(207b) Pharmacometric Model Guided Control for Improved Therapeutic Exposure

An adaptive model-predictive controller was simulated which, directed by a semi-PBPK model, reduces the cardiotoxic effect of doxorubicinol (DOXol), the primary metabolite of doxorubicin (DOX), while maintaining the effectiveness of DOX.

A PBPK model of DOX [1] was expanded to include multiple species and a heart compartment that captures the known myocardial toxicity of DOXol. Model parameters were fit using allometric scaling of subclinical DOX/DOXol tissue data combined with human clinical tissue data. For the adaptive model-predictive control, the DOX and DOXol concentrations in the blood are measured in real-time using a photoplysmograph [2]; the PBPK model predicts the DOXol heart concentration and DOX AUC; and the controller gathers heart concentration predictions and adjusts the infusion based on the continuous measurements model predictions to stay within a target AUC range without exceeding a maximum heart concentration. A simulated study was conducted with a control group and an intervention group that implemented the model-guided controller.

The intervention group showed a 46.9% improvement in maintaining therapeutic AUC levels (control 29.0%, intervention 75.9%). The intervention group also never exceeded the preset maximum heart concentration, indicating the simulation did not allow for the concentration to reach a toxic point. The control group exceeded maximum concentration in 19.8% of simulation iterations.

A PBPK model coupled with the controller code has shown promise in reducing the occurrence of over-prescribing DOX. Simultaneously controlling these values through infusion manipulation will help achieve optimal therapeutic dose with minimal side effects. Tunable user-inputs and physical parameters make this system potentially versatile for broader application.

References:

[1] Dubbelboer, et al. (2017). Mol. Pharmaceutics, 14(3):686-698.

[2] Michalak, et al. (2010). Optics Express, 18(25):26535-26549.