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.

The Clinical Relevance of the Infiltrating Immune Cell Composition in Kidney Transplant Rejection.

BACKGROUND: The link between the histology of kidney transplant rejection, especially Antibody-mediated rejection, T cell-mediated rejection and Mixed rejection, and the types of infiltrating immune cells is currently not well charted. Cost and technical complexity of single cell analysis hinder large scale studies of the relationship between cell infiltrate profiles and histological heterogeneity. METHODS: In this cross-sectional study, we assessed the composition of nine intragraft immune cell types by using a validated kidney transplant-specific signature matrix for deconvolution of bulk transcriptomics in three different kidney transplant biopsy datasets (N=403, N=224, N=282). The association and the discrimination of the immune cell types with the Banff histology and the association with graft failure were assessed individually and with multivariable models. Unsupervised clustering algorithms were applied on the overall immune cells composition and compared to the Banff phenotypes. RESULTS: Banff-defined rejection was related to high presence of CD8+ effector T cells, Natural Killer cells, monocytes/macrophages and to a lesser extent B cells, whereas CD4+ memory T cells were lower in rejection compared to no rejection. Estimated intragraft effector memory-expressing CD45RA (TEMRA) CD8+ T cells were strongly and consistently associated with graft failure. The large heterogeneity in immune cell composition across rejection types prevented supervised and unsupervised methods to accurately recover the Banff phenotypes based solely on immune cell estimates. The lack of correlation between immune cell composition and Banff-defined rejection types was validated using multiplex immunohistochemistry. CONCLUSIONS: Although some specific cell types (FCGR3A+ myeloid cells, CD14+ monocytes/macrophages and NK cells), partly discriminate between rejection phenotypes, the overall estimated immune cell composition of kidney transplants is ill related to main Banff-defined rejection categories and adds to the Banff lesion scoring and evaluation of rejection severity. The estimated intragraft CD8temra cells bear strong and consistent association with graft failure and independent of Banff-grade rejection.

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.

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.

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.

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.

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.

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.

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.

Risk factors for mood disorders among offspring of parents with bipolar disorder: Findings from a discordant-sibling study.

The purpose of this naturalistic, prospective study was to identify risk factors for mood disorders in offspring of parents with bipolar disorder (BPD) using the discordant-sibling design by comparing premorbid psychopathology or symptoms, temperament, personality traits and coping style as well as the perception of family-related characteristics among affected and unaffected siblings within the same family. This approach controls for confounding by unmeasured genetic and environmental factors shared within families. Our sample comprised 24 families of a parent with BPD with at least one child that developed BPD or major depressive disorder (n = 31), and at least one child who did not. Offspring were followed for a mean duration of 16.2 (s.d: 4.6) years. Information was collected from the offspring themselves. Generalized linear mixed models only revealed differences in three dimensions of the Dimension of Temperament Survey-Revised (DOTS-R) version: Offspring with mood disorders scored higher on "Approach-withdrawal", "Rhythmicity for daily habits", and "Task orientation" than their unaffected siblings. The higher scores, and not lower scores as expected, on these temperament dimensions observed in offspring that subsequently developed mood disorders may reflect increased vulnerability, but they could also mirror premorbid mood swings or strategies to cope with them.

A robust and reliable methodology to perform GECI-based multi-time point neuronal calcium imaging within mixed cultures of human iPSC-derived cortical neurons.

Introduction: Human induced pluripotent stem cells (iPSCs), with their ability to generate human neural cells (astrocytes and neurons) from patients, hold great promise for understanding the pathophysiology of major neuropsychiatric diseases such as schizophrenia and bipolar disorders, which includes alterations in cerebral development. Indeed, the in vitro neurodifferentiation of iPSCs, while recapitulating certain major stages of neurodevelopment in vivo, makes it possible to obtain networks of living human neurons. The culture model presented is particularly attractive within this framework since it involves iPSC-derived neural cells, which more specifically differentiate into cortical neurons of diverse types (in particular glutamatergic and GABAergic) and astrocytes. However, these in vitro neuronal networks, which may be heterogeneous in their degree of differentiation, remain challenging to bring to an appropriate level of maturation. It is therefore necessary to develop tools capable of analyzing a large number of cells to assess this maturation process. Calcium (Ca2+) imaging, which has been extensively developed, undoubtedly offers an incredibly good approach, particularly in its versions using genetically encoded calcium indicators. However, in the context of these iPSC-derived neural cell cultures, there is a lack of studies that propose Ca2+ imaging methods that can finely characterize the evolution of neuronal maturation during the neurodifferentiation process. Methods: In this study, we propose a robust and reliable method for specifically measuring neuronal activity at two different time points of the neurodifferentiation process in such human neural cultures. To this end, we have developed a specific Ca2+ signal analysis procedure and tested a series of different AAV serotypes to obtain expression levels of GCaMP6f under the control of the neuron-specific human synapsin1 (hSyn) promoter. Results: The retro serotype has been found to be the most efficient in driving the expression of the GCaMP6f and is compatible with multi-time point neuronal Ca2+ imaging in our human iPSC-derived neural cultures. An AAV2/retro carrying GCaMP6f under the hSyn promoter (AAV2/retro-hSyn-GCaMP6f) is an efficient vector that we have identified. To establish the method, calcium measurements were carried out at two time points in the neurodifferentiation process with both hSyn and CAG promoters, the latter being known to provide high transient gene expression across various cell types. Discussion: Our results stress that this methodology involving AAV2/retro-hSyn-GCaMP6f is suitable for specifically measuring neuronal calcium activities over multiple time points and is compatible with the neurodifferentiation process in our mixed human neural cultures.

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.

Impact of hypoxia and reoxygenation on the extra/intracellular metabolome and on transporter expression in a human kidney proximal tubular cell line.

INTRODUCTION: Ischemia-reperfusion injury (IRI) induces several perturbations that alter immediate kidney graft function after transplantation and may affect long-term graft outcomes. Given the IRI-dependent metabolic disturbances previously reported, we hypothesized that proximal transporters handling endo/exogenous substrates may be victims of such lesions. OBJECTIVES: This study aimed to determine the impact of hypoxia/reoxygenation on the human proximal transport system through two semi-targeted omics analyses. METHODS: Human proximal tubular cells were cultured in hypoxia (6 or 24 h), each followed by 2, 24 or 48-h reoxygenation. We investigated the transcriptomic modulation of transporters. Using semi-targeted LC-MS/MS profiling, we characterized the extra/intracellular metabolome. Statistical modelling was used to identify significant metabolic variations. RESULTS: The expression profile of transporters was impacted during hypoxia (y + LAT1 and OCTN2), reoxygenation (MRP2, PEPT1/2, rBAT, and OATP4C1), or in both conditions (P-gp and GLUT1). The P-gp and GLUT1 transcripts increased (FC (fold change) = 2.93 and 4.11, respectively) after 2-h reoxygenation preceded by 24-h hypoxia. We observed a downregulation (FC = 0.42) of y+LAT1 after 24-h hypoxia, and of PEPT2 after 24-h hypoxia followed by 2-h reoxygenation (FC = 0.40). Metabolomics showed that hypoxia altered the energetic pathways. However, intracellular metabolic homeostasis and cellular exchanges were promptly restored after reoxygenation. CONCLUSION: This study provides insight into the transcriptomic response of the tubular transporters to hypoxia/reoxygenation. No correlation was found between the expression of transporters and the metabolic variations observed. Given the complexity of studying the global tubular transport systems, we propose that further studies focus on targeted transporters.

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.

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.

Semi-synthetic human albumin isoforms: Production, structure, binding capacities and influence on a routine laboratory test.

Human Serum Albumin (HSA) undergoes Post-Translational-Modifications (PTMs) leading to isoforms affecting its oncotic and non-oncotic properties. HSA is comprised of several isoforms whose abundance may vary with pathologies such as diabetes, kidney and liver diseases. Studying their impact separately may help to understand their sources and potential pathogenicity and further their evaluation as biomarkers. The present study examined semi-synthetic HSA isoforms to investigate independently their structure by means of advanced mass spectrometry techniques (LC-TOF-MS and ICP-MS), influence on the HSA binding/antioxidant activities using a binding capacity test, and potential impact on albumin quantification by a routine immunoturbidimetric assay. Applying different chemical reactions to a commercial HSA solution, we obtained different solutions enriched up to 53 % of native HSA, 78 % of acetylated HSA, 71 % of cysteinylated HSA, 94 % of oxidized HSA, 58 % of nitrosylated HSA and 96 % of glycated HSA, respectively. Moreover, the semi-synthetic isoforms showed differently altered binding capacities for a panel of ligands (Cu, Cd, Au, Ds and L-T4). Furthermore, immunoturbidimetry was found to be insensitive to the presence and abundance of the different isoforms. The fully characterized semi synthetic HSA isoforms obtained should be useful to further investigate their pathogenicity and potential roles as biomarkers.