Unit MEDICINAL CHEMISTRY III

Course
Chemistry and technology of drugs
Study-unit Code
65003906
Location
PERUGIA
Curriculum
In all curricula
Teacher
Antimo Gioiello
Teachers
  • Antimo Gioiello
  • Andrea Carotti (Codocenza)
Hours
  • 38 ore - Antimo Gioiello
  • 10 ore (Codocenza) - Andrea Carotti
CFU
6
Course Regulation
Coorte 2017
Offered
2021/22
Learning activities
Caratterizzante
Area
Discipline chimiche, farmaceutiche e tecnologiche
Academic discipline
CHIM/08
Type of study-unit
Obbligatorio (Required)
Type of learning activities
Attività formativa monodisciplinare
Language of instruction
Italian
Contents
Provide a broad knowledge on the process of drug discovery with insights into the strategies and methods of computational chemistry, synthesis and chemical technologies that allow its successful completion.
Reference texts
1) Introduzione alla Chimica Farmaceutica, Graham L. Patrick, II Edizione (Editore: EdiSes).
2) Drugs: From Discovery to Approval, 2nd Edition Rick Ng (Editore: Wiley-Blackwell).
Educational objectives
The overall aim of the course Medicinal Chemistry III is to provide the student a wide knowledge on the process of drug discovery and development with insights on key steps that allow its successful completion.

The main knowledge gained will be:
- Methods to identify and validate a biological target
- Design and synthesis of chemical libraries according to the concepts of molecular diversity and drug-likeness.
- Approaches to discover a "Lead compound" and optimize its pharmacodynamic and pharmacokinetic properties.
- Methods to study ligand / target interaction.
- Sustainable drug discovery
- Enabling technologies in drug discovery

The main skills that will be acquired at the end of the course include:
- Ability to set up an experimental study aimed to discover new biological targets.
- Analyze the process of drug discovery identify problems and propose more sustainable solutions
- Design and building a chemical library for the discovery of lead compounds and (pre)clinical candidates
- Set-up approaches for hit-to-lead exploration and lead optimization
Prerequisites
The contents and objectives of the course will be fully acquired and achieved by students if they possess the following knowledge.

Essential knowledge that a student must possess at the beginning of the course:
- Medicinal Chemistry (I and II); Organic Chemistry (I and II); Pharmacology, pharmacognosy and toxicology general;

Skills which the student must have at the beginning of the course:
- Laboratory of drug extration and synthesis; Physiology; English.

Useful knowledge that a student must possess at the beginning of the course:
- Mathematics (statistics); Physical chemistry.
Teaching methods
- Frontal and/or remote lectures on all topics of the course.

- Seminars on topics of general interest as well as based on technological and methodological innovation.
Other information
The supplementary and workshop/seminar activities are determined in agreement with students. They may cover seminars, technical and practical discussions, and reinterpretations of common laboratory practices.
Learning verification modality
The exam includes an oral exam consisting of a discussion aimed at ascertaining the extent of the level of understanding reached by the student and his depth of understanding in the topics of the program.

In addition, it will be evaluated the ability to speak with propriety of language and organization of the subject exposed.

For information on support services for students with disabilities see: http://www.unipg.it/disabilita-e-dsa
Extended program
- Definition and evolution of the process of drug discovery.
- Methods for identification and validation of a biological target.
- Definition and strategies for the discovery of hit and lead compounds.
- Synthetic strategies for the construction of chemical libraries.
- Enabling technologies for drug discovery.
- Approaches and enabling chemical technologies for lead optimization
- Study of the interactions of a compound of biological interest with its target.
- Development of predictive models of biological activity (SAR, QSAR).
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