Clinical application of a semimechanistic-physiologic population PK/PD model for neutropenia following pemetrexed therapy
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abstract
PURPOSE: The objective of these analyses was to examine the effect of variations in the explanatory factors of neutropenic response, identified by semimechanistic-physiologic population pharmacokinetic/pharmacodynamic (PK/PD) modeling, on clinically important features of the absolute neutrophil count (ANC)-time profile (e.g, the nadir of the ANC [NANC], its timing [T (Nadir)], and the timecourse of recovery [T (Rec)]). METHODS: Correlation analyses were used to evaluate the relationship of NANC, T (Nadir), and T (Rec) as a function of overall systemic exposure (AUC) and each of the covariates contained in the population PK/PD model. Simulations using the final PK/PD model were used to generate complete ANC-time profiles. Frequency counts of NANCs from the simulated profiles were used to quantitatively explore differences in the incidence and severity of neutropenia associated with a variety of scenarios (500 mg/m2 versus 600 mg/m2, normal vitamin deficiency markers versus elevated vitamin deficiency markers, and body surface area-based versus renal function-based dosing) and to evaluate the effect of individual explanatory factors with respect to neutropenic response. RESULTS: Information obtained from correlation analysis and simulations was helpful in quantitatively exploring the impact of dose, exposure, and/or patient characteristics on neutropenic response. The information gained from these simulations provided supportive evidence for the decision to routinely include vitamin supplementation during pemetrexed treatment as a means of managing the risk of severe neutropenia secondary to pemetrexed administration. These techniques also provided information regarding the specific T (Nadir) and T( Rec) for inclusion in product labeling and suggested that a 14-day treatment cycle might be feasible for pemetrexed. CONCLUSION: For population PK/PD models, to provide useful information for the practicing clinician or the clinical development team, it is not sufficient to look only at influences of covariates on model parameters. Rather, the modeling results need to be carefully investigated in terms of clinically relevant measures.
