Pharmacokinetic modeling of tranexamic acid for patients undergoing cardiac surgery with normal renal function and model simulations for patients with renal impairment

Tranexamic acid (TXA), an effective anti-fibrinolytic agent that is cleared by glomerular filtration, is used widely for cardiopulmonary bypass (CPB) surgery. However, an effective dosing regimen has not been fully developed in patients with renal impairment. The aims of this study were to characterize the inter-patient variability associated with pharmacokinetic parameters and to recommend a new dosing adjustment based on the BART dosing regimen for CPB patients with chronic renal dysfunction (CRD). Recently published data on CPB patients with normal renal function (n = 15) were re-examined with a two-compartment model using the ADAPT5® and NONMEMVII® to identify covariates that explain inter-patient variability and to ascertain whether sampling strategies might affect parameter estimation. A series of simulations was performed to adjust the BART dosing regimen for CPB patients with renal impairment. Based on the two-compartmental model, the number of samples obtained after discontinuation of TXA infusion was found not to be critical in parameter estimation (p > 0.05). Both body weight and creatinine clearance were identified as significant covariates (p < 0.005). Simulations showed significantly higher than normal TXA concentrations in CRD patients who received the standard dosing regimen in the BART trial. Adjustment of the maintenance infusion rate based on the percent reduction in renal clearance resulted in predicted plasma TXA concentrations that were safe and therapeutic (~100 mg·L(-1) ). Our proposed dosing regimen, with consideration of renal function, is predicted to maintain effective target plasma concentrations below those associated with toxicity for patients with renal failure for CPB

Clinical Trial Simulation to Evaluate Population Pharmacokinetics and Food Effect: Capturing Abiraterone and Nilotinib Exposures

The objectives of this study were to determine (1) the accuracy with which individual patient level exposure can be determined and (2) whether a known food effect can be identified in a trial simulation of a typical population pharmacokinetic trial. Clinical trial simulations were undertaken using NONMEM VII to assess a typical oncology pharmacokinetic trial design. Nine virtual trials for each compound were performed for combinations of different level of between-occasion variability, number of patients in the trial and magnitude of a food covariate on oral clearance. Less than 5% and 20% bias and precision were obtained in individual clearance estimated for both abiraterone and nilotinib using this design. This design resulted biased and imprecise population clearance estimates for abiraterone. The between-occasion variability in most trials was captured with less than 30% of percent bias and precision. The food effect was detectable as a statistically significant covariate on oral clearance for abiraterone and nilotinib with percent bias and precision of the food covariate less than 20%. These results demonstrate that clinical trial simulation can be used to explore the ability of specific trial designs to evaluate the power to identify individual and population level exposures,covariate and variability effects

Nicotine and cotinine exposure from electronic cigarettes: a population approach

BACKGROUND AND OBJECTIVES: Electronic cigarettes (e-cigarettes) are a recent technology that has gained rapid acceptance. Still, little is known about them in terms of safety and effectiveness. A basic question is how effectively they deliver nicotine; however, the literature is surprisingly unclear on this point. Here, a population pharmacokinetic model was developed for nicotine and its major metabolite cotinine with the aim to provide a reliable framework for the simulation of nicotine and cotinine concentrations over time, based solely on inhalation airflow recordings and individual covariates [i.e., weight and breath carbon monoxide (CO) levels]. METHODS: This study included ten adults self-identified as heavy smokers (at least one pack of cigarettes per day). Plasma nicotine and cotinine concentrations were measured at regular 10-min intervals for 90 min while human subjects inhaled nicotine vapor from a modified e-cigarette. Airflow measurements were recorded every 200 ms throughout the session. A population pharmacokinetic model for nicotine and cotinine was developed based on previously published pharmacokinetic parameters and the airflow recordings. All of the analyses were performed with the non-linear mixed-effect modeling software NONMEM(®) version 7.2. RESULTS: The results show that e-cigarettes deliver nicotine effectively, although the pharmacokinetic profiles are lower than those achieved with regular cigarettes. Our pharmacokinetic model effectively predicts plasma nicotine and cotinine concentrations from the inhalation volume, and initial breath CO. CONCLUSION: E-cigarettes are effective at delivering nicotine. This new pharmacokinetic model of e-cigarette usage might be used for pharmacodynamic analysis where the pharmacokinetic profiles are not available

Modeling Sitagliptin Effect on Dipeptidyl Peptidase 4 (DPP4) Activity in Adults with Hematological Malignancies After Umbilical Cord Blood (UCB) Hematopoietic Cell Transplant (HCT)

Background and Objectives— Dipeptidyl peptidase-4 (DPP4) inhibition is a potential strategy to increase the engraftment rate of hematopoietic stem/progenitor cells. A recent clinical trial using sitagliptin, a DPP4 inhibitor approved for type 2 diabetes mellitus, has shown to be a promising approach in adults with hematological malignancies after umbilical cord blood (UCB) hematopoietic cell transplant (HCT). Based on data from this clinical trial, a semi-mechanistic model was developed to simultaneously describe DPP4 activity after multiple doses of sitagliptin in subjects with hematological malignancies after a single-unit UCB HCT. Methods— The clinical study included 24 patients that received myeloablative conditioning followed by 4 oral sitagliptin 600mg with single-unit UCB HCT. Using a nonlinear mixed effects approach, a semi-mechanistic pharmacokinetic/pharmacodynamic model was developed to describe DPP4 activity from this trial data using NONMEM 7.2. The model was used to drive Monte-Carlo simulations to probe various dosage schedules and the attendant DPP4 response. Results— The disposition of sitagliptin in plasma was best described by a 2-compartment model. The relationship between sitagliptin concentration and DPP4 activity was best described by an indirect response model with a negative feedback loop. Simulations showed that twice a day or three times a day dosage schedules were superior to once daily schedule for maximal DPP4 inhibition at the lowest sitagliptin exposure. Conclusion— This study provides the first pharmacokinetic/pharmacodynamic model of sitagliptin in the context of HCT, and provides a valuable tool for exploration of optimal dosing regimens, critical for improving time to engraftment in patients after UCB HCT

Prediction of Nephropathy in Type 2 Diabetes: An Analysis of the ACCORD Trial Applying Machine Learning Techniques

Applying data mining and machine learning (ML) techniques to clinical data might identify predictive biomarkers for diabetic nephropathy (DN), a common complication of type 2 diabetes mellitus (T2DM). A retrospective analysis of the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial was intended to identify such factors using ML. The longitudinal data were stratified by time after patient enrollment to differentiate early and late predictors. Our results showed that Random Forest and Simple Logistic Regression methods exhibited the best performance among the evaluated algorithms. Baseline values for glomerular filtration rate (GFR), urinary creatinine, urinary albumin, potassium, cholesterol, low-density lipoprotein, and urinary albumin to creatinine ratio were identified as DN predictors. Early predictors were the baseline values of GFR, systolic blood pressure, as well as fasting plasma glucose (FPG) and potassium at month 4. Changes per year in GFR, FPG, and triglycerides were recognized as predictors of late development. In conclusion, ML-based methods successfully identified predictive factors for DN among patients with T2DM

Population Pharmacokinetic Modeling To Estimate the Contributions of Genetic and Nongenetic Factors to Efavirenz Disposition

Efavirenz pharmacokinetics is characterized by large between-subject variability, which determines both therapeutic response and adverse effects. Some of the variability in efavirenz pharmacokinetics has been attributed to genetic variability in cytochrome P450 genes that alter efavirenz metabolism, such as CYP2B6 and CYP2A6. While the effects of additional patient factors have been studied, such as sex, weight, and body mass index, the extent to which they contribute to variability in efavirenz exposure is inconsistently reported. The aim of this analysis was to develop a pharmacometric model to quantify the contribution of genetic and nongenetic factors to efavirenz pharmacokinetics. A population-based pharmacokinetic model was developed using 1,132 plasma efavirenz concentrations obtained from 73 HIV-seronegative volunteers administered a single oral dose of 600 mg efavirenz. A two-compartment structural model with absorption occurring by zero- and first-order processes described the data. Allometric scaling adequately described the relationship between fat-free mass and apparent oral clearance, as well as fat mass and apparent peripheral volume of distribution. Inclusion of fat-free mass and fat mass in the model mechanistically accounted for correlation between these disposition parameters and sex, weight, and body mass index. Apparent oral clearance of efavirenz was reduced by 25% and 51% in subjects predicted to have intermediate and slow CYP2B6 metabolizer status, respectively. The final pharmacokinetic model accounting for fat-free mass, fat mass, and CYP2B6 metabolizer status was consistent with known mechanisms of efavirenz disposition, efavirenz physiochemical properties, and pharmacokinetic theory. (This study has been registered at ClinicalTrials.gov under identifier NCT00668395.

Tardive Dyskinesia in Relation to Estimated Dopamine D2 Receptor Occupancy in Patients with Schizophrenia: Analysis of the CATIE data

Objective The objective of this study was to evaluate the relationship between antipsychotic-induced tardive dyskinesia (TD) and estimated dopamine D2 receptor occupancy levels in patients with schizophrenia, using the dataset from the Clinical Antipsychotic Trials in Intervention Effectiveness (CATIE). Methods The dataset from 218 subjects (risperidone, N=78; olanzapine, N=100; ziprasidone, N=40) who presented with a score of zero on the Abnormal Involuntary Movement Scale (AIMS) at baseline in Phase 1 of the CATIE study, and remained for ≥6 months, was used. Peak and trough dopamine D2 receptor occupancy levels on the day of the AIMS assessment at the endpoint were estimated from plasma antipsychotic concentrations, using population pharmacokinetic analysis and our D2 prediction model. The estimated dopamine D2 receptor occupancy levels were compared between patients who presented an AIMS score of ≥2 at endpoint and those with a score of zero, using the Mann-Whitney U test. Results Estimated dopamine D2 receptor occupancy levels at trough were significantly higher in subjects who developed involuntary movements (N=23) than those who did not (N=195) (71.7±14.4% vs. 64.3±19.3%, p<0.05) while no significant difference was found in the estimated peak D2 receptor occupancy between them (75.4±8.7% vs. 72.1±9.9%, p=0.07). When the analyses were separately conducted for the three drugs, there were no significant differences in estimated peak or trough D2 occupancy although the values were consistently numerically higher among those developing involuntary movements. Conclusion Greater dopamine D2 receptor blockade with antipsychotics at trough might increase the risk of tardive involuntary movements although this finding needs to be replicated in larger trials

Hyperprolactinemia and estimated dopamine D2 receptor occupancy in patients with schizophrenia : analysis of the CATIE data

Background Large-scale data are still lacking on the relationship between serum prolactin concentration and dopamine D2 receptor occupancy in patients with schizophrenia treated with antipsychotics. Methods The dataset from 481 subjects (risperidone, N = 172, olanzapine, N = 211, and ziprasidone, N = 98) who participated in Phase 1 of the Clinical Antipsychotic Trials in Intervention Effectiveness (CATIE) was used in the present analysis. Dopamine D2 receptor occupancy levels on the day of the measurement of serum prolactin level were estimated from plasma antipsychotic concentrations. A multivariate general linear model was used to examine effects of clinical and demographic characteristics, including estimated D2 occupancy levels, on serum prolactin concentrations. Individual subjects were divided into two groups, stratified by the presence of hyperprolactinemia. To evaluate the performance of this binary classification, sensitivity, specificity, and accuracy of consecutive cut-off points in the D2 occupancy were calculated. Results The multivariate general linear model revealed that estimated D2 occupancy levels had significant effects on serum prolactin concentrations while any other variables failed to show significant effects. The cut-off point associated with 0.5 or greater, in both sensitivity and specificity with the greatest accuracy, was 73% (sensitivity, 0.58; specificity, 0.68; accuracy = 0.64) (68–70% for risperidone, 77% for olanzapine, and 55% for ziprasidone.). Conclusion The threshold for hyperprolactinemia in D2 occupancy may lie somewhat on a lower side of the established therapeutic window with antipsychotics (i.e. 65–80%). This finding highlights the need for the use of the lowest possible dose to avoid this hormonal side effect in the treatment of schizophrenia.peer-reviewe

Application of a single-objective, hybrid genetic algorithm approach to pharmacokinetic model building.

A limitation in traditional stepwise population pharmacokinetic model building is the difficulty in handling interactions between model components. To address this issue, a method was previously introduced which couples NONMEM parameter estimation and model fitness evaluation to a single-objective, hybrid genetic algorithm for global optimization of the model structure. In this study, the generalizability of this approach for pharmacokinetic model building is evaluated by comparing (1) correct and spurious covariate relationships in a simulated dataset resulting from automated stepwise covariate modeling, Lasso methods, and single-objective hybrid genetic algorithm approaches to covariate identification and (2) information criteria values, model structures, convergence, and model parameter values resulting from manual stepwise versus single-objective, hybrid genetic algorithm approaches to model building for seven compounds. Both manual stepwise and single-objective, hybrid genetic algorithm approaches to model building were applied, blinded to the results of the other approach, for selection of the compartment structure as well as inclusion and model form of inter-individual and inter-occasion variability, residual error, and covariates from a common set of model options. For the simulated dataset, stepwise covariate modeling identified three of four true covariates and two spurious covariates; Lasso identified two of four true and 0 spurious covariates; and the single-objective, hybrid genetic algorithm identified three of four true covariates and one spurious covariate. For the clinical datasets, the Akaike information criterion was a median of 22.3 points lower (range of 470.5 point decrease to 0.1 point decrease) for the best single-objective hybrid genetic-algorithm candidate model versus the final manual stepwise model: the Akaike information criterion was lower by greater than 10 points for four compounds and differed by less than 10 points for three compounds. The root mean squared error and absolute mean prediction error of the best single-objective hybrid genetic algorithm candidates were a median of 0.2 points higher (range of 38.9 point decrease to 27.3 point increase) and 0.02 points lower (range of 0.98 point decrease to 0.74 point increase), respectively, than that of the final stepwise models. In addition, the best single-objective, hybrid genetic algorithm candidate models had successful convergence and covariance steps for each compound, used the same compartment structure as the manual stepwise approach for 6 of 7 (86 %) compounds, and identified 54 % (7 of 13) of covariates included by the manual stepwise approach and 16 covariate relationships not included by manual stepwise models. The model parameter values between the final manual stepwise and best single-objective, hybrid genetic algorithm models differed by a median of 26.7 % (q₁ = 4.9 % and q₃ = 57.1 %). Finally, the single-objective, hybrid genetic algorithm approach was able to identify models capable of estimating absorption rate parameters for four compounds that the manual stepwise approach did not identify. The single-objective, hybrid genetic algorithm represents a general pharmacokinetic model building methodology whose ability to rapidly search the feasible solution space leads to nearly equivalent or superior model fits to pharmacokinetic data