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Pseudoxanthoma elasticum (PXE) is a rare inherited systemic disease responsible for a juvenile peripheral arterial calcification disease. The clinical diagnosis of PXE is only based on a complex multi-organ phenotypic score and/or genetical analysis. Reduced plasma inorganic pyrophosphate concentration [PPi]p has been linked to PXE. In this study, we used a novel and accurate method to measure [PPi]p in one of the largest cohorts of PXE patients, and we reported the valuable contribution of a cutoff value to PXE diagnosis. Plasma samples and clinical records from two French reference centers for PXE (PXE Consultation Center, Angers, and FAVA-MULTI South Competent Center, Nice) were assessed. Plasma PPi were measured in 153 PXE and 46 non-PXE patients. The PPi concentrations in the plasma samples were determined by a new method combining enzymatic and ion chromatography approaches. The best match between the sensitivity and specificity (Youden index) for diagnosing PXE was determined by ROC analysis. [PPi]p were lower in PXE patients (0.92 ± 0.30 µmol/L) than in non-PXE patients (1.61 ± 0.33 µmol/L, p < 0.0001), corresponding to a mean reduction of 43 ± 19% (SD). The PPi cutoff value for diagnosing PXE in all patients was 1.2 µmol/L, with a sensitivity of 83.3% and a specificity of 91.1% (AUC = 0.93), without sex differences. In patients aged <50 years (i.e., the age period for PXE diagnosis), the cutoff PPi was 1.2 µmol/L (sensitivity, specificity, and AUC of 93%, 96%, and 0.97, respectively). The [PPi]p shows high accuracy for diagnosing PXE; thus, quantifying plasma PPi represents the first blood assay for diagnosing PXE. [ABSTRACT FROM AUTHOR]

Aims: Monitoring drug safety in real‐world settings is the primary aim of pharmacovigilance. Frequent adverse drug reactions (ADRs) are usually identified during drug development. Rare ones are mostly characterized through post‐marketing scrutiny, increasingly with the use of data mining and disproportionality approaches, which lead to new drug safety signals. Nonetheless, waves of excessive numbers of reports, often stirred up by social media, may overwhelm and distort this process, as observed recently with levothyroxine or COVID‐19 vaccines. As human resources become rarer in the field of pharmacovigilance, we aimed to evaluate the performance of an unsupervised co‐clustering method to help the monitoring of drug safety. Methods: A dynamic latent block model (dLBM), based on a time‐dependent co‐clustering generative method, was used to summarize all regional ADR reports (n = 45 269) issued between 1 January 2012 and 28 February 2022. After analysis of their intra and extra interrelationships, all reports were grouped into different cluster types (time, drug, ADR). Results: Our model clustered all reports in 10 time, 10 ADR and 9 drug collections. Based on such clustering, three prominent societal problems were detected, subsequent to public health concerns about drug safety, including a prominent media hype about the perceived safety of COVID‐19 vaccines. The dLBM also highlighted some specific drug–ADR relationships, such as the association between antiplatelets, anticoagulants and bleeding. Conclusions: Co‐clustering and dLBM appear as promising tools to explore large pharmacovigilance databases. They allow, 'unsupervisedly', the detection, exploration and strengthening of safety signals, facilitating the analysis of massive upsurges of reports. [ABSTRACT FROM AUTHOR]

Aims: Iohexol clearance has been proposed to estimate the glomerular filtration rate (GFR). A population pharmacokinetics (popPK) model was developed from heterogeneous 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 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 performance for GFR estimation was 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 0.1–1‐9 h exhibiting a relative mean prediction error (MPE) (RMSE) = −3.7% (14.3%) and better performance 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 9 hours for the third sample were associated with an increased imprecision. Conclusion: A single MAP‐BE of iohexol based on a three‐sample LSS for four heterogeneous populations 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. [ABSTRACT FROM AUTHOR]

The use of synthetic data in pharmacology research has gained significant attention due to its potential to address privacy concerns and promote open science. In this study, we implemented and compared three synthetic data generation methods, CT‐GAN, TVAE, and a simplified implementation of Avatar, for a previously published pharmacogenetic dataset of 253 patients with one measurement per patient (non‐longitudinal). The aim of this study was to evaluate the performance of these methods in terms of data utility and privacy trade off. Our results showed that CT‐GAN and Avatar used with k = 10 (number of patients used to create the local model of generation) had the best overall performance in terms of data utility and privacy preservation. However, the TVAE method showed a relatively lower level of performance in these aspects. In terms of Hazard ratio estimation, Avatar with k = 10 produced HR estimates closest to the original data, whereas CT‐GAN slightly underestimated the HR and TVAE showed the most significant deviation from the original HR. We also investigated the effect of applying the algorithms multiple times to improve results stability in terms of HR estimation. Our findings suggested that this approach could be beneficial, especially in the case of small datasets, to achieve more reliable and robust results. In conclusion, our study provides valuable insights into the performance of CT‐GAN, TVAE, and Avatar methods for synthetic data generation in pharmacogenetic research. The application to other type of data and analyses (data driven) used in pharmacology should be further investigated. [ABSTRACT FROM AUTHOR]

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., Competing Interests: The authors declare no conflict of interest.

Introduction: Rituximab is a first-line treatment for membranous nephropathy. Nephrotic syndrome limits rituximab exposure due to urinary drug loss. Rituximab underdosing (serum level <2 μg/ml at month-3) is a risk factor for treatment failure. We developed a machine learning algorithm to predict the risk of underdosing based on patients' characteristics at rituximab infusion. We investigated the relationship between the predicted risk of underdosing and the cumulative dose of rituximab required to achieve remission., Methods: Rituximab concentrations were measured at month-3 in 92 sera from adult patients with primary membranous nephropathy, split into a training (75%) and a testing set (25%). A forward-backward machine-learning procedure determined the best combination of variables to predict rituximab underdosing in the training data set, which was tested in the test set. The performances were evaluated for accuracy, sensitivity, and specificity in 10-fold cross-validation training and test sets., Results: The best variables combination to predict rituximab underdosing included age, gender, body surface area (BSA), anti-phospholipase A2 receptor type 1 (anti-PLA2R1) antibody titer on day-0, serum albumin on day-0 and day-15, and serum creatinine on day-0 and day-15. The accuracy, sensitivity, and specificity were respectively 79.4%, 78.7%, and 81.0% (training data set), and 79.2%, 84.6% and 72.7% (testing data set). In both sets, the algorithm performed significantly better than chance ( P  < 0.05). Patients with an initial high probability of underdosing experienced a longer time to remission with higher rituximab cumulative doses required to achieved remission., Conclusion: This algorithm could allow for early intensification of rituximab regimen in patients at high estimated risk of underdosing to increase the likelihood of remission., (© 2023 Published by Elsevier, Inc., on behalf of the International Society of Nephrology.)

An antikinetoplastid pharmacomodulation study was conducted at position 6 of the 8-nitroquinolin-2(1 H )-one pharmacophore. Fifteen new derivatives were synthesized and evaluated in vitro against L. infantum , T. brucei brucei , and T. cruzi , in parallel with a cytotoxicity assay on the human HepG2 cell line. A potent and selective 6-bromo-substituted antitrypanosomal derivative 12 was revealed, presenting EC 50 values of 12 and 500 nM on T. b. brucei trypomastigotes and T. cruzi amastigotes respectively, in comparison with four reference drugs (30 nM ≤ EC 50 ≤ 13 μM). Moreover, compound 12 was not genotoxic in the comet assay and showed high in vitro microsomal stability (half life >40 min) as well as favorable pharmacokinetic behavior in the mouse after oral administration. Finally, molecule 12 ( E ° = -0.37 V/NHE) was shown to be bioactivated by type 1 nitroreductases, in both Leishmania and Trypanosoma , and appears to be a good candidate to search for novel antitrypanosomal lead compounds., Competing Interests: The authors declare no competing financial interest., (Copyright © 2020 American Chemical Society.)