Application of machine-learning models to predict the ganciclovir and valganciclovir exposure in children using a limited sampling strategy.

Intravenous ganciclovir and oral valganciclovir display significant variability in ganciclovir pharmacokinetics, particularly in children. Therapeutic drug monitoring currently relies on the area under the concentration-time (AUC). Machine-learning (ML) algorithms represent an interesting alternative to Maximum-a-Posteriori Bayesian-estimators for AUC estimation. The goal of our study was to develop and validate an ML-based limited sampling strategy (LSS) approach to determine ganciclovir AUC0-24 after administration of either intravenous ganciclovir or oral valganciclovir in children. Pharmacokinetic parameters from four published population pharmacokinetic models, in addition to the World Health Organization growth curve for children, were used in the mrgsolve R package to simulate 10,800 pharmacokinetic profiles of children. Different ML algorithms were trained to predict AUC0-24 based on different combinations of two or three samples. Performances were evaluated in a simulated test set and in an external data set of real patients. The best estimation performances in the test set were obtained with the Xgboost algorithm using a 2 and 6 hours post dose LSS for oral valganciclovir (relative mean prediction error [rMPE] = 0.4% and relative root mean square error [rRMSE] = 5.7%) and 0 and 2 hours post dose LSS for intravenous ganciclovir (rMPE = 0.9% and rRMSE = 12.4%). In the external data set, the performance based on these two sample LSS was acceptable: rMPE = 0.2% and rRMSE = 16.5% for valganciclovir and rMPE = -9.7% and rRMSE = 17.2% for intravenous ganciclovir. The Xgboost algorithm developed resulted in a clinically relevant individual estimation using only two blood samples. This will improve the implementation of AUC-targeted ganciclovir therapeutic drug monitoring in children.

The Cost-effectiveness of Valganciclovir Prophylaxis Versus Preemptive Therapy in CMV R+ Kidney Transplant Recipients Over the First Year Posttransplantation.

Background: In kidney transplant recipients with positive serology (R+) for the cytomegalovirus (CMV), 2 strategies are used to prevent infection, whose respective advantages over the other are still debated. This study aimed to evaluate the cost-effectiveness and cost utility of antiviral prophylaxis against CMV versus preemptive therapy, considering CMV infection-free survival over the first year posttransplantation as the main clinical outcome. Methods: Clinical, laboratory, and economic data were collected from 186 kidney transplant patients CMV (R+) included in the cohort study (85 patients who benefited from CMV prophylaxis and 101 from preemptive therapy). Costs were calculated from the hospital perspective and quality-adjusted life years (QALYs) using the EQ5D form. Using nonparametric bootstrapping, the incremental cost-effectiveness ratio (ICER) and cost utility were estimated (euros) for each case of infection avoided and each QALY gained for 1 y, respectively. Results: Prophylaxis significantly decreased the risk of CMV infection over the first year posttransplantation (hazard ratio 0.22, 95% confidence interval = 0.12-0.37, P < 0.01). Compared with preemptive therapy, prophylaxis saved financial resources (€1155 per patient) and was more effective (0.42 infection avoided per patient), resulting in an ICER = €2769 per infection avoided. Prophylaxis resulted in a net gain of 0.046 in QALYs per patient and dominated over preemptive therapy with €1422 cost-saving for 1 QALY gained. Conclusions: This study shows that CMV prophylaxis, although considered as a more expensive strategy, is more cost-effective than preemptive therapy for the prevention of CMV infections in renal transplant patients. Prophylaxis had a positive effect on quality of life at reasonable costs and resulted in net savings for the hospital.

A Machine Learning Algorithm to Predict the Starting Dose of Daptomycin.

BACKGROUND AND OBJECTIVE: The dosage of daptomycin is usually based on body weight. However, it has been shown that this approach yields too high an exposure in obese patients. Pharmacokinetic and pharmacodynamic indexes (PK/PD) have been proposed for daptomycin's antibacterial effect (AUC/CMI >666) and toxicity (C0 > 24.3 mg/L). We previously developed machine learning (ML) algorithms to predict starting doses based on Monte Carlo simulations. We propose a new way to perform probability of target attainment based on an ML algorithm to predict the daptomycin starting dose. METHODS: The Dvorchik model of daptomycin was implemented in the mrgsolve R package and 4950 pharmacokinetic profiles were simulated with doses ranging from 4 to 12 mg/kg. We trained and benchmarked four machine learning algorithms and selected the best to iteratively search for the optimal dose of daptomycin maximizing the event (AUC/CMI > 666 and C0 < 24.3 mg/L). The ML algorithm was evaluated in simulations and an external database of real patients in comparison with population pharmacokinetics. RESULTS: The performance of the Xgboost algorithms developed to predict the event (ROC AUC) in the training and test set were 0.762 and 0.761, respectively. The most important prediction variables were dose, creatinine clearance, body weight and sex. In the external database of real patients, the starting dose administered based on the ML algorithm significantly improved the target attainment by 7.9% (p-value = 0.02929) in comparison with the dose administered based on body weight. CONCLUSION: The developed algorithm improved the target attainment for daptomycin in comparison with weight-based dosing. We built a Shiny app to calculate the optimal starting dose.

Gut microbiota biotransformation of drug glucuronides leading to gastrointestinal toxicity: Therapeutic potential of bacterial ß-glucuronidase inhibition in mycophenolate-induced enteropathy.

AIMS: Drug-induced enteropathy is often associated with the therapeutic use of certain glucuronidated drugs. One such drug is mycophenolic acid (MPA), a well-established immunosuppressant of which gastrointestinal adverse effects are a major concern. The role of bacterial ß-glucuronidase (ß-G) from the gut microbiota in MPA-induced enteropathy has recently been discovered. Bacterial ß-G hydrolyzes MPAG, the glucuronide metabolite of MPA excreted in the bile, leading to the digestive accumulation of MPA that would favor in turn these adverse events. We therefore hypothesized that taming bacterial ß-G activity might reduce MPA digestive exposure and prevent its toxicity. MAIN METHODS: By using a multiscale approach, we evaluated the effect of increasing concentrations of MPA on intestinal epithelial cells (Caco-2 cell line) viability, proliferation, and migration. Then, we investigated the inhibitory properties of amoxapine, a previously described bacterial ß-G inhibitor, by using molecular dynamics simulations, and evaluated its efficiency in blocking MPAG hydrolysis in an Escherichia coli-based ß-G activity assay. The pharmacological effect of amoxapine was evaluated in a mouse model. KEY FINDINGS: We observed that MPA impairs intestinal epithelial cell homeostasis. Amoxapine efficiently blocks the hydrolysis of MPAG to MPA and significantly reduces digestive exposure to MPA in mice. As a result, administration of amoxapine in MPA-treated mice significantly attenuated gastrointestinal lesions. SIGNIFICANCE: Collectively, these results suggest that the digestive accumulation of MPA is involved in the pathophysiology of MPA-gastrointestinal adverse effects. This study provides a proof-of-concept of the therapeutic potential of bacterial ß-G inhibitors in glucuronidated drug-induced enteropathy.

Identification of non-adherence to adjuvant letrozole using a population pharmacokinetics approach in hormone receptor-positive breast cancer patients.

BACKGROUND: Letrozole, an aromatase inhibitor metabolised via CYP2A6 and CYP3A4/5 enzymes, is used as adjuvant therapy for women with hormone receptor (HR)-positive early breast cancer. The objective of this study was to quantify the impact of CYP2A6 genotype on letrozole pharmacokinetics (PK), to identify non-adherent patients using a population approach and explore the possibility of a relationship between non-adherence and early relapse. METHODS: Breast cancer patients enrolled in the prospective PHACS study (ClinicalTrials.gov NCT01127295) and treated with adjuvant letrozole 2.5 mg/day were included. Trough letrozole concentrations (Css,trough) were measured every 6 months for 3 years by a validated LC-MS/MS method. Concentration-time data were analysed using non-linear mixed effects modelling. Three methods were evaluated for identification of non-adherent subjects using the base PK model. RESULTS: 617 patients contributing 2534 plasma concentrations were included and led to a one-compartment PK model with linear absorption and elimination. Model-based methods identified 28 % of patients as non-adherent based on high fluctuations of their Css,trough compared to 3 % based on patient declarations. The covariate analysis performed in adherent subjects revealed that CYP2A6 intermediate (IM) and slow metabolisers (SM) had 21 % (CI95 % = 12 - 30 %) and 46 % (CI95 % = 41 - 51 %) lower apparent clearance, respectively, compared to normal and ultrarapid metabolisers (NM+UM). Early relapse (19 patients) was not associated with model-estimated, concentration-based or declared adherence in the total population (p = 0.41, p = 0.37 and p = 0.45, respectively). CONCLUSIONS: These findings will help future investigations focusing on the exposure-efficacy relationship for letrozole in adjuvant setting.

Population Pharmacokinetics and Limited Sampling Strategy of Mycophenolate Mofetil for Indian Patients with Lupus Nephritis.

BACKGROUND: Mycophenolic acid is widely used to treat lupus nephritis (LN). However, it exhibits complex pharmacokinetics with large interindividual variability. This study aimed to develop a population pharmacokinetic (popPK) model and a 3-sample limited sampling strategy (LSS) to optimize therapeutic drug monitoring in Indian patients with LN. METHODS: Five blood samples from each LN patient treated with mycophenolic acid were collected at steady-state predose and 1, 2, 4, and 6 hours postdose. Demographic parameters were tested as covariates to explain interindividual variability. PopPK analysis was performed using Monolix and the stochastic approximation expectation-maximization algorithm. An LSS was derived from 500 simulated pharmacokinetic (PK) profiles using maximum a posteriori Bayesian estimation to estimate individual PK parameters and area under the curve (AUC). The LSS-calculated AUC was compared with the AUC calculated using the trapezoidal rule and all the simulated samples. RESULTS: A total of 51 patients were included in this study. Based on the 245 mycophenolic acid concentrations, a 1-compartmental model with double absorption using gamma distributions best fitted the data. None of the covariates improved the model significantly. The model was internally validated using diagnostic plots, prediction-corrected visual predictive checks, and bootstrapping. The best LSS included samples at 1, 2, and 4 hours postdose and exhibited good performances in an external dataset (root mean squared error, 21.9%; mean bias, -4.20%). CONCLUSIONS: The popPK model developed in this study adequately estimated the PK of mycophenolic acid in adult Indian patients with LN. This simple LSS can optimize TDM based on the AUC in routine practice.

High Precision Ping-pong Auto-zeroed Lock-in Fluorescence Photometry Sensor.

This paper presents a high-precision CMOS fluorescence photometry sensor using a novel lock-in amplification scheme based on switched-biasing and ping-pong auto-zeroing techniques. The CMOS sensor includes two photodiodes and a lock-in amplifier (LIA) operating at 1 kHz. The LIA comprises a differential low-noise amplifier using a novel switched-biasing ping-pong auto-zeroed scheme, an automatic phase aligner, a programmable gain amplifier, a band-pass filter, a mixer, and an output low-pass filter. The design is fabricated in 0.18-µm CMOS process, and the measurement shows that the LIA can retrieve noisy input signals with a dynamic reserve of 42 dB, while consuming only 0.7 mW from a 1.8 V supply voltage. The measured results show that the LIA can detect a wide range of incident light power from 8 nW to 24 µW. The proposed design is encapsulated in a 3D-printed housing allowing for real-time in vitro biomarker detection. This ambulatory platform uses an LED and a fiber optic to convey the excitation light to the sample and retrieve the fluorescence signal. Experiments with a beads solution diluted in PBS demonstrate that the sensor has a sensitivity of 1:100 k. Experimental results obtained in vitro with NIH3T3 mouse cells tagged with membrane dye show the ability of the prototype to detect different densities of cell culture. The portable prototype, which includes optical filters and a small 30 mm × 36 mm × 30 mm printed circuit board enclosed inside the 3D-printed housing, consumes 36.7 mW and weighs 120 g.

Improvement of Charcot-Marie-Tooth Phenotype with a Nanocomplex Treatment in Two Transgenic Models of CMT1A.

Curcumin has been shown to exert beneficial effects in peripheral neuropathies. Despite its known biological activities, curcumin has unfavorable pharmacokinetics. Its instability has been linked to its failure in clinical trials of curcumin for the treatment of human pathologies. For this reason, we developed curcumin-loaded cyclodextrin/cellulose nanocrystals (NanoCur) to improve its pharmacokinetics. The present study aims to assess the potency of a low dose of NanoCur in 2 Charcot-Marie-Tooth disease type 1A (CMT1A) rodent models at different stages of the disease. The efficiency of NanoCur is also compared to that of Theracurmin (Thera), a commercially available curcumin formulation. The toxicity of a short-term and chronic exposure to the treatment is investigated both in vitro and in vivo, respectively. Furthermore, the entry route, the mechanism of action and the effect on the nerve phenotype are dissected in this study. Overall, the data support an improvement in sensorimotor functions, associated with amelioration in peripheral myelination in NanoCur-treated animals; an effect that was not evident in the Thera-treated group. That was combined with a high margin of safety both in vivo and in vitro. Furthermore, NanoCur appears to inhibit inflammatory pathways that normally include macrophage recruitment to the diseased nerve. This study shows that NanoCur shows therapeutic benefits with minimal systemic toxicity, suggesting that it is a potential therapeutic candidate for CMT1A and, possibly, for other neuropathies.

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.

Activities of clinical pharmacologists across Europe: A survey by the European association of clinical pharmacology and therapeutics.

PURPOSE: In order to explore clinical pharmacology and therapeutics (CPT) teaching and practices across continental Europe, the European Association of Clinical Pharmacology and Therapeutics (EACPT) made a survey in 2022 amongst its 27 affiliated societies. METHODS: The survey was made available online to EACPT representatives, and 47 filled-in questionnaires were received from 25 countries (one to five per country), representing all geographic areas of Europe. RESULTS: Clinical pharmacologists (CPs) spend 25%, 30%, 15%, and 25% of their time in teaching, hospital activities, committees, and research, respectively, with large variations across and within countries. CPT courses are given at Schools of Medicine in all the countries except one, mostly organized and taught by medical doctors (MDs). In Central, Western, and Southern Europe, the teachers may have medicine or pharmacy training. Therapeutic drug monitoring and pharmacovigilance were the hospital activities most frequently reported, and clinical/forensic toxicology, rounds of visits, and pharmacogenetics the least. Two-thirds of the panel think CPs should be MDs. However, the transversal nature of CPT was underlined, with patients/diseases and drugs as gravity centres, thus calling for the complementary skills of MDs and PharmDs. Besides, most respondents reported that clinical pharmacists in their country are involved in rounds of visits, pharmacovigilance, TDM, and/or pharmacogenetic testing and that collaborations with them would be beneficial. CONCLUSION: CPT comes with a plurality of backgrounds and activities, all required to embrace the different pathologies and the whole lifecycle of medicinal products, but all of them being rarely performed in any given country. The willingness to use common CPT teaching material and prescribing exams at the European level is a good sign of increasing harmonisation of our discipline Europewide.

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.

Solid organ transplantation and gut microbiota: a review of the potential immunomodulatory properties of short-chain fatty acids in graft maintenance.

Transplantation is the treatment of choice for several end-stage organ defects: it considerably improves patient survival and quality of life. However, post-transplant recipients may experience episodes of rejection that can favor or ultimately lead to graft loss. Graft maintenance requires a complex and life-long immunosuppressive treatment. Different immunosuppressive drugs (i.e., calcineurin inhibitors, glucocorticoids, biological immunosuppressive agents, mammalian target of rapamycin inhibitors, and antiproliferative or antimetabolic agents) are used in combination to mitigate the immune response against the allograft. Unfortunately, the use of these antirejection agents may lead to opportunistic infections, metabolic (e.g., post-transplant diabetes mellitus) or cardiovascular (e.g., arterial hypertension) disorders, cancer (e.g., non-Hodgkin lymphoma) and other adverse effects. Lately, immunosuppressive drugs have also been associated with gut microbiome alterations, known as dysbiosis, and were shown to affect gut microbiota-derived short-chain fatty acids (SCFA) production. SCFA play a key immunomodulatory role in physiological conditions, and their impairment in transplant patients could partly counterbalance the effect of immunosuppressive drugs leading to the activation of deleterious pathways and graft rejection. In this review, we will first present an overview of the mechanisms of graft rejection that are prevented by the immunosuppressive protocol. Next, we will explain the dynamic changes of the gut microbiota during transplantation, focusing on SCFA. Finally, we will describe the known functions of SCFA in regulating immune-inflammatory reactions and discuss the impact of SCFA impairment in immunosuppressive drug treated patients.

Understanding the nervous system: lessons from Frontiers in Neurophotonics.

The Frontiers in Neurophotonics Symposium is a biennial event that brings together neurobiologists and physicists/engineers who share interest in the development of leading-edge photonics-based approaches to understand and manipulate the nervous system, from its individual molecular components to complex networks in the intact brain. In this Community paper, we highlight several topics that have been featured at the symposium that took place in October 2022 in Québec City, Canada.

Better Rejection-Free Survival at Three Years in Kidney Transplant Recipients With Model-Informed Precision Dosing of Mycophenolate Mofetil.

The clinical impact of individual dose adjustment of mycophenolate mofetil is still debated, due to conflicting results from randomized clinical trials. This retrospective study aimed to compare 3-year rejection-free survival and adverse effects between adult kidney transplant recipients (KTRs) with or without mycophenolate mofetil model-informed precision dosing (MIPD). MIPD is defined here as mycophenolic acid area under the curve (AUC0-12h) estimation using a limited sampling strategy, pharmacokinetic models and Bayesian estimators; dose recommendation to reach AUC0-12h = 45 mg.h/L; using a widely used online expert system. The study, nested in two multicenter prospective cohort studies, focused on patients who received a mycophenolate drug and were followed up for 1-3 years. Mycophenolate mofetil MIPD was prescribed as per local practice, on a regular basis, when deemed necessary, or not at all. The MIPD group included 341 KTRs and the control group 392. At 3 years, rejection-free survival was respectively 91.2% and 80.6% (P < 0.001) and the cumulative incidence of rejection 5.08% vs. 12.7% per patient × year (hazard ratio = 0.49 (0.34, 0.71), P < 0.001), corresponding to a 2.5-fold reduction. Significant association with rejection-free survival was confirmed in patients at low or high risk of rejection (P = 0.017 and 0.013) and in patients on tacrolimus, but not on cyclosporine (P < 0.001 and 0.205). The mycophenolate mofetil MIPD group had significantly more adverse effects, but most occurred before the first AUC0-12h, suggesting some may be the reason why MIPD was ordered.

Early detection of liver injuries by the Serum enhanced binding test sensitive to albumin post-transcriptional modifications.

Early and sensitive biomarkers of liver dysfunction and drug-induced liver injury (DILI) are still needed, both for patient care and drug development. We developed the Serum Enhanced Binding (SEB) test to reveal post-transcriptional modifications (PTMs) of human serum albumin resulting from hepatocyte dysfunctions and further evaluated its performance in an animal model. The SEB test consists in spiking serum ex-vivo with ligands having specific binding sites related to the most relevant albumin PTMs and measuring their unbound fraction. To explore the hypothesis that albumin PTMs occur early during liver injury and can also be detected by the SEB test, we induced hepatotoxicity in male albino Wistar rats by administering high daily doses of ethanol and CCl4 over several days. Blood was collected for characterization and quantification of albumin isoforms by high-resolution mass spectrometry, for classical biochemical analyses as well as to apply the SEB test. In the exposed rats, the appearance of albumin isoforms paralleled the positivity of the SEB test ligands and histological injuries. These were observed as early as D3 in the Ethanol and CCl4 groups, whereas the classical liver tests (ALT, AST, PAL) significantly increased only at D7. The behavior of several ligands was supported by structural and molecular simulation analysis. The SEB test and albumin isoforms revealed hepatocyte damage early, before the current biochemical biomarkers. The SEB test should be easier to implement in the clinics than albumin isoform profiling.

Absolute Quantification of Human Serum Albumin Isoforms by Internal Calibration Based on a Top-Down LC-MS Approach.

Well-characterized biomarkers using reliable quantitative methods are essential for the management of various pathologies such as diabetes, kidney, and liver diseases. Human serum albumin (HSA) isoforms are gaining interest as biomarkers of advanced liver pathologies. In view of the structural alterations observed for HSA, insights into its isoforms are required to establish them as reliable biomarkers. Therefore, a robust absolute quantification method seems necessary. In this study, we developed and validated a far more advanced top-down liquid chromatography-mass spectrometry (LC-MS) method for the absolute quantification of HSA isoforms, using myoglobin (Mb) as an internal standard for quantification and for mass recalibration. Two different quantification approaches were investigated based on peak integration from the deconvoluted spectrum and extracted ion chromatogram (XIC). The protein mixture human serum albumin/myoglobin eluted in well-shaped separated peaks. Mb allowed a systematic mass recalibration for every sample, resulting in extremely low mass deviations compared to conventional deconvolution-based methods. In total, eight HSA isoforms of interest were quantified. Specific-isoform calibration curves showing good linearity were obtained by using the deconvoluted peaks. Noticeably, the HSA ionization behavior appeared to be isoform-dependent, suggesting that the use of an enriched isoform solution as a calibration standard for absolute quantification studies of HSA isoforms is necessary. Good repeatability, reproducibility, and accuracy were observed, with better sensitivity for samples with low albumin concentrations compared to routine biochemical assays. With a relatively simple workflow, the application of this method for absolute quantification shows great potential, especially for HSA isoform studies in a clinical context, where a high-throughput method and sensitivity are needed.

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.