Short Course: Quantitative-System-Pharmacology: Why, How and When in Drug Discovery and Development Quantitative system pharmacology (QSP) integrates the drug related information (physico-chemical properties, exposures, pharmacokinetics, etc.) to the biology and pharmacology (disease, target, biomarkers, etc.) and attempts to understand the system as a whole. Although complex, such an approach provides immense opportunities in terms of drug discovery, lead identification, optimization, and development in the clinic. This short course will offer a holistic approach to understanding the vital role of QSP by taking a step- wise approach to its application throughout the drug development process. Through the use of real- world case studies and examples, this course provides a unique opportunity for attendees to better understand the rapidly growing field of QSP as well as appreciate how it can be applied immediately to their work. Learning Objectives: • Understand QSP as it relates to the minimum information required to build QSP model. • Gain insight on QSP’s value and utility to various steps of drug discovery and development. • Attempt to construct and apply QSP in their own scenario to address critical questions.
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Short Course: Quantitative-System-Pharmacology: Why, How and When in Drug
Discovery and Development
Quantitative system pharmacology (QSP) integrates the drug related information (physico-chemical
properties, exposures, pharmacokinetics, etc.) to the biology and pharmacology (disease, target,
biomarkers, etc.) and attempts to understand the system as a whole. Although complex, such an
approach provides immense opportunities in terms of drug discovery, lead identification, optimization,
and development in the clinic.
This short course will offer a holistic approach to understanding the vital role of QSP by taking a step-
wise approach to its application throughout the drug development process. Through the use of real-
world case studies and examples, this course provides a unique opportunity for attendees to better
understand the rapidly growing field of QSP as well as appreciate how it can be applied immediately to
their work.
Learning Objectives:
• Understand QSP as it relates to the minimum information required to build QSP model.
• Gain insight on QSP’s value and utility to various steps of drug discovery and development.
• Attempt to construct and apply QSP in their own scenario to address critical questions.
Presentations All presentations will be available on the workshop website, no later than 24 hours following the
workshop. Presentations will remain online for registered attendees until August 7, 2019.
AAPS Disclaimer Statement All scientific presentations at AAPS-sponsored events must adhere to the highest standards of scientific
ethics, including acknowledgements or references to sources (both scientific and financial), and the
absence of promotional content or endorsement of commercial products. Any conflict of interest must
be disclosed prior to the meeting. Authors and speakers are responsible for the content and ideas stated
in their oral and written presentations. AAPS is not responsible for, nor do we endorse, the material
published in any final program materials or any oral or written statements made by presenters at this
Current Landscape of QSP in Pharmaceutical Industry To better understand the current landscape in preclinical QSP modeling and to stimulate the exchange
of knowledge in QSP practices across the biopharmaceutical industry, a preclinical QSP working group
within the IQ DMLG was formed in 2016, consisting of representatives from 17 pharmaceutical
companies, ranging from small biotech to large pharma companies. One of the objectives of the
preclinical QSP working group was to understand current challenges and opportunities for preclinical
QSP modeling within R&D, as well as evaluate the organizational structures of preclinical QSP modelers
within the industry and their interface with other functional experts across R&D and regulatory
agencies. Therefore, a survey was conducted across 50 pharmaceutical companies and was developed
with the support of the International Consortium for Innovation & Quality in Pharmaceutical
Development (IQ). IQ is a not-for-profit organization of pharmaceutical and biotechnology companies
with a mission of advancing science-based and scientifically-driven standards and regulations for
pharmaceutical and biotechnology products worldwide. The results of the survey will be shared in this
workshop session and will provide an understanding of the landscape of QSP across the Pharmaceutical
Industry and its role in drug discovery & development and lead to further dialogues on the value and
application of this approach to improve the probability of clinical success for future drugs.
Maria Nijsen, Ph.D., Abbvie
Marjoleen Nijsen holds a Ph.D. in Pharmacology from the faculty of Medicine at
the University of Utrecht, the Netherlands. Since then she has focused her
pharmacology career in the areas of Neuroscience, Cardiovascular System and
Gastrointestinal Emerging Diseases. In 2003 she switched her career towards the
areas of ADME, bioanalysis, human PK/DDI projection, mechanistic physiological-
based PK (PBPK) and translational PKPD modeling as well as quantitative systems
pharmacology (QSP). She’s currently Vice President of DMPK-BA at Abbvie, to
support drug discovery and development in target validation, compound selection, biomarker
measurements, clinical efficacious dose and DDI predictions across several therapeutic areas, including
Oncology, Neuroscience and Immunology. She is a member of AAPS, ISSX, ISoP, chair of the IQ DMLG
QSP working group and co-member of the IQ CPLG QSP working group.