A tool for neutrophil guided dose adaptation in chemotherapy

Area of Science:

• Pharmacology
• Mathematical Modeling
• Oncology

Background:

• Chemotherapy dosing requires balancing efficacy and toxicity.
• Neutropenia, a common side effect, increases infection risk.
• Predictive models can aid individualized dose selection.

Purpose of the Study:

• To transfer a semi-mechanistic myelosuppression model to a user-friendly dosing tool.
• To demonstrate the tool's utility for individual dose selection in anticancer treatment.
• To evaluate the tool's performance against established software.

Main Methods:

• Developed a semi-mechanistic model for drug-induced myelosuppression.
• Transferred the model from NONMEM to a Microsoft Excel-based dosing tool.
• Utilized a Bayesian estimation procedure for dose optimization.

Main Results:

• The Excel tool successfully solved the pharmacokinetic and pharmacodynamic differential equations.
• Estimation performance was comparable to NONMEM.
• The tool enables users to request optimal doses based on prior neutrophil data.

Conclusions:

• The developed dosing tool provides a practical method for individualizing chemotherapy doses.
• This approach can help manage chemotherapy-induced neutropenia.
• The tool facilitates personalized anticancer treatment strategies.