A continuous-time multistate Markov model to describe the occurrence and severity of diarrhea events in metastatic breast cancer patients treated with lumretuzumab in combination with pertuzumab and paclitaxel

Abstract

Purpose

To inform lumretuzumab and pertuzumab dose modifications in order to decrease the incidence, severity, and duration of the diarrhea events in metastatic breast cancer patients treated with a combination therapy of lumretuzumab (anti-HER3) in combination with pertuzumab (anti-HER2) and paclitaxel using quantitative clinical pharmacology modeling approaches.

Methods

The safety and pharmacokinetic (PK) data from three clinical trials (lumretuzumab monotherapy n = 47, pertuzumab monotherapy n = 78, and the combination therapy of lumretuzumab, pertuzumab and paclitaxel n = 35) were pooled together to develop a continuous-time discrete states Markov model describing the dynamics of the diarrhea events.

Results

The model was able to capture the time course of different severities of diarrhea reasonably well. The effect of lumretuzumab and pertuzumab was well described by an E_max function indicating an increased rate of transition from moderate to mild or more severe diarrhea with higher doses. The concentration needed to trigger or worsen diarrhea episodes was estimated to be 120-fold lower in combination therapy compared to monotherapy, suggesting strong synergy between the two monoclonal antibodies. The prophylactic effect of loperamide in a subset of patients was also well captured by the model with a clear tendency to reduce the occurrence of diarrhea events.

Conclusions

This work shows that PK-toxicity modeling provides insight into how the severity of key adverse events evolves over time and highlights the potential use to support decision making in drug development.

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