IntiQuan Boston QSP Workshop
Introduction to Advanced QSP Modeling in R
Date and Time: Wednesday, September 18th, 2019 (8:00 am – 3:15 pm)
Registration required (scroll down)
In the past two decades, quantitative systems pharmacology (QSP), a mechanistically oriented form of drug and disease modeling, has established itself as a powerful tool to quantitatively integrate data and knowledge. Its scope is to support the assessment of drug efficacy and safety problems in model-informed drug discovery and development. The impact of QSP in model-informed drug discovery and development continues to grow and is increasingly recognized within the pharmaceutical industry, from early stages in drug discovery to late-stage development and life-cycle management, up to support of regulatory submission.
Methodologically, QSP has not yet reached its full potential; it requires further maturation for definitions and standardization and communication of QSP models to enable the timely delivery of high‐quality, reproducible, fit‐for‐purpose results. Recently, an approach to a QSP workflow has been proposed, serving as a guide to data programmers and modeling scientists. This workflow covers the entire QSP data structuring and modeling process by providing a recipe with the ingredients needed for a QSP modeling activity to proceed.
The workflow aims at bringing together QSP and population‐based, mixed‐effect pharmacometrics modeling, two approaches that have more modeling and computational techniques in common than not. For example, the distribution of a certain parameter value (e.g., reflecting inter-individual variability) has become an important feature in QSP modeling and may benefit from related parameter estimation techniques borrowed from pharmacometrics modeling. Consequently, hybrid models combining mechanistic and population modeling have emerged and proven necessary for applicability toward practical problems in pharmaceutical research and development. Such models require a workflow and associated tools to enable model development and qualification, with flexible switching from a simplified QSP model without variability, to a QSP model with some variability and uncertainty features included, to possibly a full nonlinear mixed‐effects (NLME) model, depending on the questions to be addressed and the data available.
The workshop is picking up on the workflow described in the paper and shows how to put the abstract concepts into application on a realistic QSP modeling example. The major topics covered in the workshop are:
- Description of models and their simulation (ODE and biochemical reaction-based syntax)
- Reuse of models from model databases – import of SBML models to R
- Introduction to a realistic QSP modeling example
- Standardized general data format applicable to both QSP and NLME modeling
- Robust parameter estimation based on sensitivity equations and multi-start optimization in R
- Analyzing models, informing modeling decisions, using profile likelihood and other methods
- Can I trust my model / the parameter estimates?
- Which experiments should be conducted to be able to decide between different mechanistic hypotheses?
- Seamless incorporation of multi-subject data and estimation of parameter variability
- Transition to full NLME parameter estimation approach
- Evaluation of hybrid models (NLME+QSP)
The workshop is designed as a hands-on tutorial. Each topic will first be presented on slides and will be illustrated based on realistic examples. Between topics, the participants will have the chance to implement and reproduce the different modeling steps on their own using a provided example model.
After the workshop, the participants will have learned to write their own mechanistic QSP models, simulate models, graphically explore their data, and perform parameter estimation – with and without consideration of parameter variability. In addition, participants will have gained insight into how models can be analyzed to better inform the design of new experiments to increase mechanistic understanding and trust in the models.
Basic knowledge of writing scripts in R is an advantage but not strictly necessary.
Computer and Software Requirements
- Participants are supposed to bring their laptops with installations of R and ideally RStudio.
- The IntiQuan R package IQR Tools will be used for all modeling and simulation during the workshop. Installation instructions are available here.
- Two weeks prior to the workshop date participants will receive detailed information for the setup of their computers.
- All workshop material (slides, example files, etc.) will be made available on USB sticks at the day of the workshop.
- After the workshop each participant is entitled to a personal full(*) 1 year license key for IQR Tools by sending an email to email@example.com. (*) Participants from consulting companies can obtain a 1 year license key for personal use but not for commercial use on customer projects.
|Wednesday September 18 – 2019 (8:00 am – 3:15 pm)|
|08:00-08:15||Welcome and Introduction||Henning Schmidt|
|08:15-09:00||Modeling & Simulation
|Setup of a QSP Modeling Project
|11:15-12:00||Can I trust my model?
|12:00-1:00||Lunch Break||Henning Schmidt|
|2:00-3:00||Special Workflow Topics
|3:00-3:15||Concluding Remarks & End of Workshop||Henning Schmidt|
|3:15-5:00||Availability of Presenter for additional Q&A.||Henning Schmidt|
* Exact agenda and content is subject to small changes to adapt to new developments and profile of the audience
|Henning Schmidt, PhD, IntiQuan GmbH
Henning Schmidt is an expert in Model-Informed Drug Development with over fourteen years of industry experience. He has been supporting projects from target validation in the early phases of drug discovery to study design for post-marketing commitments. During the last decade, he has provided decision-making support to drug discovery and development teams in various therapeutic areas, including oncology, dermatology, immunology, respiratory, bone and muscle wasting diseases. This has led to several go/no-go decisions on the progression of novel drugs through their development cycle and successful registrations. In addition, Henning is active in the area of tool development for improving the efficiency, quality and compliance in the areas of systems biology, QSP, and pharmacometrics. Henning received undergraduate education at Darmstadt University, Germany and SUPELEC in Paris, France. He obtained his PhD in Control Theory and Systems Biology at the Royal Institute of Technology in Stockholm, Sweden. He has worked for Fraunhofer Chalmers Research Center (Gothenburg, Sweden) and Novartis Pharma AG (Basel, Switzerland) prior to founding IntiQuan in 2015.
The workshop fee is 250 USD. This includes workshop material and coffee breaks. Payment can be done conveniently and securely via PayPal. By clicking the “Pay Now” button you are redirected to PayPal, allowing secure payment of the workshop fee. An invoice will be sent to you by email. We send out this invoice manually, so please give us a minute to do that.
Refund policy: No refunds.
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In case of question about the workshop, etc., contact Henning Schmidt, IntiQuan GmbH, Spalenring 150, 4055 Basel, Switzerland; Phone: +41 76 603 28 06; Email: firstname.lastname@example.org