ANALISI CORRELATIVE STRUTTURA-ATTIVITA'

Structure-activity relationships (SAR) are key to many aspects of drug discovery, ranging from primary screening to lead optimization. Working with SAR starts from identifying whether an SAR actually exists in a collection of molecules and their associated activities to trying to elucidate the details of one or more SARs and subsequently using that information to make structural modifications to optimize some property or activity. Fundamentally, an understanding of the SAR for a set of molecules, allows one to rationally explore chemical space, which, in the absence of “sign posts” is essentially infinite. Invariably, the development of a chemical series involves optimizing multiple physicochemical and biological properties simultaneously . While the intuition and experience of a medicinal chemist is vital to these efforts, the data generated by modern high throughput experimental techniques can overwhelm the capabilities of a single chemist. In these scenarios, in silico methods allow rapid and efficient characterization of SARs. Coupled with in silico modifications of structures, one can easily prioritize large screening decks or even generate new compounds de novo and ascertain whether they belong to the SAR being studied or not. It should be kept in mind that computational methods do not replace medicinal chemistry domain knowledge. Rather, they can provide a guide by integrating and summarizing large amounts of pre-existing data to suggest useful structural modifications. Misuse of these techniques can lead to misleading results. SAR models are a reduced or simplified representation of reality, replete with assumptions and limitations. These methods cover the spectrum in terms of complexity and utility. The goal of this course is to highlight the different type of SAR modeling methods, and specifically, how they support the task of exploring chemical space to elucidate and optimize structure-activity relationships in a drug discovery setting. We can broadly divide them into two groups - those based on statistical or data mining methods (e.g., regression models) and those based on physical approaches (e.g., pharmacophore models). It is important to realize that the choice of modeling technique can influence to what extent and in how much detail an SAR can be explored. 3D approaches on the other hand are generally more explicitly informative, in the sense that one can directly understand the nature of ligand - receptor interactions that underlie an observed SAR. Some 3D approaches are more explicit than others - docking for example. The expanding role of computational science will be examined and will examine the use of: target analysis, virtual screening for lead discovery, structure-based and ligand-based design methods and the use of computational techniques in library preparation and data handling. On completing this course successfully students will be able to:

1. Understand the modern drug discovery process, from start to finish, including both small molecule and biological agents.
2. Have a detailed understanding of modern medicinal chemistry techniques, including structure-activity relationships, quantitative structure-activity relationships and the use of computer techniques within this, including molecular modelling and docking studies.
3. Appreciate the role of modern computational techniques in the drug discovery process .


Skills Learning Outcomes:

A. Competency in effective inter-disciplinary understanding and communication;
B. Critical evaluation of data;
C. Develop molecular modelling and expand computational laboratory skills;
D. Develop independent study skills.

Course contents and teaching

Principal aims

To introduce students to molecular modelling techniques as applied to biological systems with particular emphasis on the methods used and their underlying theory. The student should gain a basic understanding of the available computational methods and their theoretical foundations; what time scales and length scales are accessible; what properties can be computed and to what level of accuracy; and what methods are most appropriate for different molecular systems and properties.

Relevant in silico tools along with success stories, possibilities and difficulties.will be also briefly presented.

Subject knowledge and understanding

Have an understanding of the theoretical background and application of computer modelling in medicinal chemistry; Understand the origins of intermolecular interactions, how to model them, and how to relate them to experimental data; Appreciate the advantages and disadvantages (critical ability) of different modelling methodologies .

Principal Learning Outcomes

1. Ability to implement the above methodologies in practice; b) Ability to analyse a given problem and select a suitable computational method for studying it; (c) Cognitive Skills: The key challenge for this module is for students to be able to design a molecular modelling experiment, and implement it efficiently on a computer. They will also further understand the statistical analysis and interpretation of the results and the relationship to laboratory experiments.(d) Subject-Specific/Professional Skills: Able to undertake molecular modelling to solve specified problems and critically evaluate data and articles.

Frontal Lessons.

According to "Regolamento Didattico di Ateneo (R.D.A.) i.e. University Didactic Regulations attendance of lessons is mandatory.

Should teaching be carried out in mixed mode or remotely, it may be necessary to introduce changes with respect to previous statements, in line with the programme planned and outlined in the syllabus.

Learning assessment may also be carried out on line, should the conditions require it.

Information for students with disabilities and / or SLD: To guarantee equal opportunities and in compliance with the laws in force, interested students can ask for a personal interview in order to plan any compensatory and / or dispensatory measures, based on the teaching objectives and specifications needs. It is also possible to contact the CInAP contact person (Center for Active and Participatory Integration - Services for Disabilities and / or SLD) of the Department of Drug and Health Sciences prof. Teresa Musumeci.

Synopsis: This course will present drug development “Hit to lead selection and validation” as a process involving target selection, lead discovery and optimization using computer based method. Along the way the student will learn about molecular recognition and computer aided drug design as applied to the development of new drugs.

Course contents and teaching

Principal aims

To introduce students to molecular modelling techniques as applied to biological systems with particular emphasis on the methods used and their underlying theory. The student should gain a basic understanding of the available computational methods and their theoretical foundations; what time scales and length scales are accessible; what properties can be computed and to what level of accuracy; and what methods are most appropriate for different molecular systems and properties.

Relevant in silico tools along with success stories, possibilities and difficulties.will be also briefly presented.

• Process of action of drugs. Pharmacodynamics: molecular targets: interactions between bio-active molecules and drug targets. Pharmacokinetics: adsorption, distribution, metabolism, elimination.
• Introduction to basic principles of protein-ligand interactions and a number of concepts in modern drug discovery.
• Rational drug design and introduction to computational methods.
• Chemoinformatic
• Conformational analysis: Geometry optimization and Energy Minimization methods. Quantum- and Molecular-mechanics methods (Force Field).
• Commercial(Cambridge Structural Database: CSD) and non-profit (Protein Brookaven Databank: PDB) crystallographic databases.
• Structure based methods, binding site analysis, dock­ing, scoring functions and virtual screening.
• Application of docking techniques to the prediction of drug-target interactions.
• MIF methods : GRID, CoMFA.
• Ligand based design approaches including “traditional” (2D) QSAR (QSPR), 3D-QSAR (CoMFA) , Pharmacophore modelling.
• Chemical and Drug Databases.
• Property calculations and property filtering.
• Molecular Similarity.
• Prediction of ADME (Administration-Distribution-Metabolism-Excretion) and toxicity of Drug molecule.
• Structural Bioinformatics in Drug Development (Protein Homology modeling).
• Molecular Dynamics.

1. TEXTBOOKS AND OTHER RESOURCES

Due to the cutting edge nature of this course and the rapid advances made in the field , a single primary text which adequately covers the content of this course has not been identified. Therefore each lecturer will provide the student with additional resources to supplement their lecture material. These resources will take the form of text books, journal articles (if available links to the electronic form of these resources will be provided) or web based resources.

Notes from the class; Chemometry booklet; Useful readings suggested from the Teacher.