NNT722 - NANOTECHNOLOGICAL APPROACH TO DRUG TARGETING

Course Name Code Semester Theory
(hours/week)
Application
(hours/week)
Credit ECTS
NANOTECHNOLOGICAL APPROACH TO DRUG TARGETING NNT722 Any Semester/Year 3 0 3 9
PrequisitesNone
Course languageTurkish
Course typeElective 
Mode of DeliveryFace-to-Face 
Learning and teaching strategiesLecture
Discussion
 
Instructor (s)Assist. Prof. Dr. Cem Varan 
Course objectiveThe aim of the course is to give information about nanotechnological drug targeting 
Learning outcomes
  1. Students who completed this course would be able to explain and interpret Purpose and basis of drug targeting, surface properties to be used for drug targeting, types and strategies of targeting, preparation, characterization, stability, efficacy, administration routes and utilization of targeted delivery systems.
Course ContentPurpose and basis of drug targeting, surface properties to be used for drug targeting, types and strategies of targeting, preparation, characterization, stability, efficacy, administration routes and utilization of targeted delivery systems 
ReferencesLecture notes, internet.
Nanoparticles for Pharmaceutical Applications, Ed: A.J. Domb, Y.Tabato, M.N.V. Ravi Kumar and S Farber.
Nanoparticle Technology for Drug Delivery, Ed: Ram B. Gupta, Uday B. Kompella 

Course outline weekly

WeeksTopics
Week 1Nanotechnological Approaches on Drug Targeting
Week 2Targeting into the cells
Week 3Drug targeting to the brain
Week 4Drug targeting to the brain (continues)
Week 5Tumor targeting strategies for nanoparticles
Week 6Midterm exam
Week 7Nanotechnology and vaccine
Week 8Drug targeting to lung
Week 9Nanoparticles interaction with biological surfaces and biological and biological fate of nanoparticles.
Week 10Characterization of nanoparticles? surface properties and modification methods
Week 11Seminars
Week 12Final exam

Assesment methods

Course activitiesNumberPercentage
Attendance00
Laboratory00
Application00
Field activities00
Specific practical training00
Assignments00
Presentation00
Project00
Seminar150
Midterms150
Final exam00
Total100
Percentage of semester activities contributing grade succes150
Percentage of final exam contributing grade succes150
Total100

WORKLOAD AND ECTS CALCULATION

Activities Number Duration (hour) Total Work Load
Course Duration (x14) 14 3 42
Laboratory 0 0 0
Application000
Specific practical training000
Field activities000
Study Hours Out of Class (Preliminary work, reinforcement, ect)148112
Presentation / Seminar Preparation17070
Project000
Homework assignment000
Midterms (Study duration)000
Final Exam (Study duration) 14646
Total Workload30127270

Matrix Of The Course Learning Outcomes Versus Program Outcomes

D.9. Key Learning OutcomesContrubition level*
12345
1. To be able to use mathematics, science and engineering knowledge to develop new methods in nanotechnology and nanomedicine     X
2. To have comprehensive information on the current techniques and methods applied in nanotechnology and nanomedicine    X
3. To develop methods and tools for the identification and understanding of functions and interaction mechanisms at the atomic and molecular level  X  
4. To understand the effects of universal and social aspects in nanotechnology and nanomedicine applications.  X  
5. To be able to use new technological developments, databases and other knowledge sources efficiently by adopting the importance of life-long learning   X  
6. To acquire the ability of analysis, synthesis and evaluation of new ideas and developments in nanotechnology and nanomedicine    X 
7. To have awareness of entrepreneurship and innovativeness  X  
8. To be able to design an experiment, analyze and interpret the experimental results as a written report.    X
9. An ability to perform disciplinary and interdisciplinary team work     X
10. An ability to present the results of the studies orally or written in national and international platforms and contribute to the scientific literature.   X 
11. To have consciousness about professional ethics and social responsibility     X

*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest