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QSPC2022 Satellite Event: Advanced QSP Modeling in R

Special emphasis is put on the estimation of individual parameters based on clinical data, allowing to determine Virtual Subjects, Cohorts, and Populations in an estimation-based approach that is suitable even for large scale models and a large number of patients in the considered data. The ability to estimate such individual level parameters in QSP models has the potential to open up advances in diagnostics through the use of advanced statistical methods.

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IntiQuan’s collaboration with BioNotus in pharmacometrics modeling gets Flemish government support

IntiQuan’s collaboration with BioNotus in pharmacometrics (PMX) modeling has been recognized by the Vlaamse Agentschap Innoveren & Ondernemen (VLAIO). The Flemish government agency has granted a subsidy for scientific staff and consultancy costs (KMOGS.2019.0661) for this joint project, confirming it as a sound business decision. With BioNotus as one of its launch customers, IntiQuan has a further opportunity to test and improve its novel proprietary modeling platform, IQdesktop. BioNotus is using the software to implement an auditable PMX workflow. The…

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Population PK/PD modeling in R

WCoP2020 Workshop on Population PKPD Modeling in R

On Monday, April 6, 2020, IntiQuan will present a full day hands-on workshop on PKPD/NLME modeling workflow from data exploration, parameter estimation, and reporting in the R environment using our IQR Tools R package. After the workshop, the participants will be able to set up their own PKPD/NLME workflow using IQR Tools to: explore the modeling dataset, perform parameter estimation in supported NLME tools, and evaluate parameter estimation results. Furthermore, participants will be able to generate a short report in Microsoft Word directly from the results generated in R.

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Advanced QSP Modeling – From Clinical Data to Virtual Subjects, Cohorts, and Populations

COVID-19 Update: QSPC2020 Workshop replaced by Free Webinar

Special emphasis is put on the estimation of individual parameters based on clinical data, allowing to determine Virtual Subjects, Cohorts, and Populations in an estimation-based approach that is suitable even for large scale models and a large number of patients in the considered data. The ability to estimate such individual level parameters in QSP models has the potential to open up advances in diagnostics through the use of advanced statistical methods.

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Boston QSP Modeling Workshop

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.

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Population PK/PD modeling in R

PAGE2019 Workshop on Population PKPD Modeling in R
On Tuesday, June 11, 2019, IntiQuan will present a full day hands-on workshop on PKPD/NLME modeling workflow from data exploration, parameter estimation, and reporting in the R environment using our IQR Tools R package. After the workshop, the participants will be able to set up their own PKPD/NLME workflow using IQR Tools to: explore the modeling dataset, perform parameter estimation in supported NLME tools, and evaluate parameter estimation results. Furthermore, participants will be able to generate a short report in Microsoft Word directly from the results generated in R.

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QSP modeling in R

PAGE2019 Workshop on QSP modeling in R
On Monday, June 10, 2019, IntiQuan will present a half-day workshop entitled “Using R and the IntiQuan R tools to support efficient QSP modeling, simulation and parameter estimation”. The workshop is designed as a hands-on tutorial. The different topics will be presented on slides and will be illustrated based on a realistic QSP example. For each topic, the participants will have the chance to reproduce the different modeling steps on their own resorting on a provided example model that is suitable for the workshop. Basic knowledge of writing R scripts is of advantage.

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