Exploring carbon source related localization and phosphorylation in the Snf1/Mig1 network using population and single cell-based approaches.

The AMPK/SNF1 pathway governs energy balance in eukaryotic cells, notably influencing glucose de-repression. In S. cerevisiae, Snf1 is phosphorylated and hence activated upon glucose depletion. This activation is required but is not sufficient for mediating glucose de-repression, indicating further glucose-dependent regulation mechanisms. Employing fluorescence recovery after photobleaching (FRAP) in conjunction with non-linear mixed effects modelling, we explore the spatial dynamics of Snf1 as well as the relationship between Snf1 phosphorylation and its target Mig1 controlled by hexose sugars. Our results suggest that inactivation of Snf1 modulates Mig1 localization and that the kinetic of Snf1 localization to the nucleus is modulated by the presence of non-fermentable carbon sources. Our data offer insight into the true complexity of regulation of this central signaling pathway in orchestrating cellular responses to fluctuating environmental cues. These insights not only expand our understanding of glucose homeostasis but also pave the way for further studies evaluating the importance of Snf1 localization in relation to its phosphorylation state and regulation of downstream targets.

Population pharmacokinetics of primaquine and its metabolites in African males.

BACKGROUND: Primaquine (PQ) is the prototype 8-aminoquinoline drug, a class which targets gametocytes and hypnozoites. The World Health Organization (WHO) recommends adding a single low dose of primaquine to the standard artemisinin-based combination therapy (ACT) in order to block malaria transmission in regions with low malaria transmission. However, the haemolytic toxicity is a major adverse outcome of primaquine in glucose-6-phosphate dehydrogenase (G6PD)-deficient subjects. This study aimed to characterize the pharmacokinetic properties of primaquine and its major metabolites in G6PD-deficient subjects. METHODS: A single low-dose of primaquine (0.4-0.5 mg/kg) was administered in twenty-eight African males. Venous and capillary plasma were sampled up to 24 h after the drug administration. Haemoglobin levels were observed up to 28 days after drug administration. Only PQ, carboxy-primaquine (CPQ), and primaquine carbamoyl-glucuronide (PQCG) were present in plasma samples and measured using liquid chromatography mass spectrometry. Drug and metabolites' pharmacokinetic properties were investigated using nonlinear mixed-effects modelling. RESULTS: Population pharmacokinetic properties of PQ, CPQ, and PQCG can be described by one-compartment disposition kinetics with a transit-absorption model. Body weight was implemented as an allometric function on the clearance and volume parameters for all compounds. None of the covariates significantly affected the pharmacokinetic parameters. No significant correlations were detected between the exposures of the measured compounds and the change in haemoglobin or methaemoglobin levels. There was no significant haemoglobin drop in the G6PD-deficient patients after administration of a single low dose of PQ. CONCLUSIONS: A single low-dose of PQ was haematologically safe in this population of G6PD-normal and G6PD-deficient African males without malaria. Trial registration NCT02535767.

Modeling Developmental Changes in Caffeine Clearance Considering Differences between Pre- and Postnatal Period.

Taguchi et al. reported that postmenstrual age (PMA) is a promising factor in describing and understanding the developmental change of caffeine (CAF) clearance. The aim of the present study was to quantify how developmental changes occur and to determine the effect of the length of the gestational period on CAF clearance. We performed a nonlinear mixed effect model (NONMEM) analysis and evaluated the fit of six models. A total of 115 samples were obtained from 52 patients with a mean age of 34.3 ± 18.2 d. The median values of gestational age (GA) and postnatal age (PNA) were 196 and 31 d, respectively. Serum CAF levels corrected for dose per body surface area (BSA) (C/D ratioBSA) were dependent on PMA rather than PNA, which supports the findings of a previous study. NONMEM analysis provided the following final model of oral clearance: CL/F = 0.00603∙WT∙∙0.877GA ≤ 196 L/h. This model takes into account developmental changes during prenatal and postnatal periods separately. The model successfully described the variation in clearance of CAF. Our findings suggest that the dosage of CAF in preterm infants should be determined based not only on body weight (WT) but also on both PNA and GA.

Why the racing industry and equestrian disciplines need to implement population pharmacokinetics: To learn, explain, summarize, harmonize, and individualize.

Population pharmacokinetics (POP PK) is a powerful pharmacokinetic tool, which measures quantitatively, and explains the variability in drug exposure and drug effect between individuals. POP PK uses an observational (nonexperimental) approach; it is conducted in the target population living in its normal environment (e.g., farm and race-track). The strength of the POP PK approach lies in its greater relevance for the population studied in its different natural environments than experimental studies carried out in more or less biased laboratory conditions. In clinical settings, it is commonly necessary to restrict the number of samples per subject collected for analysis and the derived data cannot be analyzed using traditional individual data analytical methods; rather data are merged and analyzed with an appropriate statistical tool: the nonlinear mixed effect model (NLMEM). POP PK modeling is frequently used with the objective of adjusting drug dosage, and hence drug exposure, not only for the whole population but also for subgroups of animals (e.g., for a given breed, sex, and age). It can also have application at the individual subject level, in the context of precision medicine. For horses, the use of the POP PK/PD model will allow prescribers to estimate an individual Withdrawal Time for a given horse whose treatment they are supervising. Another potential field of application will be meta-analysis of existing data to generate new knowledge on a drug or to collate and synthesize, in an objective and transparent manner, existing data; this will facilitate harmonization of screening limits at an international level.

Significant predictors for olanzapine pharmacokinetics: a systematic review of population pharmacokinetic studies.

INTRODUCTION: Olanzapine is widely used for treating schizophrenia and bipolar I disorder. Due to its high pharmacokinetic variability, several population pharmacokinetic studies have been performed to identify factors contributing to the variability and thus facilitate individualized dosing. This review aims to provide a comprehensive overview of published population pharmacokinetic studies and explore potential covariates. METHODS: We systematically searched PubMed, Web of Science, and EMBASE databases from their inception to 31 December 2022. Information on the study design, characteristics, and final parameter estimates was summarized and compared. Monte Carlo simulations provided visual predictive distributions to compare eligible studies. Forest plots were constructed to explore the effects of covariates on olanzapine pharmacokinetics. RESULTS: A total of 10 population pharmacokinetic and three population pharmacokinetic/pharmacodynamic studies involving infants, children, adolescents, and adults were finally included. The median apparent clearance was 0.253 L/h/kg in adults, 27-43% lower than that of infants and children. Men and smokers increased the apparent clearance of olanzapine by 32% and 34%, respectively. The concentration required to achieve half of the maximum effect for the Positive and Negative Syndrome Scale total score was 24.80 ng/mL, comparable with 22.32 ng/mL for dopamine D2 receptor occupancy. CONCLUSIONS: A higher dosage may be required for men or heavy smokers than for women or nonsmokers to reach the same exposure. Moreover, further population studies are essential to be conducted to clarify the dose-exposure-response relationship of olanzapine. PROSPERO REGISTRATION: CRD42022368637.

Population Pharmacokinetics of Levetiracetam: A Systematic Review.

BACKGROUND: The use of levetiracetam (LEV) has been increasing, given its favorable pharmacokinetic profile. Numerous population pharmacokinetic studies for LEV have been conducted. However, there are some discrepancies regarding factors affecting its pharmacokinetic variability. Therefore, this systematic review aimed to summarize significant predictors for LEV pharmacokinetics as well as the need for dosage adjustments. METHODS: We performed a systematic search for population pharmacokinetic studies of LEV conducted using a nonlinear-mixed effect approach from PubMed, Scopus, CINAHL Complete, and Science Direct databases from their inception to March 2020. Information on study design, model methodologies, significant covariate-parameter relationships, and model evaluation was extracted. The quality of the reported studies was also assessed. RESULTS: A total of 16 studies were included in this review. Only two studies were conducted with a two-compartment model, while the rest were performed with a one-compartment structure. Bodyweight and creatinine clearance were the two most frequently identified covariates on LEV clearance (CLLEV). Additionally, postmenstrual age (PMA) or postnatal age (PNA) were significant predictors for CLLEV in neonates. Only three studies externally validated the models. Two studies conducted pharmacodynamic models for LEV with relatively small sample size. CONCLUSION: Significant predictors for LEV pharmacokinetics are highlighted in this review. For future research, a population pharmacokinetic-pharmacodynamic model using a larger sample size should be conducted. From a clinical perspective, the published models should be externally evaluated before clinical implementation.

Finding optimal design in nonlinear mixed effect models using multiplicative algorithms.

BACKGROUND AND OBJECTIVES: To optimize designs for longitudinal studies analyzed by nonlinear mixed effect models (NLMEMs), the Fisher information matrix (FIM) can be used. In this work, we focused on the multiplicative algorithms, previously applied in standard individual regression, to find optimal designs for NLMEMs. METHODS: We extended multiplicative algorithms to mixed models and implemented the algorithm both in R and in C. Then, we applied the algorithm to find D-optimal designs in two longitudinal data examples, one with continuous and one with binary outcome. RESULTS: For these examples, we quantified the improved speed when C is used instead of R. Design optimization using the multiplicative algorithm led to designs with D-efficiency gains between 13% and 25% compared to non-optimized designs. CONCLUSION: We found that the multiplicative algorithm can be used efficiently to design longitudinal studies.

A Modified Hybrid Wald's Approximation Method for Efficient Covariate Selection in Population Pharmacokinetic Analysis.

One important objective of population pharmacokinetic (PPK) analyses is to identify and quantify relationships between covariates and model parameters such as clearance and volume. To improve upon existing covariate model development methods including stepwise procedures and Wald's approximation method (WAM), this paper introduces an innovative method named the hybrid first-order conditional estimation (FOCE)/Monte-Carlo parametric expectation maximization (MCPEM)-based Wald's approximation method with backward elimination (BE), or H-WAM-BE. Compared with WAM, this new method uses MCPEM to obtain full covariance matrix after running FOCE to obtain full model parameter estimates, followed by BE to select the final covariate model. Two groups of datasets (simulation datasets and rituximab datasets) were used to compare the performance of H-WAM-BE with two other methods, likelihood ratio test (LRT)-based stepwise covariate method (SCM) and H-WAM with full subset approach (H-WAM-F) in NONMEM. Different scenarios with different sample sizes and sampling schemes were used for simulating datasets. The nominal model was used as the reference to evaluate the three methods for their ability to accurately identify parameter-covariate relationships. The methods were compared using the number of true and false positive covariates identified, number of times that they identified the reference model, computation times, and predictive performance. Best-performing H-WAM-BE methods (M2 and M4) showed comparable results with LRT-based SCM. H-WAM-BE required shorter or comparable computation times than LRT-based SCM and H-WAM-F regardless of the model structure, sample size, or sampling design used in this study.

Sources of lamotrigine pharmacokinetic variability: A systematic review of population pharmacokinetic analyses.

BACKGROUND: Lamotrigine (LTG) is a new generation antiepileptic drug. However, relatively high interindividual pharmacokinetic variability of this drug has been documented. Therefore, several population pharmacokinetic studies of lamotrigine were conducted to identify factors influencing its pharmacokinetics. OBJECTIVE: This systematic review aimed to summarize significant factors influencing LTG pharmacokinetics and their relationships with pharmacokinetic parameters as well as the magnitude of pharmacokinetic variability. METHODS: Four databases i.e. PubMed, Scopus, CINAHL Complete, and Science Direct were systematically searched from their inception to March 2020. Population pharmacokinetic studies of LTG conducted in humans using a nonlinear-mixed effect approach were eligible for a systematic review. RESULTS: Nineteen studies were included in this systematic review. Most studies characterized LTG pharmacokinetics as a one-compartment model structure. The three most frequently identified significant covariates influencing LTG clearance included concomitant antiepileptic drugs, body weight, and genetic polymorphisms. Approximately 58% of the studies did not externally validate the models. CONCLUSIONS: For clinical application, LTG maintenance dose could be optimized using population pharmacokinetic models employing covariates such as concomitant antiepileptic drugs, body weight, and genetic polymorphisms. However, these models should be assessed for their predictability in the target population before utilizing such models in clinical settings.

Implementing PRED Subroutine of NONMEM for Versatile Pharmacokinetic Analysis Using Fast Inversion of Laplace Transform (FILT).

In pharmacokinetic (PK) analysis, conventional models are described by ordinary differential equations (ODE) that are generally solved in their Laplace transformed forms. The solution in the Laplace transformed forms is inverse Laplace transformed to derive an analytical solution. However, inverse Laplace transform is often mathematically difficult. Consequently, numerical inverse Laplace transform methods have been developed. In this study, we focus on extending the modeling functions of Nonlinear Mixed Effect Model (NONMEM), a standard software for PK and population pharmacokinetic (PPK) analyses, by adding the Fast Inversion of Laplace Transform (FILT) method, one of the representative numerical inverse Laplace transform methods. We implemented PREDFILT, a specialized PRED subroutine, which functions as an internal model unit in NONMEM to enable versatile FILT analysis with second-order precision. The calculation results of the compartment models and a dispersion model are in good agreement with the ordinary analytical solutions and theoretical values. Therefore, PREDFILT ensures enhanced flexibility in PK or PPK analyses under NONMEM environments.

Bayesian Individual Dynamic Predictions with Uncertainty of Longitudinal Biomarkers and Risks of Survival Events in a Joint Modelling Framework: a Comparison Between Stan, Monolix, and NONMEM.

Given a joint model and its parameters, Bayesian individual dynamic prediction (IDP) of biomarkers and risk of event can be performed for new patients at different landmark times using observed biomarker values. The aim of the present study was to compare IDP, with uncertainty, using Stan 2.18, Monolix 2018R2 and NONMEM 7.4. Simulations of biomarker and survival were performed using a nonlinear joint model of prostate-specific antigen (PSA) kinetics and survival in metastatic prostate cancer. Several scenarios were evaluated, according to the strength of the association between PSA and survival. For various landmark times, a posteriori distribution of PSA kinetic individual parameters was estimated, given individual observations, with each software. Samples of individual parameters were drawn from the posterior distribution. Bias and imprecision of individual parameters as well as coverage of 95% credibility interval for PSA and risk of death were evaluated. All software performed equally well with small biases on individual parameters. Imprecision on individual parameters was comparable across software and showed marked improvements with increasing landmark time. In terms of coverage, results were also comparable and all software were able to well predict PSA kinetics and survival. As for computing time, Stan was faster than Monolix and NONMEM to obtain individual parameters. Stan 2.18, Monolix 2018R2 and NONMEM 7.4 are able to characterize IDP of biomarkers and risk of event in a nonlinear joint modelling framework with correct uncertainty and hence could be used in the context of individualized medicine.

Application of a hemodialysis clearance prediction model using quantitative structure-pharmacokinetic relationship analysis.

Hemodialysis (HD) is a method used to remove biogenic substances or blood components that cause disease and some drugs used by patients to treat their diseases. Therefore, dosing schedule must be planned according to HD clearance (CLHD ) when medical treatment is provided to patients receiving HD. We aimed to clarify the physical properties (eg, octanol-water partition coefficient and molecular electronegativity) or pharmacokinetic parameters (eg, volume of distribution) of compounds affecting CLHD and to construct a mathematical model to predict CLHD . The analysis covered individual CLHD data for nine compounds from the literature. The molecular descriptors which are physical properties or pharmacokinetic parameters were calculated using the structural formula of each compound, and searched for factors related to CLHD among the calculated 148 molecular descriptors. Nonlinear mixed-effects model analysis with CLHD as objective variable and molecular descriptors as explanatory variable was conducted to examine the factor affecting CLHD and develop a model for predicting CLHD . The logarithm of the brain/blood partition coefficient was detected as a factor affecting CLHD . The predictive accuracy of CLHD using the constructed mathematical model with the logarithm of the brain/blood partition coefficient as explanatory variable was adequate.

Population Pharmacokinetics of Teicoplanin in Chinese Adult Patients / 中国药学杂志

OBJECTIVE: To establish a population pharmacokinetics(PPK) model of teicoplanin(TEC) in Chinese adult patients and investigate the factors influencing TEC pharmacokinetic parameters. METHODS: A total of 222 blood samples and related information were prospectively collected from 139 inpatients with Gram-positive bacterial infection receiving TEC intravenously. A one-compartment model with first order elimination was used to perform the PPK analysis and the PPK model of TEC was developed via nonlinear mixed effects modeling(NONMEM) approach. The stability and prediction of the final model were evaluated by Bootstrap and normalized predictive distribution error (NPDE). Monte Carlo simulation was used to evaluate the effective of currently recommended dosing regimen. RESULTS: The creatinine clearance(CLcr) and albumin(ALB) were identified as the most significant covariate on the clearance rate of TEC. The established final model was: CL(L•h-1)=1.24×(CLcr/77)0.564×31/ALB;V(L)=69.2. It is verified that the established final model is stable, effective and predictable. For most patients with different serum albumin concentration and CLcr, the initial loading dose of 400 mg/q12h, iv, 3 times, and the maintenance dose of 400-800 mg•d-1 can achieve effective treatment of trough concentration. Severe infections need to adjust the loading dose to 800 mg/q12h, iv, 3 times, and maintain a dose of 400-800 mg•d-1 of the dosing regimens to ensure that the blood concentration reached 15 mg•L-1. CONCLUSION: This study reports that CLcr, ALB has a significant effect on TEC clearance and the model has important value for the individualization of TEC therapy in Chinese adult patients.

Optimal Dosing Regimen of Phenytoin for Korean Epilepsy Patients: From Premature Babies to the Elderly.

Phenytoin has been decreasingly used because of the high interindividual variability in drug concentration and the narrow therapeutic window. Despite such drawbacks, phenytoin is still essential as a second-line therapy for status epilepticus when patients are resistant to benzodiazepines. This study aimed to develop a population pharmacokinetic model of phenytoin and to propose the optimal dose regimen of phenytoin in Korean epilepsy patients. Concentrations collected from electronic medical records for 117 patients, with 1 or 2 measurements per patient, were analyzed using NONMEM 7.3.0. One-compartment model with first-order elimination where allometry scaling was considered best described the data, yielding the estimates of V and CL of 68.19 (L) and 0.63 (L/h), respectively, for patients with a body weight of 60 kg. Covariate analyses showed that, after birth, clearance increases with age, reaching adult level at 4 years, and after 20 years, it decreases with age. Simulation results showed that the dosing interval should be reduced to achieve optimal dosing in neonates and infants, and the optimal dose required increases with weight. This work demonstrates that a model-based approach can serve as a useful tool to individualize phenytoin therapy.

Individualized vancomycin dosage regimens in children / 国际儿科学杂志

As a glycopeptide antibiotic,vancomycin is the first choice for treatment of methicilin-resistant staphylococcus aureus (MRSA) infection.Because of the metabolic individual difference,if children were given the dose according to the instruction,few patients can achieve the valley concentration as recommended in the guideline.So it's necessary for optimizing individual vancomycin dosage regimen with the help of therapeutic drug monitoring (TDM) and population pharmacokinetics model-nonlinear mixed effect model (NONMEM),in order to realiz the combination of effect and safety.This article will introduce the population pharmacokinetics model of vancomycin in children and discuss about major parameters influencing vancomycin metabolism,including age,body mass index,renal function,health condition,and drug combination.With rational therapeutic drug monitoring and optimizing parameters of NONMEM,we hope to realize area under concentration-time curve/minimum inhibit concentration (AUC/MIC) ratio ≥400,and to provide the reference for safe and rational pediatric dosage regimen.

PFIM 4.0, an extended R program for design evaluation and optimization in nonlinear mixed-effect models.

BACKGROUND AND OBJECTIVE: Nonlinear mixed-effect models (NLMEMs) are increasingly used for the analysis of longitudinal studies during drug development. When designing these studies, the expected Fisher information matrix (FIM) can be used instead of performing time-consuming clinical trial simulations. The function PFIM is the first tool for design evaluation and optimization that has been developed in R. In this article, we present an extended version, PFIM 4.0, which includes several new features. METHODS: Compared with version 3.0, PFIM 4.0 includes a more complete pharmacokinetic/pharmacodynamic library of models and accommodates models including additional random effects for inter-occasion variability as well as discrete covariates. A new input method has been added to specify user-defined models through an R function. Optimization can be performed assuming some fixed parameters or some fixed sampling times. New outputs have been added regarding the FIM such as eigenvalues, conditional numbers, and the option of saving the matrix obtained after evaluation or optimization. Previously obtained results, which are summarized in a FIM, can be taken into account in evaluation or optimization of one-group protocols. This feature enables the use of PFIM for adaptive designs. The Bayesian individual FIM has been implemented, taking into account a priori distribution of random effects. Designs for maximum a posteriori Bayesian estimation of individual parameters can now be evaluated or optimized and the predicted shrinkage is also reported. It is also possible to visualize the graphs of the model and the sensitivity functions without performing evaluation or optimization. RESULTS: The usefulness of these approaches and the simplicity of use of PFIM 4.0 are illustrated by two examples: (i) an example of designing a population pharmacokinetic study accounting for previous results, which highlights the advantage of adaptive designs; (ii) an example of Bayesian individual design optimization for a pharmacodynamic study, showing that the Bayesian individual FIM can be a useful tool in therapeutic drug monitoring, allowing efficient prediction of estimation precision and shrinkage for individual parameters. CONCLUSION: PFIM 4.0 is a useful tool for design evaluation and optimization of longitudinal studies in pharmacometrics and is freely available at http://www.pfim.biostat.fr.

Analysis of Population Pharmacokinetics of Vancomycin Using Cystatin C as a Marker of Renal Function / 中国药学杂志

OBJECTIVE: To establish a population pharmacokinetics(PPK) model of vancomycin in adult patients and investigate the factors influencing vancomycin clearance.METHODS: The nonlinear mixed-effect model(NONMEM) was used to investigate the population characteristics of vancomycin in adult patients and the serum cystatin C was used as a marker of renal function. The final model was built by forward inclusion approach and backward elimination method. Fitting effect of the model was evaluated by the goodness of fit plots(GOFs). Nonparametric Bootstraps and normalized prediction distribution errors(NPDE) were performed to evaluate the robustness and predictive efficacy of the final model. External model evaluation was conducted using an independent dataset to evaluate the model predictability. RESULTS: Vancomycin PPK model was set up via 147 serum trough concentration data from 95 adult patients. The estimated population typical values of clearance rate and apparent volume of distribution were 3.57 L·h-1 and 63.30 L, respectively. The main factor influencing clearance was renal function. The GOFs showed that the final model was stable and effective, and the fitting degree of the final model was better than that of the base model. The robust rate verified by Bootstrap was 99.45%. All of the relative biases between the median of parameters validated by Bootstrap and the estimated parameters of final model were within ±3%, and the 95% confidence intervals of these validated parameters did not include zero. The NPDE followed the N(0,1) distribution with a global adjust P value of 0.334, which indicated that the model had a high predictive accuracy. External evaluation was performed via an independent dataset of 40 concentration data from 20 patients. The mean prediction error(MPE) and mean absolute prediction error(MAPE) based on population predictions(PRED) was -1.90% and 24.34%, respectively. CONCLUSION: Vancomycin PPK model established in the study is of as a good stability and high predictive accuracy, as a reference for developing individualized administration regimens.

Population pharmacokinetics of oxcarbazepine in Chinese epilepsy patients / 药学学报

Oxcarbazepine (OXC) is a common antiepileptic drugs. In this study, one hundred and eighty four epilepsy patients with 196 observations of oxcarbazepine's active metabolite, 10,11-dihydro-10-monohydroxy carbazepine (MHD) were collected prospectively from routine clinical monitoring. Nonlinear mixed effect modeling was employed to develop a population pharmacokinetic model of oxcarbazepine in Chinese patients with epilepsy to investigate the impact of gender, age, weight, co-medications and genetic polymorphisms of UGT2B7 c.802T>C, ABCC2 c.1249G>A, ABCC 23972C>T on pharmacokinetic characteristics of OXC. The population estimate of apparent clearance (CL/F) and apparent volume of distribution (V/F) was 1.84 L·h−1 and 275 L, respectively. Gender and UGT2B7 c.802T>C affected the clearance rate of MHD significantly. The established model was:CL/F=1.84×0.848UGT2B7×1.17GENDER. Where the genotype of UGT2B7 c.802T>C was CC, UGT2B7=0, otherwise UGT2B7=1. When the patient was male, GENDER=1, otherwise GENDER=0. The final model was evaluated by normalized predictive distribution error (NPDE) and bootstrap method. The model was stable and reliable, which offers a powerful approach for rational use of OXC in epilepsy patients.

Population pharmacokinetic study of tacrolimus in pediatric patients with primary nephrotic syndrome / 药学学报

Tacrolimus is commonly used in the treatment for the refractory primary nephrotic syndrome (PNS) in the pediatric patients. Data were retrospectively obtained from 100 children with 357 tacrolimus trough concentrations in our center between May 2010 and March 2016. Information of age, sex, body weight, drug dose, co-therapy medications, laboratory tests and sampling time were collected. The population pharmacokinetic model was developed using nonlinear mixed effect modeling (NONMEM) software. A one-compartment model with first-order absorption and elimination best described the data. The population estimate of apparent clearance (CL/F) and apparent volume of distribution (V/F) was 6.54 L·h-1 and 86.2 L, respectively. Body weight (WT, kg), daily dose of tacrolimus (DD, mg·day-1) and co-therapy azole antifungal agent have a significant impact on the CL/F. The final PPK model of CL/F was:CL/F=6.54×((WT)/25)K×((DD)/1.5)0.293×0.657Azole,K=(WT-30.9)/(WT-30.9+10.4-30.9). When combined with azole antifungal agents, Azole was 1, whereas vice versa was 0. This is the first PPK study of tacrolimus conducted in pediatric patients with PNS, which may facilitate individualized drug therapy of tacrolimus.

Effects of Cystatin C on the Pharmacokinetic Parameters of Vancomycin Based on Population Pharmacokinetics / 中国药房

OBJECTIVE:To establish population pharmacokinetics(PPK)model of vancomycin so as to evaluate the effects of cystatin C(Cys C)on the pharmacokinetics parameters of vancomycin. METHODS:Totally 333 times therapeutic drug monitoring (TDM)were retrospectively collected from 225 patients who received vancomycin. Using sex,age,body weight(mT),Scr and Cys C as covariates,PPK model was established by using nonlinear mixed effect model method. Bootstrap method and normal prediction distribution error(NPDE)method were adopted for internal validation of model. Forty times of TDM data were collected from other 27 patients for external validation. Predicted accuracy and precision of model were investigated with mean prediction error (MPE) and root mean square error (RMSE). The effects of Cys C change on pharmacokinetic parameters of vancomycin were evaluated with steady state trough concentration and apparent clearance rate (CL/F) of vancomycin in typical patient (65 year-old,64 kg,Scr 66 μmol/L,1 000 mg,q12 h)forecasted with the final model at different levels of Cys C. RESULTS:CL/F of vancomycin was significantly influenced by age,body weight,the levels of Scr and Cys C. The final model was CL/F(L/h)＝3.68×(Scr/66)－0.431×(mT/64)1.1×(Age/65)－0.368×(Cys C/1.04)－0.693,V/F was equal to 82.5 L. The robust rate verified by Bootstrap method was 100%. Except for the interindividual variation of V/F,the relative bias of other pharmacokinetic parameters was less than 5%,and the estimated parameters of the final model were in the 95% confidence intervals of estimated values of Bootstrap. NPDE results showed that the homogeneity of variance was consistent with normal distribution (P＞0.05). In external validation,MPE and RMSE of the simplest model were －1.52 μg/mL and 6.87 μg/mL. MPE and RMSE of the final model were －0.32 μg/mL and 4.27 μg/mL,the accuracy and precision were improved significantly in the final model. When Cys C levle of typical patient was 0.3-4.0 mg/L,the steady state trough concentration predicted by final model were 5.25-29.97 μ g/mL and CL/F were 1.45-8.71 L/h. CONCLUSIONS:Age,body weight,the levels of Scr and Cys C significantly influence the pharmacokinetic parameters of vancomycin;moreover,the level of Cys C can change blood concentration of vancomycin. Established PPK model is of great predictive performance,which can be used to estimate the individual pharmacokinetics parameters of vancomycin.