Search result: Catalogue data in Spring Semester 2019

Pharmaceutical Sciences Master Information
Core Courses
Core Courses II
Pharmaceutical Skills Training
NumberTitleTypeECTSHoursLecturers
511-0013-00LEthics in Research and Drug Development Restricted registration - show details
Only for MSc Pharmaceutical Sciences.
O1 credit1GE. Kut Bacs
AbstractThe course provides an introduction into the concepts and tools of ethics with a special emphasis on ethical dilemmas in biomedicine and drug development.
ObjectiveStudents
• are able to elaborate on basic concepts and tools of ethics, specifically medical ethics and bioethics
• know about key ethical questions in biomedical research and drug development
• are able to critically reflect on experiments and studies in animals and humans taking into account core ethical values
• know where to find more information on international ethical declarations and Swiss ordinances on human and animal research (e.g. swissethics, SAMW)
• are able to weigh conflicting ethical values and to develop and take a stance in an ethical debate
• know about the conflicting interests in the pharmaceutical industry from a global perspective on drug development (i.e. economization vs. solidarity with less economically developed countries)
Prerequisites / NoticeThis course is part of ETH's Critical Thinking Initiative (CTETH).
511-0010-00LScientific Concepts and Methods Restricted registration - show details
Only for MSc Pharmaceutical Sciences.
O2 credits3GE. Kut Bacs, V. I. Otto, N. Sieroka
AbstractThe module is an introductory course fostering critical thinking about scientific concepts and methods in the natural sciences, particularly in pharmaceutical and biomedical research.
ObjectiveStudents
• have the ability to explain and reflect upon core themes in philosophy of science and cutting edge methods that are relevant in modern pharmaceutical and biomedical research.
• are able to explain the role experiments, models, images, and quantifications play in the formation of a theory, and the constitution and illustration of a scientific fact.
• are able to actively engage in a critical discussion about scientific concepts, methods and approaches in the field of biomedical research and philosophy of science.
• are able to critically evaluate the basic scientific assumptions, concepts and approaches underlying their own research project.
• have learned how to “closely read” and analyse a scientific paper and are able to present their paper analysis to an audience that is not expert in the research field.
ContentThis course is part of the ETH "Critical Thinking" initiative.
Prerequisites / NoticeThe course is best suited for students who have recently performed a research project of their own.
511-0014-00LProcess & Project Management Restricted registration - show details O1 credit2GE. Walter
AbstractThis course provides knowledge about the core of the Process Excellence (PE) methodology as a data-driven, systematic approach to problem solving, with a focus on customer impact. With the help of this tool box, students learn basic project management tools and are prepared to run process improvement projects successfully.
ObjectiveStudents are able to:
• describe and apply effectively the basic methodologies of project management and Process Excellence;
• evaluate systematically processes and identify, visualize, measure, and analyze problems;
• create and formulate recommended solutions to identified and analyzed problems.
ContentProcess Excellence (PE) is used to improve existing processes. PE aims at sustainable results and satisfied customers. It removes the waste in the organization and improves the flow in the processes. It makes the process outcomes predictable and reliable. PE helps to take the right decision based on facts and figures and to set the right priorities. The successful management of both, processes and projects, is important for sustainable growth in the pharmaceutical industry and requires varying technical skills and soft skills.

Process Excellence is also referred to DMAIC, which stands for Define, Measure, Analyze, Improve, and Control. DMAIC encompasses the following steps: Define the process improvement goals that are consistent with customer demands and enterprise strategy (business case, project charter, voice of the customer); Measure the current process and collect relevant data for future comparison (process mapping, data collection); Analyze relationship and causality of factors, determine what the relationship is, and attempt to ensure that all factors have been considered (process analysis); Improve or optimize the process based upon the analysis using rational and creative techniques (generation and implementation of solutions); Control to ensure that any variances are corrected before they result in defects. Set up pilot runs to establish process capability, move to production, monitor the process, and install control mechanisms. Problem-solving and prioritization: priority matrix; cause & effect diagram; failure mode & effect analysis (FMEA).
Prerequisites / NoticeCourse prepares the ground for process and project management. Active participation and teamwork are required and assessed. Topics are illustrated with concrete examples. Case study and a business game are used to practice the tools explored during the course
511-0012-00LPharmaceutical Biostatistics Restricted registration - show details O2 credits2GK. Grosch
AbstractThe course conveys skills necessary to understand, plan, and conduct statistical analyses. This includes a short recapitulation of statistical basics and extends on statistical methods used and applied in research and development of pharmaceutical industry. The student will also apply their knowledge in practical examples using a statistical tool.
ObjectiveDescribe the importance of exploratory data analysis (EDA). Describe and be able to generate the most frequently used statistics for EDA.

Summarize basics of probability theory and statistics. Describe the concept of point estimation, statistical tests, interval estimation, and sample size.

Describe and apply different statistical methods such as ANOVA, linear regressions, and other pertinent statistical methods. Explain their assumptions, limitations, and diagnostic checks. Be able to plan, analyze experiments, and to interpret results from respective statistical analyses.

In practical applications, learn to handle challenges of real data, understand the difference between statistical significance and relevance, learn methods to overcome data issues, understand the risks of multiplicity issues in experiments and be able to apply methods for multiplicity control.
ContentDay I: Exploratory Data Analysis (EDA) and short recapitulation of Probability & Statistics
Day II: ANOVA & linear regression
Day III: Dealing with real data
Lecture notespdf presentation will be available prior to course
Prerequisites / NoticeAs a preparation for the course, please, use books that were recommended during your basic statistic course (e.g. Stahel, W. 2008, Statistische Datenanalyse, Eine Einführung für Naturwissenschaftler, Springer Vieweg Verlag or others).
You may also use the following textbook which can be purchased or downloaded for free under following link:
Link
David M Diez, Christopher D Barr, Mine Cetinkaya-Rundel, Leah Dorazio 2017: Advanced High School Statistics

It is expected that the student has an understanding of basic statistical concepts.

During the course R Studio will be used as a statistical tool. The student is requested to install latest version of R and R Studio on a laptop/tablet which should be brought to the course. As we will use latest graphical applications, please, make sure you installed ggplot2 and its respective libraries in R as well and get used to the underlying syntax. This course IS NOT an introductory course to R or R Studio and the student is expected to be familiar with the use of R and R Studio.

Useful links:
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Data sets which will be used during the course will be delivered prior to or during the course.
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