmacology where she provided expert advice on the PBPK modelling of drug-drug interactions /special populations. In addition to MPharm programme, she teaches drug disposition and pharmacokinetics on MRes in Experimental Cancer Medicine and MSc course for Independent Prescribers. She has published over 75 research papers in highly cited peer reviewed journals (H index 36) and supervised/mentored 30 PhD students and postdoctoral research associates.
Amin Rostami-Hochaghan, PharmD, PhD, FCP
|
Amin joined the University of Manchester in December 2009 as a Professor of Systems Pharmacology at the Centre for Applied Pharmacokinetic Research in the School of Pharmacy and Pharmaceutical Sciences at the University of Manchester.
Previously Amin held a chair in Systems Pharmacology at the University of Sheffield. He joined the University of Sheffield as Research Assistant to Professor Geoff Tucker in 1996 before progressing to Lecturer, Senior Lecturer, Reader and Full Professorship posts in 1997, 2002, 2005 and 2007, respectively.
As the Vice President of Research and Development at Simcyp Limited, Amin leads a team of over 30 scientists working on extrapolation of in vitro data on drug metabolism to predict in vivo pharmacokinetics and pharmacodynamics in “virtual patient populations”.
A decade ago, Professor Rostami founded the postgraduate course in Modelling and Simulation in Pharmacokinetics & Pharmacodynamics, which has become this current course in Model-based Drug Development.
Daniel Scotcher, PhD
|
Dr Scotcher is a Lecturer in Applied Pharmacokinetics in the Division of Pharmacy and Optometry. Dan received his PhD from University of Manchester in 2016; he then visited the US Food and Drug Administration as an ORISE research fellow, and returned to University of Manchester as a post-doc, and subsequently appointed as Lecturer in 2019.
Dan’s expertise is in quantitative translation of in vitro data to predict pharmacokinetic outcomes. He draws upon direct and collaborative research experiences with pharmaceutical industry and regulatory institutions, to understand real-world practice and problems that can be addressed using combination of in vitro and in silico research methodologies. Dan is based within the Centre for Applied Pharmacokinetic Research (CAPKR), a consortium of academic researchers at The University of Manchester and industrial pharmaceutical companies.
Dissertations
The dissertation involves working closely with your supervisor to develop and implement an empirical research project. Details of what is involved, and of how and when to submit the dissertation are provided in the Dissertation Handbook. The University’s Guidance for the presentation of Taught Dissertations can be found on the A-Z of Student Services. This guidance tends to change annually, so confirm the dissertation unit lead that you are using the correct version. It is useful to note that the Faculty no longer requires submission of a printed dissertation, although you may want to print and bind your own personal copy. Unlike PhD theses at the University, MSc dissertations are not routinely made “open access”. We often deal with projects involving confidential data; if that is the case with your project, you should discuss with your supervisor measures to protect confidentiality.
Supervisory Arrangements
Each student’s progress will be ultimately under the direction of the Programme Directors. At the beginning of the dissertation unit (PHAR69920) students will be provided with a list of available academic supervisors, their topic areas and potential projects. On the basis of this information, students will be encouraged to speak to individual members of staff to discuss project ideas. Students will be given a few weeks in order to select supervisors/projects/research areas of their choice. Please note: it is not always possible to allocate students to the academic supervisor of choice, but every attempt will be made to match students to a research area of their choice.
Supervisor
Each student will be supervised by an academic Supervisor within the programme and in the case of part-time students co-supervised by a work-based contact whenever possible. Supervision is governed by the University Manual of Academic Procedures, which outlines in more detail the responsibilities of the Supervisor and the Student. Briefly, the responsibilities of the Supervisor include: giving guidance about the nature of research and the standard expected; the planning of the research programme; and pointing the Student towards relevant literature and other sources of information.
The relationship between the Student and his/her Supervisor is of central importance. Both the Student and the Supervisor have a responsibility to ensure that the dissertation is completed within the prescribed period of the programme. Supervisors and students should establish at their initial meeting clear and explicit expectations of each other in order to minimise the risks and problems of misunderstanding, personality clashes, inadequate supervision and unsatisfactory work. Timetables for Progress Monitoring meetings must be closely observed.
Advisor
Each student will also be allocated an adviser. The role of the Advisor is not in any way meant to disturb the special relationship between Student and Supervisor. However, if a student feels the need to discuss matters, whether academic or otherwise, with another person, the Adviser will be available. Such discussions can be in the absence of the Supervisor outside of the framework of the formal meetings and confidential.
If you have any queries or concerns at any time during your period of study, there is a range of people you can approach:
- Your Student Representatives
- The Course Administrator
- Your Supervisor
- Your Adviser
- The Student Support Officer
- The Programme Director
- The Consortium PGT Lead (Dr Ellen Schafheutle)
- The Head of Division (Prof. Jayne Lawrence)
Course Assessments
Full details of modes of assessment for each Course Unit are provided in Section B.
The programmes contain a range of both formative and summative assessment tasks which have been designed to establish student’s knowledge and understanding of the stated learning outcomes for the course unit.
Formative assessments
- These are developmental assessments which assess your learning as you work through the unit and whenever possible form part of the preparatory work for, and link to the summative assessments.
- Formative assessments do not contribute towards the final mark but are an important form of your assessment in that feedback from these assessments will enable you to develop and improve before moving on to the summative assessment.
- Formative assessments are marked as a pass or fail, feedback will be offered to guide your learning.
- You must attempt all formative assessments within a course unit and if you do not pass you should discuss your learning needs with the course unit lead.
Summative assessments
- Each unit includes at least one summative assessment. These have been designed to assess your learning and the practice-based application of it.
- Each assessment task is allocated a percentage weighting towards the final mark.
- The minimum weighting of any individual summative assessment will be 10%.
Exemptions to the Postgraduate Taught Degree Regulations
Please be aware that the MSc in MBDD has some higher requirements to the University degree regulations and details of these are outlined below.
- The course unit pass mark for all levels (i.e. Postgraduate Certificate, Diploma and Masters) is 50%.
- The programme will not apply any compensation rules
Criteria of Levels of Achievement
To obtain a pass for the Postgraduate Certificate you are required to successfully pass 60 credits, Postgraduate Diploma – 120 credits, and Masters you are required to successfully pass 180 credits.
You will be eligible for the award of a distinction at Masters level only, provided you achieve an average mark of 70% or more, based on the weighted programme as a whole. If credit has been awarded as a result of referral, you will not be eligible for the award of distinction.
You will be eligible for the award of a merit at Masters level only, provided you achieve an average mark of 60% or more, based on the weighted programme as a whole. If credit has been awarded as a result of referral, you may still be eligible for the award of merit.
Reassessment
Reassessment as a result of a fail is known as a “Referral”. Reassessment as a result of approved and verified mitigating circumstances is known as “Deferral” and may be permitted where students are reassessed as a first attempt, for which no penalty applies.
Students may be referred in up to half of the total taught credits. The combined total number of credits referred and compensated cannot exceed half the taught credits. Decisions with regard to which components should be reassessed are made by the Examination Board. When a student is referred they will normally be permitted to retake the assessment/exam on one further occasion.
At the recommendation of the Board of Examiners, students will normally be allowed one resubmission of a failed dissertation or project and this will normally be within four months of the date of the publication of the result.
The pass mark for a reassessment is the same as the first attempt (i.e. 50% for masters and 40% for Postgraduate Diploma/Certificate). When a reassessment is passed, the mark is capped at the lowest compensable fail mark (i.e. 40R for Masters and 30R for Postgraduate Diploma/Certificate), unless the previous mark was within the compensation zone, in which case the original mark will stand with a suffix ‘R’. This mark is used in the weighted average/total mark for the final award. The capped mark is applied to the whole unit and not the failed component.
Referrals may also be compensated providing the number of quota of compensations has not been exceeded. When a student’s referral mark is in the compensation zone (and the student/unit is eligible for compensation), the student’s mark will be capped at the lowest compensable fail mark (i.e. 40R for Masters and 30R for Postgraduate Diploma/Certificate).
Please note that some programmes do not allow referrals. Please refer to the ‘Programme Exemptions to PGT Degree Regulations’ section of the handbook where specific exemptions applicable to the programme will be listed.
External Examiners
The External Examiner for this programme is: Dr Raj K. Singh Badhan
Name of Institution: Aston University
Position at current Institution: Lecturer in Pharmacokinetics (Pharmacometrics)
Please see the SHS Student Handbook for further information on External Examiners.
Section B: Syllabus, Course Units and Route through the Programme
Syllabus
| Unit Sequence | Full-time & Part-time, Year 1 | Full-time & Part-time, Year2 |
| Semester 1 (Sept-Jan) | PHAR69921 Basic PKPD;
PHAR69931 Introduction to Biostatistics |
PHAR72010 Intro to clinical trials;
PHAR69922 Physiologically-based PKPD, part 1 |
| Semester 2 (Feb-May) | PHAR69923 Data analysis in PKPD | PHAR69932 Physiologically-based PKPD, part 2
PHAR69924 Project preparation |
| Summer FT: June-Sept of Year 1, PT: June–Jan of Year 2 |
PHAR69920 (Dissertation) |
Course Units
Study Method & Unit content
For each unit there is a unit description outlining what is required, these are listed below. Throughout your studies help and support is available from your Academic Advisor, the Programme Director and the PGT Programmes Support team. All elements of each unit must be undertaken in order to complete the unit, they are all compulsory components.
The unit descriptions and handbook provide an overall outline for the course, any further details or advice on specifics of the programme should be sought from the unit leader. Students are advised to keep the unit leader and administrator up to date on the progress of their study, including any difficulties they are encountering. Regular updates between the students and unit leader will also take place through a series of tutorials based on specific essays and coursework questions and these will be available to Distance-Learning students through on-line virtual meetings.
PHAR69921 Basic Pharmacokinetics and Pharmacodynamics (PKPD)
Unit Lead: Prof. Leon Aarons
This introductory unit is designed to give the student an understanding of fundamental concepts in pharmacokinetics and pharmacodynamics, essentially how drugs get into the body, how they get around the body and how they get out of the body. Topics include the processes of absorption, distribution, metabolism and excretion (ADME), the concept of compartmental analysis, and basic statistical concepts.
Emphasis is given to explaining how chemical properties of drug interact with physiological aspects of the human body to affect the behaviour of different drugs and the variation between individual patients. Quantitative assessment of the processes (modelling and data analysis) is described with reference to drug discovery, drug development and therapeutic usage. The unit also provides experience in solving numerical problems relating the time-course of drugs and their metabolites in the body.
This unit is a prerequisite for subsequent units which focus on physiologically-based models and advanced approaches to data analysis.
Theoretical knowledge will be disseminated through a series of lectures and tutored workshops. Students will then work on a structured assignment that emphasises the application of theory by solving problems.
Summary of Teaching
Lectures (15) 30 hours
Tutored workshops (6) 12 hours
Tutorial/feedback (3 to 6) 6 hours
Independent Study 90 hours
(reading, case study)
Assessment
Assignment– Case study report and oral presentation 60%
Formal Examination (January exam period) 40%
Learning Outcomes
Knowledge:
- Describe the detailed mechanisms involved in drug absorption, distribution, metabolism and excretion (ADME).
- Explain the crucial roles of pharmacometric methods in developing and gaining regulatory approval for a medicine, and specifically the role of pharmacokinetics and biostatistics in guiding the design and conduct of clinical trials.
Skills:
- Make informed predictions on the influence of any change in (ADME) mechanism on plasma drug concentration-time profile for a given substance,
- Critically analyse observations on plasma drug concentration-time profiles and characterise them quantitatively for the purpose of making inferences between different drugs, different patients, different conditions etc.
- Identify the reasons for differences in the time-courses of drug effect and plasma drug concentration.
- Apply basic biostatistical concepts and interpret statistical information arising from clinical trials.
Transferable Skills:
- Perform calculations using fundamental pharmacokinetic equations
- Produce written reports on the pharmacokinetics of a given drug, making effective use of pharmacokinetic terminology.
PHAR69922 In Vitro- In Vivo Extrapolation (IVIVE) of ADME & Physiologically- Based Pharmacokinetics [PBPK1]
Unit Lead – Dr Dan Scotcher
This unit provides a focused introduction to the use of mechanistic pharmacokinetic models, which use mathematical descriptions of physiological processes to predict the fate of drug molecules within the human body. Therefore, 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. Implementation of this approach will be described in the following areas:
- Prediction of drug-drug interactions by extrapolating from in vitro laboratory tests before commencing clinical studies
- Prediction of oral absorption; guiding the design of oral drug formulations
Topics outline
Introduction to physiologically-based pharmacokinetics (PBPK) and systems pharmacology
Modelling of in vitro experiments
- metabolic assays
- permeability
- mDDI assay data
- transporter/transporter-metabolic DDI assay
Quantitative prediction and IVIVE of metabolic clearance
- Physiology of hepatic and extrahepatic metabolism
- In vitro-in vivo extrapolation (IVIVE) of metabolism
- PBPK modelling and simulation (M&S)
- IVIVE in drug development
Predicting absorption
- Principles of drug absorption
- The evolution of PBPK absorption models
- PBPK M&S of oral formulation effects
Drug distribution and binding
- Principles of drug distribution
- Models for predicting volume of distribution
- Whole-body PBPK M&S
- Drug binding and local tissue concentrations
mDDIs – PBPK M&S to inform drug labelling and clinical care
- Principles of metabolic drug-drug interactions (mDDIs): competitive inhibition, mechanism-based inhibition and induction
- IVIVE of metabolic mDDIs
- PBPK modelling of mDDIs
Summary of Teaching
Lectures [16] 16 h
Seminars (Webinars) [7] 14 h
Hands-on workshops [15] 30 h
Independent Study 90 h
Assessment
Final exam 40%
Coursework, Case Study report and presentation 60%
Knowledge:
- Describe the details of in vitro systems used to characterise drug absorption, distribution, metabolism and excretion.
- Describe the relationships (scaling) between data obtained from in vitro systems and in vivo observations on ADME.
Skills:
- Critically analyse observations on plasma drug concentration-time profiles and characterise them quantitatively for the purpose of making inferences between different drugs, different patients, different conditions etc.
- Identify the reasons for differences in the time-courses of drug effect and plasma drug concentration.
- Make informed predictions of the behaviour of drugs in body with respect to plasma drug concentration-time profile.
- Apply basic biostatistical concepts and interpret statistical information arising from clinical trials.
Transferable Skills:
- extracting key points from scientific literature
- effective written communication
Suggested Reading
Lecture notes cite key research articles and reviews, which include…
Wienkers, L.C. and T.G. Heath (2005) Predicting in vivo drug interactions from in vitro drug discovery data. Nat Rev Drug Discov 4:825-33.
Houston, J.B. and A. Galetin (2008) Methods for predicting in vivo pharmacokinetics using data from in vitro assays. Curr Drug Metab. 9:940-51.
Rostami-Hodjegan, A. (2012) Physiologically Based Pharmacokinetics Joined With In Vitro-In Vivo Extrapolation of ADME: A Marriage Under the Arch of Systems Pharmacology. Clin Pharmacol Ther 92:50-61.
Jamei, M., S. Marciniak, K. Feng, A. Barnett, G. Tucker, and A. Rostami-Hodjegan (2009) The Simcyp population-based ADME simulator. Expert Opin Drug Metab Toxicol. 5:211-23.
Rowland, M., C. Peck, and G. Tucker (2011) Physiologically-based pharmacokinetics in drug development and regulatory science. Annu Rev Pharmacol Toxicol 51:45-73.
Zhao, P., M. Rowland, and S.M. Huang (2012) Best practice in the use of physiologically based pharmacokinetic modeling and simulation to address clinical pharmacology regulatory questions. Clin Pharmacol Ther 92:17-20.
Tsamandouras, N., A. Rostami-Hodjegan, and L. Aarons (2013) Combining the “bottom-up” and “top-down” approaches in pharmacokinetic modelling: Fitting PBPK models to observed clinical data. Br J Clin Pharmacol.
PHAR69932 Advanced topics in Physiologically-based PK models [PBPK2]
Unit Lead: Dr Dan Scotcher
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. Completion of the introductory unit is an essential prerequisite. 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. Implementation of this approach will be described in the following areas:
Topics outline Interspecies extrapolation and first-in-human (FIH) design
Complex drug interactions
Special populations –
PBPK M&S of biologics Coupling PBPK M&S with pharmacodynamic (PD) modelling |
Summary of Teaching
Lectures 15 h
Seminars (Webinars) 14 h
Hands-on workshops 30 h
Independent Study 90 h
Assessment
Final exam 40%
Coursework, Case Study report and presentation 60%
Suggested Reading
Wienkers, L.C. and T.G. Heath (2005) Predicting in vivo drug interactions from in vitro drug discovery data. Nat Rev Drug Discov 4:825-33.
Houston, J.B. and A. Galetin (2008) Methods for predicting in vivo pharmacokinetics using data from in vitro assays. Curr Drug Metab. 9:940-51.
Rostami-Hodjegan, A. (2012) Physiologically Based Pharmacokinetics Joined With In Vitro-In Vivo Extrapolation of ADME: A Marriage Under the Arch of Systems Pharmacology. Clin Pharmacol Ther 92:50-61.
Jamei, M., S. Marciniak, K. Feng, A. Barnett, G. Tucker, and A. Rostami-Hodjegan (2009) The Simcyp population-based ADME simulator. Expert Opin Drug Metab Toxicol. 5:211-23.
Rowland, M., C. Peck, and G. Tucker (2011) Physiologically-based pharmacokinetics in drug development and regulatory science. Annu Rev Pharmacol Toxicol 51:45-73.
Zhao, P., M. Rowland, and S.M. Huang (2012) Best practice in the use of physiologically based pharmacokinetic modeling and simulation to address clinical pharmacology regulatory questions. Clin Pharmacol Ther 92:17-20.
Tsamandouras, N., A. Rostami-Hodjegan, and L. Aarons (2013) Combining the “bottom-up” and “top-down” approaches in pharmacokinetic modelling: Fitting PBPK models to observed clinical data. Br J Clin Pharmacol.
PHAR69923 Data Analysis in PKPD
Unit Lead: Dr. Kayode Ogungbenro
This is a taught unit which covers data analysis in PKPD studies. This is a specialised area of modelling that combines mathematical and statistical knowledge in data fitting with a sound knowledge of the human body as a system in order to choose more mechanistic models. Analyses can describe the observed data but can also be used for the purpose of prediction. The mathematical background provided in this unit relates to linear and non-linear fitting, optimisation procedures that are applied to objective function(s) and optimal design issues. Statistical input relates to finding inferences, covariates and application of non-linear mixed effect modelling amongst other subjects. This unit also provides information on available tools for data analysis and their applications. Most commonly used platforms for data analysis such as Phoenix, Monolix, nlmixr, and NONMEM are part of the unit where students analyse data with a number of different packages and learn their advantages and disadvantages.
Summary of Teaching
Lectures (15L x 2H) = 30 h
Seminars (Webinars) (6W x 2H) = 12 h
Tutorials (12T x 2H) = 24 h
Assignment/Problem Solving (4P x 45H) = 180 h
Independent Study (10P x 12H) = 45 h
KEY:
W = Week; D = Day; L= Lecture; T= Tutorial; P = Problem Solving Assignment; H = Hour
Assessment
Examination (end of year 1) 40%
Coursework (2 equally weighted assignments) 60%
Learning Outcomes
Knowledge:
- Evaluate the use of different software packages to perform certain types of data analysis
Skills:
- Understand the different types of models that exist in PK and PD
- Understand basic concepts and mathematical principles associated with parameter estimation in pharmacokinetic (PK) and pharmacodynamics (PD) modelling
- Implement basic models in PK and PD for estimation and simulation purposes
- Understand how to choose between the different software packages available for parameter estimation depending on the situation in PK and PD
- Interpret results obtained from different software packages in PK and PD
Transferable Skills:
- Critically assess published reports in the field of Pharmacometrics for consistency in use of methods and other assumptions
PHAR69931 Introduction to Biostatistics
Unit Lead: Prof. Leon Aarons
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.
Emphasis is given to the use of simple hands-on exercises to build confidence in the application of concepts and in the practical use of software for statistical problem solving.
This unit is a prerequisite for the subsequent unit (PHAR69923) on advanced approaches to data analysis, including mixed effects models and population pharmacokinetics.
Theoretical knowledge will be disseminated first by lecture, but the unit will emphasise the application of theory by solving problems in workshops and coursework assignments. Some of the assignments will be computer based, to ensure students are comfortable with basic software for data analysis and presentation before encountering more specialised software in subsequent units.
Students will complete exercises using Microsoft Excel, SPSS and R.
Summary of Teaching
Lecture 10h
Workshop/tutorials 20h
Academic Advisor 4h
Independent study 100h
Assessment
Biostatistics problems coursework 100%
Learning Outcomes
Knowledge
- Define and apply the statistical terms relevant to clinical trial data collection and interpretation.
- Explain the crucial role biostatistics plays in guiding the design and conduct of clinical trials.
Skills
- Apply basic biostatistical concepts and interpret statistical information arising from clinical trials.
- Understand the statistical terms presented in clinical trial documents
- Use computer software such as Excel and SPSS to perform basic computational tasks including file input/output, calculations, and production of effective graphs
Transferable skills
- Independent learning, mastering new software
Suggested reading
Senn S. Statistical Issues in Drug Development. John Wiley & Sons. Chichester. 1997
Armitage P, Berry, G, Mathews JNS. Statistical Methods in Medical Research. Blackwell Publishing, Oxford. 2002.
BMJ statistics notes. (Altman et al.)
PHAR72010 Introduction to Clinical Trials
This introductory unit provides an overview of the basic requirements and procedures employed in planning and conducting clinical trials. It also provides guidance on ethical, regulatory and statistical aspects as well as the managerial requirements.
Aims:
- Acquire and develop an advanced knowledge and in depth understanding of the theory of setting up and running clinical trials
- Acquire and develop the advanced skills to make an effective contribution to clinical trial practice
- Demonstrate the ability to analyse the different clinical trial phases and the purpose of each
- Identify their own learning needs, develop themselves as critically reflective practitioners and advance their own learning to sustain continuing professional development
- Critically evaluate literature, theories and methodologies and apply approaches to creating a clinical trials project plan
Summary of Teaching
This unit is delivered entirely through distance learning. A printed manual consisting of ten chapters is issued to the student on registration and receipt of payment. Each chapter is designed to contain ten hours of assessed learning.
Additional learning activities are directed which, together with the assessment, require 50 hours of self-directed learning and assessment
Assessment
Assignment (3000 words) 100%
Learning Outcomes
Knowledge
Demonstrate an advanced knowledge and detailed understanding of the:
- historical background of how clinical trials have developed
- and evolved to modern standards
- drug development process
- basic requirements for developing and conducting clinical trials
- practical and theoretical aspects of the design, conduct,
- analysis of outcomes and reporting of clinical trials
- ethical and regulatory aspects of the proper conduct of trials
Skills
Critically analyse, evaluate and where appropriate formulate an informed opinion about:
- Clinical trial reporting
- Impact of poor practices in clinical trial conduct
- Effectiveness of current systems and processes
- Analyse approaches to project management
- Offer guidance on Project Management in Clinical Trials, including Project Management Tools and how to use them effectively, and how to
- Plan approaches to manage when things don’t go as planned, for example when there are unexpected outcomes or accidents in the conduct of clinical trials
- Complete practical exercises to demonstrate an understanding of the application of knowledge derived from the course material
- Engage in critically reflective practice as a process of continuous personal development
- Network with key stakeholders in own organisation to drive processes forward based on knowledge gained through course participation
Transferable skills
- The ability to critique information from a range of sources to formulate opinions
- Project manage in accordance with organisational requirements, demonstrating high level knowledge on a process driven approach
- The ability to assess current working practices based on knowledge and experience gained and adapt working practices as necessary
- The ability to prioritise and effectively manage time to complete work by required deadlines
PHAR69924 Literature Review and Research Protocol in Modelling and Simulation
Unit Lead: Dr Kayode Ogungbenro
This is a research unit, in which students conduct a literature review on a topic of current importance in the general area of Modelling, Simulation and Data Analysis. The primary aim is to synthesise new knowledge by gathering the discrete pieces of information, identifying the gap in knowledge and writing a detailed research proposal.
Assessment
Research proposal of approximately 5,000 words (100% of unit marks)
The report should comprise a focussed literature review concluding with a detailed modelling & simulation project proposal (research protocol).
Skills to be assessed:
- Recognise and explain the role of M&S in PK/PD and drug development
- Perform a thorough review of M&S literature in a specific therapeutic area and identify the key references
- Summarise the previous work, showing insight into the strengths and weaknesses in the key references
- Identify gaps that can be addressed by M&S
- Construct a feasible modelling project to address the gaps identified in the existing literature
- Present a convincing rationale for carrying out the work, including sufficient detail on modelling approach and data
- Produce a written document (research proposal) that communicates in a professional, precise and clear manner what you are hoping to achieve
Assessors are asked to comment on the extent to which the paper is successful in these areas:
- Context and rationale [20% of marks]
- Does the paper describe the clinical and drug-development background to the problem? Is the role and potential contribution of PK/PD modelling clear, so that the reader can appreciate the rationale for modelling in this situation?
- Literature review [30%]
- Does the summary of PK/PD background reference all the key work in the area? Does it indicate understanding of the important points, rather than just listing results without explaining significance? Does the discussion of strengths and weaknesses demonstrate insight and critical analysis of the published modelling approaches?
- Proposal [30%]
- Is the proposed modelling work novel? Does it address a real gap, and is that gap clearly explained? Is the work described with sufficient detail as to the modelling approach and the data required? Does the proposal present a convincing case that 1) there is a need for the work, and a significant anticipated reward for a successful outcome, and 2) the proposal contains sufficient detail to give confidence of successful outcome.
- Presentation [20%]
- Is the presentation clear and professional in tone, so as to give the reader confidence in the abilities of the writer? Is the referencing appropriate? Are Figures and Tables clear and relevant to the review and proposal, with informative captions that point out the key features and intended purpose of the figure/table? Is the language grammatical and free of typographic errors?
PHAR69920 Dissertation / Supervised Research Project
Unit Lead: Dr Kayode Ogungbenro
This is a research unit and follows on directly from the literature review and research protocol in modelling and simulation (PHAR69924). Students carry out the research project proposed in PHAR69924, i.e. on a topic of current interest that addresses the issues raised in their review project. The research should have elements of data analysis, modelling or simulation and is supervised by one academic supervisor. The dissertation should be written as an MSc thesis and the major results should be worthy of publication in pharmacokinetic journals.
The total time spent on the research and writing the dissertation should be based on 10 notional learning hours per credit (i.e. a total of 600 hours teaching for this 60-credit unit). The projected hours of independent study are intended as guidelines only, but are important to ensure a balanced workload between the units.
Supervisory Sessions (10T x 2H) = 20 h
Independent Study (P x 560H) = 560 h
Assessment
Dissertation (12,000 – 15,000 words) 90%
Oral Presentation (via Web Link for part-time students) 10%
(The oral presentation will normally take place before the final Dissertation submission.)
Learning Outcomes
This unit aims to give the opportunity and environment where the students take a leading edge piece of research work and employ the knowledge that is gathered through other modules of the course. The research must be an applied PKPD or M&S project and it should add to the existing knowledge in the area.
Knowledge:
- Gain scholarly knowledge in a focused area of M&S
- Knowing how to identify an appropriate research question
- Select methodology
- Appraise the relevant literature
- Gained the knowledge and understanding to be able to produce a report that is publishable in a peer reviewed journal relevant to the area
Skills:
- Gain scholarly knowledge in a focused area of M&S
- Apply the knowledge and skills gathered in other modules within a well defined research project,
- Execute and complete a piece of research which is narrowly focused on specific area of M&S,
- Develop the ability to debate the outcome of research and argue for validity of the methods used in the study and the conclusions drawn from the results.
Transferable Skills:
Develop the ability to debate the outcome of research and argue for validity of the methods used in the study and the conclusions drawn from the results.
Progress Committee
The MSc Programme Board, and ultimately the Pharmacy and Vision Sciences Postgraduate Consortium Committee, considers issues of poor student progress, student dissatisfaction with academic supervision and other mitigating circumstances that may be influencing progress.
Failure to submit progress forms or assessments on time will result in investigation.