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Course Code: 
PTX619
Semester: 
Spring
Course Type: 
Elective
Course Language: 
İngilizce
Course Objectives: 
• Comprehend basic terms and concepts in “in silico toxicology”, acquire knowledge about data collection and processing in toxicology. • Learn to apply toxicity prediction tools. • Apprehend methods for developing toxicity prediction and classification models for pharmaceuticals and other compounds. • Acquire skills necessary to perform computational toxicology studies.
Course Content: 
a)Therotical Part

The concept of in silico toxicology, basic terms of in silico toxicology, molecular identifiers, similarity, graphical representation of data, toxicity prediction tools, modeling types, regulatory guidelines of in silico toxicology, toxicity prediction model application and development.

b)Case Study

Case studies from the scientific literature are evaluated with in silico toxicology perspective.

Course Methodology: 
1: Lecture, 2: Question-Answer, 3: Discussion, 4: Presentation, 5:Simulation, 6: Video, 7: Applications, 8:Case Study
Course Evaluation Methods: 
A:Written exam, B: Multiple choice , C:Filing the blank D: True/False, E: Oral Exam F: Portfolio, G: Contribution of course activities H:Homework

Vertical Tabs

Course Learning Outcomes

Learning Outcomes Programme learning outcomes Teaching Methods Assessment Methods
  1. lists the basic terms and concepts in in silico toxiciology
 1,2,3,4 1,2,3,4,7,8 E,G,H
  1. lists molecular identifiers and knows conversion methods
 1,2,3,4 1,2,3,4,5,7,8 E,G,H
  1. comprehends and measure similarity for molecules
 1,2,3,4 1,2,3,4,5,7,8 E,G,H
  1. evaluates graphical demonstration of the data
 1,2,3,4 1,2,3,4,7,8 E,G,H
  1. uses widely used toxicity prediction tools
 1,2,3,4 1,2,3,4,5,7,8 E,G,H
  1. differentiate the modeling types
 1,2,3,4 1,2,3,4,7,8 E,G,H
  1. applies regulatory rules to developed QSAR models
 1,2,3,4 1,2,3,4,7,8 E,G,H
  1. comprehends which tool to apply for prediction
 1,2,3,4 1,2,3,4,7,8 E,G,H
     9.   develops and applies a toxicity prediction model and interprets the results 1,2,3,4 1,2,3,4,5,7,8 E,G,H

Course Flow

Week Theorical Topics                                      Study Materials
1 Main concepts of in silico toxicology Lecture notes and terms for in silico toxicology
2 Molecular identifiers, similarity, structural alerts, molecule drawing tools, and database search Web-based and stand-alone databases and molecule drawing software
3 OECD QSAR Toolbox applications QSAR Toolbox software and user manual
4 Basic concepts in data analysis- Regression and classification Software and websites for data processing and model development
5 Quantitative structure-activity relationships Molecular descriptor definition and descriptor calculation programs
6 Grouping chemicals and read-across Related articles from the literature and read-across based software
7 Adverse outcome pathways Concepts of AOP, related articles from the literature, and applications that facilitate AOP development
8 Three-dimensional modeling-molecular docking, binding affinity prediction, and homology modeling Concepts and terminology of 3D modeling
9 Application of in silico methods within the ICH M7 guideline for potentially genotoxic impurities In silico methods applied in ICH M7
10 Rule-based and statistical-based systems for toxicity prediction Examples of rule-based and statistical-based models
11 Literature studies-I Articles from the related literature
12 Literature studies-II Articles from the related literature
13 Applications-I Databases, data from the literature, prediction tools
14 Applications-II Databases, data from the literature, prediction tools

Recommended Sources

Textbook 1. Benfenati, E. (Ed.). (2016). In silico methods for predicting drug toxicity. Humana Press.
Additional Resources
  1. In Silico Toxicology Lecture Notes
  2. OECD Guidelines for the Validation of (Quantitative) Structure-Activity Relationships [(Q)SAR]  Models
  3. OECD (2017), Guidance on Grouping of Chemicals, Second Edition, OECD Series on Testing and Assessment, No. 194, OECD Publishing, Paris
  4. OECD Users' Handbook supplement to the Guidance Document for developing and assessing Adverse Outcome Pathways
  5. M7(R1) Assessment and Control of DNA Reactive (Mutagenic) Impurities in Pharmaceuticals To Limit Potential Carcinogenic Risk
  6. OECD QSAR Toolbox application manual

Material Sharing

Documents All lecture notes and scientific articles are shareable.
Assignments Shareable.
Exams Not shareable.

Assessment

IN-TERM STUDIES NUMBER PERCENTAGE
Case studies 3 100
Total   100
CONTRIBUTION OF FINAL EXAMINATION TO OVERALL GRADE   60
CONTRIBUTION OF IN-TERM STUDIES TO OVERALL GRADE   40
Total   100

Course’s Contribution to Program

No Program Learning Outcomes Contribution
1 2 3 4 5
1 Being able to understand and interpret basic concepts of in silico toxicology and being able to build on this basis to develop further in silico toxicological evaluation.         X
2 Being able to access, search, filter, and evaluate previous related literature research and utilize it for their own benefit.

Being able to pursue interdisciplinary literature surveys and construct analogies between similar problems in different domains.

      X  
3 Developing the necessary skills for abstract thinking and governing the methodologies for conceptualizing and constructing abstract models.

Having the ability to construct their own mathematical models from these abstract models.

Having the ability to utilize mathematical and computational tools and techniques to devise their own unique solution for the problem at hand.

        X
4 Having the ability to design testing scenarios for performance evaluation.

Having the ability to analyze and evaluate several methods in a scientific manner.

        X
5 Having enough knowledge about statistical methods to be able to analyze the results of toxicological studies.

Having the ability to employ data analysis for model development as well as performance evaluation.

        X
6 Having the ability to do toxicological risk assessments and safety assessments of chemicals and products.         X
7 Being able to propose projects by themselves, carry out and complete them and evaluate the obtained results independently and efficiently.       X  

ECTS

Activities Quantity Duration
(Hour) 		Total
Workload
(Hour)
Course Duration (Including the exam week: 12x Total course hours) 12 3 42
Hours for off-the-classroom study (Pre-study, practice) 12 2 28
Case studies 3 2 6
Lab. Applications      
Hours for off-the-Lab. study (Pre-study, practice)      
Lab. Midterm Exem      
Lab. Final      
Homework 2 3 6
Final 1 14 14
Total Work Load     96
Total Work Load / 25 (h)     3.84
ECTS Credit of the Course     7.5
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