Quantitative Chemical Structure and Activity Relationship
A.Y. 2024/2025
Learning objectives
The purpose of this course is that participants gain knowledge on and understand:
- the computational strategies for modelling targets, responsible for biological activity and toxicity, simulating both their interaction with xenobiotics or biotechnological products and their molecular recognition mechanisms at an atomistic level;
- methods to predict and validate the mechanism of action (MoA) of xenobiotics and biotechnological products, with particular attention to a better rational design of experiments on animal models, according to the 3Rs principle.
- the principal physicochemical properties of xenobiotics of relevance to health risk assessment;
- the accuracy of in silico approaches used in scientific studies and risk assessment reports.
- the computational strategies for modelling targets, responsible for biological activity and toxicity, simulating both their interaction with xenobiotics or biotechnological products and their molecular recognition mechanisms at an atomistic level;
- methods to predict and validate the mechanism of action (MoA) of xenobiotics and biotechnological products, with particular attention to a better rational design of experiments on animal models, according to the 3Rs principle.
- the principal physicochemical properties of xenobiotics of relevance to health risk assessment;
- the accuracy of in silico approaches used in scientific studies and risk assessment reports.
Expected learning outcomes
At the end of the course, the student is expected to know:
- the application of the computational methods used in toxicological research;
to critically evaluate:
- the pros and cons of in silico prediction approaches used in risk assessment reports;
to gain:
- the bases for deeply understanding methods and results of a toxicological paper and/or data reports;
to obtain:
a multifaceted bioinformatics knowledgebase, useful for further student's personal study of this topic.
- the application of the computational methods used in toxicological research;
to critically evaluate:
- the pros and cons of in silico prediction approaches used in risk assessment reports;
to gain:
- the bases for deeply understanding methods and results of a toxicological paper and/or data reports;
to obtain:
a multifaceted bioinformatics knowledgebase, useful for further student's personal study of this topic.
Lesson period: year
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course can be attended as a single course.
Course syllabus and organization
Single session
Responsible
Lesson period
year
Prerequisites for admission
The course requires the knowledge of basic notions of mathematics, statistics, chemistry, biochemistry and molecular biology, needed for proficient comprehension of the lessons.
Assessment methods and Criteria
In silico Methods in Toxicology module: The examination will be done in the form of a written exam, for which ~ 3 hours time are given. The examination will have open text questions, and can include practical application of software tools for the prediction of toxicological properties (tools that have been introduced in the course). The answers to the written exam are sent by e-mail to the tutors after 3 hours. Follow-up of the written examination is an individual oral examination of ~30 minutes with each candidate. In the oral examination the answers to the written exam will be discussed with possibly some follow-up questions. The written and the oral exam together will determine the grade.
Structural Bioinformatics module: an oral discussion about two different topics, selected from the lessons.
Structural Bioinformatics module: an oral discussion about two different topics, selected from the lessons.
In Silico Methods in Toxicology
Course syllabus
The module in silico methods in toxicology is aimed at providing students with the fundamental concepts of in silico approaches used to predict physico-chemical, fate and toxicological properties of chemicals, and specifically the application of existing methods in human health and eco-toxicology. Advantages deriving from a complementary use of experimental and in silico techniques will be deeply discussed during the course. The focus is not on developing QSAR/AOP or PBK models, but to learn how to apply, assess and integrate those data into hazard assessment.
1. Introductions and overview of chemoinformatics and computational toxicology
2. Chemical structure representation (SMILES, MOL, INChI, etc)
3. Structure fingerprints, pairwise similarity
4. Introduction to QSAR and computational tox endpoints
5. Use and prediction of Molecular and physicochemical properties (QSAR)
6. Prediction of ecotoxicological properties (QSAR)
7. Chemical database searching and retrieval; exact, substructure, and similarity searches using tools like the OECD QSAR toolbox and/or KNIME
8. QSAR and Rules for Skin Sensitization and AOP; Skin Permeability
9. QSAR and Rules for Bacterial Reverse Mutagenicity
10. Read-Across and its application its human health toxicology
11. TTC - concepts, application and pittfalls in human health toxicology
12. TTC applied to a mixture problem, and limitations of software implementation of TTC
13. Introduction to PBK modeling - basic and first application
14. Chemical Safety Assessment - Future perspectives on integration of New Approach Methods including in silico approaches
15. Discussion and course wrap up
1. Introductions and overview of chemoinformatics and computational toxicology
2. Chemical structure representation (SMILES, MOL, INChI, etc)
3. Structure fingerprints, pairwise similarity
4. Introduction to QSAR and computational tox endpoints
5. Use and prediction of Molecular and physicochemical properties (QSAR)
6. Prediction of ecotoxicological properties (QSAR)
7. Chemical database searching and retrieval; exact, substructure, and similarity searches using tools like the OECD QSAR toolbox and/or KNIME
8. QSAR and Rules for Skin Sensitization and AOP; Skin Permeability
9. QSAR and Rules for Bacterial Reverse Mutagenicity
10. Read-Across and its application its human health toxicology
11. TTC - concepts, application and pittfalls in human health toxicology
12. TTC applied to a mixture problem, and limitations of software implementation of TTC
13. Introduction to PBK modeling - basic and first application
14. Chemical Safety Assessment - Future perspectives on integration of New Approach Methods including in silico approaches
15. Discussion and course wrap up
Teaching methods
Frontal lessons. Each classroom lesson will take 2 hours, and start with lecture(s) but also include hands-on exercises based on topics and concepts covered during that session. Exercises can also be assigned as home-work, to be discussed at the start of the next lecture.
Teaching Resources
No textbook is required for this module. The instructors will, however, provide a list of recommended software tools, books, papers, and other materials that students may wish to explore for more in-depth coverage of particular topics. A computer will be needed to follow the examples and to perform homework. All the lectures will be made available for self-study. As the course does not use a text-book it is highly recommended to be present during all lectures. The presentations are not always sufficient for self-study. Experience from earlier courses indicates that doing the exam without have attended the lectures is very difficult, although not impossible if students actively investigate the topics addressed in the lectures. If possible lectures will be recorded and made available for self-study if a student cannot attend a specific lecture.
Structural bioinformatics
Course syllabus
The module Structural bioinformatics is aimed at depicting a landscape of the most up-to-date bioinformatics techniques for the study of the biopolymers (nucleic acids and proteins) that are the main targets of xenobiotics. Students will be introduced to the theoretical bases of the most used computational approaches (4 CFU). Bioinformatics laboratories, totalling 16 hours (1 CFU), will be provided together with lectures, and will be carried out by the Molecular Operating Environment (MOE, https://www.chemcomp.com/Products.htm) fully integrated drug discovery software package, kindly offered by the Chemical Computing Group (http://www.chemcomp.com/index.htm).
Frontal lessons (4 CFU)
1. Introduction to bioinformatics
2. Genome organization and evolution
3. Databases, archives and information retrieval
4. Substitution matrices, pairwise and multiple alignments, database search and phylogenetic trees
5. Protein structure and architecture
6. Protein structure prediction and validation: comparative modelling, threading and ab initio approaches
7. Case studies: chimeric comparative modelling of a receptor/target; distant homology modelling of an enzyme
8. Xenobiotic and novel drug risk assessment and prioritization
9. Case study: evaluation of endocrine active substances through an efficient in silico pipeline
10. Introduction to systems biology
Bioinformatics laboratories (1 CFU)
1. Building small molecules, biopolymers and introducing their PTMs
2. Protein comparative modelling
3. Molecular docking
4. Quantitative structure-activity relationship in practice
Frontal lessons (4 CFU)
1. Introduction to bioinformatics
2. Genome organization and evolution
3. Databases, archives and information retrieval
4. Substitution matrices, pairwise and multiple alignments, database search and phylogenetic trees
5. Protein structure and architecture
6. Protein structure prediction and validation: comparative modelling, threading and ab initio approaches
7. Case studies: chimeric comparative modelling of a receptor/target; distant homology modelling of an enzyme
8. Xenobiotic and novel drug risk assessment and prioritization
9. Case study: evaluation of endocrine active substances through an efficient in silico pipeline
10. Introduction to systems biology
Bioinformatics laboratories (1 CFU)
1. Building small molecules, biopolymers and introducing their PTMs
2. Protein comparative modelling
3. Molecular docking
4. Quantitative structure-activity relationship in practice
Teaching methods
Frontal lessons and practical laboratories to be carried out through computational software provided to students by teacher and tutor.
Teaching Resources
Arthur M. Lesk, Introduction to Bioinformatics, Fifth Edition. Oxford University Press 2019
In Silico Methods in Toxicology
CHIM/08 - PHARMACEUTICAL CHEMISTRY - University credits: 5
Lectures: 40 hours
Professor:
Roncaglioni Alessandra
Structural bioinformatics
BIO/10 - BIOCHEMISTRY - University credits: 5
Individual laboratory activities: 16 hours
Lectures: 32 hours
Lectures: 32 hours
Professor:
Eberini Ivano
Shifts:
Turno
Professor:
Eberini IvanoProfessor(s)
Reception:
On Mondays, Wednesdays and Fridays from 9 to 10 am and on appointment previously taken via Microsoft Teams or email
Microsoft Teams