The Antibody Drug Absorption Following Subcutaneous or Intramuscular Administration and Its Mathematical Description by Coupling Physiologically Based Absorption Process with the Conventional Compartment Pharmacokinetic Model : The Journal of Clinical Pharmacology

Although it is known that FcRn, the neonatal Fc-receptor, functions to protect immune gamma globulin (IgG) from elimination, the influence of FcRn on the tissue distribution of IgG has not been quantified. In the present work, a physiologically-based pharmacokinetic (PBPK) model has been developed to characterize and predict IgG disposition in plasma and in tissues. The model includes nine major compartments, connected in an anatomical manner, to represent tissues known to play a significant role in IgG disposition. Each tissue compartment was subdivided into vascular, endosomal and interstitial spaces. IgG transport between the blood and interstitial compartments may proceed by convection through paracellular pores in the vascular endothelium, or via FcRn-mediated transcytosis across vascular endosomal cells. The model was utilized to characterize plasma concentration-time data for 7E3, a monoclonal antiplatelet IgG1 antibody, in control and FcRn-knockout (KO) mice. These data showed that high dose intravenous immunoglobulin (IVIG), 1g/kg, increased 7E3 clearance in control mice from 5.2 +/- 0.3 to 14.4 +/- 1.4 ml/d/kg; however, IVIG failed to increase the clearance of 7E3 in KO mice (72.5 +/- 4.0 vs. 61.0 +/- 3.6 ml/d/kg). Based on model fitting to the 7E3 plasma concentration data, simulations were conducted to predict tissue concentrations of IgG in control and in KO mice, and the predictions were then tested by assessing 7E3 tissue distribution in KO mice and control mice. 7E3 was radiolabeled with Iodine-125 using chloramine T method, and (125)I-7E3 IgG was administered at a dose of 8 mg/kg to control and KO mice. At various time points, sub-groups of 3 mice were sacrificed, blood and tissue samples were collected, and radioactivity assessed by gamma counting. PBPK model performance was assessed by comparing model predictions with the observed data. The model accurately predicted 7E3 tissue concentrations, with mean predicted vs. observed AUC ratios of 1.04 +/- 0.2 and 0.86 +/- 0.3 in control and FcRn-KO mice. The PBPK model, which incorporates the influence of FcRn on IgG clearance and disposition, was found to provide accurate predictions of IgG tissue kinetics in control and FcRn-knockout mice.

In clinical development stages, an a priori assessment of the sensitivity of the pharmacokinetic behavior with respect to physiological and anthropometric properties of human (sub-) populations is desirable. A physiology-based pharmacokinetic (PBPK) population model was developed that makes use of known distributions of physiological and anthropometric properties obtained from the literature for realistic populations. As input parameters, the simulation model requires race, gender, age, and two parameters out of body weight, height and body mass index. From this data, the parameters relevant for PBPK modeling such as organ volumes and blood flows are determined for each virtual individual. The resulting parameters were compared to those derived using a previously published model (P(3)M). Mean organ weights and blood flows were highly correlated between the two models, despite the different methods used to generate these parameters. The inter-individual variability differed greatly especially for organs with a log-normal weight distribution (such as fat and spleen). Two exemplary population pharmacokinetic simulations using ciprofloxacin and paclitaxel as model drugs showed good correlation to observed variability. A sensitivity analysis demonstrated that the physiological differences in the virtual individuals and intrinsic clearance variability were equally influential to the pharmacokinetic variability but were not additive. In conclusion, the new population model is well suited to assess the influence of individual physiological variability on the pharmacokinetics of drugs. It is expected that this new tool can be beneficially applied in the planning of clinical studies.

To characterize the population pharmacokinetics of trastuzumab in patients with metastatic breast cancer. A nonlinear mixed effect model was based on pharmacokinetic data from phase I, II, and III studies of 476 patients. The phase I study enrolled patients with advanced solid tumors. The phase II and III studies enrolled patients with HER2-positive metastatic breast cancer. Patients in the pivotal phase II and III studies were treated with a 4 mg/kg loading dose of trastuzumab followed by 2 mg/kg weekly for up to 840 days. The model adequately predicted observed trastuzumab concentrations. Model stability and performance were verified using bootstrap simulations. Percentiles, mean, and standard deviation of observed levels were compared with their distributions from 100 replicates of datasets simulated under the model. A two-compartment linear pharmacokinetic model best described the data and accounted for the long-term accumulation observed following weekly administration of trastuzumab. Population estimates from the base model for clearance (CL) and volume of distribution of the central compartment (V1) of trastuzumab were 0.225 L/day, and 2.95 L, respectively. Estimated terminal halflife (t1/2) based on the population estimate was 28.5 days. Interpatient variabilities in clearance and volume were 43 and 29%, respectively. The number of metastatic sites, plasma level of extracellular domain of the HER2 receptor, and patient weight were significant baseline covariates for clearance, volume, or both (P<0.005). However, these covariate effects on trastuzumab exposure were modest and not clinically important in comparison with the large inter-patient variability of CL. Concomitant chemotherapy (anthracycline plus cyclophosphamide, or paclitaxel) did not appear to influence clearance. This population pharmacokinetic model can predict trastuzumab exposure in the long-term treatment of patients with metastatic breast cancer and provide comparison of alternative dosage regimens via simulation.