Integrative work 1

Teacher(s)

. SOMEBODY; Elens Laure (coordinator); Hermans Emmanuel;

Language

English

Main themes

The course features practical sessions where students will apply fundamental concepts in PK-PD, including pattern recognition and both non-compartmental and compartmental analysis with PK parameter calculations and interpretation. These sessions will also involve PKPD modeling using Pkanalix, covering various case studies such as direct Emax, compartment effect, and turnover. Additionally, coaching sessions are planned to support students in their group work on a PKPD database, encompassing contextualization, data handling, and basic non-compartmental and compartmental analysis of PKPD data.

Content

This course will be divided in different hands-on and case series sessions to familiarize the students with:

• PK pattern recognition
• PD pattern recognition
• Non compartimental analysis
• Bioequivalence and bioavailibility study analysis

Additionnally, the schedule will intergatre supervised coaching sessions to let the students working on their datasets with support from teachers and teaching assistants. 

Teaching methods

This course is part of Block I and is composed of Practical sessions that integrate the competencies acquired through all Bloc 1 Teaching units

1. WPMTX2001: Drug life cycle
2. WPMTX2002 and WPMTX2003: Stat and PK-PD basics
3. WPMTX2004: Data handling
4. Practical sessions of WPMTX2005

Evaluation methods

Students will receive a data set per group of 2-6 students. The work will be evaluated through a written report and an oral defense
Data will relate to a PK-PD study, we ask them to

1. Contextualize the data set:
2. How my dataset integrates in drug life cycle?
3. Describe the molecule/drug
   1. PD aspects
      1. pharmacological class
      2. Indication(s)
      3. Mode of action and notion of receptor
      4. Biomarkers 
   2. PK aspects of the work
      1. Route(s) of administration
      2. ADME pathway
      3. Influencing factors
      4. Special populations
      5. Clinical PK: TDM, PK markers, steady state…
4. Handle the data:
   1. Exploring, cleaning, normalizing, visualizing, summarizing & reporting
5. Draft a DMP
6. NCA of PK data 
7. PK/PD pattern recognition and basic CA modeling

Other information

Slide decks available on Moodle

Teaching materials

• Slide decks available on Moodle

Faculty or entity

> FARM