Clinical pharmacy meets the Saarland University - Individualization of

Clinical pharmacy meets the Saarland University - Individualization of
pharmacotherapy using pharmacometric and pharmacogenetic approaches
Schaeftlein A.1, Lehr T.1
1Clinical
Pharmacy, Saarland University, Campus C2 2, 66123 Saarbrücken, Germany
BACKGROUND
In October 2012 a clinical pharmacy group was established at the Saarland University. One year later the group consists of 1 junior professor, 1 post-doc, 4 PhD
students, 1 diploma student and 2 graduate assistants establishing an in silico modeling and simulation (M&S) research focus area and extended clinical
pharmacy teaching concepts for the host university. The objective of this presentation is to introduce the main research fields and teaching projects of the new
clinical pharmacy group at the Saarland University.
Research
Optimal
design
Machine
Learning
Neruodegenerative
diseases
Therapeutic
Areas
Oncology
Regression
techniques
Software
NONMEM® MONOLIX®
PK SIM®
SAS®
Berkley Madonna®
R®
Pirana®,PsN®
Matlab®
Antiinfectives
Mechanistic Models
• Descriptive
• Mainly based on
observations rather than on
the underlying physiological
processes
• Simplified mechanistic models
• Capture key biological
processes
• Biological and drug
dependent parameters
• Reflect underlying
physiological system as
much as possible
• Separates system- and drug
dependent parameters
Qpul
Growth
α
Absorption
CMT
Qpul
lung
Qfa
Qfa
fat
Qbra
Qbra
brain
Qca
Target
cells
Intercompartmental
clearance
Central
CMT
β
Qre
Infected
cells
I
T
Peripheral
CMT
Qca
heart
Infection
Absorption
rate
excretion
Qmu
Qpan
Qha
Oncolytic Virus
OV
Example: Dabigatran PK during
hemodialysis [1]
Example: Effect of oncolytic
reovirus on tumor cell growth [2]
Qpan
Qsk
skin
Hemodialysis clearance
Total clearance
Qmu
muscle
pancreas
Qsk
Qsp
Total body clearance
Qre
kidney
Qsp
spleen
Qha
liver
Qgu
metabolism
artery
Figure 2. Interaction between the clinical pharmacy working group and the collaboration partners.
Based on the question of interest and the underlying data different kind of
mathematical models are developed: (i) empirical models, (ii) semimechanistic models and (iii) mechanistic models (fig. 3).
Such models characterize, understand, and predict a drug’s pharmacokinetic
and pharmacodynamic behavior (biomarker, efficacy and safety response)
and are used to rationalize data-driven decision making in pharmacotherapy
(e.g. dosing recommendations in special populations like pediatric or renally
impaired patients, clinical trial simulations).
For realization of the PMx and PGx in silico approaches, a dedicated highperformance cluster was established and is exclusively available for the
clinical pharmacy group, which fulfills requirements to handle sensitive
patient data from clinical studies.
Semi-mechanistic Models
vein
Regulatory
Empirical Models
δ
Data Analysis
Study Design
Death
Saarbrücken
Academia
PGx
p
Clinical
Pharmacy
PMx
PBPK
Release
Cooperation
Literature
Databases
Data
mining
Figure 1. Methods and software used (left) by members of the clinical pharmacy group in Saarbrücken
for answering questions in different therapeutic areas (right).
Industry
Data Generation
Cardiovascular
diseases
Methods
Population
PK&PD
Techniques
Individualization of pharmacotherapy using pharmacometric (PMx) and
pharmacogenetic (PGx) approaches as well as the development of new
methodologies for joined PMx/PGx analyses are main research topics of the
group. PMx and PGx data analyses technologies are primarily applied in the
therapeutic areas of central nervous system diseases, cardiovascular
diseases, oncology and antiinfectives (fig. 1). Necessary in vitro, preclinical
and clinical data for in silico approaches are provided by collaboration
partners from academia, pharmaceutical industry and regulatory agency
enabling a multi-disciplinary interaction between pharmacists, medical
practitioner, mathematicians and bioinformaticians (fig. 2).
Qgu
gut
Example: Physiologically-based
PK models of clarithromycin [3]
Figure 3. Overview of the PMx models.
Teaching
During a doctoral thesis, a diploma or an internship, the group offers training in the emerging fields of PMx and PGx to undergraduate and graduate pharmacy
students and to students from neighboring disciplines, e.g. bioinformatics. Apart from this, the group revised the clinical pharmacy teaching concept for
undergraduate pharmacy students at Saarland University focusing on pharmaceutical care based on real-life patient files, pharmacokinetics, -dynamics, epidemiology and –economics as well as therapeutic drug monitoring (fig. 4). The clinical pharmacy lectures are established during the 6th and 7th semester.
Students of the 8th semester have the opportunity to strengthen their knowledge within clinical pharmacy seminars.
CONCLUSION
Teaching
Undergrate students
• Lectures
• Seminars
• Chosen compulsory subject
Characteristics
• Case managment of real-life patient
files (RP-DOC®, TCI works®)
• Integration of student response
systems like Socrative®
• Teaching circles including real-life oneon-one consultations
• Regular exchange (including visits) with
regional hospitals
PhD-, Diploma- and internship students
• Training in techniques and softwares
of PMx and PGx modelling and
simulation
Characteristics
• Interdisciplinary cooperation's
(Medical practitioners,
Mathematicians, Informaticians)
• Member of graduate programs (e.g.
Phar-MetrX program)
• Insights in drug discovery and
development, clinical trial design and
pharmaceutical industry
Figure 4. Fundamental principles in the teaching concept of the clinical pharmacy
working group at the Saarland university .
•
The techniques of the new clinical pharmacy group allow contributing to
improved drug safety and efficacy by predictions of therapeutic failures
and adverse events prior to drug administration in (special) patient
populations by using new in silico M&S strategies.
•
Apart from this, teaching of PMx/PGx and extended clinical pharmacy
contents enables the training of new PMx/PGx and pharmaceutical care
experienced people for pharmacies, academia, industries and authorities.
REFERENCES
[1]
[2]
[3]
Liesenfeld, KH. et al.: Clin Pharmacokinet. 2013, 52:453-462.
Titze, MI. et al.: Jahrestagung der Deutschen Pharmazeutischen Gesellschaft (DPhG), Freiburg, Deutschland, 09.11.10.2013.
Moj, D. et al.: Jahrestagung der Deutschen Pharmazeutischen Gesellschaft (DPhG), Freiburg, Deutschland, 09.11.10.2013.