Data Analysis in Pharmacokinetics and Pharmacodynamics

PHAR69923 Data Analysis in Pharmacokinetics and Pharmacodynamics 30 Semester 2 Postgraduate Taught Level 7 Kayode Ogungbenro Unit coordinator 1 FHEQ level (Framework for Higher Education Qualifications) ' Masters/Integrated Masters P4 ' European Credit Transfer & Accumulation System Rating : 15.0 Data analysis in PKPD studies is a specialised area of modelling that combines mathematical and statistical knowledge in data fitting with the sound knowledge of the human body as a system to enable choosing more mechanistic models. The analysis can describe the observed data but also it can be used for the purpose of prediction. The mathematical background provided in this module relates to linear and non-linear fitting, optimisation procedures that are applied to objective function(s). Statistical input relates to finding inferences, covariates, application of non-linear mixed effect modelling. This Unit also provides information on available tools for data analysis and their applications. Commonly used platforms for data analysis (such as NONMEM, MONOLIX, Phoenix, MATLAB and R (rxode/nlmixr)) are explored within this  module, where students analyse data and learn the advantages and disadvantages of these platforms/packages, as well as their limitations   Data analysis in PKPD studies is a specialised area of modelling that combines mathematical and statistical knowledge in data fitting with the sound knowledge of the human body as a system to enable choosing more mechanistic models. The analysis can describe the observed data but also it can be used for the purpose of prediction. The mathematical background provided in this module relates to linear and non-linear fitting, optimisation procedures that are applied to objective function(s). Statistical input relates to finding inferences, covariates, application of non-linear mixed effect modelling. This Unit also provides information on available tools for data analysis and their applications. Commonly used platforms for data analysis (such as NONMEM, MONOLIX, Phoenix, MATLAB and R (rxode/nlmixr)) are explored within this  module, where students analyse data and learn the advantages and disadvantages of these platforms/packages, as well as their limitations   <ul><li>Understand different types of models that exist in pharmacokinetics and pharmacodynamics</li><li>Understand basic concepts and mathematical principles associated with parameter estimation in pharmacokinetic and pharmacodynamic modelling</li><li>Understand how to choose between different software and methods available for parameter estimation depending on the situation in pharmacokinetics and pharmacodynamics</li><li>Understanding limitations of common packages for modelling and simulation</li><li>Understand the role of modelling and simulation in drug development  </li></ul> <ul><li>Implement basic models in pharmacokinetics and pharmacodynamics for estimation and simulation purposes</li><li>Use common software that are available for parameter estimation and simulation in pharmacokinetics and pharmacodynamics</li><li>Interpret results obtained from different software and methods in pharmacokinetics and pharmacodynamics</li><li>Describe the theory of parameter estimation in non-linear mixed effects modelling</li><li>Use modelling and software for dose optimisation during drug development  </li></ul> <ul><li>Extracting key point from scientific literature for a pharmacokinetic and pharmacodynamic model</li><li>Effective communication of pharmacokinetic and pharmacodynamic modelling results  </li></ul> <ul><li>Lectures - with series of exercises based on simulated and real datasets</li><li>Workshops - entailing guided sequences of analyses with interactive discussion with tutor.</li><li>Webinars (part-time students only)</li><li>Directed reading.</li><li>Formative-assessment - provide feedback.</li><li>Summative assessments – based on real life drug development/dose optimisation problems and feedback provided.</li><li>The total teaching and assessment methods would be based on 10 notional learning hours per credit (i.e. a total of 300 learning hours teaching and assessment for this 30-credit unit).</li></ul> 0 Other 100% <figure class="table"><table style="border:1px solid hsl(0, 0%, 0%);"><tbody><tr><td style="border:1px solid hsl(0, 0%, 0%);">Assessment Methods</td><td style="border:1px solid hsl(0, 0%, 0%);">Weighting (%)</td></tr><tr><td style="border:1px solid hsl(0, 0%, 0%);">Written exam</td><td style="border:1px solid hsl(0, 0%, 0%);">40%</td></tr><tr><td style="border:1px solid hsl(0, 0%, 0%);">Written assignment (simulation and modelling based)</td><td style="border:1px solid hsl(0, 0%, 0%);">60%</td></tr></tbody></table></figure> Feedback will be given within 15 working days for assignments and after the exam board has met for exams. MSc MBDD (PT) MSc MBDD (PT) PGDT Taught Component Mandatory MSc MBDD MSc MBDD PGDT Taught Component Mandatory CPD Model-based Drug Dev. CPD Model-based Drug Dev. PGDT Taught Component Optional N <ul><li>Pharmacokinetic-Pharmacodynamic Modeling and Simulation. Bonate Peter L. 2nd edition. New York, Dordrecht, Heidelberg, London: Springer, 2011.</li><li>Pharmacokinetics & Pharmacodynamic Data Analysis: Concept and Applications. Gabrielson J and Weiner D. 4th edition. Stockholm; Sweden Pharmaceutical Press, 2006.</li><li>Nonlinear Models for Repeated Measurement Data. Davidian M and Giltinan DM. 1st edition, Chapman & Hall, 1995.   </li></ul> 0