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Serum creatinine (sCr) is a commonly measured biomarker to estimate glomerular filtration rate (GFR) and therefore widely used as a covariate in population pharmacokinetic models of renally excreted drugs. In neonates, sCr dynamically changes during the first few weeks after birth. Missing covariates are a common problem in pharmacokinetic modeling of neonates due to the limited availability of blood sampling in number and volume. The objective of this work is to develop a parsimonious population model describing time courses of sCr in neonates with the intent to be incorporated into pharmacokinetic models of various drugs where sCr values are sparse or missing. The data for model development consisted of sCr measurements in 1080 newborns with a gestational age of 24–42 weeks. The model is based on a pharmacokinetic model of sCr that involves GFR, backflow of creatinine from the renal tubules, and urinary flow. Gestational age is the only covariate explaining between-subject variability of sCr. The model adequately describes distinct features of the sCr time course such as a peak and decline to a plateau. For a neonate with a GA of 35 weeks, the typical value of sCr at birth was 0.584 mg/dL, the peak (0.794 mg/dL) occurred 2.3 days after birth, to reach a plateau of 0.255 mg/dL approximately after 24.7 days. Model simulations reveal that in neonates with a similar postnatal age, sCr decreases with increasing GA. In summary, our model is designed to be a part of full random effects pharmacokinetic models where sCr is a significant covariate.

Serum creatinine (sCr) is a commonly measured biomarker to estimate glomerular filtration rate (GFR) and therefore widely used as a covariate in population pharmacokinetic models of renally excreted drugs. In neonates, sCr dynamically changes during the first few weeks after birth. Missing covariates are a common problem in pharmacokinetic modeling of neonates due to the limited availability of blood sampling in number and volume. The objective of this work is to develop a parsimonious population model describing time courses of sCr in neonates with the intent to be incorporated into pharmacokinetic models of various drugs where sCr values are sparse or missing. The data for model development consisted of sCr measurements in 1080 newborns with a gestational age of 24-42 weeks. The model is based on a pharmacokinetic model of sCr that involves GFR, backflow of creatinine from the renal tubules, and urinary flow. Gestational age is the only covariate explaining between-subject variability of sCr. The model adequately describes distinct features of the sCr time course such as a peak and decline to a plateau. For a neonate with a GA of 35 weeks, the typical value of sCr at birth was 0.584 mg/dL, the peak (0.794 mg/dL) occurred 2.3 days after birth, to reach a plateau of 0.255 mg/dL approximately after 24.7 days. Model simulations reveal that in neonates with a similar postnatal age, sCr decreases with increasing GA. In summary, our model is designed to be a part of full random effects pharmacokinetic models where sCr is a significant covariate.

Shortened red blood cell (RBC) lifespan is one of the major factors contributing to anemia in end-stage renal disease (ESRD) patients and should be taken into account in anemia management protocols. In this study, we aimed to estimate RBC lifespan and the source of between-subject variability in ESRD patients. The resulting individual parameters (empirical Bayes estimates) were used to predict hemoglobin concentrations 2 weeks in advance. The reticulocyte-based estimation of RBC lifespan (REBEL) and the population modeling of RBC count data were used. A total of 120 blood samples collected biweekly over 10 weeks in 24 patients receiving maintenance doses of recombinant human erythropoietin (rHuEPO) subcutaneously were included in this analysis. Typical RBC lifespan was estimated to be 63.3 days. RBC lifespan was found to increase with erythroferrone, a recently identified hormone participating in iron metabolism. Approximately, a 10% increase in plasma erythroferrone was associated with a 5% increase in RBC lifespan. In addition, RBC lifespan was 18.7% shorter in females compared with males. Out of 24 subjects, 16 had hemoglobin concentrations predicted within 95% prediction intervals. The median absolute prediction error was 15.9% (interquartile range, 9.5 to 24.7%). We demonstrated that REBEL coupled with the population modeling technique can be used effectively to estimate RBC lifespan. Then, individual parameters can be used to predict future hemoglobin concentrations in ESRD patients.

This study aimed to develop a method for implementing dose correction in a Michaelis-Menten (M-M) approximation of a target-mediated drug disposition (TMDD) model with multiple intravenous (IV) bolus administrations. We derived the formula of a correction factor (Fcorr) for each dose in a multiple IV bolus dosing regimens for M-M model. Fcorr depends on the residual free drug amount prior IV bolus dosing event and dose amount. We conducted a stochastic simulation and estimation (SSE) exercise based on therapeutic antibody PK parameters to evaluate the effect of Fcorr on parameter estimation. Previously published clinical PK data of recombinant human erythropoietin (rHuEPO) from four clinical trials in healthy subjects receiving multiple IV bolus doses were analyzed by both M-M model with and without dose correction (MMC and MMNC) as well as the rapid-binding/quasi-steady-state (RB/QSS) TMDD models. Our results showed that MMNC introduced bias to fixed-effect parameter estimates and overestimated random-effect variables. Compared with MMC, MMNC was not able to adequately characterize the nonlinearity in the PK data of antibody and rHuEPO. The MMC-based simulation demonstrated that thricely weekly 10 IU/kg rHuEPO dosing regimen yielded Fcorr = 0.5. This result suggested that the lower-than-expected exposure for rHuEPO at low dose is due to target binding. An M-M approximation of the TMDD model should include a dose correction to avoid model misfitting and potential bias in the estimated PK parameters.

Hepcidin (H25) is a hormone peptide synthesized by the liver that binds to ferroportin and blocks iron export. In this study, H25 was inhibited by administration of single and multiple doses of an anti-H25 monoclonal antibody Ab 12B9m in cynomolgus monkeys. The objective of this analysis was to develop a pharmacodynamic model describing the role of H25 in regulating iron homeostasis and the impact of hepcidin inhibition by Ab 12B9m. Total serum H25 and Ab 12B9m were determined in each animal. Corresponding measurements of serum iron and hemoglobin (Hb) were obtained. The PD model consisted of iron pools in serum (FeS), reticuloendothelial macrophages (FeM), hemoglobin (FeHb), and liver (FeL). The iron was assumed to be transported between the FeS, FeHb, and FeM unidirectionally at rates k S, k Hb, and k M. H25 serum concentrations were described by the previously developed PK model with the parameters fixed at their estimates. The serum iron and Hb data were fitted simultaneously. The corresponding estimates of the rate constants were k S/Fe0 = 0.113 h−1, k M = 0.00191 h−1, and k Hb = 0.00817 h−1. The model-based IC50 value for the H25 inhibitory effect on ferroportin activity was 0.398 nM. The PD model predicted a negligible effect of Ab 12B9m on Hb levels for the tested doses. The presented PD model adequately described the serum iron time courses following single and multiple doses of Ab 12B9m. Ab 12B9m-induced inhibition of H25 resulted in a temporal increase in serum and liver iron and a decrease in the iron stored in reticuloendothelial macrophages.

Prediction of human pharmacokinetics (PK) can be challenging for monoclonal antibodies (mAbs) exhibiting target-mediated drug disposition (TMDD). In this study, we performed a quantitative analysis of a diverse set of six mAbs exhibiting TMDD to explore translational rules that can be utilized to predict human PK. A TMDD model with rapid-binding approximation was utilized to fit PK and PD (i.e., free and/or total target levels) data, and average absolute fold error (AAFE) was calculated for each model parameter. Based on the comparative analysis, translational rules were developed and applied to a test antibody not included in the original analysis. AAFE of less than two-fold was observed between monkey and human for baseline target levels (R 0), body-weight (BW) normalized central elimination rate (K el/BW(-0.25)) and central volume (V c/BW(1.0)). AAFE of less than three-fold was estimated for the binding affinity constant (K D). The other four parameters, i.e., complex turnover rate (K int), target turnover rate (K deg), central to peripheral distribution rate constant (K pt) and peripheral to central rate constant (K tp) were poorly correlated between monkey and human. The projected human PK of test antibody based on the translation rules was in good agreement with the observed nonlinear PK. In conclusion, we recommend a TMDD model-based prediction approach that integrates in vitro human biomeasures and in vivo preclinical data using translation rules developed in this study.

Sensitivity analysis is commonly used to characterize the effects of parameter perturbations on model output. One use for the approach is the optimization of an experimental design enabling estimation of model parameters with improved accuracy. The primary objective of this study is to conduct a sensitivity analysis of selected target-mediated pharmacokinetic models, ascertain the effect of parameter variations on model predictions, and identify influential model parameters. One linear model (Model 1, control) and 2 target-mediated models (Models 2 and 3) were evaluated over a range of dose levels. Simulations were conducted with model parameters being perturbed at the higher and lower ends from literature mean values. Profiles of free plasma drug concentrations and their partial derivatives with respect to each parameter vs time were analyzed. Perturbations resulted in altered outputs, the extent of which reflected parameter influence. The model outputs were highly sensitive to perturbations of linear disposition parameters in all 3 models. The equilibrium dissociation constant (K(D)) was less influential in Model 2 but was influential in the terminal phase in Model 3, highlighting the role of K(D) in this region. An equation for Model 3 in support of the result for K(D) was derived. Changes in the initial receptor concentration [R(tot) (0)] paralleled the observed effects of initial plasma volume (V(c)) perturbations, with increased influence at higher values. Model 3 was also sensitive to the rates of receptor degradation and internalization. These results suggest that informed sampling may be essential to accurately estimate influential parameters of target-mediated models.

Monoclonal antibodies (mAbs) exhibit biexponential profiles in plasma that are commonly described with a standard two-compartment model with elimination from the central compartment. These models adequately describe mAb plasma PK. However, these models ignore elimination from the peripheral compartment. This may lead to underestimation of the volume of distribution of the peripheral compartment and thus over-predicts concentration in the peripheral compartment. We developed a simple and physiologically relevant model that incorporates information on binding and dissociation rates between mAb and FcRn receptor, mAb uptake, reflection, and catabolic degradation. We employed a previously published PBPK model and, with assumptions regarding rates of processes controlling mAb disposition, reduced the complex PBPK model to a simpler circular model with central, peripheral, and lymph compartments specifying elimination from both central and peripheral. We successfully applied the model to describe the PK of an investigational mAb. Our model presents an improvement over standard two-compartmental models in predicting whole-body average tissue concentrations while adequately describing plasma PK with minimal complexity and physiologically more meaningful parameters.

γ-Hydroxybutyric acid (GHB), a drug of abuse, demonstrates complex toxicokinetics with capacity-limited metabolism and active renal reabsorption. The objectives of the present study were to conduct a local sensitivity analysis of a mechanistic model for the active renal reabsorption of GHB and to use the results to inform the design of future studies aimed at developing therapeutic strategies for treating GHB overdoses. A local sensitivity analysis was used to assess the influence of parameter perturbations on model outputs (plasma concentrations and urinary excretion of GHB). Further, a sensitivity index was calculated for each perturbed parameter to assess the specific segments of the time course that are critical to parameter estimation. Model outputs were simulated for rats dosed with 200, 400, 600, and 1,000 mg/kg GHB intravenously and individual parameters were perturbed by two-, five-, and tenfold higher and lower than the nominal value. Model outputs were sensitive to perturbations in clearance and volume parameters. In contrast, model outputs were found to be insensitive to changes in distributional parameters suggesting that additional tissue distribution data is required. Based on the sensitivity analysis the 1,000-mg/kg GHB dose can be eliminated from future studies as the parameters can be adequately estimated from the lower doses. To further validate the use of this model, dose-specific sampling schedules were designed based on model predictions for doses of 600 and 1,500 mg/kg. These sampling schedules were able to adequately capture the inflection point and terminal elimination phase of the plasma concentration–time profiles obtained.