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Rational Drug Design: Principles and Applications

01UCBMV

A.A. 2019/20

Course Language

Inglese

Course degree

Master of science-level of the Bologna process in Biomedical Engineering - Torino

Course structure
Teaching Hours
Lezioni 30
Esercitazioni in laboratorio 30
Teachers
Teacher Status SSD h.Les h.Ex h.Lab h.Tut Years teaching
Tuszynski Jacek Adam   Professore Ordinario ING-IND/34 30 0 10 0 1
Teaching assistant
Espandi

Context
SSD CFU Activities Area context
ING-IND/34 6 B - Caratterizzanti Ingegneria biomedica
2019/20
Rational drug design to treat a variety of diseases plaguing humans is a dream, which is fast becoming a practically achievable goal of computer-aided drug discovery research. This course will expose the student to methods and applications of computational drug design providing a historical overview followed by an in-depth introduction to present-day methods. The history of drug research can be divided into several phases characterized by: empirical methods, targeted isolation of active compounds from plants, systematic search for new synthetic materials with desired biological effects and the introduction of animal models as surrogates for patients, the use of in vitro test systems as a replacement for animal experiments, the introduction of molecular-level methods such as protein crystallography, molecular modeling. Today, quantitative structure–activity relationships are found for targeted structure-based and computer-aided design of drugs. Discoveries of new targets and the validation of their therapeutic value is achieved through genomic, transcriptomic, metabolomic and proteomic analysis, knock-in and knockout animal models, and gene silencing with siRNA. Main focus will be placed on both ligand-based and structure-based computer-aided design of an active substance, which is validated by in vitro and in vivo tests to determine the activity of new investigational compounds. The highly mathematical area of pharmacokinetics will also be discussed in detail as well. Examples of specific applications will be described.
Rational drug design to treat a variety of diseases plaguing humans is a dream, which is fast becoming a practically achievable goal of computer-aided drug discovery research. This course will expose the student to methods and applications of computational drug design providing a historical overview followed by an in-depth introduction to present-day methods. The history of drug research can be divided into several phases characterized by: empirical methods, targeted isolation of active compounds from plants, systematic search for new synthetic materials with desired biological effects and the introduction of animal models as surrogates for patients, the use of in vitro test systems as a replacement for animal experiments, the introduction of molecular-level methods such as protein crystallography, molecular modeling. Today, quantitative structure–activity relationships are found for targeted structure-based and computer-aided design of drugs. Discoveries of new targets and the validation of their therapeutic value is achieved through genomic, transcriptomic, metabolomic and proteomic analysis, knock-in and knockout animal models, and gene silencing with siRNA. Main focus will be placed on both ligand-based and structure-based computer-aided design of an active substance, which is validated by in vitro and in vivo tests to determine the activity of new investigational compounds. The highly mathematical area of pharmacokinetics will also be discussed in detail as well. Examples of specific applications will be described.
The student will gain competence in specific computational techniques at the level of molecular target modeling, ligand-protein interactions and pharmacokinetic simulations. To better understand the practical use of these methods, case studies will discuss examples of drug development from oncology, virology and immunology At the end of the course the student will be able to: • Understand molecular modeling approaches and force fields applied to drug-target systems. • Develop homology models of proteins • Perform bioinformatic and chemo-informatic database mining • Employ ligand-based drug screening/discovery/design algorithms • Employ structure-based drug screening/discovery/design algorithms with particular focus on drug-target docking simulations and fast binding affinity predictions • Understand the basis of pharmacokinetic modeling, use software for ADMET prediction, • Develop pharmacophore models, perform a QSAR determination. This course will help students to develop their independent thinking through self-assessment tests. The ability to learn is stimulated by a training program that alternates, in an organized schedule, methodological principles, application examples, and exercises. The course will help to improve both written and oral communication skills through classroom exercises, group and individual tutorials and through the development of a short applied project focused on drug design. The applied project will encourage the students to undertake surveys on websites, to view the scientific literature and to become aware of the applied research areas related to the course. The course will provide students with marketable skills for prospective employment in the biotech and pharma industries.
The student will gain competence in specific computational techniques at the level of molecular target modeling, ligand-protein interactions and pharmacokinetic simulations. To better understand the practical use of these methods, case studies will discuss examples of drug development from oncology, virology and immunology At the end of the course the student will be able to: • Understand molecular modeling approaches and force fields applied to drug-target systems. • Develop homology models of proteins • Perform bioinformatic and chemo-informatic database mining • Employ ligand-based drug screening/discovery/design algorithms • Employ structure-based drug screening/discovery/design algorithms with particular focus on drug-target docking simulations and fast binding affinity predictions • Understand the basis of pharmacokinetic modeling, use software for ADMET prediction, • Develop pharmacophore models, perform a QSAR determination. This course will help students to develop their independent thinking through self-assessment tests. The ability to learn is stimulated by a training program that alternates, in an organized schedule, methodological principles, application examples, and exercises. The course will help to improve both written and oral communication skills through classroom exercises, group and individual tutorials and through the development of a short applied project focused on drug design. The applied project will encourage the students to undertake surveys on websites, to view the scientific literature and to become aware of the applied research areas related to the course. The course will provide students with marketable skills for prospective employment in the biotech and pharma industries.
Good knowledge of the basics of engineering with particular attention to physics, mathematics, chemistry, biology, mechanics, materials science. The lecturer will fill specific background gaps by ad hoc lectures.
Good knowledge of the basics of engineering with particular attention to physics, mathematics, chemistry, biology, mechanics, materials science. The lecturer will fill specific background gaps by ad hoc lectures.
• Historical overview of drug discovery • Introduction to modern drug design and development • Bioinformatic, chemi-informatic and pharmacological databases • Drug design and discovery • Protein homology modelling • Ligand/receptor docking for affinity calculation. Drug binding kinetics • Virtual Screening • Pharmacophore development • QSAR methodology • Pharmacokinetics and ADMET prediction • Basis of Molecular Mechanics, Molecular Dynamics and QM/MM for precise investigation of drug-protein binding interface
• Historical overview of drug discovery • Introduction to modern drug design and development • Bioinformatic, chemi-informatic and pharmacological databases • Drug design and discovery • Protein homology modelling • Ligand/receptor docking for affinity calculation. Drug binding kinetics • Virtual Screening • Pharmacophore development • QSAR methodology • Pharmacokinetics and ADMET prediction • Basis of Molecular Mechanics, Molecular Dynamics and QM/MM for precise investigation of drug-protein binding interface
The course can be taken in both the first and second year of the M.Sc. degree program. A half of the lectures will be dedicated to computational tools. Personalized tutoring by the teacher or collaborators will be useful for the project development. The idea of the course is to provide the students the ability to employ new techniques, software and tools in the field of drug design.
The course can be taken in both the first and second year of the M.Sc. degree program. A half of the lectures will be dedicated to computational tools. Personalized tutoring by the teacher or collaborators will be useful for the project development. The idea of the course is to provide the students the ability to employ new techniques, software and tools in the field of drug design.
Lectures and hands on tutorials in the computational lab
Lectures and hands on tutorials in the computational lab
• D.C. Young, Computational Chemistry, John Wiley &Sons, 2001 • Leach, A.R., 2001. Molecular modelling : principles and applications. Prentice Hall. • R.D. Hoffmann, A. Gohier and P. Pospisil (eds.) Data Mining in Drug Discovery, Wiley-VCH, 2014 • G. Klebe (ed.), Drug Design: Methodology, Concepts and Mode-of-Action, Springer, 2013
• D.C. Young, Computational Chemistry, John Wiley &Sons, 2001 • Leach, A.R., 2001. Molecular modelling : principles and applications. Prentice Hall. • R.D. Hoffmann, A. Gohier and P. Pospisil (eds.) Data Mining in Drug Discovery, Wiley-VCH, 2014 • G. Klebe (ed.), Drug Design: Methodology, Concepts and Mode-of-Action, Springer, 2013
Modalità di esame: progetto di gruppo;
Team Project. The project will be based on an applied problem on drug design to be solved by a student team (e.g., 5 students). The team will develop a report (docx format) and will deliver an oral presentation (pptx format). Each member will be required to present a part of the work and to answer a few questions related to the project.
Exam: group project;
Team Project. The project will be based on an applied problem on drug design to be solved by a student team (e.g., 5 students). The team will develop a report (docx format) and will deliver an oral presentation (pptx format). Each member will be required to present a part of the work and to answer a few questions related to the project.


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