To meet regulatory expectations for Donidalorsen’s BLA, Ionis Pharmaceuticals engaged Certara’s Pharmacometrics experts to develop a validated exposure–response model linking drug exposure, biomarker response, and clinical outcomes. The resulting analysis supported dose optimization and accelerated submission timelines.
FAQs
What is exposure–response (E–R) analysis?
Exposure–response analysis quantitatively characterizes the relationship between drug exposure (concentration in the body) and the resulting pharmacologic or clinical response. It helps determine how different doses affect efficacy, safety, and biomarkers, guiding optimal dosing strategies throughout drug development.
How is E–R analysis performed?
E–R modeling integrates pharmacokinetic, biomarker, and clinical data from trials to quantify dose–response or concentration–response relationships. Methods may include regression modeling, nonlinear mixed-effects models, or simulation-based approaches to explore different dosing scenarios and patient covariates.
What types of data are typically used in E–R models?
E–R models often leverage clinical data (such as efficacy endpoints and safety events), pharmacokinetic (PK) data, and relevant biomarkers. Together, these data types enable developers to evaluate the full exposure–response continuum.
What role does E–R analysis play in dose optimization?
E–R modeling can simulate outcomes under different dosing frequencies or patient characteristics, identifying regimens that maximize efficacy and minimize risk. For Donidalorsen, simulations supported the potential for every-two-month dosing in well-responding patients.
How was E–R analysis applied in the Ionis Donidalorsen program?
In the Donidalorsen development program for hereditary angioedema (HAE), E–R analysis was used to quantify the relationship between drug concentration, biomarker activity (prekallikrein), and attack rate. Modeling supported justification for a less frequent dosing regimen while maintaining efficacy.
Why is E–R analysis important in regulatory submissions?
Regulators such as the FDA and EMA expect E–R analyses to justify dose selection, support labeling, and strengthen benefit–risk assessments. A well-designed E–R model provides scientific evidence for the proposed dosing regimen and can reduce the need for additional clinical trials.
How is E–R analysis performed?
E–R modeling integrates pharmacokinetic, biomarker, and clinical data from trials to quantify dose–response or concentration–response relationships. Methods may include regression modeling, nonlinear mixed-effects models, or simulation-based approaches to explore different dosing scenarios and patient covariates.
How can E–R analysis streamline development?
By predicting responses across patient populations or dosing regimens, E–R modeling supports smarter trial design, reduces uncertainty, and can accelerate regulatory approval—especially when used to justify dose optimization or support accelerated or adaptive pathways.
What types of data are typically used in E–R models?
E–R models often leverage clinical data (such as efficacy endpoints and safety events), pharmacokinetic (PK) data, and relevant biomarkers. Together, these data types enable developers to evaluate the full exposure–response continuum.
How is model validation handled in E–R analysis?
Validation typically involves using external or cross-study data to confirm predictive performance. In the Donidalorsen program, the model was externally validated using Phase II data, demonstrating reliability and reproducibility for regulatory review.
