A machine learning approach to predict daptomycin exposure from two concentrations based on Monte Carlo simulations

Daptomycin is a concentration-dependent lipopeptide antibiotic for which exposure/effect relationships have been shown. Machine learning (ML) algorithms, developed to predict the individual exposure to drugs, have shown very good performances in comparison to maximum a posteriori Bayesian estimation (MAP-BE). The aim of this work was to predict the area under the blood concentration curve (AUC) of daptomycin from two samples and a few covariates using XGBoost ML algorithm trained on Monte Carlo simulations. Five thousand one hundred fifty patients were simulated from two literature population pharmacokinetics models. Data from the first model were split into a training set (75%) and a testing set (25%). Four ML algorithms were built to learn AUC based on daptomycin blood concentration samples at pre-dose and 1 h post-dose. The XGBoost model (best ML algorithm) with the lowest root mean square error (RMSE) in a 10-fold cross-validation experiment was evaluated in both the test set and the simulations from the second population pharmacokinetic model (validation). The ML model based on the two concentrations, the differences between these concentrations, and five other covariates (sex, weight, daptomycin dose, creatinine clearance, and body temperature) yielded very good AUC estimation in the test (relative bias/RMSE = 0.43/7.69%) and validation sets (relative bias/RMSE = 4.61/6.63%). The XGBoost ML model developed allowed accurate estimation of daptomycin AUC using C0, C1h, and a few covariates and could be used for exposure estimation and dose adjustment. This ML approach can facilitate the conduct of future therapeutic drug monitoring (TDM) studies.

Optimization of Ganciclovir and Valganciclovir Starting Dose in Children by Machine Learning

BACKGROUND AND OBJECTIVES

Ganciclovir (GCV) and valganciclovir (VGCV) show large interindividual pharmacokinetic variability, particularly in children. The objectives of this study were (1) to develop machine learning (ML) algorithms trained on simulated pharmacokinetics profiles obtained by Monte Carlo simulations to estimate the best ganciclovir or valganciclovir starting dose in children and (2) to compare its performances on real-world profiles to previously published equation derived from literature population pharmacokinetic (POPPK) models achieving about 20% of profiles within the target.

MATERIALS AND METHODS

The pharmacokinetic parameters of four literature POPPK models in addition to the World Health Organization (WHO) growth curve for children were used in the mrgsolve R package to simulate 10,800 pharmacokinetic profiles. ML algorithms were developed and benchmarked to predict the probability to reach the steady-state, area-under-the-curve target (AUC0-24 within 40-60 mg × h/L) based on demographic characteristics only. The best ML algorithm was then used to calculate the starting dose maximizing the target attainment. Performances were evaluated for ML and literature formula in a test set and in an external set of 32 and 31 actual patients (GCV and VGCV, respectively).

RESULTS

A combination of Xgboost, neural network, and random forest algorithms yielded the best performances and highest target attainment in the test set (36.8% for GCV and 35.3% for the VGCV). In actual patients, the best GCV ML starting dose yielded the highest target attainment rate (25.8%) and performed equally for VGCV with the Franck model formula (35.3% for both).

CONCLUSION

The ML algorithms exhibit good performances in comparison with previously validated models and should be evaluated prospectively.

Limited sampling strategy and population pharmacokinetic model of mycophenolic acid in pediatric patients with systemic lupus erythematosus: application of a double gamma absorption model with SAEM algorithm

INTRODUCTION

Mycophenolic acid (MPA), the active metabolite of mycophenolate mofetil (MMF), is widely used in the treatment of systemic lupus erythematosus (SLE). It has been shown that its therapeutic drug monitoring based on the area under the curve (AUC) improves treatment efficacy. MPA exhibits a complex bimodal absorption, and a double gamma distribution model has been already proposed in the past to accurately describe this phenomenon. These previous population pharmacokinetics models (POPPK) have been developed using iterative two stage Bayesian (IT2B) or non-parametric adaptive grid (NPAG) methods. However, non-linear mixed effect (NLME) approaches based on stochastic approximation expectation-maximization (SAEM) algorithms have never been published so far for this particular model. The objectives of this study were (i) to implement the double absorption gamma model in Monolix, (ii) to compare different absorption models to describe the pharmacokinetics of MMF, and (iii) to develop a limited sampling strategy (LSS) to estimate AUC in pediatric SLE patients.

MATERIAL AND METHODS

A data splitting of full pharmacokinetic profiles sampled in 67 children extracted either from the expert system ISBA (n = 34) or the hospital Saint Louis (n = 33) was performed into train (75%) and test (25%) sets. A POPPK was developed for MPA in the train set using a NLME and the SAEM algorithm and different absorption models were implemented and compared (first order, transit, or simple and double gamma). The best limited sampling strategy was then determined in the test set using a maximum-a-posteriori Bayesian method to estimate individual PK parameters and AUC based on three blood samples compared to the reference AUC calculated using the trapezoidal rule applied on all samples and performances were assessed in the test set.

RESULTS

Mean patient age and dose was 13 years old (5-18) and 18.1 mg/kg (7.9-47.6), respectively. MPA concentrations (764) from 107 occasions were included in the analysis. A double gamma absorption with a first-order elimination from the central compartment best fitted the data. The optimal LSS with samples at 30 min, 2 h, and 3 h post-dose exhibited good performances in the test set (mean bias - 0.32% and RMSE 21.0%).

CONCLUSION

The POPPK developed in this study adequately estimated the MPA AUC in pediatric patients with SLE based on three samples. The double absorption gamma model developed with the SAEM algorithm showed very accurate fit and reduced computation time.

In Silico Pharmacokinetics Evaluation of Forgiveness for Doravirine and Rilpivirine

BACKGROUND

This study aimed to evaluate the concentrations of rilpivirine (RLP) and doravirine (DOR) after 3 days-off using simulations from population pharmacokinetics models.

METHODS

The authors conducted a series of 500 sets of 10,000 Monte Carlo simulations to examine the steady-state conditions for 2 common dosage levels: 25 mg/d for RLP and 100 mg/d for DOR. These simulations were conducted under 2 scenarios: 1 without drug cessation and another after a 3-day break. The validity of the implementation was established through a comparison of median trough concentrations (C24h) with previously reported data. Subsequently, the proportion of simulated patients with C24h and C72h after 3 days-off (C72h/3do) that exceeded the inhibitory concentration 50 (IC50), 5.2 mcg/L for DOR and 20.5 mcg/L for RLP respectively, was calculated. The inhibitory quotient (IQ) was also computed, which was 6 times IC50 for DOR and 4.5 times IC50 for RLP. Finally, nomograms were constructed to estimate the probability of having C72h/3do > IC50 or > IQ for different ranges of C24h.

RESULTS

Simulated C24h median ± SD for RLP were 61.8 ± 0.4 mcg/L and for DOR 397 ± 0 mcg/L. For RLP, 99.3 ± 0.1% exceeded IC50 at C24h, 16.4 ± 0.4% at C72h/3do, and none surpassed the IQ threshold. In contrast, DOR had 100% ± 0% above IC50 at C24h, 93.6 ± 0.2% at C72h/3do, and 58.6 ± 0.5% exceeded the IQ.

CONCLUSIONS

These findings suggest that treatment with DOR may offer a more forgiving therapeutic profile than RLP, given the larger proportion of patients achieving effective drug exposure with DOR. However, it is important to acknowledge a significant limitation of this study, namely, the assumption that drug concentration is a perfect surrogate for drug effectiveness.

Tacrolimus population pharmacokinetics in adult heart transplant patients

INTRODUCTION

Tacrolimus is an immunosuppressant largely used in heart transplantation. However, the calculation of its exposure based on the area under the curve (AUC) requires the use of a population pharmacokinetic (PK) model. The aims of this work were (i) to develop a population PK model for tacrolimus in heart transplant patients, (ii) to derive a maximum a posteriori Bayesian estimator (MAP-BE) based on a limited sampling strategy (LSS) and (iii) to estimate probabilities of target attainment (PTAs) for AUC and trough concentration (C0).

MATERIAL AND METHODS

Forty-seven PK profiles (546 concentrations) of 18 heart transplant patients of the Pharmacocinétique des Immunosuppresseurs chez les patients GREffés Cardiaques study receiving tacrolimus (Prograf®) were included. The database was split into a development (80%) and a validation (20%) set. PK parameters were estimated in MONOLIX® and based on this model a Bayesian estimator using an LSS was built. Simulations were performed to calculate the PTA for AUC and C0.

RESULTS

The best model to describe the tacrolimus PK was a two-compartment model with a transit absorption and a linear elimination. Only the CYP3A5 covariate was kept in the final model. The derived MAP-BE based on the LSS (0-1-2 h postdose) yielded an AUC bias ± SD = 2.7 ± 10.2% and an imprecision of 9.9% in comparison to the reference AUC calculated using the trapezoidal rule. PTAs based on AUC or C0 allowed new recommendations to be proposed for starting doses (0.11 mg·kg-1 ·12 h-1 for the CYP3A5 nonexpressor and 0.22 mg·kg1 ·12 h-1 for the CYP3A5 expressor).

CONCLUSION

The MAP-BE developed should facilitate estimation of tacrolimus AUC in heart transplant patients.

Diagnostic, prognostic and clinical value of left ventricular radial strain to identify paradoxical septal motion in ventilated patients with the acute respiratory distress syndrome: an observational prospective multicenter study

BACKGROUND

Acute cor pulmonale (ACP) is prognostic in patients with acute respiratory distress syndrome (ARDS). Identification of paradoxical septal motion (PSM) using two-dimensional echocardiography is highly subjective. We sought to describe feature-engineered metrics derived from LV radial strain changes related to PSM in ARDS patients with ACP of various severity and to illustrate potential diagnostic and prognostic yield.

METHODS

This prospective bicentric study included patients under protective ventilation for ARDS related to COVID-19 who were assessed using transesophageal echocardiography (TEE). Transgastric short-axis view at mid-papillary level was used to visually grade septal motion, using two-dimensional imaging, solely and combined with LV radial strain: normal (grade 0), transient end-systolic septal flattening (grade 1), prolonged end-systolic septal flattening or reversed septal curvature (grade 2). Inter-observer variability was calculated. Feature engineering was performed to calculate the time-to-peak and area under the strain curve in 6 LV segments. In the subset of patients with serial TEE examinations, a multivariate Cox model analysis accounting for new-onset of PSM as a time-dependent variable was used to identify parameters associated with ICU mortality.

RESULTS

Overall, 310 TEE examinations performed in 182 patients were analyzed (age: 67 [60-72] years; men: 66%; SAPSII: 35 [29-40]). Two-dimensional assessment identified a grade 1 and grade 2 PSM in 100 (32%) and 48 (15%) examinations, respectively. Inter-rater reliability was weak using two-dimensional imaging alone (kappa = 0.49; 95% CI 0.40-0.58; p < 0.001) and increased with associated LV radial strain (kappa = 0.84, 95% CI 0.79-0.90, p < 0.001). The time-to-peak of mid-septal and mid-lateral segments occurred significantly later in systole and increased with the grade of PSM. Similarly, the area under the strain curve of these segments increased significantly with the grade of PSM, compared with mid-anterior or mid-inferior segments. Severe acute cor pulmonale with a grade 2 PSM was significantly associated with mortality. Requalification in an upper PSM grade using LV radial strain allowed to better identify patients at risk of death (HR: 6.27 [95% CI 2.28-17.2] vs. 2.80 [95% CI 1.11-7.09]).

CONCLUSIONS

In objectively depicting PSM and quantitatively assessing its severity, TEE LV radial strain appears as a valuable adjunct to conventional two-dimensional imaging.

Bridging the Worlds of Pharmacometrics and Machine Learning

Precision medicine requires individualized modeling of disease and drug dynamics, with machine learning-based computational techniques gaining increasing popularity. The complexity of either field, however, makes current pharmacological problems opaque to machine learning practitioners, and state-of-the-art machine learning methods inaccessible to pharmacometricians. To help bridge the two worlds, we provide an introduction to current problems and techniques in pharmacometrics that ranges from pharmacokinetic and pharmacodynamic modeling to pharmacometric simulations, model-informed precision dosing, and systems pharmacology, and review some of the machine learning approaches to address them. We hope this would facilitate collaboration between experts, with complementary strengths of principled pharmacometric modeling and flexibility of machine learning leading to synergistic effects in pharmacological applications.

Within-subject pharmacokinetic variability has a strong influence on individual exposure ratios in bioequivalence studies, hence on drug formulation interchangeability

INTRODUCTION

Bioequivalence between a reference and a generic drug is based on the hypothesis that a ± 20% change in blood exposure (or ± 10% for drugs with narrow therapeutic index, NTI) following the generic/reference switch will not have any therapeutic consequences. However, the individual exposure ratio between generic and reference can be higher than 1.20 (or 1.10). This study aims to analyse the different parameters influencing the individual exposure ratio, hence the conditions for reference/generic interchangeability.

METHODS

Bioequivalence studies with a double cross-over design for a virtual drug were simulated using 100 random sets of 12, 24, 48 or 100 pairs of areas under the curve (AUC), varying the generic/reference AUC geometric mean ratios between 0.80 and 1.25 and the within-subject exposure variance of the reference and the generic formulations.

RESULTS

The proportion of subjects with an exposure generic/reference ratio outside the ± 10% or ± 20% acceptance intervals increases when (1) the reference within-subject variance increases; (2) the ratio of the generic within-subject variance on the reference within-subject variance increases; and (3) the generic/reference mean AUC ratio diverges from 1.0. When only considering replicated administrations of the reference, the individual exposure ratio increases with the within-subject variance, yielding values outside the usually accepted individual exposure ratio range of 0.5 to 2 for drugs with narrow therapeutic index as soon as the within-subject variance standard deviation is ≥ 0.25 (equivalent to within-patient CV% > 25%).

CONCLUSIONS

Interchangeability between reference and generic formulations, especially for drugs with narrow therapeutic index can only be assumed if, the within-subject variance of generic is less or equal to the within-subject variance of reference or, if this is not the case, if the distribution of the generic/generic individual exposure ratios is included within the therapeutic margins of the reference drug.

Mycophenolate Mofetil Dose Adjustment in Pediatric Kidney Transplant Recipients

BACKGROUND

The Immunosuppressant Bayesian Dose Adjustment web site aids clinicians and pharmacologists involved in the care of transplant recipients; it proposes dose adjustments based on the estimated area under the concentration-time curve (AUCs). Three concentrations (T 20 min , T 1 h , and T 3 h ) are sufficient to estimate mycophenolic acid (MPA) AUC 0-12 h in pediatric kidney transplant recipients. This study investigates mycophenolate mofetil (MMF) doses and MPA AUC values in pediatric kidney transplant recipients, and target exposure attainment when the proposed doses were followed, through a large-scale analysis of the data set collated since the inception of the Immunosuppressant Bayesian Dose Adjustment web site.

METHODS

In this study, 4051 MMF dose adjustment requests, corresponding to 1051 patients aged 0-18 years, were retrospectively analyzed. AUC calculations were performed in the back office of the Immunosuppressant Bayesian Dose Adjustment using published Bayesian and population pharmacokinetic models.

RESULTS

The first AUC request was posted >12 months posttransplantation for 41% of patients. Overall, only 50% had the first MPA AUC 0-12 h within the recommended 30-60 mg.h/L range. When the proposed dose was not followed, the proportion of patients with an AUC in the therapeutic range for MMF with cyclosporine or tacrolimus at the subsequent request was lower (40% and 45%, respectively) than when it was followed (58% and 60%, respectively): P = 0.08 and 0.006, respectively. Furthermore, 3 months posttransplantation, the dispersion of AUC values was often lower at the second visit when the proposed doses were followed, namely, P = 0.03, 0.003, and 0.07 in the 4 months-1 year, and beyond 1 year with <6-month or >6-month periods between both visits, respectively.

CONCLUSIONS

Owing to extreme interindividual variability in MPA exposure, MMF dose adjustment is necessary; it is efficient at reducing such variability when based on MPA AUC.

Tacrolimus Exposure Before and After a Switch From Twice-Daily Immediate-Release to Once-Daily Prolonged Release Tacrolimus: The ENVARSWITCH Study

LCP-tacrolimus displays enhanced oral bioavailability compared to immediate-release (IR-) tacrolimus. The ENVARSWITCH study aimed to compare tacrolimus AUC0-24 h in stable kidney (KTR) and liver transplant recipients (LTR) on IR-tacrolimus converted to LCP-tacrolimus, in order to re-evaluate the 1:0.7 dose ratio recommended in the context of a switch and the efficiency of the subsequent dose adjustment. Tacrolimus AUC0-24 h was obtained by Bayesian estimation based on three concentrations measured in dried blood spots before (V2), after the switch (V3), and after LCP-tacrolimus dose adjustment intended to reach the pre-switch AUC0-24 h (V4). AUC0-24 h estimates and distributions were compared using the bioequivalence rule for narrow therapeutic range drugs (Westlake 90% CI within 0.90-1.11). Fifty-three KTR and 48 LTR completed the study with no major deviation. AUC0-24 h bioequivalence was met in the entire population and in KTR between V2 and V4 and between V2 and V3. In LTR, the Westlake 90% CI was close to the acceptance limits between V2 and V4 (90% CI = [0.96-1.14]) and between V2 and V3 (90% CI = [0.96-1.15]). The 1:0.7 dose ratio is convenient for KTR but may be adjusted individually for LTR. The combination of DBS and Bayesian estimation for tacrolimus dose adjustment may help with reaching appropriate exposure to tacrolimus rapidly after a switch.

Doravirine Exposure Decreased by Dialysis in a HIV Patient: A Grand Round

BACKGROUND

The authors report the case of a 66-year-old male patient who was hemodialyzed 3 times per week for chronic renal failure and treated with 100 mg of doravirine once daily in combination with dolutegravir for HIV-1. No dose adjustment is required for doravirine in cases of severe renal injury, but the effect of dialysis on its exposure is poorly understood.

METHODS RESULTS

Two series of 2 samples were drawn before and after 4-hour hemodialysis and showed an average doravirine concentration decrease of 48.1 ± 6.7%. The effects of hemodialysis were important, contrary to what was expected and has been previously reported. In addition, intraindividual variability was low. Nevertheless, because the concentrations reported were largely above the inhibitory concentration 50 (IC 50 ), no dose adjustment was required.

CONCLUSIONS

The decrease in doravirine concentration due to hemodialysis observed in this case report was quite significant. Therefore, therapeutic drug monitoring might be recommended in certain patients undergoing doravirine treatment also on hemodialysis.

A Hybrid Algorithm Combining Population Pharmacokinetic and Machine Learning for Isavuconazole Exposure Prediction

OBJECTIVES

Maximum a posteriori Bayesian estimation (MAP-BE) based on a limited sampling strategy and a population pharmacokinetic (POPPK) model is used to estimate individual pharmacokinetic parameters. Recently, we proposed a methodology that combined population pharmacokinetic and machine learning (ML) to decrease the bias and imprecision in individual iohexol clearance prediction. The aim of this study was to confirm the previous results by developing a hybrid algorithm combining POPPK, MAP-BE and ML that accurately predicts isavuconazole clearance.

METHODS

A total of 1727 isavuconazole rich PK profiles were simulated using a POPPK model from the literature, and MAP-BE was used to estimate the clearance based on: (i) the full PK profiles (refCL); and (ii) C24h only (C24h-CL). Xgboost was trained to correct the error between refCL and C24h-CL in the training dataset (75%). C24h-CL as well as ML-corrected C24h-CL were evaluated in a testing dataset (25%) and then in a set of PK profiles simulated using another published POPPK model.

RESULTS

A strong decrease in mean predictive error (MPE%), imprecision (RMSE%) and the number of profiles outside ± 20% MPE% (n-out20%) was observed with the hybrid algorithm (decreased in MPE% by 95.8% and 85.6%; RMSE% by 69.5% and 69.0%; n-out20% by 97.4% and 100% in the training and testing sets, respectively. In the external validation set, the hybrid algorithm decreased MPE% by 96%, RMSE% by 68% and n-out20% by 100%.

CONCLUSION

The hybrid model proposed significantly improved isavuconazole AUC estimation over MAP-BE based on the sole C24h and may improve dose adjustment.

A Method for Evaluating Robustness of Limited Sampling Strategies—Exemplified by Serum Iohexol Clearance for Determination of Measured Glomerular Filtration Rate

In combination with Bayesian estimates based on a population pharmacokinetic model, limited sampling strategies (LSS) may reduce the number of samples required for individual pharmacokinetic parameter estimations. Such strategies reduce the burden when assessing the area under the concentration versus time curves (AUC) in therapeutic drug monitoring. However, it is not uncommon for the actual sample time to deviate from the optimal one. In this work, we evaluate the robustness of parameter estimations to such deviations in an LSS. A previously developed 4-point LSS for estimation of serum iohexol clearance (i.e., dose/AUC) was used to exemplify the effect of sample time deviations. Two parallel strategies were used: (a) shifting the exact sampling time by an empirical amount of time for each of the four individual sample points, and (b) introducing a random error across all sample points. The investigated iohexol LSS appeared robust to deviations from optimal sample times, both across individual and multiple sample points. The proportion of individuals with a relative error greater than 15% (P15) was 5.3% in the reference run with optimally timed sampling, which increased to a maximum of 8.3% following the introduction of random error in sample time across all four time points. We propose to apply the present method for the validation of LSS developed for clinical use.

Vascular endothelial growth factor, tissue factor, coagulation and fibrinolysis markers in slow-flow vascular malformations: a prospective study of treatment with sirolimus

Slow-flow vascular malformations (VMs), especially those with venous components, can be complicated by localized intravascular coagulopathy (LIC), responsible for pain and impaired quality of life. Several studies have shown the effectiveness of mTOR inhibitors (especially sirolimus) on slow-flow VMs but its effect on coagulation has been poorly studied, especially in children. Our study shows that venous and combined VMs are associated with coagulation abnormalities and provides novel evidence that sirolimus improves coagulopathy in venous malformations. However we did not clearly evidence predictive biomarkers of response to sirolimus but this is the first study attempting to highlight predictive markers of response to sirolimus.

Machine learning-supported interpretation of kidney graft elementary lesions in combination with clinical data

Interpretation of kidney graft biopsies using the Banff classification is still heterogeneous. In this study, extreme gradient boosting classifiers learned from two large training datasets (n = 631 and 304 cases) where the "reference diagnoses" were not strictly defined following the Banff rules but from central reading by expert pathologists and further interpreted consensually by experienced transplant nephrologists, in light of the clinical context. In three external validation datasets (n = 3744, 589, and 360), the classifiers yielded a mean ROC curve AUC (95%CI) of: 0.97 (0.92-1.00), 0.97 (0.96-0.97), and 0.95 (0.93-0.97) for antibody-mediated rejection (ABMR); 0.94 (0.91-0.96), 0.94 (0.92-0.95), and 0.91 (0.88-0.95) for T cell-mediated rejection; >0.96 (0.90-1.00) with all three for interstitial fibrosis-tubular atrophy. We also developed a classifier to discriminate active and chronic active ABMR with 95% accuracy. In conclusion, we built highly sensitive and specific artificial intelligence classifiers able to interpret kidney graft scoring together with a few clinical data and automatically diagnose rejection, with excellent concordance with the Banff rules and reference diagnoses made by a group of experts. Some discrepancies may point toward possible improvements that could be made to the Banff classification.

Iohexol plasma and urinary concentrations in cirrhotic patients: A pilot study

BACKGROUND

Renal failure is an independent prognostic factor for survival in patients with cirrhosis. Equations to calculate serum creatinine significantly overestimate the glomerular filtration rate (GFR). Plasma clearance of direct biomarkers has been used to improve the accuracy of evaluations of GFR in this population, but no study has simultaneously measured plasma and urinary clearance, which is the gold standard.

AIM

To study calculated plasma and urinary concentrations of iohexol, based on the kinetics of samples collected over 24 h from cirrhotic patients with three different grades of ascites.

METHODS

One dose of iohexol (5 mL) was injected intravenously and plasma concentrations were measured 11 times over 24 h in nine cirrhotic patients. The urinary concentration of iohexol was also measured, in urine collected at 4, 8, 12 and 24 h.

RESULTS

The plasma and urinary curves of iohexol were similar; however, incomplete urinary excretion was detected at 24 h. Within the estimated GFR limits of our population (> 30 and < 120 mL/min/1.73 m²), the median measured GFR (mGFR) was 63.7 mL/min/1.73 m² (range: 41.3–111.3 mL/min/1.73 m²), which was an accurate reflection of the actual GFR. Creatinine-based formulas for estimating GFR showed significant bias and imprecision, while the Brochner–Mortensen (BM) equation accurately estimated the mGFR (r = 0.93).

CONCLUSION

Plasma clearance of iohexol seems useful for determining GFR regardless of the ascites grade. We will secondly devise a pharmacokinetics model requiring fewer samples andvalidate the BM equation.

Optimization of Vancomycin Initial Dose in Term and Preterm Neonates by Machine Learning

INTRODUCTION

Vancomycin is one of the antibiotics most used in neonates. Continuous infusion has many advantages over intermittent infusions, but no consensus has been achieved regarding the optimal initial dose. The objectives of this study were: to develop a Machine learning (ML) algorithm based on pharmacokinetic profiles obtained by Monte Carlo simulations using a population pharmacokinetic model (POPPK) from the literature, in order to derive the best vancomycin initial dose in preterm and term neonates, and to compare ML performances with those of an literature equation (LE) derived from a POPPK previously published.

MATERIALS AND METHODS

The parameters of a previously published POPPK model of vancomycin in children and neonates were used in the mrgsolve R package to simulate 1900 PK profiles. ML algorithms were developed from these simulations using Xgboost, GLMNET and MARS in parallel, benchmarked and used to calculate the ML first dose. Performances were evaluated in a second simulation set and in an external set of 82 real patients and compared to those of a LE.

RESULTS

The Xgboost algorithm yielded numerically best performances and target attainment rates: 46.9% in the second simulation set of 400-600 AUC/MIC ratio vs. 41.4% for the LE model (p = 0.0018); and 35.3% vs. 28% in real patients (p = 0.401), respectively). The Xgboost model resulted in less AUC/MIC > 600, thus decreasing the risk of nephrotoxicity.

CONCLUSION

The Xgboost algorithm developed to estimate the initial dose of vancomycin in term or preterm infants has better performances than a previous validated LE and should be evaluated prospectively.

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.

Pharmacokinetics of mycophenolic acid and external evaluation of two limited sampling strategies of drug exposure in patients with juvenile systematic lupus erythematosus

INTRODUCTION

Mycophenolate mofetil (MMF), a pro-drug of mycophenolic acid (MPA), has become a major therapeutic option in juvenile systemic lupus erythematosus (jSLE). Monitoring MPA exposure using area under curve (AUC) has proved its value to increase efficacy and safety in solid organ transplantation both in children and adults, but additional data are required in patients with autoimmune diseases. In order to facilitate MMF therapeutic drug monitoring (TDM) in children, Bayesian estimators (BE) of MPA AUC_0-12 h using limited sampling strategies (LSS) have been developed. Our aim was to conduct an external validation of these LSS using rich pharmacokinetics and compare their predictive performance.

METHODS

Pharmacokinetic blood samples were collected from jSLE treated by MMF and MPA plasma concentrations were determined using high-performance liquid chromatography system with ultraviolet detection (HPLC-UV). Individual AUC_0-12 h at steady state was calculated using the trapezoid rule and compared with two LSS: (1) ISBA, a two-stage Bayesian approach developed for jSLE and (2) ADAPT, a non-linear mixed effects model with a parametric maximum likelihood approach developed with data from renal transplanted adults.

RESULTS

We received 41 rich pediatric PK at steady state from jSLE and calculated individual AUC_0-12 h. The external validation MPA AUC_0-12 h was conducted by selecting the concentration-time points adapted to ISBA and ADAPT: (1) ISBA showed good accuracy (bias: - 0.8 mg h/L), (2) ADAPT resulted in a bias of 6.7 mg L/h. The corresponding relative root mean square prediction error (RSME) was 23% and 43% respectively.

CONCLUSION

According to our external validation of two LSS of drug exposure, the ISBA model is recommended for Bayesian estimation of MPA AUC_0-12 h in jSLE. In the literature focusing on MMF TDM, an efficacy cut-off for MPA AUC_0-12 h between 30 and 45 mg h/L is proposed in jSLE but this requires additional validation.

A single Bayesian estimator for iohexol clearance estimation in ICU, liver failure and renal transplant patients

AIM

Iohexol clearance has been proposed to estimate the glomerular filtration rate (GFR). A population pharmacokinetics (popPK) model was developed from heterogenous patients. A Bayesian estimator (MAP-BE) based on a limited sampling strategy (LSS) was derived and evaluated in external patients.

METHODS

Full pharmacokinetic data (7-12 samples) from 172 patients receiving iohexol for measurement of their GFR (unstable and stable ICU patients, liver failure patients and kidney transplant patients) were split into a development (n=136) and validation (n=36) datasets. A PopPK model was developed in Monolix and was used to develop MAP-BE based on LSS. Its performances for GFR estimation were evaluated in the validation set.

RESULTS

A two-compartment model with first-order elimination best described the data. The final model included the type of patients on volume of distribution (Vd), clearance and intercompartmental constants, serum creatinine on clearance and body weight on Vd. The best LSS included samples at 0.1-1-9h exhibiting a relative MPE(RMSE) = -3.7%(14.3%) and better performances than the Bröchner-Mortensen Formula (-3.0%/17%). Split by type of patients, the highest interindividual variability and imprecision was observed in unstable ICU patients MPE(RMSE)=3.7%(18.8%) while the best performances were obtained for renal transplant patients MPE(RMSE)=1.0%(5.8%). All LSS that included samples before 9h for the third sample were associated with an increased imprecision.

CONCLUSION

A single MAP-BE of iohexol based on a 3-sample-LSS for 4 heterogeneous population was developed and allowed accurate estimation of GFR in kidney transplant patients, slightly biased in stable ICU patients and slightly imprecise in unstable ICU patients.

Sirolimus (Rapamycin) for Slow-Flow Malformations in Children: The Observational-Phase Randomized Clinical PERFORMUS Trial

Importance

Sirolimus is increasingly being used to treat various vascular anomalies, although evidence of its efficacy is lacking.

Objective

To assess the efficacy and safety of sirolimus for children with slow-flow vascular malformations to better delineate the indications for treatment.

Design, Setting and Participants

This multicenter, open-label, observational-phase randomized clinical trial included 59 children aged 6 to 18 years with a slow-flow vascular malformation who were recruited between September 28, 2015, and March 22, 2018, in 11 French tertiary hospital centers. Statistical analysis was performed on an intent-to-treat basis from December 4, 2019, to November 10, 2020.

Interventions

Patients underwent an observational period, then switched to an interventional period when they received oral sirolimus (target serum levels, 4-12 ng/mL). The switch time was randomized from month 4 to month 8, and the whole study period lasted 12 months for each patient.

Main Outcomes and Measures

The primary outcome was change in the volume of vascular malformations detected on magnetic resonance imaging scan (with centralized interpretation) per unit of time (ie, between the interventional period and the observational period). Secondary outcomes included subjective end points: pain, bleeding, oozing, quality of life, and safety.

Results

Among the participants (35 girls [59.3%]; mean [SD] age, 11.6 [3.8] years), 22 (37.3%) had a pure venous malformation, 18 (30.5%) had a cystic lymphatic malformation, and 19 (32.2%) had a combined malformation, including syndromic forms. Variations in the volume of vascular malformations detected on magnetic resonance imaging scans associated with the duration period were not overall significantly different between the interventional period and the observational period (all vascular malformations: mean [SD] difference, -0.001 [0.007]; venous malformations: mean [SD] difference, 0.001 [0.004]; combined malformations: mean [SD] difference, 0.001 [0.009]). However, a significant decrease in volume was observed for children with pure lymphatic malformations (mean [SD] difference, -0.005 [0.005]). Overall, sirolimus had positive effects on pain, especially for combined malformations, and on bleeding, oozing, self-assessed efficacy, and quality of life. During sirolimus treatment, 56 patients experienced 231 adverse events (5 serious adverse events, none life-threatening). The most frequent adverse event was an oral ulcer (29 patients [49.2%]).

Conclusions and Relevance

This observational-phase randomized clinical trial allows for clarifying the goals of patients and families when starting sirolimus therapy for children older than 6 years. Pure lymphatic malformations seem to be the best indication for sirolimus therapy because evidence of decreasing lymphatic malformation volume per unit of time, oozing, and bleeding and increasing quality of life was found. In combined malformations, sirolimus significantly reduced pain, oozing, and bleeding. Benefits seemed lower for pure venous malformations than for the 2 other subgroups, also based on symptoms.

Trial Registration

ClinicalTrials.gov Identifier: NCT02509468; clinicaltrialsregister.eu Identifier: 2015-001096-43.

Parametric and Nonparametric Methods in Population Pharmacokinetics: Experts' Discussion on Use, Strengths, and Limitations

Population pharmacokinetics consists of analyzing pharmacokinetic (PK) data collected in groups of individuals. Population PK is widely used to guide drug development and to inform dose adjustment via therapeutic drug monitoring and model-informed precision dosing. There are 2 main types of population PK methods: parametric (P) and nonparametric (NP). The characteristics of P and NP population methods have been previously reviewed. The aim of this article is to answer some frequently asked questions that are often raised by scholars, clinicians, and researchers about P and NP population PK methods. The strengths and limitations of both approaches are explained, and the characteristics of the main software programs are presented. We also review the results of studies that compared the results of both approaches in the analysis of real data. This opinion article may be informative for potential users of population methods in PK and guide them in the selection and use of those tools. It also provides insights on future research in this area.

Pharmacokinetics, Pharmacodynamics, and Therapeutic Drug Monitoring of Valganciclovir and Ganciclovir in Transplantation

Ganciclovir and valganciclovir are first choice drugs for the prevention and treatment of cytomegalovirus infection and disease in solid organ and stem cell transplant recipients. Only a few studies on the pharmacokinetics and exposure/efficacy or exposure/safety relationships of ganciclovir and valganciclovir in transplant recipients have been published so far, and there are still controversies about the exposure parameter to use for therapeutic drug monitoring (TDM). We performed an extensive literature review of the clinical pharmacokinetics data, the exposure/effect relationships in terms of efficacy and safety, and the available tools for valganciclovir and ganciclovir TDM in adults and pediatrics transplant recipients. The pharmacokinetics of ganciclovir and valganciclovir is well described in adults and children, and a high interindividual variability is commonly observed. In contrast, the drug pharmacodynamics has been poorly described in adults and barely in children. The average 24-hour area under the concentration-time curve (AUC_0-24h ) seems to be the best predictor of efficacy and toxicity. The benefit of TDM remains controversial in adult patients but should be considered in children due to higher interindividual variability and lower probability of target attainment. Several bayesian estimators based on limited sampling strategies have been developed with this aim and may be used in clinical practice for the AUC-based individual dose adjustment of ganciclovir and valganciclovir.

Neurotoxicity Despite a Renal Function-Adjusted Cefepime Regimen: A Case Study

An 83-year-old man, presenting decreased renal function (estimated glomerular filtration rate 21 mL/min/1.73 m), was treated for a bone and joint infection (on a trans-metatarsal right foot amputation) caused by Klebsiella Pneumonia sensitive to cefepime. The starting dose (1 g bid) was based on recommendations for patients presenting severe infections. One week after treatment initiation, the patient developed neurotoxicity, exhibiting extremely high plasma cefepime concentrations. Based on TDM, the dose was reduced by 8 times the original dose. This case report highlights the importance of therapeutic drug monitoring for cefepime, especially in patients presenting altered renal functions, as typical recommendations are estimated for standard patients.

A Population Pharmacokinetic Modeling Approach to Determine the Efficacy of Intravenous Amikacin in Children with Cystic Fibrosis

BACKGROUND

In children with cystic fibrosis (CF), the currently recommended amikacin dose ranges between 30 and 35 mg/kg/d; however, data supporting this dosing efficacy are lacking. In this article, the objectives were to develop a nonparametric pharmacokinetic population model for amikacin in children with CF and investigate the efficacy and toxicity at different dose rates for distinct minimum inhibitory concentration (MIC) clinical breakpoints using Monte Carlo simulations.

METHODS

Data from 94 children with CF (613 serum concentrations) from the Bordeaux University Hospital's CF-centre were analyzed. After determination of nonparametric pharmacokinetic population model parameters and associated influent covariates in Pmetrics, 1000 Monte Carlo simulations were performed for 7 different dose rates between 30 and 60 mg/kg/d, to predict the probability of obtaining peak serum amikacin ≥10 × MIC and trough level ≤2.5 mg/L, for MIC values between 1 and 16 mg/L.

RESULTS

The median (min-max) age and weight were 10 (0.3-17) years and 29 (6-71) kg, respectively, with only 2 children younger than 1 year of age. Body weight and creatinine clearance significantly impacted the amikacin volume of distribution and clearance. The mean relative bias/root mean squared error between observed and individual predicted concentrations was -0.68%/8.1%. Monte Carlo simulations showed that for sensitive bacteria with MICs ≤ 4, 30 mg/kg/d was most appropriate for a 100% success rate; for bacteria with MICs ≥ 8 [eg, Pseudomonas aeruginosa (MICamikacin = 8)], a dose of at least 40 mg/kg/d allowed a high success probability (90%), with a trough level below 2.5 mg/L.

CONCLUSIONS

For intermediate pathogens, a dose of at least 40 mg/kg/d can improve efficacy, with an acceptable calculated residual trough level in cases of normal or hyperfiltration. Because amikacin undergoes renal clearance, which is immature until 1 year of age, dosing recommendations for this age group may be markedly high, warranting cautious interpretation.

Towards therapeutic drug monitoring of mycophenolic acid in mucous membrane pemphigoid: A retrospective single-centre study

BACKGROUND

Mycophenolate mofetil (MMF) is theoretically a treatment of choice for mucous membrane pemphigoid (MMP), due to its good long-term tolerance and efficacy especially in elderly patients. However, no therapeutic monitoring is currently performed despite its large inter-individual variability.

OBJECTIVES

The aim of this study was to investigate the exposure/effect relationship based on the area under the curve (AUC) or trough level of mycophenolic acid in MMP patients.

METHODS

Thirteen patients (n = 29 AUC measurements performed between February 2013 and November 2016) treated for MMP at Limoges University Hospital were evaluated using the Mucous Membrane Pemphigoid Disease Area Index score, and patients were classified as improvement (>50% decrease vs. baseline) vs. stabilisation (<50%) or non-response (no improvement). AUC was estimated using a population pharmacokinetic model and Bayesian estimation. The association between exposure parameters, demographic variables and response group was investigated using time-dependent Cox models, and an AUC threshold for 'improvement' was also investigated.

RESULTS

An improvement was observed in approximately 70% of the patients. Only the MPA AUC_0-24 h was retained in the multivariate analysis with a decreased risk of stabilisation/non-response per 10 mg*h/L increase, (HR = 0.64, 95% CI = [0.43-0.94], P = 0.0038). That led to an AUC_0-24 h threshold of 89 mg*h/L associated with excellent performances (AUC ROC = 0.828, Sen = 75%, Spe = 100%, P = 0.0001).

DISCUSSION/CONCLUSION

An association between MPA exposure and disease was observed. Therapeutic drug monitoring can be proposed with an AUC_0-24 h threshold of 89 mg*h/L. It might improve the long-term response of patients to this drug with better tolerance than rituximab or cyclophosphamide.

Personalized Therapy for Mycophenolate: Consensus Report by the International Association of Therapeutic Drug Monitoring and Clinical Toxicology

ABSTRACT

When mycophenolic acid (MPA) was originally marketed for immunosuppressive therapy, fixed doses were recommended by the manufacturer. Awareness of the potential for a more personalized dosing has led to development of methods to estimate MPA area under the curve based on the measurement of drug concentrations in only a few samples. This approach is feasible in the clinical routine and has proven successful in terms of correlation with outcome. However, the search for superior correlates has continued, and numerous studies in search of biomarkers that could better predict the perfect dosage for the individual patient have been published. As it was considered timely for an updated and comprehensive presentation of consensus on the status for personalized treatment with MPA, this report was prepared following an initiative from members of the International Association of Therapeutic Drug Monitoring and Clinical Toxicology (IATDMCT). Topics included are the criteria for analytics, methods to estimate exposure including pharmacometrics, the potential influence of pharmacogenetics, development of biomarkers, and the practical aspects of implementation of target concentration intervention. For selected topics with sufficient evidence, such as the application of limited sampling strategies for MPA area under the curve, graded recommendations on target ranges are presented. To provide a comprehensive review, this report also includes updates on the status of potential biomarkers including those which may be promising but with a low level of evidence. In view of the fact that there are very few new immunosuppressive drugs under development for the transplant field, it is likely that MPA will continue to be prescribed on a large scale in the upcoming years. Discontinuation of therapy due to adverse effects is relatively common, increasing the risk for late rejections, which may contribute to graft loss. Therefore, the continued search for innovative methods to better personalize MPA dosage is warranted.

Estimation of drug exposure by machine learning based on simulations from published pharmacokinetic models: The example of tacrolimus

We previously demonstrated that Machine learning (ML) algorithms can accurately estimate drug area under the curve (AUC) of tacrolimus or mycophenolate mofetil (MMF) based on limited information, as well as or even better than maximum a posteriori Bayesian estimation (MAP-BE). However, the major limitation in the development of such ML algorithms is the limited availability of large databases of concentration vs. time profiles for such drugs. The objectives of this study were: (i) to develop a Xgboost model to estimate tacrolimus inter-dose AUC based on concentration-time profiles obtained from a literature population pharmacokinetic (POPPK) model using Monte Carlo simulation; and (ii) to compare its performance with that of MAP-BE in external datasets of rich concentration-time profiles. The population parameters of a previously published PK model were used in the mrgsolve R package to simulate 9000 rich interdose tacrolimus profiles (one concentration simulated every 30 min) at steady-state. Data splitting was performed to obtain a training set (75%) and a test set (25%). Xgboost algorithms able to estimate tacrolimus AUC based on 2 or 3 concentrations were developed in the training set and the model with the lowest RMSE in a ten-fold cross-validation experiment was evaluated in the test set, as well as in 4 independent, rich PK datasets from transplant patients. ML algorithms based on 2 or 3 concentrations and a few covariates yielded excellent AUC estimation in the external validation datasets (relative bias < 5% and relative RMSE < 10%), comparable to those obtained with MAP-BE. In conclusion, Xgboost machine learning models trained on concentration-time profiles simulated using literature POPPK models allow accurate tacrolimus AUC estimation based on sparse concentration data. This study paves the way to the development of artificial intelligence at the service of precision therapeutic drug monitoring in different therapeutic areas.

Population pharmacokinetic model of isoniazid in patients with tuberculosis in Tunisia

AIMS

To develop a pharmacokinetic model of isoniazid (INH) concentration taking into account demographic factors and genetic variables [N-acetyltransferase 2 (NAT2) genotype], and to propose an initial INH dosage that could maximize the probability of achieving the desired INH concentration.

METHODS

A retrospective analysis was undertaken of INH concentration data collected from patients with tuberculosis in Tunisia.

RESULTS

In total, 118 patients were included in this study. The one-compartment model [volume of distribution (V), elimination rate (Ke)] was found to have good predictive performance. Multi-variate analysis showed that NAT2 affected both V and Ke significantly, but age, gender and weight did not. Internal validation of the final model showed correlation of 0.95 between individual predicted INH concentration 3 h after drug intake (C3) and observed C3. External validation showed that percentage mean absolute prediction error and percentage root mean squared error were 9.11% (range 0.62-35.8%) and 11.6%, respectively. Monte-Carlo simulation showed that doses of at least 225 mg/24 h and at least 450 mg/24 h attained a therapeutic concentration in >80% of patients in the NAT2 slow acetylator group and the NAT2 rapid/intermediate acetylator group, respectively.

CONCLUSION

The pharmacokinetic model allowed optimization of individual dosing regimens of INH in patients with tuberculosis in Tunisia. This tool may facilitate improved efficacy of INH and prevent its toxicity in this population.

Population pharmacokinetics of ganciclovir and valganciclovir in paediatric solid organ and stem cell transplant recipients

AIMS

Ganciclovir (GCV) and its prodrug valganciclovir (VGCV) are first-line agents to prevent and treat cytomegalovirus in transplant recipients. There is high pharmacokinetic (PK) interindividual variability and PK data are scarce, especially in paediatric stem cell transplant (SCT) recipients. We sought to determine the optimal GCV and VGCV dosing in transplanted children.

METHODS

We conducted a single-centre retrospective population PK (POPPK) study of IV GCV and enteral VGCV in paediatric solid organ transplant (SOT) and SCT recipients. We included children who were transplanted and had available plasma GCV concentrations, done per standard of care. POPPK analysis was performed using a nonlinear mixed effects modelling approach with NONMEM. Optimal dosing was determined based on the achievement of the surrogate efficacy target: GCV 24 h area under the concentration-time curve (AUC_0-24h ) of 40-60 mg.h.L^-1 .

RESULTS

Fifty children with a median [range] age of 7.5 years [0.5-17.4] contributed 580 PK samples. A two-compartment model with first-order absorption with a lag time and first-order elimination fit the data well. Creatinine clearance and body weight (WT) were significant covariates for GCV clearance (CL); and WT for the volumes of distribution. IV GCV 15-20 mg.kg^-1 .day^-1 divided every 12 hours achieved the highest probability of target achievement (PTA) (33.0-33.8%). Enteral VGCV 30 and 40 mg.kg^-1 .day^-1 divided every 12 hours in children 0-<6 years, and 6-18 years, respectively, achieved the highest PTA (29.1-33.0%).

CONCLUSION

This is the first POPPK model developed in children with either SOT or SCT. Concentration target achievement was low, suggesting a potential benefit for therapeutic drug monitoring to ensure optimal exposure.

Clinical Pharmacokinetics and Bayesian Estimators for the Individual Dose Adjustment of a Generic Formulation of Tacrolimus in Adult Kidney Transplant Recipients

BACKGROUND

Tacrolimus has a narrow therapeutic range and requires dose adjustment, usually based on the trough blood concentration but preferably on the area under the concentration-time curve over 12 h post-dose (AUC_0-12h). The single-arm, multicentre, clinical study IMPAKT aimed: (i) to develop, in de novo kidney transplant recipients, pharmacokinetic models and maximum a-posteriori Bayesian estimators for a generic, immediate-release, oral formulation of tacrolimus to estimate tacrolimus AUC_0-12h at different post-transplant periods using a limited sampling strategy, and considering the CYP3A5*3 polymorphism as a covariate and (ii) to compare the performance of these Bayesian estimators to those previously developed for the original formulation.

METHODS

Thirty patients were enrolled and 29 provided nine blood samples over 9 h at day 7 and months 1 and 3 post-transplant. Tacrolimus blood profiles measured with liquid chromatography-tandem mass spectrometry were modelled using one-compartment, double gamma absorption, linear elimination models developed in-house. Different limited sampling strategies of three time-points within 4 h post-dose were tested for the maximum a-posteriori Bayesian estimator of tacrolimus AUC_0-12h. The models and estimators were validated internally and their performance compared to that of models previously developed for the original formulation.

RESULTS

The concentration-time curves, AUC_0-12h/dose and trough blood concentration/dose exhibited wide inter-individual variability. The covariate-free pharmacokinetic models developed for the three post-transplant periods closely fitted the individual profiles. Maximum a-posteriori Bayesian estimators based on three different limited sampling strategies and no covariate yielded accurate AUC_0-12h estimates, including for the five cytochrome P450 3A5 expressers and for the four patients without corticosteroids. The 0-1 h-3 h strategy finally chosen had very low bias (- 4.0 to - 2.5%) and imprecision (root mean square error 5.5-9.2%). The maximum a-posteriori Bayesian estimators previously developed for the reference product fitted the generic profiles with similar performance.

CONCLUSIONS

We developed original pharmacokinetic models and accurate maximum a-posteriori Bayesian estimators to estimate patient exposure and adjust the dose of generic tacrolimus, and confirmed that the robust tools previously developed for the original formulation can be applied to this generic.

Nonparametric Methods in Population Pharmacokinetics

Population pharmacokinetic (PK) modeling is a widely used approach to analyze PK data obtained from groups of individuals, in both industry and academic research. The approach can also be used to analyze pharmacodynamic (PD) data and pooled PK/PD data. There are 2 main families of population PK methods: parametric and nonparametric. The objectives of this article are to present an overview of nonparametric methods used in population pharmacokinetic modeling and to explain their specific characteristics to inform scientists and clinicians about their potential value for data analysis, simulation, dosage design, and therapeutic drug monitoring (TDM). Nonparametric methods have several interesting characteristics for population PK analysis, including computation of exact likelihoods, the ability to accommodate parameter probability distributions of any shape (eg, non-Gaussian), and to detect subpopulations and outliers. Nonparametric population methods are also highly relevant for model-based TDM and design of individualized drug dosage regimens. Several algorithms have been developed to estimate model parameter values within an individual and compute that individual's dosage to achieve target drug exposure with maximum precision and accuracy. Nonparametric modeling methods for both population and individual PK analysis are available under user-friendly packages.

A Machine Learning Approach to Estimate the Glomerular Filtration Rate in Intensive Care Unit Patients Based on Plasma Iohexol Concentrations and Covariates

OBJECTIVE

This work aims to evaluate whether a machine learning approach is appropriate to estimate the glomerular filtration rate in intensive care unit patients based on sparse iohexol pharmacokinetic data and a limited number of predictors.

METHODS

Eighty-six unstable patients received 3250 mg of iohexol intravenously and had nine blood samples collected 5, 30, 60, 180, 360, 540, 720, 1080, and 1440 min thereafter. Data splitting was performed to obtain a training (75%) and a test set (25%). To estimate the glomerular filtration rate, 37 candidate potential predictors were considered and the best machine learning approach among multivariate-adaptive regression spline and extreme gradient boosting (Xgboost) was selected based on the root-mean-square error. The approach associated with the best results in a ten-fold cross-validation experiment was then used to select the best limited combination of predictors in the training set, which was finally evaluated in the test set.

RESULTS

The Xgboost approach yielded the best performance in the training set. The best combination of covariates comprised iohexol concentrations at times 180 and 720 min; the relative deviation from these theoretical times; the difference between these two concentrations; the Simplified Acute Physiology Score II; serum creatinine; and the fluid balance. It resulted in a root-mean-square error of 6.2 mL/min and an r² of 0.866 in the test set. Interestingly, the eight patients in the test set with a glomerular filtration rate < 30 mL/min were all predicted accordingly.

CONCLUSIONS

Xgboost provided accurate glomerular filtration rate estimation in intensive care unit patients based on two timed blood concentrations after iohexol intravenous administration and three additional predictors.

[Summary of the recommendations of the International Association of Therapeutic Drug Monitoring and Clinical Toxicology (IATDMCT) on the therapeutic drug monitoring of tacrolimus]

Recently, the International Association of Therapeutic Drug Monitoring (IATDMCT), that is the learning society for biological pharmacology and toxicology, issued recommendations on the therapeutic drug monitoring (TDM) of tacrolimus. This is the second consensus after the one issued in 2009. In this document, the role of tacrolimus TDM for the four principal transplanted organs is discussed. The analytical aspects, pharmacogenetics, TDM alternative approaches and the positioning of biomarkers are also presented. Stronger recommendations are about trough concentration targets in kidney and liver transplantation and for other indication of tacrolimus use. For the first time, an area under the curve of tacrolimus concentrations target is recommended for recipients management. Eventually, another set of recommendations are proposed for pharmacodynamic biomarkers used in patients' follow-up.

Fasting Status and Circadian Variation Must be Considered When Performing AUC-based Therapeutic Drug Monitoring of Tacrolimus in Renal Transplant Recipients

Therapeutic drug monitoring (TDM) is mandatory for the immunosuppressive drug tacrolimus (Tac). For clinical applicability, TDM is performed using morning trough concentrations. With recent developments making tacrolimus concentration determination possible in capillary microsamples and Bayesian estimator predicted area under the concentration curve (AUC), AUC‐guided TDM may now be clinically applicable. Tac circadian variation has, however, been reported, with lower systemic exposure following the evening dose. The aim of the present study was to investigate tacrolimus pharmacokinetic (PK) after morning and evening administrations of twice‐daily tacrolimus in a real‐life setting without restrictions regarding food and concomitant drug timing. Two 12 hour tacrolimus investigations were performed; after the morning dose and the following evening dose, respectively, in 31 renal transplant recipients early after transplantation both in a fasting‐state and under real‐life nonfasting conditions (14 patients repeated the investigation). We observed circadian variation under fasting‐conditions: 45% higher peak‐concentration and 20% higher AUC following the morning dose. In the real‐life nonfasting setting, the PK‐profiles were flat but comparable after the morning and evening doses, showing slower absorption rate and lower AUC compared with the fasting‐state. Limited sampling strategies using concentrations at 0, 1, and 3 hours predicted AUC after fasting morning administration, and samples obtained at 1, 3, and 6 hours predicted AUC for the other conditions (evening and real‐life nonfasting). In conclusion, circadian variation of tacrolimus is present when performed in patients who are in the fasting‐state, whereas flatter PK‐profiles and no circadian variation was present in a real‐life, nonfasting setting.

Pharmacogenetic-Whole blood and intracellular pharmacokinetic-Pharmacodynamic (PG-PK2-PD) relationship of tacrolimus in liver transplant recipients

Tacrolimus (TAC) is the cornerstone of immunosuppressive therapy in liver transplantation. This study aimed at elucidating the interplay between pharmacogenetic determinants of TAC whole blood and intracellular exposures as well as the pharmacokinetic-pharmacodynamic relationship of TAC in both compartments. Complete pharmacokinetic profiles (Predose, and 20 min, 40 min, 1h, 2h, 3h, 4h, 6h, 8h, 12h post drug intake) of twice daily TAC in whole blood and peripheral blood mononuclear cells (PBMC) were collected in 32 liver transplanted patients in the first ten days post transplantation. A non-parametric population pharmacokinetic model was applied to explore TAC pharmacokinetics in blood and PBMC. Concurrently, calcineurin activity was measured in PBMC. Influence of donor and recipient genetic polymorphisms of ABCB1, CYP3A4 and CYP3A5 on TAC exposure was assessed. Recipient ABCB1 polymorphisms 1199G>A could influence TAC whole blood and intracellular exposure (p<0.05). No association was found between CYP3A4 or CYP3A5 genotypes and TAC whole blood or intracellular concentrations. Finally, intra-PBMC calcineurin activity appeared incompletely inhibited by TAC and less than 50% of patients were expected to achieve intracellular IC₅₀ concentration (100 pg/millions of cells) at therapeutic whole blood concentration (i.e.: 4–10 ng/mL). Together, these data suggest that personalized medicine regarding TAC therapy might be optimized by ABCB1 pharmacogenetic biomarkers and by monitoring intracellular concentration whereas the relationship between intracellular TAC exposure and pharmacodynamics biomarkers more specific than calcineurin activity should be further investigated.

Population pharmacokinetics and Bayesian estimators for intravenous mycophenolate mofetil in haematopoietic stem cell transplant patients

AIMS

Intravenous mycophenolate mofetil (IV MMF), a prodrug of mycophenolic acid (MPA), is used during nonmyeloablative and reduced-intensity conditioning haematopoetic stem cell transplantation (HCT) to improve engraftment and reduce graft-versus-host disease. The aims of this study were to develop population pharmacokinetic models and Bayesian estimators based on limited sampling strategies to allow for individual dose adjustment of intravenous mycophenolate mofetil administered by infusion in haematopoietic stem cell transplant patients.

METHODS

Sixty-three MPA concentration-time profiles (median [min-max] = 6 [4-7] samples) were collected from 34 HCT recipients transplanted for 14 (1-45) days and administered IV MMF every 8 hours, concomitantly with cyclosporine. The database was split into development (75%) and validation (25%) datasets. Pharmacokinetic models characterized by a single compartment with first-order elimination, combined with two gamma distributions to describe the transformation of MMF into mycophenolic acid, were developed using in parallel nonparametric (Pmetrics) and parametric (ITSIM) approaches. The performances of the models and the derived Bayesian estimators were evaluated in the validation set.

RESULTS

The best limited sampling strategy led to a bias (min, max), root mean square error between observed and modeled interdose areas under the curve in the validation dataset of -11.72% (-31.08%, 5.00%), 14.9% for ITSIM and -2.21% (-23.40%, 30.01%), 12.4% for Pmetrics with three samples collected at 0.33, 2 and 3 hours post dosing.

CONCLUSION

Population pharmacokinetic models and Bayesian estimators for IV MMF in HCT have been developed and are now available online (https://pharmaco.chu-limoges.fr) for individual dose adjustment based on the interdose area under the curve.

Effect of genetic polymorphisms in CYP3A4, CYP3A5, and m-TOR on everolimus blood exposure and clinical outcomes in cancer patients

Genetic variations in CYP3A4, CYP3A5, and m-TOR could contribute to interpatient variability regarding m-TOR inhibitors pharmacokinetics or cellular effects. The purpose of this study was to evaluate the influence of selected candidate variations in these genes on everolimus pharmacokinetics, efficacy, and toxicity in cancer patients. Thirty-four patients receiving everolimus for breast (n = 22) or renal (n = 10) cancers, or neuroendocrine tumors of pancreatic origin (n = 2) were included in the study. Six variants in genes related to everolimus pharmacokinetics (CYP3A4*22 and CYP3A5*3) or pharmacodynamics (m-TOR rs2295079, rs2295080, rs2024627 and rs1057079) were genotyped. Associations with trough concentrations (C₀), dose reductions, or treatment interruptions due to toxicity and progression-free survival were investigated using generalized estimating equations and Cox models. CYP3A5 nonexpressers had significantly higher C₀ as compared with expressers (βGG vs AG = + 6.32 ± 2.22 ng/mL, p = 0.004). m-TOR rs2024627 was significantly associated with an increased risk of cancer progression studied alone or as part of an haplotype (T vs C: HR = 2.60, 95% CI [1.16-5.80], p = 0.020; CTCG vs other haplotypes HR = 2.29, 95% CI [1.06-4.95], p = 0.035, respectively). This study showed that CYP3A5 expression impacts everolimus pharmacokinetics in cancer patients and identified a genetic variation in m-TOR associated with the risk of cancer progression.

Identification of Factors Affecting Tacrolimus Trough Levels in Latin American Pediatric Liver Transplant Patients

Tacrolimus is the cornerstone in pediatric liver transplant immunosuppression. Despite close monitoring, fluctuations in tacrolimus blood levels affect safety and efficacy of immunosuppressive treatments. Identifying the factors related to the variability in tacrolimus exposure may be helpful in tailoring the dose. The aim of the present study was to characterize the clinical, pharmacological, and genetic variables associated with systemic tacrolimus exposure in pediatric liver transplant patients. De novo transplant patients with a survival of more than 1 month were considered for inclusion and were genotyped for cytochrome P450 3A5 (CYP3A5). Peritransplant clinical factors and laboratory covariates were recorded retrospectively between 1 month and 2 years after transplant, including alanine aminotransferase (ALT), aspartate aminotransferase, hematocrit, and tacrolimus predose steady-state blood concentrations collected 12 hours after tacrolimus dosing. A linear mixed effect (LME) model was used to assess the association of these factors and the log-transformed tacrolimus dose-normalized trough concentration (logC0/D) levels. Bootstrapping was used to internally validate the final model. External validation was performed in an independent group of patients who matched the original population. The developed LME model described that logC0/D increases with increases in time after transplant (β = 0.019, 95% confidence interval [CI], 0.010-0.028) and ALT values (β = 0.00030, 95% CI, 0.00002-0.00056), whereas logC0/D is significantly lower in graft CYP3A5 expressers compared with nonexpressers (β = -0.349, 95% CI, -0.631 to -0.062). In conclusion, donor CYP3A5 genotype, time after transplant, and ALT values are associated with tacrolimus disposition between 1 month and 2 years after transplant. A better understanding of tacrolimus exposure is essential to minimize the occurrence of an out-of-range therapeutic window that may lead to adverse drug reactions or acute rejection.

Big data in pharmacogenomics: current applications, perspectives and pitfalls

The efficiency of new generation sequencing methods and the reduction of their cost has led pharmacogenomics to gradually supplant pharmacogenetics, leading to new applications in personalized medicine along with new perspectives in drug design or identification of drug response factors. The amount of data generated in genomics fits the definition of big data, and need a specific bioinformatics processing following standard steps: data collection, processing, analysis and interpretation. Pitfalls of pharmacogenomics studies are directly related to these steps. This review aims to describe these steps from a pharmacogenomic point of view, focusing on bioinformatics aspects.

A donor and recipient candidate gene association study of allograft loss in renal transplant recipients receiving a tacrolimus-based regimen

This work investigated, in two large cohorts of French renal transplants treated with tacrolimus, the influence of donor and recipient ABCB1, CYP3A4, and CYP3A5 genotypes on the risk of allograft loss. A discovery and a replication population of 330 and 369 adult renal transplant patients, each from a different transplantation center and all receiving a tacrolimus-based immunosuppressive regimen, were retrospectively genotyped. The influence of genetic factors and other known risk factors on allograft loss was investigated using multivariate Cox proportional hazard analyses. The existence of previous transplantations (per unit HR = 1.89 [1.10-3.26] P = .0216) and the donor ABCB1 c.1199GA/AA genotype (GA/AAvs GG: HR = 3.22 [1.14-9.09], P = .0288) were associated with an increased risk of allograft loss in the discovery cohort and with graft loss due to humoral rejection in the replication cohort (per unit HR = 2.26 [1.34-3.81], P = .00229; GA/AAvs GG HR = 3.42 [1.28-9.16], P = .0142). Genotyping the donor for the ABCB1 c.1199 G>A (exon 11, rs2229109) allele may be of interest before prescribing tacrolimus to the recipient, although this polymorphism is rather rare and its effect may be limited to certain mechanisms of graft loss.

Self-poisoning with 60 tablets of Apixaban, a pharmacokinetics case report

A 67-year-old man was admitted to the emergency department about 5 h after deliberate self-poisoning with 300 mg of Apixaban. The clinical examination did not show any organ dysfunctions or haemorrhagic signs, and the patient's life was not in danger. The first analysis, upon admission, showed a concentration of 2655 μg l^-1 of Apixaban. The Cmax was observed 17 h after the intake (3654 μg l^-1 ), about four times the classical Tmax value (median [range]: 4 h [2-4]). The Apixaban was then eliminated following a first order elimination with a calculated half-life of 10.8 h. The anti-Xa activity seems to be linearly related to concentration up to 4000 μg l^-1 . This report suggests that the use of activated charcoal should be effective up to 17 h after a massive intake.

Treatment of voluminous and complicated superficial slow-flow vascular malformations with sirolimus (PERFORMUS): protocol for a multicenter phase 2 trial with a randomized observational-phase design

BACKGROUND

Slow-flow superficial vascular malformations (VMs) are rare congenital anomalies that can be responsible for pain and functional impairment. Currently, we have no guidelines for their management, which can involve physical bandages, sclerotherapy, surgery, anti-inflammatory or anti-coagulation drugs or no treatment. The natural history is progressive and worsening. Mammalian target of rapamycin (mTOR) is a serine/threonine kinase that acts as a master switch in cell proliferation, apoptosis, metabolism and angio/lymphangiogenesis. Sirolimus directly inhibits the mTOR pathway, thereby inhibiting cell proliferation and angio/lymphangiogenesis. Case reports and series have reported successful use of sirolimus in children with different types of vascular anomalies, with heterogeneous outcomes.

OBJECTIVE

The objective of this trial is to evaluate the efficacy and safety of sirolimus in children with complicated superficial slow-flow VMs.

METHODS/DESIGN

This French multicenter randomized observational-phase, phase 2 trial aims to include 50 pediatric patients 6 to 18 years old who have slow-flow (lymphatic, venous or lymphatico-venous) voluminous complicated superficial VM. Patients will be followed up for 12 months. All patients will start with an observational period (no treatment). Then at a time randomly selected between month 4 and month 8, they will switch to the experimental period (switch time), when they will receive sirolimus until month 12. Each child will undergo MRI 3 times: at baseline, at the switch time, and at month 12. For both periods (observational and treatment), we will calculate the relative change in volume of the VM divided by the study period duration. This relative change weighted by the study period duration will constitute the primary endpoint. VM will be measured by MRI images, which will be centralized and interpreted by the same radiologist who will be blinded to the study period. Hence, each patient will be his/her own control. Secondary outcomes will include assessment of safety and efficacy by viewing standardized digital photographs and according to the physician, the patient or proxy; impact on quality of life; and evolution of biological makers (coagulation factors, vascular endothelial growth factor, tissue factor).

DISCUSSION

The main benefit of the study will be to resolve uncertainty concerning the efficacy of sirolimus in reducing the volume of VMs and limiting related complications and the safety of the drug in children with slow-flow VMs. This trial design is interesting in these rare conditions because all included patients will have the opportunity to receive the drug and the physician can maintain it after the end of the protocol if is found efficient (which would not be the case in a classical cross-over study).

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT02509468 , first received: 28 July 2015. EU Clinical Trials Register EudraCT Number: 2015-001096-43.

Endogenous Metabolites-Mediated Communication Between OAT1/OAT3 and OATP1B1 May Explain the Association Between SLCO1B1 SNPs and Methotrexate Toxicity

Although OATP1B1 is not expressed in the kidney, polymorphisms in SLCO1B1 have been associated with methotrexate clearance or toxicity. This unexpected pharmacogenetic association may reflect remote communication between liver and kidney transporters. This study confirms the pharmacogenetic association with methotrexate toxicity in adult patients with hematological malignancies. Using a targeted urinary metabolomics approach, we identified 38 and 34 metabolites which were differentially excreted between wildtype and carriers of the c.388A>G or c.521T>C variant alleles, respectively, half of them being associated with methotrexate toxicity. These metabolites mainly consisted of fatty acid derivatives and microbiota catabolites, including glycine conjugates and other uremic toxins, all known OATs substrates. These results suggest that dysfunction of a transporter affects the excretion profile of endogenous or exogenous substrates, possibly through metabolite-mediated interactions involving other transport systems, even in distant organs. This opens the way for better comprehension of complex pharmacokinetics and transporter-mediated drug-drug or nutrient-drug interactions.

Comparative clinical trial of the variability factors of the exposure indices used for the drug monitoring of two tacrolimus formulations in kidney transplant recipients

BACKGROUND

Several studies found differences in tacrolimus whole blood trough levels (C0) or area-under-the curve (AUC) between the twice-daily (Tac-BID) and once-daily (Tac-OD) formulations given to kidney transplant recipients at equal doses. As C0 is widely used as a surrogate of the AUC for individual dose adjustment, this study investigated the correlation and proportionality between C0 and the 24h-AUC, depending on the formulation, time post-transplantation, pharmacogenetics traits and other individual characteristics.

METHODS

45 adult kidney transplant recipients were randomized to receive either Tac OD or Tac BID. On days 8±1 (D8) and 90±3 (month 3, M3), blood samples were collected over 24h in both groups. Tacrolimus concentrations were determined using HPLC-MS/MS and common CYP3A5, CYP3A4 and ABCB1 genotypes characterized using allelic discrimination assays. Tacrolimus population pharmacokinetics was studied in the two patient groups using the Iterative Two Stage (ITS) technique, considering a one-compartment model with two gamma laws to describe the absorption phase. Bayesian estimation based on the C0, C1h and C3h concentrations was employed to estimate individual Tac AUC_0-12h and AUC_12-24h (for Tac BID), or AUC_0-24h (for Tac OD). Multiple linear regression was used to evaluate the influence of Tac formulation, post-transplantation period, recipient gender, existing glucose metabolism disorders, and CYP3A5, CYP3A4 and ABCB1 genotypes on C0, AUC_0-24h and the AUC-to-trough concentration ratios.

RESULTS

The Full Analysis Set comprised 22 patients on Tac OD and 20 on Tac BID. Tac exposure indices as well as their time evolution were similar in the two groups. Multi-linear modeling analysis showed that the Tac dose was higher with Tac-OD than Tac-BID, on D8 than at M3 and in CYP3A5 expressors (p<0.0001 for all). No such influence was found on C0 or C24h, while the AUC_0-24h was significantly higher on D8 than at M3. The AUC_0-24h/C0 ratio was not affected by the drug formulation and the polymorphisms studied, but it was significantly lower on D8 than at M3 (p=7.8×10^-5). In contrast, both the post-transplantation period (p=1.53×10^-4), and CYP3A5 expression (p=0.003) had a significant influence on the AUC_0-24h/C24h ratio, explaining 19% and 12% of its variability, respectively. Consistently, for both Tac formulations, the AUC_0-24h was better correlated with C24h than C0, and for Tac-BID the AUC_0-12h was better correlated with C12h than C0.

CONCLUSIONS

This study confirms that the precisely timed 12h- or 24h-post-dose blood concentration (as opposed to the vaguely defined 'trough level') is a convenient surrogate of the 24h-AUC of tacrolimus for the two TAC formulations over the first 3 months post-transplantation. Still, for a given C24h value, AUC_0-24h was higher on D8 and in CYP3A5 expressors. Bayesian estimation of AUC_0-12h for TAC BID and AUC_0-24h for TAC OD is feasible using only 3 time points within the first 3h, thus giving access to the actual overall exposure.