ProductLife Group (PLG), a global leader in regulatory, scientific, compliance, and digital transformation consulting services for the life sciences industry, is pleased to announce the acquisition of IntiQuan AG, a premier Swiss boutique specializing in pharmacometric services for the biopharmaceutical sector.
Webinar series on efficient support of MIDD
IntiQuan is holding a series of free webinars based on IntiQuan’s R-based open source modeling & simulation platform
IQR Tools. The topics range from model representation over population modeling, both in the context of PK(PD) and QSP, to advanced clinical trial simulation, considering drop-out and compliance models.
You can register from the following webpage: https://training.intiquan.com
Efficient and reproducible non-compartmental analysis in R
On Tuesday, June 28th, 2022 (9:00 – 12:00 CEST), IntiQuan will present a 3 hour interactive webinar entitled “Efficient and reproducible non-compartmental analysis in R”. In this workshop, participants will learn to how to conduct non-compartmental analyses (NCA) in a script-based manner, using the open source and freely available IQnca R package.
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.
IntiQuan is seeking modeling and simulation scientists (Associate Director or Director level) to join our team and make a difference
Webinar series on efficient support of MIDD
IntiQuan is holding a series of free webinars based on IntiQuan’s R-based open source modeling & simulation platform
IQR Tools. The topics range from model representation over population modeling, both in the context of PK(PD) and QSP, to advanced clinical trial simulation, considering drop-out and compliance models.
You can register from the following webpage: https://training.intiquan.com
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…
IntiQuan is fully online and working normally. We have taken early measures to ensure 100% business continuity.
PAGE2020 Workshop on Advanced QSP Modeling in R
On Tuesday, June 9, 2020, IntiQuan will present a full day hands-on workshop on 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.
PAGE2020 Workshop on Population PKPD Modeling in R
On Tuesday, June 9, 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.