Understanding hemoglobin contribution to high-dose methotrexate disposition-population pharmacokinetics in pediatric patients with hematological malignancies.

PURPOSE: The aim of the present study was to develop a population pharmacokinetic model for methotrexate (MTX) during high-dose treatment (HDMTX) in pediatric patients with acute lymphoblastic leukemia (ALL) and non-Hodgkin's lymphoma (NHL) and to describe the influence of variability factors. METHODS: The study included 50 patients of both sexes (aged 1-18 years) who received 3 or 5 g/m2 of HDMTX. A nonlinear mixed effect modeling approach was applied for data analysis. Parameter estimation was performed by first-order conditional estimation method with interaction (FOCEI), whereas stepwise covariate modeling was used to assess variability factors. RESULTS: The final model is a two-compartment model that incorporates the effect of body surface area and the influence of hemoglobin and serum creatinine on MTX clearance (CL). Population pharmacokinetic values for a typical subject were estimated at 5.75 L/h/m2 for clearance (CL), 21.3 L/m2 for volume of the central compartment (V1), 8.2 L/m2 for volume of the peripheral compartment (V2), and 0.087 L/h/m2 for intercompartmental clearance (Q). According to the final model, MTX CL decreases with increasing serum creatinine, whereas a positive effect was captured for hemoglobin. A difference of almost 32% in MTX CL was observed among patients' hemoglobin values reported in the study. CONCLUSION: The developed population pharmacokinetic model can contribute to the therapy optimization during HDMTX in pediatric patients with ALL and NHL. In addition to renal function and body weight, it describes the influence of hemoglobin on CL, allowing better understanding of its contribution to the disposition of HDMTX.

Operational characteristics of full random effects modelling ('frem') compared to stepwise covariate modelling ('scm').

An adequate covariate selection is a key step in population pharmacokinetic modelling. In this study, the automated stepwise covariate modelling technique ('scm') was compared to full random effects modelling ('frem'). We evaluated the power to identify a 'true' covariate (covariate with highest correlation to the pharmacokinetic parameter), precision, and accuracy of the parameter-covariate estimates. Furthermore, the predictive performance of the final models was assessed. The scenarios varied in covariate effect sizes, number of individuals (n = 20-500) and covariate correlations (0-90% cov-corr). The PsN 'frem' routine provides a 90% confidence intervals around the covariate effects. This was used to evaluate its operational characteristics for a statistical backward elimination procedure, defined as 'fremposthoc' and to facilitate the comparison to 'scm'. 'Fremposthoc' had a higher power to detect the true covariate with lower bias in small n studies compared to 'scm', applied with commonly used settings (forward p < 0.05, backward p < 0.01). This finding was vice versa in a statistically similar setting. For 'fremposthoc', power, precision and accuracy of the covariate coefficient increased with higher number of individuals and covariate effect magnitudes. Without a backward elimination step 'frem' models provided unbiased coefficients with highly imprecise coefficients in small n datasets. Yet, precision was superior to final 'scm' model precision obtained using common settings. We conclude that 'fremposthoc' is also a suitable method to guide covariate selection, although intended to serve as a full model approach. However, a deliberated selection of automated methods is essential for the modeller and using those methods in small datasets needs to be taken with caution.

Methotrexate concentrations and associated variability factors in high dose therapy of children with acute lymphoblastic leukemia and non-Hodgkin lymphoma.

Monitoring and optimization procedures improved high dose methotrexate (HDMTX) treatment outcomes. However, there are still some concerns regarding unexplained concentration variability. The objective of this study was to evaluate drug concentrations and associated variability factors in pediatric patients with acute lymphoblastic leukemia (ALL) and non-Hodgkin lymphoma (NHL) on HDMTX. Fifty patients (aged 1-18 years), receiving in total 184 HDMTX cycles of 3 or 5 g/m2/24 h infusion, were included in the study. Comparisons of MTX concentrations and concentrations to dose ratio between two dosing groups were conducted by Mann-Whitney U test. Regression analysis was performed with transformed data to assess relationship between MTX concentration to dose ratio and patient characteristics, biochemical analysis and therapy data. Statistically significant difference in concentrations between 3 and 5 g/m2 dosing groups was detected only at 24 h after the start of infusion (p < 0.001), but not at 48 and 72 h (p > 0.05). There was no difference between dose-normalized concentrations. Regression analysis showed that 73.9% of variability in dependent variable can be explained by included variables: time since dose, creatinine clearance (CrCl), hemoglobin and certain concomitant therapy. Our results highlight the importance of not only renal function and concomitant therapy, but also hemoglobin in reducing the variation in MTX concentrations. Therefore, monitoring of aforementioned biochemical parameters during HDMTX is important not only to assess toxicity, but also in predicting their impact on drug level.

Population pharmacokinetic modeling and simulation of HPPH in Chinese patients with esophageal carcinoma.

1. 2-[1-Hexyloxyethyl]-2-devinyl pyropheophorbide-a (HPPH), a second-generation photosensitizer, has been widely employed in photodynamic therapy (PDT) for the treatment of malignant lesions. The objective of this study was to characterize the pharmacokinetics of HPPH in Chinese patients using a population pharmacokinetic (PopPK) approach.2. For the first time, a PopPK model of HPPH for Chinese (n = 20) was developed (registration number: CTR20160425). The pharmacokinetics of HPPH was described by a three-compartment model with linear elimination. Through the stepwise addition (p < 0.05) and backward elimination (p < 0.001) approach, fat-free mass (FFM) was identified to be the most significant covariate and V1 increased with FFM. Visual predictive check (VPC) was employed for the evaluation of the final model. Subsequent full covariate analysis indicated that FFM has considerable impact (∼30%) on HPPH exposure and fat mass also has a modest (∼25%) impact.3. The simulations suggested that a dose adjustment of HPPH may be necessary for Chinese and the dose of 3 mg/m2 should be appropriate. HPPH exposure increases with fat mass while being inversely related to FFM. HPPH-PDT for overweight patients should be monitored with more caution and PDT conditions should be optimized if necessary.

Population pharmacokinetics and covariate analysis of Sym004, an antibody mixture against the epidermal growth factor receptor, in subjects with metastatic colorectal cancer and other solid tumors.

Sym004 is an equimolar mixture of two monoclonal antibodies, futuximab and modotuximab, which non-competitively block the epidermal growth factor receptor (EGFR). Sym004 has been clinically tested for treatment of solid tumors. The present work characterizes the non-linear pharmacokinetics (PK) of Sym004 and its constituent antibodies and investigates two types of covariate models for interpreting the interindividual variability of Sym004 exposure. Sym004 serum concentration data from 330 cancer patients participating in four Phase 1 and 2 trials (n = 247 metastatic colorectal cancer, n = 87 various types advanced solid tumors) were pooled for non-linear mixed effects modeling. Dose regimens of 0.4-18 mg/kg Sym004 dosed by i.v. infusion weekly or every 2nd week were explored. The PK profiles for futuximab and modotuximab were parallel, and the parameter values for their population PK models were similar. The PK of Sym004 using the sum of the serum concentrations of futuximab and modotuximab was well captured by a 2-compartment model with parallel linear and saturable, Michaelis-Menten-type elimination. The full covariate model including all plausible covariates included in a single step showed no impact on Sym004 exposure of age, Asian race, renal and hepatic function, tumor type and previous anti-EGFR treatments. The reduced covariate model contained statistically and potentially clinically significant influences of body weight, albumin, sex and baseline tumor size. Population PK modeling and covariate analysis of Sym004 were feasible using the sum of the serum concentrations of the two constituent antibodies. Full and reduced covariate models provided insights into which covariates may be clinically relevant for dose modifications and thus may need further exploration.

Operating characteristics of stepwise covariate selection in pharmacometric modeling.

Stepwise covariate modeling (SCM) is a widely used tool in pharmacometric analyses to identify covariates that explain between-subject variability (BSV) in exposure and exposure-response relationships. However, this approach has several potential weaknesses, including over-estimated covariate effect and incorrect selection of covariates due to collinearity. In this work, we investigated the operating characteristics (i.e., accuracy, precision, and power) of SCM in a controlled setting by simulating sixteen scenarios with up to four covariate relationships. The SCM analysis showed a decrease in the power to detect the true covariates as model complexity increased. Furthermore, false highly correlated covariates were frequently selected in place of or in addition to the true covariates. Relative root mean square errors (RMRSE) ranged from 1 to 51% for the fixed effects parameters, increased with the number of covariates included in the model, and were slightly higher than the RMRSE obtained with a simple re-estimation exercise with the true model (i.e., stochastic simulation and estimation). RMRSE for BSV increased with the number of covariates included in the model, with a covariance parameter RMRSE of almost 135% in the most complex scenario. Loose boundary conditions on the continuous covariate power relation appeared to have an impact on the covariate model selection in SCM. A stricter boundary condition helped achieve high power (> 90%), even in the most complex scenario. Finally, reducing the sample size in terms of number of subjects or number of samples proved to have an impact on the power to detect the correct model.

Population pharmacokinetics of cyclosporine A in Japanese renal transplant patients: comprehensive analysis in a single center.

PURPOSE: Cyclosporine A (CyA), a potent immunosuppressive agent used in renal transplantation, has a narrow therapeutic window and a large variability in blood concentrations. This study aimed to develop a population pharmacokinetic (PPK) model of CyA in living-donor renal transplant patients at a single center and identify factors influencing CyA pharmacokinetics (PK). METHODS: A total of 660 points (preoperative) and 4785 points (postoperative) of blood concentration data from 98 patients who underwent renal transplantation were used. Pre- and postoperative CyA model structure and PPK parameters were separately estimated with a non-linear mixed-effect model, and subsequently, covariate analysis of postoperative data were comprehensively estimated, including preoperative PK parameters. RESULTS: A two-compartment model with first-order absorption and absorption lag time was selected in this study. Aspartate aminotransferase, body surface area (BSA), pretransplant area under the whole blood concentration-time curve/dose, and postoperative days were identified as the covariates on oral clearance. BSA was selected as a covariate of the distribution volume of the central compartment. In addition, diabetes mellitus was selected as a covariate of the first-order absorption rate. CONCLUSIONS: This PPK study used the largest number of blood concentration data among previous reports of living-donor renal transplant patients. Moreover, all patients received the same immunosuppressive regimen in a single center. Therefore, the validity of the selected covariates is reliable with high precision. The developed PPK model and selected covariates provide useful information about factors influencing CyA PK and greatly contributes to the identification of the most suitable dosing regimen for CyA.

Population Pharmacokinetics of Anlotinib, a Multiple Receptor Tyrosine Kinase Inhibitor, in Chinese Patients With Malignant Tumors.

The objective of this study was to investigate the pharmacokinetic behavior of anlotinib in Chinese patients with malignant tumors using the population approach. A total of 407 anlotinib plasma concentrations from 16 patients were analyzed in this study. Anlotinib was administered orally 12 or 16 mg in the single-dose phase and 12 mg once daily in the multiple-dose phase. A population pharmacokinetic model was established using nonlinear mixed-effects model method. The potential influence of demographic and pathophysiological factors on oral anlotinib pharmacokinetics was investigated in a covariate analysis. The final model was evaluated using goodness-of-fit plots, visual predictive check, and bootstrap methods. The pharmacokinetic profile of anlotinib was best described by a 1-compartment model with first-order absorption and first-order elimination. The population estimates of the apparent total clearance, apparent volume of distribution, and absorption rate constant were 8.91 L/h, 1950 L, and 0.745/h, respectively. Body weight was identified as a significant covariate on apparent volume of distribution. Patients with low body weight tended to show higher exposure to anlotinib than those with high body weight. However, these differences were not clinically significant based on the simulations of the individual body weight effects. Taken together, this population pharmacokinetic model adequately described the pharmacokinetics of anlotinib in patients with malignant tumors and supports the same starting dose among them.

Population Pharmacokinetics as a Tool to Reevaluate the Complex Disposition of Ethanol in the Fed and Fasted States.

The pharmacokinetics (PK) of ethanol are important in pharmacology and therapeutics because of potential drug-alcohol interactions as well as in forensic science when alcohol-related crimes are investigated. The PK of ethanol have been extensively studied since the 1930s, although some issues remain unresolved, such as the significance of first-pass metabolism, whether zero-order kinetics apply, and the effects of food on bioavailability. We took advantage of nonlinear mixed-effects modeling to describe blood-alcohol concentration (BAC) profiles derived from 3 published clinical studies involving oral, intraduodenal, and intravenous administration of ethanol with and without food. The overall data set included 1510 BACs derived from 72 healthy subjects (60 men, 12 women) aged between 20 and 60 years. Two-compartment models with first-order absorption and Michaelis-Menten elimination kinetics adequately described the BAC profiles. Food intake had 2 separate effects: It reduced the absorption rate constant and accelerated the maximum elimination rate. Estimates of the maximum elimination rate (fasted) and the food effect (as a factor) were 6.31 g/h (95%CI, 6.04-6.59 g/h) and 1.39-fold (95%CI, 1.33-1.46-fold), respectively. Simulations showed that the area under the BAC-time curve (AUC) was smaller with lower input rate of ethanol, irrespective of any first-pass metabolism. The AUC from time 0 to 10 hours for a 75-kg subject was 2.34 g â€¢ h/L (fed) and 3.83 g â€¢ h/L (fasted) after an oral dose of 45 g ethanol. This difference was mainly attributable to the food effect on ethanol elimination and depended less on the absorption rate. Our new approach to explain the complex human PK of ethanol may help when BAC predictions are made in clinical pharmacology and forensic medicine.

Mini-PBPK-Based Population Model and Covariate Analysis to Assess the Complex Pharmacokinetics and Pharmacodynamics of RO7449135, an Anti-KLK5/KLK7 Bispecific Antibody in Cynomolgus Monkeys.

RO7449135, an anti-kallikrein (KLK)5/KLK7 bispecific antibody, is in development as a potential therapy against Netherton's syndrome (NS). In cynomolgus monkey studies, RO7449135 bound to KLK5 and KLK7, causing considerable accumulation of total KLKs, but with non-dose-proportional increase. To understand the complex PKPD, a population model with covariate analysis was developed accounting for target binding in skin and migration of bound targets from skin to blood. The covariate analysis suggested the animal batch as the categorical covariate impacting the different KLK5 synthesis rates between the repeat-dose study and single-dose study, and the dose as continuous covariate impacting the internalization rate of the binary and ternary complexes containing KLK7. To comprehend the mechanism underlying, we hypothesized that inhibition of KLK5 by RO7449135 prevented its cleavage of the pro-enzyme of KLK7 (pro-KLK7) and altered the proportion between pro-KLK7 and KLK7. Besides the pro-KLK7, RO7449135 can interact with other proteins like LEKTI through KLK7 connection in a dose-dependent manner. The different high-order complexes formed by RO7449135 interacting with pro-KLK7 or LEKTI-like proteins can be subject to faster internalization rate. Accounting for the dose and animal batch as covariates, the model-predicted free target suppression is well aligned with the visual target engagement check. The population PKPD model with covariate analysis provides the scientific input for the complex PKPD analysis, successfully predicts the target suppression in cynomolgus monkeys, and thereby can be used for the human dose projection of RO7449135.

Population Pharmacokinetics of Total and Free Erdafitinib in Adult Healthy Volunteers and Cancer Patients: Analysis of Phase 1 and Phase 2 Studies.

A population pharmacokinetic (PK) model was developed using data pooled from 6 clinical studies (3 in healthy volunteers and 3 in cancer patients) to characterize total and free plasma concentrations of erdafitinib following single- and multiple-dose administration, to understand clinically relevant covariates, and to quantify the inter- and intraindividual variability in erdafitinib PK. An open, linear, 3-compartment disposition model with first-order absorption and a lag time was used to describe the PK profile of total and free erdafitinib plasma concentrations. The PK of erdafitinib were linear and time independent. After oral administration, erdafitinib was rapidly absorbed, with a time to maximum concentration between 2 and 4 hours. In patients, erdafitinib total apparent oral clearance was 0.200 L/h (median free fraction = 0.24%), and the effective terminal half-life of total drug was 76.4 hours. Interindividual variability in PK parameters was moderate for oral clearance and central volume of distribution, and large for absorption rate and peripheral volume of distribution. Sex and renal function were significant covariates on free oral clearance, while weight, sex, and α1 -acid-glycoprotein were significant on oral central volume of distribution. Age, race, and mild hepatic impairment were not significant covariates of erdafitinib exposure. Given that the magnitude of the covariate effects were within 25% of reference values and that the recommended dosing regimen of erdafitinib comprises individual dose up-titrations and reductions based on presence or absence of toxicities, the clinical relevance of the investigated covariates is expected to be limited, and no dose adjustments are warranted.

A quick and accurate method for the estimation of covariate effects based on empirical Bayes estimates in mixed-effects modeling: Correction of bias due to shrinkage.

Nonlinear mixed-effects modeling is a popular approach to describe the temporal trajectory of repeated measurements of clinical endpoints collected over time in clinical trials, to distinguish the within-subject and the between-subject variabilities, and to investigate clinically important risk factors (covariates) that may partly explain the between-subject variability. Due to the complex computing algorithms involved in nonlinear mixed-effects modeling, estimation of covariate effects is often time-consuming and error-prone owing to local convergence. We develop a fast and accurate estimation method based on empirical Bayes estimates from the base mixed-effects model without covariates, and simple regressions outside of the nonlinear mixed-effect modeling framework. Application of the method is illustrated using a pharmacokinetic dataset from an anticoagulation drug for the prevention of major cardiovascular events in patients with acute coronary syndrome. Both the application and extensive simulations demonstrated that the performance of this high-throughput method is comparable to the commonly used maximum likelihood estimation in nonlinear mixed-effects modeling.

Intrinsic Connectivity Provides the Baseline Framework for Variability in Motor Performance: A Multivariate Fusion Analysis of Low- and High-Frequency Resting-State Oscillations and Antisaccade Performance.

Intrinsic brain activity provides the functional framework for the brain's full repertoire of behavioral responses; that is, a common mechanism underlies intrinsic and extrinsic neural activity, with extrinsic activity building upon the underlying baseline intrinsic activity. The generation of a motor movement in response to sensory stimulation is one of the most fundamental functions of the central nervous system. Since saccadic eye movements are among our most stereotyped motor responses, we hypothesized that individual variability in the ability to inhibit a prepotent saccade and make a voluntary antisaccade would be related to individual variability in intrinsic connectivity. Twenty-three individuals completed the antisaccade task and resting-state functional magnetic resonance imaging (fMRI). A multivariate analysis of covariance identified relationships between fMRI oscillations (0.01-0.2 Hz) of resting-state networks determined using high-dimensional independent component analysis and antisaccade performance (latency, error rate). Significant multivariate relationships between antisaccade latency and directional error rate were obtained in independent components across the entire brain. Some of the relationships were obtained in components that overlapped substantially with the task; however, many were obtained in components that showed little overlap with the task. The current results demonstrate that even in the absence of a task, spectral power in regions showing little overlap with task activity predicts an individual's performance on a saccade task.