A Pharmacometric Model to Predict Chemotherapy-Induced Myelosuppression and Associated Risk Factors in Non-Small Cell...

Area of Science:

• Oncology
• Pharmacometrics
• Computational Biology

Background:

• Chemotherapy commonly causes neutropenia, a decrease in neutrophils, due to myelosuppression.
• Existing pharmacokinetic/pharmacodynamic (PK/PD) models for predicting absolute neutrophil count (ANC) are difficult to implement in clinics due to limited data.
• Predicting and managing chemotherapy-induced neutropenia remains a clinical challenge.

Purpose of the Study:

• To develop a predictive model for temporal changes in ANC for non-small cell lung cancer (NSCLC) patients receiving paclitaxel and cisplatin chemotherapy with G-CSF support.
• To create a user-friendly application for visualizing predicted ANC profiles and neutropenia risk.
• To identify patient factors influencing ANC changes during treatment.

Main Methods:

• Developed a PK/PD model incorporating transit compartments for neutrophil maturation and negative feedback.
• Utilized a K-PD model for drug effects and a constant model for G-CSF effects, accommodating limited PK/ANC data.
• Performed covariate analyses to identify factors associated with ANC changes.

Main Results:

• The model demonstrated good fit and reliable parameter estimates for predicting ANC dynamics.
• Covariate analysis indicated lower ANC in female patients and those without diabetes mellitus.
• An R Shiny application was developed to predict ANC profiles and severe neutropenia risk.

Conclusions:

• The developed model and application can serve as a valuable tool for early risk identification of grade 4 neutropenia in NSCLC patients.
• The tool can support clinical decisions, including potential chemotherapy dose adjustments.
• This approach addresses the limitations of sparse data in clinical settings for neutropenia prediction.