STRUMENTI STATISTICI E INFORMATICI PER L'ANALISI DEI DATI

Academic Year 2024/2025 - Teacher: FRANCESCO PAPPALARDO

Expected Learning Outcomes

At the end of the course, students will be able to apply advanced statistical methods for the analysis and interpretation of complex data in the field of chemistry and pharmaceutical technology. They will be capable of designing appropriate scientific experiments using experimental designs like factorial designs and utilizing advanced statistical software such as R and Python for data analysis and visualization. Additionally, they will know how to implement multivariate analyses for data dimensionality reduction and classification, perform linear and nonlinear regressions, and optimize synthetic processes. They will understand and apply advanced pharmacokinetic and pharmacodynamic models, validating analytical methods according to ICH guidelines. They will be able to manage databases using SQL and apply knowledge of artificial intelligence and in silico medicine for predictive analysis and personalization of therapies. Finally, they will conduct complete research projects—from hypothesis formulation to results presentation—while respecting current regulations and ethical principles in data management and analysis.

Course Structure

Through lessons and practical sessions at the end of each learning unit (when planned).

If the lessons are given in a mixed or remote way, the necessary changes with respect to what was previously stated may be introduced, in order to meet the program envisaged and reported in the syllabus.

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 didactic objectives and specific needs.

It is also possible to contact the referent teacher CInAP (Center for Active and Participated Integration - Services for Disabilities and / or SLD) of our Department, Prof. Santina Chiechio.

Required Prerequisites

Solid knowledge in organic, analytical, and physical chemistry.
Basic notions of statistics and mathematics.
Basic computer skills

Attendance of Lessons

Ruled by http://www.dsf.unict.it/corsi/lm-13_ctf/regolamento-didattico

Detailed Course Content

Module 1: Introduction to Statistics in the Pharmaceutical Context

Importance of statistical analysis in drug research and development.

Module 2: Experimental Design and Data Analysis

Experimental design in pharmaceutical chemistry
Full and fractional factorial designs.
Analysis of Variance (ANOVA)
- One-way and two-way ANOVA.
Linear and Non-linear Regression
- Multiple regression models.

Module 3: Multivariate Analysis

Principal Component Analysis (PCA)
- Data dimensionality reduction.
Classification and Clustering Methods
- Linear Discriminant Analysis (LDA).
- Clustering algorithms (K-means, hierarchical).

Module 4: Computer Tools for Data Analysis and Visualization

Advanced Statistical Software
- Use of R and Python for statistical analysis.
Advanced Spreadsheets
- Data analysis with Excel: Solver, Data Analysis ToolPak.
Database Management
- Introduction to SQL for data extraction and manipulation.

Module 5: Statistical Applications in Chemistry and Pharmaceutical Technology

Analysis of chemical synthesis data
- Optimization of synthetic processes.
Advanced Pharmacokinetics and Pharmacodynamics
- Compartmental models.
Validation of Analytical Methods
- Validation parameters according to ICH guidelines.

Module 6: AI and In Silico Medicine: Overview

Artificial intelligence and in silico medicine in the pharmaceutical industry
- Predictive analysis and personalization of therapies.

Module 7: Final Project and Practical Applications

Practical laboratories
- Analysis of real datasets from pharmaceutical studies.
Research project
- Development of an individual or group project.
- Presentation and discussion of results.

Textbook Information

Teacher's notes will be available through studium website or made available during the lessons

Course Planning

	Subjects	Text References
1	Provided during the lessons

Learning Assessment

Learning Assessment Procedures

Written exam on the knowledge acquired and possible evaluation of the project.

Examples of frequently asked questions and / or exercises

1) Which of the following statements best describes Principal Component Analysis (PCA)?

A) It is a regression method to predict a dependent variable based on independent variables.

B) It is a supervised classification technique to assign samples to predefined categories.

C) It is a dimensionality reduction technique that transforms correlated variables into a set of uncorrelated components.

D) It is a clustering algorithm used to group similar data without predefined labels.

2) In pharmacokinetics, which compartmental model best describes a drug that distributes rapidly and uniformly throughout the body after absorption?

A) One-compartment model.

B) Two-compartment model.

C) Three-compartment model.

D) Non-compartmental model.

Degree Course in

Pharmaceutical Chemistry and Technology