OptiDose: Computing the Individualized Optimal Drug Dosing Regimen Using Optimal Control.

Area of Science:

• Pharmaceutical Sciences
• Pharmacokinetics
• Pharmacodynamics
• Computational Biology

Background:

• Individualized drug dosing is crucial for effective treatment and patient safety.
• Current dosing strategies may not fully account for complex pharmacokinetic-pharmacodynamic (PK/PD) relationships.
• Optimizing drug regimens requires sophisticated modeling approaches.

Purpose of the Study:

• To develop and validate an optimal dosing algorithm (OptiDose) for individualized patient treatment.
• To compute optimal dosing regimens using PK/PD models across diverse scenarios and administration routes.
• To minimize a cost functional representing deviation from a desired therapeutic outcome.

Main Methods:

• Developed an optimal control problem framework for PK/PD models.
• Formulated drug doses as discrete controls within a finite-dimensional optimization problem.
• Utilized adjoint techniques for efficient gradient computation.
• Employed quasi-Newton methods for solving the optimization problem.

Main Results:

• Successfully developed and validated the OptiDose algorithm.
• Demonstrated the algorithm's applicability to three distinct PK/PD scenarios.
• Efficiently computed optimal individualized dosing regimens.
• Showcased the algorithm's robustness across various administration routes.

Conclusions:

• OptiDose provides an effective computational approach for determining optimal individualized drug dosing regimens.
• The algorithm integrates PK/PD modeling with optimal control theory for enhanced therapeutic strategies.
• OptiDose has significant potential for application in clinical practice and pharmaceutical research.