Population pharmacokinetics-pharmacodynamics of alemtuzumab (Campath) in patients with chronic lymphocytic leukaemia and its link to treatment response

Abstract

Aims: To characterize alemtuzumab pharmacokinetics and its exposure-response relationship with white blood cell (WBC) count in patients with B-cell chronic lymphocytic leukaemia (CLL).

Methods: Nonlinear mixed effects models were used to characterize plasma concentration-time data and WBC count-time data from 67 patients. Logistic regression was used to relate summary measures of drug exposure to tumour response.

Results: Alemtuzumab pharmacokinetics were best characterized by a two-compartment model with nonlinear elimination where V(max) (microg h(-1)) was [1020 x (WBC count/10 x 10(9) l(-1))(0.194)], K(m) was 338 microg l(-1), V(1) was 11.3 l, Q was 1.05 l h(-1) and V(2) was 41.5 l. Intersubject variability (ISV) in V(max), K(m), V(1) and V(2) was 32%, 145%, 84% and 179%, respectively. The reduction in WBC over time was modelled by a stimulatory loss indirect response model with values of 18.2 for E(max), 306 microg l(-1) for EC(50), 1.56 x 10(9) cells l(-1) h(-1) for K(in) and 0.029 per h for K(out). The probability of achieving a complete or partial response was >/=50% when the maximal trough concentration exceeded 13.2 microg ml(-1) or when AUC(0-tau) exceeded 484 microg h(-1) ml(-1).

Conclusions: Alemtuzumab displayed time- and concentration-dependent pharmacokinetics with large interpatient variability, both in pharmacokinetics and pharmacodynamics, which was probably reflective of differences in tumour burden among patients. A direct relationship between maximal trough concentrations and clinical outcomes was observed, with increasing alemtuzumab exposure resulting in a greater probability of positive tumour response.

Figure 1

Basic diagnostic goodness of fit plots under the final pharmacokinetic model

Figure 2

Results of visual predictive check for three selected individuals: a subject with a white blood cell (WBC) count of 281 × 10⁹ cells l⁻¹ prior to dosing (top); a subject with a WBC count of 1.1 × 10⁹ cells l⁻¹ prior to dosing (middle); and a subject with a WBC count of 0.5 × 10⁹ cells l⁻¹ prior to dosing (bottom). Ninety-five percent prediction intervals from final pharmacokinetic model vs. time after the last dose overlaid with observed concentration data from that subject

Figure 3

Results of second predictive check evaluation. Distribution of ranking of observed half-life values compared with the distribution of half-life values from simulated data from selected subset

Figure 4

Basic diagnostic goodness of fit plots under the final pharmacodynamic model

Figure 5

Individual predicted alemtuzumab concentrations and white blood cell (WBC) counts under the final pharmacokinetic and pharmacodynamic model for four representative patients. •, Alemtuzumab concentration; ○, WBC count; solid line, predicted alemtuzumab concentration; dashed line, predicted WBC count

Figure 6

Scatter plot of probability of achieving a complete or partial response vs. model-predicted maximal trough concentrations (top) and AUC_0–τ (bottom plot) in responders and nonresponders using logistic regression analysis. Patients in Study 005 are denoted with solid circles. Patients in Study 213 are denoted with open circles. Solid line is the model-predicted fit from logistic regression analysis with the potentially influential observation (highlighted with an arrow) included. Dashed line is the model-predicted fit with the influential observation removed. Grey boxes are the empirical probability of response based on five bins and including the questionable observation