Related Experiment Video
Updated: Oct 23, 2025

Author Spotlight: Evaluating Clinicians' Adoption of Ultrasound-Guided Vascular Cannulation Through Simulation Training
Published on: August 9, 2024
Concentration-QT modelling in early clinical oncology settings: Simulation evaluation of performance
Gael Cantet1, Alienor Berges1,2, Rhianna O'Sullivan1
1Clinical Pharmacology and Quantitative Pharmacology, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge, UK.
Aims:
Concentration-QT modelling (C-QTc) of first-in-human data has been rapidly adopted as the primary evaluation of QTc interval prolongation risk. Here, we evaluate the performance of C-QTc in early oncology settings (i.e., patients, no placebo or supratherapeutic dose, 3 + 3 designs).
Methods:
C-QTc performance was evaluated across three oncology scenarios using a simulation-estimation approach: (scen1) typical dose-escalation testing six dose levels (n = 21); (scen2) small dose-escalation testing two dose levels (n = 9); (scen3) expansion cohorts at one dose level (n = 6-140). True ΔΔQTc effects ranged from 3 ms ("no effect") to 20 ms ("large effect"). Performance was assessed based on the upper limit of the ΔQTc two-sided 90% CI against a threshold of 10 or 20 ms.
Results:
The performance against the 10 ms threshold was limited based on C-QTc data from typical dose escalation (scen1) and acceptable performance was observed only for relatively large expansions (n ≥ 45; scen3). Performance against the 20 ms threshold was acceptable based on C-QTc data from a typical dose escalation (scen1) or dose expansion cohort n > 10 (scen3). In general, pooling C-QTc data from dose escalation and expansion cohorts substantially improved the performance and reduced the ΔQTc 90% CI width.
Conclusion:
C-QTc performance appeared limited using a 10 ms threshold, but acceptable against a 20 ms threshold. Selection of threshold may be informed by the benefit-risk balance in a specific disease area. Acceptable precision (i.e., confidence intervals) of the estimated ΔQTc, regardless of its magnitude, can be facilitated by pooling data from dose escalation and expansion cohorts.
More Related Videos
06:20Irradiator Commissioning and Dosimetry for Assessment of LQ α and β Parameters, Radiation Dosing Schema, and in vivo Dose Deposition
Published on: March 11, 2021
05:39Author Spotlight: Radiotherapy and Clonogenic Assays for Advancing Cancer Research and Personalized Medicine
Published on: April 5, 2024
Related Concept Videos
Three-Compartment Open Model
Cancer Survival Analysis
Pharmacokinetic Models: Comparison and Selection Criterion
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
Modeling in Therapy
Participant Modeling
Participant modeling involves therapists demonstrating calm and effective behaviors in...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
Pharmacokinetic Models: Overview
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal...