• Scientific Meetings
ACoP15
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  • All Day
  • Phoenix, AZ
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Join our team for some Arizona sunshine! ACoP 2024 will be held at the Arizona Grand Resort & Spa in Phoenix, Arizona, USA, from November 10-13, 2024. This year’s theme for ACoP 2024 will be: Past as Prologue, Bridge to New Horizons. Meet us in booth #27 where we will help you reach new horizons in Pharmacometrics.

ACoP15 2024 will feature a wide array of exciting scientific sessions, poster presentations, awards, Special Interest Groups (SIG) events, dedicated activities for students and trainees, tutorials, and vendor-sponsored workshops, as well as numerous networking opportunities.

This is your opportunity to connect face-to-face with our experts. Reach out and schedule time to speak with our team, to discuss your next project. On-site from our team will be Lisl Shoda, Ryan Suderman, Steve Chang, Peter Kilford, Nate Musser, Yawen Fan, and Saumitra Rahatekar. 

Can’t Miss Presentations:

Title: Thales: A unified framework for clinical-scale QSP modeling
Presenter: Ryan Suderman, Associate Director and Senior Principal Scientist, QSP
Date: Monday, November 11th
Time: 3:15-4:45 pm

A fundamental aspect of QSP modeling is the formal representation of mechanisms describing a core set of physiological processes to be perturbed by therapeutic intervention. From this core model, a virtual population is often created by sampling distinct parameter sets and then used to simulate clinical trials in the hope of ultimately informing clinical trial design. Currently within the field of QSP, numerous methods exist to build, simulate, optimize, and analyze virtual populations. By leveraging the past 20 years of QSP model building experience, Simulations Plus has developed Thales, a QSP modeling platform that instills standardized approaches both to the development of models and the fitting and validation of virtual populations with an emphasis on supporting clinical development. In this talk, we will highlight core features of Thales that improve efficiency of model development and simulation and introduce a new GUI that tames some of the complexity common to these models. Among these features are high-level, modular objects and universal methods reusable across disease indications, which enables development focused specifically on biological and clinical details and facilitates rapid exchange of knowledge between modelers, biologists, and clinicians. Our experience in both building and using Thales has demonstrated the benefits of standardization in the user interface as well as with the development process itself.

Roller Coaster Session II
Title: Hitting the Sweet Spot: Mechanism-Based Modeling to Evaluate the Interplay of Liver Compound Exposure and Liver Toxicity to Identify Safe Dosing Regimens
Presenter: Lisl Shoda, Associate Vice President and Director of Immunology, QSP
Date: Wednesday, November 13th
Time: 10:45 am – 12:15 pm

Can’t Miss Presentations!
Title: Integration of Gut Microbiome Metabolism in a PBBM-PBPK Model: Its Impact on the Sulfasalazine Absorption
Presenter: Fiona Plait, Scientist I
Date/Time: TBD
Location: TBD
Sensitivity analysis (SA) can help determine how quantitative systems pharmacology (QSP) model outputs are affected based on changes in other parameters values. Applying SA methods during QSP model development can be difficult due to model size, number of parameters, and nonlinearities. In this work, we compare three SA techniques to shed more light on the most influential parameters impacting plasma uric acid in a QSP model for gout. GOUTsym™ is composed of 25 ODEs and 39 algebraics representing pathways and processes important in representing plasma uric acid (UA) levels, including hepatic production, renal clearance pathways and gut contributions, and an embedded exposure model. The SA methods compared were partial rank correlation coefficients (PRCC), derivative-based analysis via singular value decomposition followed by QR factorization (SA-QR) of the sensitivity matrix, and variance-based Sobol’ method. The SA methods consistently identified 7-8 of the 14 total parameters as most influential. While the parameter ranking order for PRCC and SA-QR was nearly identical, the Sobol’ ranking was different but still agreed on the 7 most influential parameters with the other two methods. PRCC and SA-QR methods incorporated a clear cut-off mechanism, which distinguish influential and non-influential parameters. The Sobol’ method required user interpretation for determining a threshold. Computational times differed significantly from minutes (PRCC) to hours (SA-QR) to days (Sobol’). Through this case study, we noted that less complex SA methods like PRCC and SA-QR have similar overall rankings to the Sobol’ method even in a complex QSP model. Each increase in complexity, from correlation-based to derivative-based to variance-based, provides a tradeoff between computation time versus more flexibility and capability to capture complex model responses. Further investigations could inform generalizability of this case study, but the results herein can help inform the choice of SA method during QSP model development.