Applied Chemistry to Drug Design

Code:

Q3021

Acronym:

Q3021

Level:

300

Keywords
Classification	Keyword
OFICIAL	Chemistry

Instance: 2017/2018 - 2S

Active?	Yes
Responsible unit:	Department of Chemistry and Biochemistry
Course/CS Responsible:	Bachelor in Chemistry

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
L:BQ	6	Official Study Plan	3	-	6	56	162
L:Q	30	study plan from 2016/17	3	-	6	56	162

Teaching language

Portuguese

Objectives

The student must have a broad knowledge of the entire pipeline of drug development, from the identification of the target to the market entry, including economic and legal aspects and registration of patents. He/she should also be able to, given a biologic target, identify a lead compound. To optimize computationally the energy of interaction between the two species when the target structure is known. Estimate the energetic contribution of the solvent in the receptor-ligand equilibrium as well as the role of hydrophobicity and flexibility. It must know the requirements that a drug must possess to have good absorption, distribution, metabolism and excretion properties. In short, must have the capacity, on an autonomous basis, to make consistent and relevant proposals of ligands with good affinity for the target, with favorable pharmacokinetic properties and with viability for commercial development.

Learning outcomes and competences

To choose a hit/lead compound among a large database; to improve the affinity of the hit/lead compound; to predict, and improve, the pharmacokinetic properties of the hit/lead; to work with all intervenients in the drug discovery and development process.

Working method

Presencial

Pre-requirements (prior knowledge) and co-requirements (common knowledge)

Not applicable

Program

1. Introduction to Drug Design 1.1. What is a drug? 1.2. The origin of drugs. 1.3. Drug formulation, different kinds of formulationsa and theur strenghts and wikenesses 1.4. The multiple names of a drug: internal name,IUPAC name, generic name and commercial name. 1.5. Drug likeliness. ADME-tox properties. 2. Drug Discovery and Development Pipeline. 2.1. Target identification. Methodologies. 2.2. Pharmacophores and hit coumpounds. Methods to find hit compunds: serendipity, screening and rational design. 2.3. From a hit to a lead. 2.4. Lead optimisation. Combinatorial chemistry, parallel synthesis and rational modification. 2.5. Measuring the biological activity. Sinergy between design and assay. 2.6. Pre-development and development. Clinical trials. Post-Comercial survey. 2.7. Scientific, technical and economical reasons for drug discontinuation. 2.8. Marketing of a drug. Legal aspects. 2.9. Patents. 3. Absortion, Distribution, Metabolism and Excretion 3.1. Pharmacokynetics and Pharmacodynamics. Relation with drug dose. 3.2. Bioavailability. The relation between bioavailability and the amount of drug available at the target site. 3.3. Transport Across Biological Membranes. 3.4. Absortion. Barriers to absortion. Molecular properties that modulate absortion. 3.5. Distribution. Molecular properties that influence the rate and extent of drug distribution. 3.6. Metabolism and excretion. Half-life of a drug. 4. Molecular Docking 4.1. Rigid body docking, flexible ligand docking and flexible receptor docking. Strategies to include flexibility in the receptor. 4.2. Conformational search.Sistematic and stochastic algorithms. Molecular Dynamics simulations. Dealing with the solvent. 4.3. Ranking of the solutions. Empiric, force-field and knowledge based scoring functions. Consensus scoring and its limitations. 4.4. Critical evaluation of the general performance of docking methodologies. 5. Quantitative Structure-Activity Relationships (QSAR) 5.1. Introduction to QSAR. 5.2. Stages for build-up a QSAR model. Iterative nature of the derivation of the model. 5.3. Compound Selection. Property diversity, range of values for the descriptors and range of activity of the selected compounds. Interpolation and extrapolation. 5.4. Identification and selection of the most relevant set of descriptors. 5.5. Correlation between descriptors. 5.6. Derivation of the matematical model. 5.7. Complexity of the matematical model and overfitting. 5.8. Evaluation the fit and the statistical significance of the model. 5.9. Evaluation of the predictive capability of the model.

Mandatory literature

Graham Patrick; An Introduction to Medicinal Chemistry, 2009

Teaching methods and learning activities

Classical lectures, centred in the professor, with smaller interventions from the students.

The laboratory classes will follow the problem-based learning paradigm, centred on the students, where the professor will help and guide the student through its search for knowledge, motivated by the necessity to solve the problem it has in hand.

Software

Osiris Property Explorer - http://www.organic-chemistry.org/prog/peo
Marvin Property Plugin - http://www.chemaxon.com/marvin/sketch/index.php
Programa de visualização VMD - http://www.ks.uiuc.edu/Research/vmd

Evaluation Type

Distributed evaluation with final exam

Assessment Components

designation	Weight (%)
Exame	75,00
Participação presencial	25,00
Total:	100,00

Amount of time allocated to each course unit

designation	Time (hours)
Estudo autónomo	106,00
Frequência das aulas	58,00
Total:	164,00

Eligibility for exams

The student must assist to 75% of the practical classes at least.

Calculation formula of final grade

The final classification will be the weighted average of the result of the theoretical exam (50%) and the practical classification (50%). The practical classification will be calculated as an weighted average of the continuous evaluation during the practical classes (25%) and the classification of a examination concerning the work developed during the semester ion the practical classes (25%), to be done together with the lectures examination.

Classification improvement

The student can repeat the written examinations within the calendar and rules defined by the Conselho Pedagógico. The practical classification can not be improved.