STRUMENTI STATISTICI E INFORMATICI PER L'ANALISI DEI DATI

Academic Year 2025/2026 - 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.

Pursuant to the RDA, Article 12 – University Educational Credits (CFU), within the standard workload of 25 total hours of student commitment corresponding to one credit, the following shall apply:

(a) seven (7) hours shall be devoted to lectures or equivalent instructional activities, with the remaining hours reserved for individual study;

(b) a minimum of twelve (12) and a maximum of fifteen (15) hours shall be devoted to classroom exercises or equivalent supervised activities (laboratories), with the remaining hours reserved for personal study and elaboration.

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
Fundamental concepts of descriptive statistics

Module 2: Data Analysis

Full and fractional factorial designs
Probability distributions (Binomial, Poisson, Normal, Gaussian, and others)
Risk measures
Inferential statistics and hypothesis testing
Analysis of Variance (ANOVA)
One-way and two-way ANOVA
Linear and nonlinear 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: Computational Tools for Data Analysis and Visualization

Advanced statistical software
Using R for statistical analysis
Advanced spreadsheet tools
Data analysis with Excel: Solver, Data Analysis ToolPak

Module 5: Statistical Applications in Pharmaceutical Chemistry and Technology

Advanced Pharmacokinetics and Pharmacodynamics
Validation of analytical methods according to ICH guidelines

Module 6: Quantitative Systems Pharmacology

In Silico Trials
Modeling approaches in Pharmaceutical and Life Sciences
Predictive analysis and therapy personalization

Module 7: Final Project and Practical Applications

Hands-on laboratory sessions
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	All the course content - Teaching material will be provided by the teacher

Learning Assessment

Learning Assessment Procedures

The examination dates are published on the website of the Department of Drug and Health Sciences:

https://www.dsf.unict.it/it/corsi/lm-13_ctf/orario-delle-lezioni

Learning assessment may also be conducted online should circumstances require it.

In order to guarantee equal opportunities and in compliance with current legislation, students may request a personal interview to plan any compensatory and/or exemption measures, based on the educational objectives and their specific needs.

Students may also contact the Department’s CInAP (Centre for Active and Participatory Integration – Services for Students with Disabilities and/or Specific Learning Disorders) representative, Prof. Santina Chiechio.

The examination dates are published on the website of the Department of Drug and Health Sciences: https://www.dsf.unict.it/sites/default/files/2025-07-24-calendario%20esami%20CTF.pdf

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