Understanding and Streamlining Dose Finding: From Dose Simulation to Dose Estimation

Area of Science:

• Pharmacometrics
• Clinical Pharmacology
• Drug Development

Background:

• Current dose-finding methods rely on simulations, which are resource-intensive and do not guarantee optimal dose selection.
• Ensuring drug safety and efficacy across diverse patient populations is critical in clinical practice and drug development.
• Existing simulation-based approaches can be time-consuming and may not identify the ideal dose for optimal therapeutic outcomes.

Purpose of the Study:

• To introduce and demonstrate an advanced dose estimation approach, OptiDose, for understanding therapeutic doses in patient populations.
• To streamline the dose-finding process in drug development, moving beyond traditional simulation-based methods.
• To illustrate the application of OptiDose in scenarios with complex dose-response relationships and special populations.

Main Methods:

• Utilized the OptiDose concept for advanced dose estimation.
• Applied the approach to two distinct case studies with varying safety, efficacy, and population dynamics.
• Implemented dose estimation in Monolix software to determine therapeutic dose distributions.

Main Results:

• Estimated therapeutic dose distributions for patient populations, enabling determination of doses for specific safety and efficacy fulfillment.
• Quantified the therapeutic population dose required to meet safety and efficacy requirements for 50% and 95% of patients.
• Successfully demonstrated OptiDose's capability in handling complex dose-finding challenges.

Conclusions:

• The OptiDose approach offers a more efficient paradigm for dose estimation compared to simulation-based methods.
• OptiDose can identify optimal doses across various pharmacometric and clinical pharmacology scenarios, guiding clinical practice and drug selection.
• This method is particularly valuable for dose selection in special populations, including pediatric and cancer patients.