Dose Schedule Optimization and the Pharmacokinetic Driver of Neutropenia

Abstract

Toxicity often limits the utility of oncology drugs, and optimization of dose schedule represents one option for mitigation of this toxicity. Here we explore the schedule-dependency of neutropenia, a common dose-limiting toxicity. To this end, we analyze previously published mathematical models of neutropenia to identify a pharmacokinetic (PK) predictor of the neutrophil nadir, and confirm this PK predictor in an in vivo experimental system. Specifically, we find total AUC and Cmax are poor predictors of the neutrophil nadir, while a PK measure based on the moving average of the drug concentration correlates highly with neutropenia. Further, we confirm this PK parameter for its ability to predict neutropenia in vivo following treatment with different doses and schedules. This work represents an attempt at mechanistically deriving a fundamental understanding of the underlying pharmacokinetic drivers of neutropenia, and provides insights that can be leveraged in a translational setting during schedule selection.

Translational Modeling of Drug-Induced Myelosuppression and Effect of Pretreatment Myelosuppression for AZD5153, a Selective BRD4 Inhibitor

“…A high degree of drug‐induced myelosuppression could increase the length of time a patient has severe (grade 3 or 4) TCP, even though absolute effects on platelet counts are expected to be smaller, given the reduced function of the BM. Potential mitigation strategies to overcome this are to reduce the dose level (as drug effect is proportional to drug concentrations) or, if required, a longer drug‐free period (7 days or more) should be introduced (if efficacy can be maintained), which is in line with recommendations with neutrophils with similar MTT . These results demonstrate the ability to tailor model‐based predictions with reference to a patient population without additional preclinical studies.…”

“…Potential mitigation strategies to overcome this are to reduce the dose level (as drug effect is proportional to drug concentrations) or, if required, a longer drug-free period (7 days or more) should be introduced (if efficacy can be maintained), which is in line with recommendations with neutrophils with similar MTT. 33 These results demonstrate the ability to tailor modelbased predictions with reference to a patient population without additional preclinical studies. There may be other patientspecific aspects that have not been incorporated (CRi, platelet transfusions, relapse), however these scenarios are difficult to predict from preclinical studies.…”

Systems Pharmacology Model of Gastrointestinal Damage Predicts Species Differences and Optimizes Clinical Dosing Schedules

“…Mathematical models exist for other on‐target oncology toxicities, such as neutropenia, and they allow prediction of neutrophil counts in individual patients . These models have been used to predict the drug effect across species and optimize dosing schedules in the clinic …”

“…10 These models have been used to predict the drug effect across species 11 and optimize dosing schedules in the clinic. 12 The GI tract is an ideal system for mechanistic modeling, as cell types and underlying biological processes are readily observed and measured. Cell position along the cryptvillus axis correlates with cell type and function (Figure 1a), allowing detailed studies into the cell types and dynamics.…”

Frequency‐Domain Response Analysis for Quantitative Systems Pharmacology Models

“…[3][4][5][6][7] Multiple clinical trials in adult and pediatric patients with cancer suggest that such a treatment regimen could be an interesting alternative. 8 Even though there have been some studies, especially in oncology, analyzing the impact of different dosing schedules on treatment success, [9][10][11][12][13][14][15][16][17][18][19] a mathematically sound or even analytical method is still lacking. Currently, pharmacometricians usually rely on trial-and-error methods by brute-force simulations of only a short list of possible regimens dictated by clinical practice to find the optimal regimen for their drug and model rather than applying a more quantitative or even analytical approach up front.…”