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.

Population pharmacokinetic analyses of methotrexate in pediatric patients: a systematic review.

BACKGROUND AND OBJECTIVES: Methotrexate is widely utilized in the chemotherapy of malignant tumors and autoimmune diseases in the pediatric population, but dosing can be challenging. Several population pharmacokinetic models were developed to characterize factors influencing variability and improve individualization of dosing regimens. However, significant covariates included varied across studies. The primary objective of this review was to summarize and discuss population pharmacokinetic models of methotrexate and covariates that influence pharmacokinetic variability in pediatric patients. METHODS: Systematic searches were conducted in the PubMed and EMBASE databases from inception to 7 July 2023. Reporting Quality was evaluated based on a checklist with 31 items. The characteristics of studies and information for model construction and validation were extracted, summarized, and discussed. RESULTS: Eighteen studies (four prospective studies and fourteen retrospective studies with sample sizes of 14 to 772 patients and 2.7 to 93.1 samples per patient) were included in this study. Two-compartment models were the commonly used structural models for methotrexate, and the clearance range of methotrexate ranged from 2.32 to 19.03 L/h (median: 6.86 L/h). Body size and renal function were found to significantly affect the clearance of methotrexate for pediatric patients. There were limited reports on the role of other covariates, such as gene polymorphisms and co-medications, in the pharmacokinetic parameters of methotrexate pediatric patients. Internal and external evaluations were used to assess the performance of the population pharmacokinetic models. CONCLUSION: A more rigorous external evaluation needs to be performed before routine clinical use to select the appropriate PopPK model. Further research is necessary to incorporate larger cohorts or pool analyses in specific susceptible pediatric populations to improve the understanding of predicted exposure profiles and covariate identification.

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.

Visual predictive check of longitudinal models and dropout.

Visual predictive checks (VPC) are commonly used to evaluate pharmacometrics models. However their performance may be hampered if patients with worse outcomes drop out earlier, as often occurs in clinical trials, especially in oncology. While methods accounting for dropouts have appeared in literature, they vary in assumptions, flexibility, and performance, and the differences between them are not widely understood. This manuscript aims to elucidate which methods can be used to handle VPC with dropout and when, along with a more informative VPC approach using confidence intervals. Additionally, we propose constructing the confidence interval based on the observed data instead of the simulated data. The theoretical framework for incorporating dropout in VPCs is developed and applied to propose two approaches: full and conditional. The full approach is implemented using a parametric time-to-event model, while the conditional approach is implemented using both parametric and Cox proportional-hazard (CPH) models. The practical performances of these approaches are illustrated with an application to the tumor growth dynamics (TGD) modeling of data from two cancer clinical trials of nivolumab and docetaxel, where patients were followed until disease progression. The dataset consisted of 3504 tumor size measurements from 855 subjects, which were described by a TGD model. The dropout of subjects was described by a Weibull or CPH model. Simulated datasets were also used to further illustrate the properties of the VPC methods. The results showed that the more familiar full approach might not provide meaningful improvement for TGD model evaluation over the naive approach of not adjusting for dropout, and could be outperformed by the conditional approach using either the Weibull model or the Cox proportional hazard model. Overall, including confidence intervals in VPC should improve interpretation, the conditional approach was shown to be more generally applicable when dropout occurs, and the nonparametric approach could provide additional robustness.

Pharmacokinetic comparability between two populations using nonlinear mixed effect models: a Monte Carlo study.

'Are two populations the same or are they different' is a question that is often faced in clinical pharmacology trials e.g., a pharmacokinetic trial studying a particular drug in racially different groups. To address this question, concentration-time data were simulated from a reference and test population, where in the latter the clearance, sample size, and sampling design were systematically varied. It was of interest to determine whether the estimates of clearance from the two groups were the same or different. Two approaches were used to estimate the empirical Bayes estimates (EBEs) for clearance. One approach developed a population pharmacokinetic model for the reference population and the EBEs for the reference population were estimated from this model. The parameters of the reference population were fixed to their maximum likelihood estimates. The model was then applied to the test population dataset to estimate the EBEs of the test population using the MAXEVAL = 0 option in NONMEM. A second approach, the theta approach, combined the reference and test datasets into a single dataset and used population as a covariate in the model; the EBEs were estimated from this combined model. The power and type I error rate of each approach were calculated for each treatment combination using a variety of statistical tests to determine whether there was a difference in the distribution of the EBEs in the reference population compared to the test population. Our results suggest that either MAXEVAL or theta approaches can be used with informative sampling designs. In addition to reasonable power and type I error, both approaches gave almost identical results under a dense sampling design. To statistically compare the distribution of EBEs of pharmacokinetic parameters from a reference group to that of a test group, a T-test and DTS eCDF test are equally useful.

Modeling and characterization of inter-individual variability in CD8 T cell responses in mice.

To develop vaccines it is mandatory yet challenging to account for inter-individual variability during immune responses. Even in laboratory mice, T cell responses of single individuals exhibit a high heterogeneity that may come from genetic backgrounds, intra-specific processes (e.g. antigen-processing and presentation) and immunization protocols.To account for inter-individual variability in CD8 T cell responses in mice, we propose a dynamical model coupled to a statistical, nonlinear mixed effects model. Average and individual dynamics during a CD8 T cell response are characterized in different immunization contexts (vaccinia virus and tumor). On one hand, we identify biological processes that generate inter-individual variability (activation rate of naive cells, the mortality rate of effector cells, and dynamics of the immunogen). On the other hand, introducing categorical covariates to analyze two different immunization regimens, we highlight the steps of the response impacted by immunogens (priming, differentiation of naive cells, expansion of effector cells and generation of memory cells). The robustness of the model is assessed by confrontation to new experimental data.Our approach allows to investigate immune responses in various immunization contexts, when measurements are scarce or missing, and contributes to a better understanding of inter-individual variability in CD8 T cell immune responses.

Pharmacokinetic variability of phenobarbital: a systematic review of population pharmacokinetic analysis.

AIMS AND BACKGROUND: Population pharmacokinetics with Bayesian forecasting provides for an effective approach when individualized drug dosing, while phenobarbital is a narrow therapeutic index drug that requires therapeutic drug monitoring. To date, several population pharmacokinetic models have been developed for phenobarbital, these showing a number of significant predictors of phenobarbital clearance and volume of distribution. We have therefore conducted a systematic review to summarize how these predictors affect phenobarbital pharmacokinetics as well as their relationships with pharmacokinetic parameters. METHOD: A systematic search for studies of phenobarbital population pharmacokinetics that were carried out in humans and that employed a nonlinear mixed-effect approaches was made using the PubMed, Scopus, CINAHL Complete, and ScienceDirect databases. The search covered the period from these databases' inception to March 2020. RESULTS: Eighteen studies were included in this review, all of which used a one-compartment structure. The estimated phenobarbital clearance and volume of distribution ranged from 0.0034 to 0.0104 L/h/kg and 0.37 to 1.21 L/kg, respectively, with body weight, age, and concomitant antiepileptic drugs being the three most frequently identified predictors of clearance. Most models were validated through the use of an advanced internal approach. CONCLUSION: Phenobarbital clearance may be predicted from previously developed population pharmacokinetic models and their significant covariate-parameter relationships along with Bayesian forecasting. However, when applying these models in a target population, an external evaluation of these models using the target population is warranted, and it is recommended that future research be conducted to investigate the link between population pharmacokinetics and pharmacodynamics.

Bayesian inference using Hamiltonian Monte-Carlo algorithm for nonlinear joint modeling in the context of cancer immunotherapy.

Treatment evaluation in advanced cancer mainly relies on overall survival and tumor size dynamics. Both markers and their association can be simultaneously analyzed by using joint models, and these approaches are supported by many softwares or packages. However, these approaches are essentially limited to linear models for the longitudinal part, which limit their biological interpretation. More biological models of tumor dynamics can be obtained by using nonlinear models, but they are limited by the fact that parameter identifiability require rich dataset. In that context Bayesian approaches are particularly suited to incorporate the biological knowledge and increase the information available, but they are limited by the high computing cost of Monte-Carlo by Markov Chains algorithms. Here, we aimed to assess the performances of the Hamiltonian Monte-Carlo (HMC) algorithm implemented in Stan for inference in a nonlinear joint model. The method was validated on simulated data where HMC provided proper posterior distributions and credibility intervals in a reasonable computational time. Then the association between tumor size dynamics and survival was assessed in patients with advanced or metastatic bladder cancer treated with atezolizumab, an immunotherapy agent. HMC confirmed limited sensitivity to prior distributions. A cross-validation approach was developed and identified the current slope of tumor size dynamics as the most relevant driver of survival. In summary, HMC is an efficient approach to perform nonlinear joint models in a Bayesian framework, and opens the way for the use of nonlinear models to characterize both the rapid dynamics and the intersubject variability observed during cancer immunotherapy treatment.

Understanding the drug exposure-response relationship of bedaquiline to predict efficacy for novel dosing regimens in the treatment of multidrug-resistant tuberculosis.

AIMS: To externally validate an earlier characterized relationship between bedaquiline exposure and decline in bacterial load in a more difficult-to-treat patient population, and to explore the performances of alternative dosing regimens through simulations. METHODS: The bedaquiline exposure-response relationship was validated using time-to-positivity data from 233 newly diagnosed or treatment-experienced patients with drug-resistant tuberculosis from the C209 open-label study. The significance of the exposure-response relationship on the bacterial clearance was compared to a constant drug effect model. Tuberculosis resistance type and the presence and duration of antituberculosis pre-treatment were evaluated as additional covariates. Alternative dosing regimens were simulated for tuberculosis patients with different types of drug resistance. RESULTS: High bedaquiline concentrations were confirmed to be associated with faster bacterial load decline in patients, given that the exposure-effect relationship provided a significantly better fit than the constant drug effect (relative likelihood = 0.0003). The half-life of bacterial clearance was identified to be 22% longer in patients with pre-extensively drug-resistant (pre-XDR) tuberculosis (TB) and 86% longer in patients with extensively drug-resistant (XDR) TB, compared to patients with multidrug-resistant (MDR) TB. Achievement of the same treatment response for (pre-)XDR TB patients as for MDR TB patients would be possible by adjusting the dose and dosing frequency. Furthermore, daily bedaquiline administration as in the ZeNix regimen, was predicted to be as effective as the approved regimen. CONCLUSION: The confirmed bedaquiline exposure-response relationship offers the possibility to predict efficacy under alternative dosing regimens, and provides a useful tool for potential treatment optimization.

Robust designs in longitudinal studies accounting for parameter and model uncertainties - application to count data.

Nonlinear mixed effect models (NLMEMs) are widely used for the analysis of longitudinal data. To design these studies, optimal designs based on the expected Fisher information matrix (FIM) can be used. A method evaluating the FIM using Monte-Carlo Hamiltonian Monte-Carlo (MC-HMC) has been proposed and implemented in the R package MIXFIM using Stan. This approach, however, requires a priori knowledge of models and parameters, which leads to locally optimal designs. The objective of this work was to extend this MC-HMC-based method to evaluate the FIM in NLMEMs accounting for uncertainty in parameters and in models. When introducing uncertainty in the population parameters, we evaluated the robust FIM as the expectation of the FIM computed by MC-HMC over the distribution of these parameters. Then, the compound D-optimality criterion (CD optimality), corresponding to a weighted product of the D-optimality criteria of several candidate models, was used to find a common CD-optimal design for the set of candidate models. Finally, a compound DE-criterion (CDE optimality), corresponding to a weighted product of the normalized determinants of the robust FIMs of all the candidate models accounting for uncertainty in parameters, was calculated to find the CDE-optimal design which was robust on both parameters and model. These methods were applied in a longitudinal Poisson count model. We assumed prior distributions on the population parameters, as well as several candidate models describing the relationship between the logarithm of the event rate parameter and the dose. We found that assuming uncertainty in parameters could lead to different optimal designs, and misspecification of models could induce designs with low efficiencies. The CD- or CDE-optimal designs therefore provided a good compromise for different candidate models. Finally, the proposed approach allows for the first time optimization of designs for repeated discrete data accounting for parameter and model uncertainties.

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.

Impact of Antibiotic Gut Exposure on the Temporal Changes in Microbiome Diversity.

Although the global deleterious impact of antibiotics on the intestinal microbiota is well known, temporal changes in microbial diversity during and after an antibiotic treatment are still poorly characterized. We used plasma and fecal samples collected frequently during treatment and up to one month after from 22 healthy volunteers assigned to a 5-day treatment by moxifloxacin (n = 14) or no intervention (n = 8). Moxifloxacin concentrations were measured in both plasma and feces, and bacterial diversity was determined in feces by 16S rRNA gene profiling and quantified using the Shannon index and number of operational taxonomic units (OTUs). Nonlinear mixed effect models were used to relate drug pharmacokinetics and bacterial diversity over time. Moxifloxacin reduced bacterial diversity in a concentration-dependent manner, with a median maximal loss of 27.5% of the Shannon index (minimum [min], 17.5; maximum [max], 27.7) and 47.4% of the number of OTUs (min, 30.4; max, 48.3). As a consequence of both the long fecal half-life of moxifloxacin and the susceptibility of the gut microbiota to moxifloxacin, bacterial diversity indices did not return to their pretreatment levels until days 16 and 21, respectively. Finally, the model characterized the effect of moxifloxacin on bacterial diversity biomarkers and provides a novel framework for analyzing antibiotic effects on the intestinal microbiome.

Factors influencing lithium pharmacokinetics in patients with acute mania: A population pharmacokinetic analysis.

OBJECTIVE: The objective of this study was to conduct a population pharmacokinetic model of lithium in Thai patients with acute mania. METHODS: Lithium concentrations from 222 acute manic patients were included in this study. The population pharmacokinetic model was developed using NONMEM 7.3 software. Influences of potential covariates including body size, age, renal function, and gender were evaluated through a stepwise approach. Bootstrap analysis and an external validation approach were used to evaluate the robustness and predictability of the final model. RESULTS: A one-compartment model adequately described lithium pharmacokinetics. Body weight and age were significant predictors for lithium clearance, with the following relationship: CL/F (L/hr) = 1.43 * (WT/65)0.425  * (age/38)-0.242 . The population estimates of lithium clearance, volume of distribution, and absorption rate constant of the final model were 1.43 L/hr, 54 L (fixed), and 0.426 hr-1 (fixed), respectively. Model evaluation showed that the final model was predictive and robust. CONCLUSIONS: A population pharmacokinetic model of lithium with good performance was developed in Thai patients with acute mania. This model can be used to assist clinicians in individualized lithium therapy.

A model-based comparative meta-analysis of the efficacy of dolutegravir-based and efavirenz-based regimens in HIV-infected patients.

Currently, combinations of typical types of antiretroviral agents have been adopted as chemotherapy for human immunodeficiency virus (HIV) infection, comprising two nucleoside analogue reverse transcriptase inhibitors plus one of a non-nucleoside reverse transcriptase inhibitor, an integrase strand-transfer inhibitor, and a protease inhibitor. Although several meta-analyses have been conducted to determine first-line combination antiretroviral therapy, this has yet to be confirmed due to the technical limitation associated. In the present study, we applied a model-based meta-analysis (MBMA) approach, because it allows integration of information from clinical trials with varying dosing, duration, and sampling time points, resulting in enlargement of available data sources. We performed a bibliographic search to identify clinical trials involving dolutegravir (DTG)-based and efavirenz (EFV)-based regimens in HIV-infected, antiretroviral therapy-naïve adults, and then identified 30 independent trial data. The time course of drug effect was described by a consecutive first-order kinetic model and analyzed using the nonlinear mixed effect modeling approach. The developed model suggests that the DTG-based regimen provides a faster-acting and more sustainable drug effect than the EFV-based regimen. Moreover, the drug effect tends to appear more slowly and decay faster in severe patients having higher viral load or smaller baseline CD4 count.

A possible solution to model nonlinearity in elimination and distributional clearances with α2 -adrenergic receptor agonists: Example of the intravenous detomidine and methadone combination in sedated horses.

The alpha(α)2 -agonist detomidine is used for equine sedation with opioids such as methadone. We retrieved the data from two randomized, crossover studies where detomidine and methadone were given intravenously alone or combined as boli (STUDY 1) (Gozalo-Marcilla et al., 2017, Veterinary Anaesthesia and Analgesia, 2017, 44, 1116) or as 2-hr constant rate infusions (STUDY 2) (Gozalo-Marcilla et al., 2019, Equine Veterinary Journal, 51, 530). Plasma drug concentrations were measured with a validated tandem Mass Spectrometry assay. We used nonlinear mixed effect modelling and took pharmacokinetic (PK) data from both studies to fit simultaneously both drugs and explore their nonlinear kinetics. Two significant improvements over the classical mammillary two-compartment model were identified. First, the inclusion of an effect of detomidine plasma concentration on the elimination clearances (Cls) of both drugs improved the fit of detomidine (Objective Function Value [OFV]: -160) and methadone (OFV: -132) submodels. Second, a detomidine concentration-dependent reduction of distributional Cls of each drug further improved detomidine (OFV: -60) and methadone (OFV: -52) submodel fits. Using the PK data from both studies (a) helped exploring hypotheses on the nonlinearity of the elimination and distributional Cls and (b) allowed inclusion of dynamic effects of detomidine plasma concentration in the model which are compatible with the pharmacology of detomidine (vasoconstriction and reduction in cardiac output).

A systematic review of population pharmacokinetics of valproic acid.

AIMS: Population pharmacokinetics is an essential tool that helps guide individualized dosing regimens. The aims of this systematic review are to provide knowledge concerning population pharmacokinetics of valproic acid (VPA) and to identify factors influencing VPA pharmacokinetic variability. METHODS: PubMed and Embase databases were systematically searched from inception to June, 2017. Relevant articles from reference lists were also included. All population pharmacokinetic studies of VPA conducted in humans and that employed a nonlinear mixed effect modelling approach were included in this review. RESULTS: Twenty-six studies were included in this review. Most studies characterized VPA pharmacokinetics as a one-compartment model. Three studies reported a two-compartment model. Body weight, dose and age were significant predictors for VPA volume of distribution (Vd ). The estimated Vd for one-compartment models ranged from 8.4 to 23.3 l. For two-compartment models, peripheral volumes of distribution ranged from 4.08 to 42.1 l. Frequently reported significant predictors for VPA clearance (CLVPA ) included body weight, VPA dose, concomitant medications, gender and age. The estimated CLVPA ranged from 0.206 to 1.154 l h-1 and the inter-individual variability ranged from 13.40 to 35.90%. Two studies reported population pharmacokinetics/pharmacodynamics of VPA in patients with epilepsy. Seventeen studies evaluated the performance of their final models. CONCLUSIONS: Significant predictors influencing VPA pharmacokinetics as well as model methodologies are highlighted in this review. For clinical application, CLVPA could be predicted using body weight, VPA dose, concomitant medications, gender or age. For future research, there is a knowledge gap regarding population pharmacokinetics/pharmacodynamics of VPA in a population other than epileptic patients.

Using the SAEM algorithm for mechanistic joint models characterizing the relationship between nonlinear PSA kinetics and survival in prostate cancer patients.

Joint modeling is increasingly popular for investigating the relationship between longitudinal and time-to-event data. However, numerical complexity often restricts this approach to linear models for the longitudinal part. Here, we use a novel development of the Stochastic-Approximation Expectation Maximization algorithm that allows joint models defined by nonlinear mixed-effect models. In the context of chemotherapy in metastatic prostate cancer, we show that a variety of patterns for the Prostate Specific Antigen (PSA) kinetics can be captured by using a mechanistic model defined by nonlinear ordinary differential equations. The use of a mechanistic model predicts that biological quantities that cannot be observed, such as treatment-sensitive and treatment-resistant cells, may have a larger impact than PSA value on survival. This suggests that mechanistic joint models could constitute a relevant approach to evaluate the efficacy of treatment and to improve the prediction of survival in patients.

Individual Bayesian Information Matrix for Predicting Estimation Error and Shrinkage of Individual Parameters Accounting for Data Below the Limit of Quantification.

PURPOSE: In mixed models, the relative standard errors (RSE) and shrinkage of individual parameters can be predicted from the individual Bayesian information matrix (MBF). We proposed an approach accounting for data below the limit of quantification (LOQ) in MBF. METHODS: MBF is the sum of the expectation of the individual Fisher information (MIF) which can be evaluated by First-Order linearization and the inverse of random effect variance. We expressed the individual information as a weighted sum of predicted MIF for every possible design composing of measurements above and/or below LOQ. When evaluating MIF, we derived the likelihood expressed as the product of the likelihood of observed data and the probability for data to be below LOQ. The relevance of RSE and shrinkage predicted by MBF in absence or presence of data below LOQ were evaluated by simulations, using a pharmacokinetic/viral kinetic model defined by differential equations. RESULTS: Simulations showed good agreement between predicted and observed RSE and shrinkage in absence or presence of data below LOQ. We found that RSE and shrinkage increased with sparser designs and with data below LOQ. CONCLUSIONS: The proposed method based on MBF adequately predicted individual RSE and shrinkage, allowing for evaluation of a large number of scenarios without extensive simulations.

Theophylline: a review of population pharmacokinetic analyses.

WHAT IS KNOWN AND OBJECTIVE: Numerous population pharmacokinetic studies of theophylline have been conducted in paediatric and adult patients. The purpose of this review was to summarize the published studies concerning population pharmacokinetics of theophylline in patients of different ages and discuss factors that might cause the large variability in the pharmacokinetics of theophylline. METHODS: A literature search was conducted in PubMed using the following keywords: 'theophylline', 'population pharmacokinetic(s)' and 'nonlinear mixed effect model'. Additionally, the relevant references listed in the retrieved articles were manually reviewed. All of the studies that reported the population pharmacokinetics of theophylline in humans were included in this review. However, articles were excluded if they were not written in English. RESULTS AND DISCUSSION: Sixteen articles were included in this review. Among them, 11 were conducted on paediatric patients, and five were conducted on adults. A one-compartment model with first-order elimination was employed in most of the included articles. A nonlinear mixed effect modelling approach (NONMEM) was the most commonly used software to develop a population pharmacokinetic model. Body weight and age (post-conceptional age and post-natal age) were the most important factors associated with the clearance (CL) of theophylline in paediatric patients. Body weight (ideal body weight and lean body mass), age and smoking status were most frequently used to estimate the CL of theophylline in adults. The median (range) estimate values of CL for paediatric and adult patients were 0·062 (0·0056-0·0949) L/h/kg and 0·053 (0·0493-0·0517) L/h/kg, respectively. The median values of the interindividual variability of CL were 33·5% in adults and 25·8% in paediatric patients. The mean values of the residual variability were 21% in paediatric patients and 14·3% in adults. WHAT IS NEW AND CONCLUSION: This review concludes that body weight and age were the most important factors associated with the clearance of theophylline in paediatric patients. Body weight, age and smoking were most frequently used to estimate the clearance of theophylline in adults. Future studies are warranted to detect the influence of new factors, such as cytochrome P450 (CYP) 1A2 gene polymorphisms, on the pharmacokinetics of theophylline because some pharmacokinetic variability was not fully explained.

Population pharmacokinetics of tacrolimus in Thai kidney transplant patients: comparison with similar data from other populations.

WHAT IS KNOWN AND OBJECTIVE: Tacrolimus, the most widely used calcineurin inhibitor in kidney transplantation, has a narrow therapeutic window with high interindividual variability in its pharmacokinetics. Clinically feasible models that combine important factors may help guide individual tacrolimus dosage adjustment in kidney transplant patients. The purpose of this study was to develop a population pharmacokinetic model and investigate the influence of clinical factors on the pharmacokinetics of tacrolimus in adult Thai kidney transplant patients from routine data monitoring. METHODS: A total of 1183 whole blood concentrations from 96 patients were characterized using nonlinear mixed-effects modelling. Clinical factors tested for influence on pharmacokinetic parameters were weight, haemoglobin, duration of tacrolimus therapy, prednisolone dose, serum albumin and estimated glomerular filtration rate. RESULTS AND DISCUSSION: A one-compartment model with first-order absorption best described the data. The population estimate of tacrolimus apparent clearance (CL/F) and apparent volume of distribution (V/F) in the final population model was 21·5 L/h (95% CI; 18·38, 24·34) and 333 L (95% CI; 222·66, 484·35), respectively. CL/F increased with decreasing haemoglobin levels and decreased with increasing duration of tacrolimus therapy (both P < 0·001). The population pharmacokinetic equation that predicted CL/F of tacrolimus was CL/F = 21·5 × exp((-0·05 () (HB) ( - 11·8)))  × (DOT/125)(-0·06) , where CL/F was tacrolimus apparent oral clearance (L/h), HB was haemoglobin levels (g/dL), and DOT was duration of tacrolimus therapy (days). No covariates significantly influenced V/F. WHAT IS NEW AND CONCLUSION: The first population pharmacokinetic model of tacrolimus in Thai adult kidney transplant patients was developed and validated. Haemoglobin and duration of tacrolimus therapy could partly explain the interindividual variability in the apparent clearance of tacrolimus. This manuscript also provides a summary review of previously reported population pharmacokinetic models of twice daily tacrolimus in adult kidney transplant recipients.