Systematic external evaluation of published population pharmacokinetic models of mycophenolate mofetil in adult kidney transplant recipients co-administered with tacrolimus

External evaluation of tacrolimus population pharmacokinetic models in adult lung transplant patients: How to enhance the predictive ability of the model?

PURPOSE

Tacrolimus is the cornerstone of current immunosuppressive strategies after lung transplantation. However, its narrow therapeutic range and considerable pharmacokinetic variability pose challenges for individualized treatment. Several tacrolimus population pharmacokinetic (popPK) models have been developed for precision dosing in adult lung transplant patients. However, their applicability across different clinical settings remains uncertain. The aim of this study was to evaluate the external predictability of these models and identify influential factors.

METHODS

Published models were systematically retrieved and assessed based on an external dataset of 39 patients (1240 tacrolimus trough concentrations) using three approaches: (1) prediction-based diagnosis using dosing records and patient characteristics; (2) simulation-based diagnosis, with prediction- and variability-corrected visual predictive checks (pvcVPC) and normalized prediction distribution error tests (NPDE); and (3) Bayesian forecasting using one to four observations for posterior predictions. We also investigated the impact of model structure and covariates on predictability.

RESULTS

The predictive performance of six published models was externally evaluated, but none demonstrated satisfactory accuracy in prediction- and simulation-based diagnosis. Bayesian forecasting yielded satisfactory results with only one prior observation and optimal predictive performance with 2-3 priors for all included models. The structural model parameterized on plasma tacrolimus concentration outperformed others. Significant correlations were observed between prediction-error and daily tacrolimus dose, postoperative day, and voriconazole co-administration.

CONCLUSIONS

The overall predictive performance of all published models was unsatisfactory, making direct extrapolation inappropriate. However, Bayesian forecasting significantly improves predictive performance. Utilizing plasma tacrolimus concentration for parameter estimation can improve the predictive ability of tacrolimus popPK models.

Machine-learning model to predict the tacrolimus concentration and suggest optimal dose in liver transplantation recipients: a multicenter retrospective cohort study

Titrating tacrolimus concentration in liver transplantation recipients remains a challenge in the early post-transplant period. This multicenter retrospective cohort study aimed to develop and validate a machine-learning algorithm to predict tacrolimus concentration. Data from 443 patients undergoing liver transplantation between 2017 and 2020 at an academic hospital in South Korea were collected to train machine-learning models. Long short-term memory (LSTM) and gradient-boosted regression tree (GBRT) models were developed using time-series doses and concentrations of tacrolimus with covariates of age, sex, weight, height, liver enzymes, total bilirubin, international normalized ratio, albumin, serum creatinine, and hematocrit. We conducted performance comparisons with linear regression and populational pharmacokinetic models, followed by external validation using the eICU Collaborative Research Database collected in the United States between 2014 and 2015. In the external validation, the LSTM outperformed the GBRT, linear regression, and populational pharmacokinetic models with median performance error (8.8%, 25.3%, 13.9%, and - 11.4%, respectively; P < 0.001) and median absolute performance error (22.3%, 33.1%, 26.8%, and 23.4%, respectively; P < 0.001). Dosing based on the LSTM model's suggestions achieved therapeutic concentrations more frequently on the chi-square test (P < 0.001). Patients who received doses outside the suggested range were associated with longer ICU stays by an average of 2.5 days (P = 0.042). In conclusion, machine learning models showed excellent performance in predicting tacrolimus concentration in liver transplantation recipients and can be useful for concentration titration in these patients.

Evaluation of Published Population Pharmacokinetic Models to Inform Tacrolimus Therapy in Adult Lung Transplant Recipients

BACKGROUND

The applicability of currently available tacrolimus population pharmacokinetic models in guiding dosing for lung transplant recipients is unclear. In this study, the predictive performance of relevant tacrolimus population pharmacokinetic models was evaluated for adult lung transplant recipients.

METHODS

Data from 43 lung transplant recipients (1021 tacrolimus concentrations) administered an immediate-release oral formulation of tacrolimus were used to evaluate the predictive performance of 17 published population pharmacokinetic models for tacrolimus. Data were collected from immediately after transplantation up to 90 days after transplantation. Model performance was evaluated using (1) prediction-based assessments (bias and imprecision) of individual predicted tacrolimus concentrations at the fourth dosing based on 1 to 3 previous dosings and (2) simulation-based assessment (prediction-corrected visual predictive check; pcVPC). Both assessments were stratified based on concomitant azole antifungal use. Model performance was clinically acceptable if the bias was within ±20%, imprecision was ≤20%, and the 95% confidence interval of bias crossed zero.

RESULTS

In the presence of concomitant antifungal therapy, no model showed acceptable performance in predicting tacrolimus concentrations at the fourth dosing (n = 33), and pcVPC plots displayed poor model fit to the data set. However, this fit slightly improved in the absence of azole antifungal use, where 4 models showed acceptable performance in predicting tacrolimus concentrations at the fourth dosing (n = 33).

CONCLUSIONS

Although none of the evaluated models were appropriate in guiding tacrolimus dosing in lung transplant recipients receiving concomitant azole antifungal therapy, 4 of these models displayed potential applicability in guiding dosing in recipients not receiving concomitant azole antifungal therapy. However, further model refinement is required before the widespread implementation of such models in clinical practice.

Population pharmacokinetics of mycophenolate in patients treated for interstitial lung disease (EVER-ILD study)

BACKGROUND

Mycophenolate mofetil (MMF) has been used to treat interstitial lung disease (ILD), but mycophenolate (MPA) pharmacokinetics was not reported for this use. This ancillary study of the EVER-ILD protocol aimed at describing the pharmacokinetic variability of MPA using population modelling in ILD.

METHODS

Concentrations of MPA were measured during an 8-h course for 27 ILD patients treated with 1000 mg MMF b.i.d. Absorption, distribution and elimination of MPA were described using population compartment models with first-order transfer and elimination rate constants, while accounting for both absorption peaks using gamma absorption models.

RESULTS

The pharmacokinetics of MPA was best described using a two-compartment model and two gamma absorption models, model performances of this model were still similar to those of a one gamma absorption model. This pharmacokinetics seemed to be notably influenced by body weight, renal function and inflammatory status. The distribubtion value area under the concentration curve between two administrations of MMF was AUC12 = 52.5 mg.h/L in median (interquartile range: 42.2-58.0 mg.h/L).

CONCLUSION

This is the first study reporting MPA pharmacokinetics in ILD. This pharmacokinetics appears to be similar to other indications and should be further investigated in future studies.

External evaluation of the predictive performance of published population pharmacokinetic models of linezolid in adult patients

OBJECTIVES

Several linezolid population pharmacokinetic (popPK) models have been established to facilitate optimal therapy; however, their extrapolated predictive performance to other clinical sites is unknown. This study aimed to externally evaluate the predictive performance of published pharmacokinetic models of linezolid in adult patients.

METHODS

For the evaluation dataset, 150 samples were collected from 70 adult patients (72.9% of which were critically ill) treated with linezolid at our center. Twenty-five published popPK models were identified from PubMed and Embase. Model predictability was evaluated using prediction-based, simulation-based, and Bayesian forecasting-based approaches to assess model predictability.

RESULTS

Prediction-based diagnostics found that the prediction error within ±30% (F30) was less than 40% in all models, indicating unsatisfactory predictability. The simulation-based prediction- and variability-corrected visual predictive check and normalized prediction distribution error test indicated large discrepancies between the observations and simulations in most of the models. Bayesian forecasting with one or two prior observations significantly improved the models' predictive performance.

CONCLUSION

The published linezolid popPK models showed insufficient predictive ability. Therefore, their sole use is not recommended, and incorporating therapeutic drug monitoring of linezolid in clinical applications is necessary.

Clinical impact using low-dose mycophenolate mofetil with tacrolimus on infectious, noninfectious complications and acute rejection, in renal transplant: A single hospital experience in Mexico

Evidence supporting a starting dose of 2 g/day of mycophenolate mofetil (MMF) in combination with tacrolimus (TAC) for renal transplantation (RT) is still limited, but maintaining a dose of <2 g could result in worse clinical outcomes in terms of acute rejection (AR). This study aimed to determine the association between AR and infectious and noninfectious complications after RT with a dose of 1.5 g vs 2 g of MMF. A prospective cohort study was performed with a 12-month follow-up of recipients of RT from living donors with low (1.5 g/day) or standard (2 g/day) doses of MMF. The association between adverse effects and complications and doses of MMF was examined using Cox proportional hazard models, and survival free of AR, infectious diseases, and noninfectious complications was evaluated using the Kaplan-Meier test. At the end of the follow-up, the incidence of infectious diseases was 52% versus 50% (P = .71) and AR was 5% versus 5% (P = .86), respectively. The survival rate free of gastrointestinal (GI) complications requiring medical attention was higher in the low-dose group than in the standard-dose dose (88% vs 45%, respectively; P < .001). The use of 1.5 g/day of MMF confers a reduction in GI complications without an increase in infectious diseases or the risk of AR.

Population pharmacokinetics of mycophenolic acid and dose optimisation in adult Chinese kidney transplant recipients

1. This study aimed to establish a population pharmacokinetic (PPK) model of mycophenolic acid (MPA), quantify the effect of clinical factors and pharmacogenomics of MPA, and optimise the dosage for adult kidney transplant recipients.2. One-hundred and four adult renal transplant patients were enrolled. The PPK model was established using the Phoenix® NMLE software and the stepwise methods were filtered for significant covariates. Monte Carlo simulations were performed to optimise the dosage regimen.3. A two-compartment model with first-order absorption and elimination (including lag time) provided a more accurate description of MPA pharmacokinetics. Serum albumin (ALB) significantly affected the central apparent clearance (CL/F), whereas post-transplant time and creatinine clearance were associated with a central apparent volume of distribution (V/F). The estimated population values obtained by the final model were 17.5 L/h and 93.97 L for CL/F and V/F, respectively. Simulation results revealed that larger mycophenolate mofetil doses are required as the ALB concentration decreases. This study established a PPK model of MPA and validated it using various methods. ALB significantly affected CL/F and recommended optimal dose strategies were given based on the final model. These results provide a reference for the personalised therapy of MPA for kidney transplant patients.

External validation of population pharmacokinetic models of vancomycin in postoperative neurosurgical patients

OBJECTIVE

Vancomycin is commonly used in the prevention and treatment of intracranial infections in postoperative neurosurgical patients with narrow therapeutic window and large pharmacokinetic variations. Several population pharmacokinetic (PPK) models of vancomycin have been established for neurosurgical patients. But comprehensive external evaluation has not been performed for almost all models. The objective of this study was to evaluate the predictive ability of published vancomycin PPK models in adult postoperative neurosurgical patients using an independent dataset.

METHOD

PubMed, Embase and China National Knowledge Internet databases were searched to identify published vancomycin PPK models in adult postoperative neurosurgical patients. Prediction-based and simulation-based diagnostics were used to evaluate model predictability. Bayesian forecasting was used to assess the influence of prior concentration on model prediction performance.

RESULT

A total of 763 vancomycin plasma concentrations from 493 postoperative neurosurgical patients were included in the external dataset. Eight population pharmacokinetic models of vancomycin in postoperative neurosurgical patients were included and evaluated. The model by Zhang et al. exhibited the best predictive performance in prediction-based diagnostics and prediction-corrected visual predictive checks, followed by the model by Shen et al. The predictive performance of other models was not satisfactory. The normalized predictive distribution error test shows that none of the models is suitable to describe our data. The predictive performance of vancomycin models was obviously improved by maximum a posteriori Bayesian forecasting.

CONCLUSION

The published PPK models for adult postoperative neurosurgical patients show extensive variation in predictive performance in our patients. Although it is challenging to recommend initial doses of vancomycin from these predictive models, the combination of model-based prediction and therapeutic drug monitoring can be used for dose optimization.

Transferability of Published Population Pharmacokinetic Models for Apixaban and Rivaroxaban to Subjects with Obesity Treated for Venous Thromboembolism: A Systematic Review and External Evaluations

Apixaban and rivaroxaban have first-line use for many patients needing anticoagulation for venous thromboembolism (VTE). The pharmacokinetics of these drugs in non-obese subjects have been extensively studied, and, while changes in pharmacokinetics have been documented in obese patients, data remain scarce for these anticoagulants. The aim of this study was to perform an external validation of published population pharmacokinetic (PPK) models of apixaban and rivaroxaban in a cohort of obese patients with VTE. A literature search was conducted in the PubMed/MEDLINE, Scopus, and Embase databases following the PRISMA statement. External validation was performed using MonolixSuite software, using prediction-based and simulation-based diagnostics. An external validation dataset from the university hospitals of Brest and Rennes, France, included 116 apixaban pharmacokinetic samples from 69 patients and 121 rivaroxaban samples from 81 patients. Five PPK models of apixaban and 16 models of rivaroxaban were included, according to the inclusion criteria of the study. Two of the apixaban PPK models presented acceptable performances, whereas no rivaroxaban PPK model did. This study identified two published models of apixaban applicable to apixaban in obese patients with VTE. However, none of the rivaroxaban models evaluated were applicable. Dedicated studies appear necessary to elucidate rivaroxaban pharmacokinetics in this population.

Fingerprick Microsampling Methods Can Replace Venepuncture for Simultaneous Therapeutic Drug Monitoring of Tacrolimus, Mycophenolic Acid, and Prednisolone Concentrations in Adult Kidney Transplant Patients

BACKGROUND

Kidney transplant patients undergo repeated and frequent venepunctures during allograft management. Microsampling methods that use a fingerprick draw of capillary blood, such as dried blood spots (DBS) and volumetric absorptive microsamplers (VAMS), have the potential to reduce the burden and volume of blood loss with venepuncture.

METHODS

This study aimed to examine microsampling approaches for the simultaneous measurement of tacrolimus, mycophenolic acid, mycophenolic acid glucuronide (MPAG), and prednisolone drug concentrations compared with standard venepuncture in adult kidney transplant patients. DBS and VAMS were simultaneously collected with venepuncture samples from 40 adult kidney transplant patients immediately before and 2 hours after immunosuppressant dosing. Method comparison was performed using Passing-Bablok regression, and bias was assessed using Bland-Altman analysis. Drug concentrations measured through microsampling and venepuncture were also compared by estimating the median prediction error (MPE) and median absolute percentage prediction error (MAPE).

RESULTS

Passing-Bablok regression showed a systematic difference between tacrolimus DBS and venepuncture [slope of 1.06 (1.01-1.13)] and between tacrolimus VAMS and venepuncture [slope of 1.08 (1.03-1.13)]. Tacrolimus values were adjusted for this difference, and the corrected values showed no systematic differences. Moreover, no systematic differences were observed when comparing DBS or VAMS with venepuncture for mycophenolic acid and prednisolone. Tacrolimus (corrected), mycophenolic acid, and prednisolone microsampling values met the MPE and MAPE predefined acceptability limits of <15% when compared with the corresponding venepuncture values. DBS and VAMS, collected in a controlled environment, simultaneously measured multiple immunosuppressants.

CONCLUSIONS

This study demonstrates that accurate results of multiple immunosuppressant concentrations can be generated through the microsampling approach, with a preference for VAMS over DBS.

Utility of salivary mycophenolic acid concentration monitoring: Modeling and Monte Carlo validation approach

The results of the previous studies demonstrated an association between mycophenolic acid (MPA) exposure, serum albumin level (ALB), and adverse effects in kidney transplant patients. The aim was the identification of mathematical correlation and association between both, total and unbound MPA concentration in relation to ALB, body mass (BM), age and estimated glomerular filtration rate (eGFR) in stable kidney transplant recipients. Furthermore, investigation was conducted with the aim to clarify the role of salivary concentration (CSAL ) of MPA in adverse effect profile. In order to analyze the association between total and salivary concentration of MPA in relation to ALB, BM, age and eGFR, a least squares method for determining the correlation between these parameters was performed. In addition, derived mathematical model based on experimental data can also be performed and simulated through the Monte Carlo (MC) approach. Adverse effects were grouped according to the nature of symptoms and scored by a previously published validated system. Numerically calculated values of CSAL from the models [CSAL  = f(ALB, BM, age, eGFR, CP ) = a00  + a10 *(ALB, BM, age, eGFR) + a01 *CP ] were then compared with those from validation set of patients, where the best fitting model was for ALB [CSAL  = 54.96-1.64*ALB +13.4*CP ]. Adverse effects estimation showed the difference in esthetic score, positively correlated with CSAL in the lower ALB group (145.41 ± 219.02 vs. 354.08 ± 262.19; with statistical significance p = .014) and almost significant for gastrointestinal score (167.69 ± 174.79 vs. 347.55 ± 320.95; p = .247). The study showed that CSAL MPA may contribute to management of adverse effects, but these findings require confirmation of clinical utility.

External Evaluation of Population Pharmacokinetic Models of Busulfan in Chinese Adult Hematopoietic Stem Cell Transplantation Recipients

Objective: Busulfan (BU) is a bi-functional DNA-alkylating agent used in patients undergoing hematopoietic stem cell transplantation (HSCT). Over the last decades, several population pharmacokinetic (pop PK) models of BU have been established, but external evaluation has not been performed for almost all models. The purpose of the study was to evaluate the predictive performance of published pop PK models of intravenous BU in adults using an independent dataset from Chinese HSCT patients, and to identify the best model to guide personalized dosing.

Methods: The external evaluation methods included prediction-based diagnostics, simulation-based diagnostics, and Bayesian forecasting. In prediction-based diagnostics, the relative prediction error (PE%) was calculated by comparing the population predicted concentration (PRED) with the observations. Simulation-based diagnostics included the prediction- and variability-corrected visual predictive check (pvcVPC) and the normalized prediction distribution error (NPDE). Bayesian forecasting was executed by giving prior one to four observations. The factors influencing the model predictability, including the impact of structural models, were assessed.

Results: A total of 440 concentrations (110 patients) were obtained for analysis. Based on prediction-based diagnostics and Bayesian forecasting, preferable predictive performance was observed in the model developed by Huang et al. The median PE% was -1.44% which was closest to 0, and the maximum F₂₀ of 57.27% and F₃₀ of 72.73% were achieved. Bayesian forecasting demonstrated that prior concentrations remarkably improved the prediction precision and accuracy of all models, even with only one prior concentration.

Conclusion: This is the first study to comprehensively evaluate published pop PK models of BU. The model built by Huang et al. had satisfactory predictive performance, which can be used to guide individualized dosage adjustment of BU in Chinese patients.

External evaluation of population pharmacokinetic models for voriconazole in Chinese adult patients with hematological malignancy

OBJECTIVES

Patients with hematological malignancies are prone to invasive fungal disease due to long-term chemotherapy or radiotherapy. Voriconazole is a second-generation triazole broad-spectrum antibiotic used to prevent or treat invasive fungal infections. Many population pharmacokinetic (pop PK) models have been published for voriconazole, and various diagnostic methods are available to validate the performance of these pop PK models. However, most of the published models have not been strictly evaluated externally. The purpose of this study is to evaluate these models externally and assess their predictive capabilities.

METHODS

The external dataset consists of adults receiving voriconazole treatment at Fujian Medical University Union Hospital. We re-established the published models based on their final estimated values in the literature and used our external dataset for initial screening. Each model was evaluated based on the following outcomes: prediction-based diagnostics, prediction- and variability-corrected visual predictive check (pvcVPC), normalized prediction distribution errors (NPDE), and Bayesian simulation results with one to two prior observations.

RESULTS

A total of 237 samples from 166 patients were collected as an external dataset. After screening, six candidate models suitable for the external dataset were finally obtained for comparison. Among the models, none demonstrated excellent predictive performance. Bayesian simulation shows that all models' prediction precision and accuracy were significantly improved when one or two prior concentrations were given.

CONCLUSIONS

The published pop PK models of voriconazole have significant differences in prediction performance, and none of the models could perfectly predict the concentrations of voriconazole for our data. Therefore, extensive evaluation should precede the adoption of any model in clinical practice.

Important lack of difference in tacrolimus and mycophenolic acid pharmacokinetics between Aboriginal and Caucasian kidney transplant recipients

AIM

To examine whether differences in tacrolimus and mycophenolic acid (MPA) pharmacokinetics contribute to the poorer kidney transplant outcomes experienced by Aboriginal Australians.

METHODS

Concentration-time profiles for tacrolimus and MPA were prospectively collected from 43 kidney transplant recipients: 27 Aboriginal and 16 Caucasian. Apparent clearance (CL/F) and distribution volume (V/F) for each individual were derived from concentration-time profiles combined with population pharmacokinetic priors, with subsequent assessment for between-group difference in pharmacokinetics. In addition, population pharmacokinetic models were developed using the prospective dataset supplemented by previously developed structural models for tacrolimus and MPA. The change in NONMEM objective function was used to assess improvement in goodness of model fit.

RESULTS

No differences were found between Aboriginal and Caucasian groups or empirical Bayes estimates, for CL/F or V/F of MPA or tacrolimus. However, a higher prevalence of CYP3A5 expressers (26% compared with 0%) and wider between-subject variability in tacrolimus CL/F (SD = 5.00 compared with 3.25 L/h/70 kg) were observed in the Aboriginal group, though these differences failed to reach statistical significance (p = .07 and p = .08).

CONCLUSION

There were no differences in typical tacrolimus or MPA pharmacokinetics between Aboriginal and Caucasian kidney transplant recipients. This means that Bayesian dosing tools developed to optimise tacrolimus and MPA dosing in Caucasian recipients may be applied to Aboriginal recipients. In turn, this may improve drug exposure and thereby transplant outcomes in this group. Aboriginal recipients appeared to have greater between-subject variability in tacrolimus CL/F and a higher prevalence of CYP3A5 expressers, attributes that have been linked with inferior outcomes.

Population Pharmacokinetics And Bayesian Estimation of Mycophenolate Mofetil In Patients With Autoimmune Hepatitis

BACKGROUND

Mycophenolate mofetil (MMF) is the most widely used second-line agent in auto-immune hepatitis (AIH). Individual dose adjustment of MMF may avoid adverse outcomes while maximizing efficacy. The aim of the present study was to develop population pharmacokinetic (popPK) models and Maximum A-Posteriori Bayesian estimators (MAP-BEs) to estimate MPA inter-dose area under the curve (AUC_0-12h ) in AIH patients administered MMF using nonlinear mixed effect modelling.

METHODS

We analyzed 50 MPA PK profiles from 34 different patients, together with some demographic, clinical, and laboratory test data. The median number of plasma samples per profile, immediately preceding and following the morning MMF dose, was 7 [4 - 10]. PopPK modeling was performed using parametric, top-down, nonlinear mixed effect modelling with NONMEM 7.3. MAP-BEs were developed based on the best popPK model and the best limited sampling strategy (LSS) selected among several.

RESULTS

The pharmacokinetic data were best described by a 2-compartment model, Erlang distribution to describe the absorption phase, and a proportional error. The mean (RSE) of popPK parameter estimates of clearance, intercompartmental clearance, central volume and absorption rate with the final model were: 21.6 L.h^-1 (11%), 22.7 L.h^-1 (19%), 35.9 L (21%) and 8.7 h^-1 (9%), respectively. The peripheral volume was fixed to 300 L. The best MAP-BE relied on the LSS at 0.33, 1 and 3 hours after mycophenolate mofetil dose administration and was very accurate (bias=5.6%) and precise (RMSE<20%).

CONCLUSION

The precise and accurate Bayesian estimator developed in this study for AIH patients on MMF can be used to improve the therapeutic management of these patients.

Population pharmacokinetics and dosage optimization of tacrolimus coadministration with Wuzhi capsule in adult liver transplant patients

1. This study aimed to establish a population pharmacokinetic model of tacrolimus coadministration with Wuzhi capsule and optimize the dosage regimen in adult liver transplant patients.2. Totally 1327 tacrolimus trough concentrations from 116 adult liver transplant patients were obtained for model development. A one-compartment model with first-order absorption and elimination was used to analyse the data, and the final model was internally verified using a goodness-of-fit diagnostic plot, bootstrap methods, and visual prediction test. A total of 29 patients with 250 tacrolimus trough concentrations was used for external validation via prediction-based diagnostics. Additionally, the simulation was used to optimize the recommended dose of tacrolimus and Wuzhi capsules.3. The estimated apparent clearance and volume of the distribution of tacrolimus were 15.4 L/h and 1210 L, respectively. The tacrolimus daily dose, Wuzhi capsule daily dose, postoperative time, alanine transaminase, haemoglobin, total bilirubin, direct bilirubin, estimated glomerular filtration rate, and urea, concomitant with voriconazole and fluconazole, were identified as significant covariates affecting the pharmacokinetic parameters. Internal and external validation showed that the final model was stable and reliable for predicting performance.4. The final model could provide guidance for dosage optimization of tacrolimus coadministered with Wuzhi capsules in adult liver transplantation patients.

The PHARMACOM-EPI Framework for Integrating Pharmacometric Modelling Into Pharmacoepidemiological Research Using Real-World Data: Application to Assess Death Associated With Valproate

In pharmacoepidemiology, it is usually expected that the observed association should be directly or indirectly related to the pharmacological effects of the drug/s under investigation. Pharmacological effects are, in turn, strongly connected to the pharmacokinetic and pharmacodynamic properties of a drug, which can be characterized and investigated using pharmacometric models. Recently, the use of pharmacometrics has been proposed to provide pharmacological substantiation of pharmacoepidemiological findings derived from real-world data. However, validated frameworks suggesting how to combine these two disciplines for the aforementioned purpose are missing. Therefore, we propose PHARMACOM-EPI, a framework that provides a structured approach on how to identify, characterize, and apply pharmacometric models with practical details on how to choose software, format dataset, handle missing covariates/dosing data, how to perform the external evaluation of pharmacometric models in real-world data, and how to provide pharmacological substantiation of pharmacoepidemiological findings. PHARMACOM-EPI was tested in a proof-of-concept study to pharmacologically substantiate death associated with valproate use in the Danish population aged ≥ 65 years. Pharmacological substantiation of death during a follow-up period of 1 year showed that in all individuals who died (n = 169) individual predictions were within the subtherapeutic range compared with 52.8% of those who did not die (n = 1,084). Of individuals who died, 66.3% (n = 112) had a cause of death possibly related to valproate and 33.7% (n = 57) with well-defined cause of death unlikely related to valproate. This proof-of-concept study showed that PHARMACOM-EPI was able to provide pharmacological substantiation for death associated with valproate use in the study population.

Estimation of Mycophenolic Acid Exposure in Heart Transplant Recipients by Population Pharmacokinetic and Limited Sampling Strategies

Purpose: The aim of this study is i) to establish a strategy to estimate the area under the curve of the dosing interval (AUC0-12h) of mycophenolic acid (MPA) in the heart transplant recipients and ii) to find the covariates that significantly affect the pharmacokinetics of MPA exposure. Methods: This single-center, prospective, open-label, observational study was conducted in 91 adult heart transplant recipients orally taking mycophenolate mofetil dispersible tablets. Samples collected intensively and sparsely were analyzed by the enzyme-multiplied immunoassay technique, and all the data were used in PPK modeling. Potential covariates were tested stepwise. The goodness-of-fit plots, the normalized prediction distribution error, and prediction-corrected visual predictive check were used for model evaluation. Optimal sampling times by ED-optimal strategy and multilinear regression (MLR) were analyzed based on the simulated data by the final PPK model. Moreover, using intensive data from 14 patients, the accuracy of AUC0-12h estimation was evaluated by Passing-Bablok regression analysis and Bland-Alman plots for both the PPK model and MLR equation. Results: A two-compartment model with first-order absorption and elimination with a lag time was chosen as the structure model. Co-medication of proton pump inhibitors (PPIs), estimated glomerular filtration rate (eGFR), and albumin (ALB) were found to significantly affect bioavailability (F), clearance of central compartment (CL/F), and the distribution volume of the central compartment (V2/F), respectively. Co-medication of PPIs decreased F by 27.6%. When eGFR decreased by 30 ml/min/1.73 m2, CL/F decreased by 23.7%. However, the impact of ALB on V2/F was limited to MPA exposure. The final model showed an adequate fitness of the data. The optimal sampling design was pre-dose and 1 and 4 h post-dose for pharmacokinetic estimation. The best-fit linear equation was finally established as follows: AUC0-12h = 3.539 × C0 + 0.288 × C0.5 + 1.349 × C1 + 6.773 × C4.5. Conclusion: A PPK model was established with three covariates in heart transplant patients. Co-medication of PPIs and eGFR had a remarkable impact on AUC0-12h of MPA. A linear equation was also concluded with four time points as an alternative way to estimate AUC0-12h for MPA.

Model-informed precision dosing to optimise immunosuppressive therapy in renal transplantation

Immunosuppressive therapy is pivotal for sustained allograft and patient survival after renal transplantation. However, optimally balanced immunosuppressive therapy is challenged by between-patient and within-patient pharmacokinetic (PK) variability. This could warrant the application of personalised dosing strategies to optimise individual patient outcomes. Pharmacometrics, the science that investigates the xenobiotic-biotic interplay using computer-aided mathematical modelling, provides options to describe and quantify this PK variability and enables identification of patient characteristics affecting immunosuppressant PK and treatment outcomes. Here, we review and critically appraise the available pharmacometric model-informed dosing solutions for the typical immunosuppressants in modern renal transplantation, to guide their initial and subsequent dosing.

Estrogen profile- and pharmacogenetics-based lamotrigine dosing regimen optimization: Recommendations for pregnant women with epilepsy

During pregnancy, various physiological changes occur that can alter the pharmacokinetics of antiepileptic drugs, such as lamotrigine (LTG). Anticipating the change in LTG dose required to achieve a pre-pregnancy target concentration is challenging. This study aimed to develop a refined population pharmacokinetic (PopPK) model of LTG in pregnant women with epilepsy (WWE) to identify factors explaining the variability in pharmacokinetics and to establish a model-informed individualized dosing regimen. On that basis, a coarsened model containing only clinical variables was also developed to examine its predictive performance compared to the refined model. In total, 322 concentration-time points from 51 pregnant WWE treated with LTG were employed to establish a refined PopPK model that included endogenous estrogen profiles, variants of candidate genes encoding LTG-metabolizing enzymes and -transporter proteins, and other clinical variables and a coarsened model that included only clinical variables, respectively. Data from an additional 11 patients were used for external validation of these two models. A nonlinear mixed-effect modeling approach was used for PopPK analysis of LTG. The standard goodness-of-fit method, bootstrap, normalized prediction distribution errors and external evaluation were adopted to estimate the stability and predictive performance of the candidate models. Akaike information criterion (AIC) was used to compare the goodness of fit between these two models. A lower AIC indicates a better fit of the data and the preferred model. Recommended dosing regimens for pregnant WWE were selected using Monte Carlo simulation based on the established optimal model. In the refined PopPK model, the population mean of apparent LTG clearance (CL/F) in pregnant WWE was estimated to be 2.82 L/h, with an inter-individual variability of 23.6%. PopPK analysis indicated that changes in estrogen profile during pregnancy were the predominant reason for the significant variations in LTG-CL/F. Up to the 3rd trimester, the concentration accumulation effect of E2 increased LTG-CL/F by 5.109 L/h from baseline levels. Contrary to effect of E2, E3 as the main circulating estrogen in pregnancy with a peak value of 34.41 ng/mL is 1000-fold higher than that in non-pregnancy reduced LTG-CL/F by 1.413 L/h. In addition, the UGT2B7 rs4356975 C > T and ABCB1 rs1128503 A > G variants may contribute to a better understanding of the inter-individual variability in LTG-CL/F. LTG-CL/F was 1.66-fold higher in UGT2B7 rs4356975 CT or TT genotype carriers than in CC genotype carriers. In contrast, ABCB1 rs1128503 GG genotype carriers had only 71.9% of the LTG-CL/F of AA or AG genotype carriers. In the coarsened PopPK model, the gestational age was a promising predictor of changes in LTG-CL/F. When comparing these two models, the refined PopPK model was favored over the coarsened PopPK model (AIC = -30.899 vs. -20.017). Monte Carlo simulation based on optimal PopPK model revealed that the LTG dosage administered to carriers of the UGT2B7 rs4356975 CT or TT genotype required a 33-50% increase to reach the pre-pregnancy target concentration, and carriers of the ABCB1 rs1128503 GG genotype required a 33-66% lower dose of LTG than carriers of the ABCB1 rs1128503 AA or AG genotype. Changes in estrogen profile during pregnancy was a better predictor of variations in LTG-CL/F than gestational age. The developed model based on estrogen profile and pharmacogenetics can serve as a foundation for further optimization of dosing regimens of LTG in pregnant WWE.

Systematic external evaluation of reported population pharmacokinetic models of vancomycin in Chinese children and adolescents

WHAT IS KNOWN AND OBJECTIVES

Various population pharmacokinetic (PopPK) models for vancomycin in children and adolescents have been constructed to optimize the therapeutic regimen of vancomycin. However, little is known about their predictive performance when extrapolated to different clinical centres. Therefore, the aim of this study was to externally validate the predictability of vancomycin PopPK model when extrapolated to different clinical centres and verify its applicability in an independent data set.

METHODS

The published models were screened from the literature and evaluated using an external data set of a total of 451 blood concentrations of vancomycin measured in 220 Chinese paediatric patients. Prediction- and simulation-based diagnostics and Bayesian forecasting were performed to evaluate the predictive performance of the models.

RESULTS

Ten published PopPK models were assessed. Prediction-based diagnostics showed that none of the investigated models met all the standards (median prediction error (MDPE) ≤ ±20%, median absolute prediction error (MAPE) ≤30%, PE% within ±20% (F₂₀ ) ≥35% and PE% within ±30% (F₃₀ ) ≥50%), indicating unsatisfactory predictability. In simulation-based diagnostics, both the visual predictive checks (VPC) and the normalized prediction distribution error (NPDE) indicated misspecification in all models. Bayesian forecasting results showed that the accuracy and precision of individual predictions could be significantly improved with one or two prior observations, but frequent monitoring might not be necessary in the clinic, since Bayesian forecasting identified that greater number of samples did not significantly improve the predictability. Model 3 established by Moffett et al showed better predictability than other models.

WHAT IS NEW AND CONCLUSION

The 10 published models performed unsatisfactorily in prediction- and simulation-based diagnostics; none of the published models was suitable for designing the initial dosing regimens of vancomycin. Pharmacokinetic characteristics and covariates, such as weight, renal function, age and underlying disease should be taken into account when extrapolating the vancomycin model. Bayesian forecasting combined with therapeutic drug monitoring based on model 3 can be used to adjust vancomycin dosing regimens.

Optimizing Mycophenolic Acid Exposure in Kidney Transplant Recipients: Time for Target Concentration Intervention

The immunosuppressive agent mycophenolate is used extensively in kidney transplantation, yet dosing strategy applied varies markedly from fixed dosing ("one-dose-fits-all"), to mycophenolic acid (MPA) trough concentration monitoring, to dose optimization to an MPA exposure target (as area under the concentration-time curve [MPA AUC0-12]). This relates in part to inconsistent results in prospective trials of concentration-controlled dosing (CCD). In this review, the totality of evidence supporting mycophenolate CCD is examined: pharmacological characteristics, observational data linking exposure to efficacy and toxicities, and randomized controlled trials of CCD, with attention to dose optimization method and exposure achieved. Fixed dosing of mycophenolate consistently leads to underexposure associated with rejection, as well as overexposure associated with toxicities. When CCD is driven by pharmacokinetic calculation to a target concentration (target concentration intervention), MPA exposure is successfully controlled and clinical benefits are seen. There remains a need for consensus on practical aspects of mycophenolate target concentration intervention in contemporary tacrolimus-containing regimens and future research to define maintenance phase exposure targets. However, given ongoing consequences of both overimmunosuppression and underimmunosuppression in kidney transplantation, impacting short- and long-term outcomes, these should be a priority. The imprecise "one-dose-fits-all" approach should be replaced by the clinically proven MPA target concentration strategy.

Systematic external evaluation of published population pharmacokinetic models of mycophenolate mofetil in adult kidney transplant recipients co-administered with tacrolimus

AIMS

Various mycophenolate mofetil (MMF) population pharmacokinetic (popPK) models have been developed to describe its PK characteristics and facilitate its optimal dosing in adult kidney transplant recipients co-administered with tacrolimus. However, the external predictive performance has been unclear. Thus, this study aimed to comprehensively evaluate the external predictability of published MMF popPK models in such populations and investigate the potential influencing factors.

METHODS

The external predictability of qualified popPK models was evaluated using an independent dataset. The evaluation included prediction- and simulation-based diagnostics, and Bayesian forecasting. In addition, factors influencing model predictability, especially the impact of structural models, were investigated.

RESULTS

Fifty full PK profiles from 45 patients were included in the evaluation dataset and 11 published popPK models were identified and evaluated. In prediction-based diagnostics, the prediction error within ±30% was less than 50% in most published models. The prediction- and variability-corrected visual predictive check and posterior predictive check showed large discrepancies between the observations and simulations in most models. Moreover, the normalized prediction distribution errors of all models did not follow a normal distribution. Bayesian forecasting demonstrated an improvement in the model predictability. Furthermore, the predictive performance of two-compartment (2CMT) models incorporating the enterohepatic circulation (EHC) process was not superior to that of conventional 2CMT models.

CONCLUSIONS

The published models showed large variability and unsatisfactory predictive performance, which indicated that therapeutic drug monitoring was necessary for MMF clinical application. Further studies incorporating potential covariates need to be conducted to investigate the key factors influencing model predictability of MMF.