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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., (© 2024 The Author(s). CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

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., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

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.

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.

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., (© 2024 British Pharmacological Society.)

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 (AUC 0-24  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., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

A significant heterogeneity prevails in antipsychotics (APs) safety monitoring recommendations. Youths are deemed more vulnerable to cardiometabolic side effects. We aimed to assess age-dependent reporting of cardiac and metabolic disorders in youths, relying on the WHO safety database (VigiBase®). VigiBase® was queried for all reports of cardiac, glucose, lipid and nutritional disorders involving APs. Patients <18 years were classified as pediatric population. Disproportionality analyses relied on the Information Component (IC): the positivity of the lower end of its 95 % confidence interval was required to suspect a signal. We yielded 4,672 pediatric reports. In disproportionality analysis, nutritional disorders were leading in youths (IC 3.9 [3.9-4.0]). Among healthcare professionals' reports, stronger signals were detected in youths than in adults. Children had the greatest signal with nutritional disorders (IC 4.7 [4.6-4.8]). In adolescents, aripiprazole was ascribed to non-alcoholic steatohepatitis (NASH). Our findings, based on real-world data, support the hypothesis of a greater propensity for nutritional disorders in youths, despite limitations of pharmacovigilance studies. We suggest specific safety profiles, such as aripiprazole and NASH. Pending more answers from population-based studies, a careful anamnesis should seek for risk factors before AP initiation. A cautious monitoring is warranted to allow earlier identification of side effects., Competing Interests: Declaration of competing interest None., (Copyright © 2024. Published by Elsevier B.V.)

Drug-induced cardiotoxicity is a primary concern in both drug development and clinical practice. Although the heart is not a common target for adverse drug reactions, some drugs still cause various adverse cardiac events, with sometimes severe consequences. Direct cardiac toxicity encompasses functional and structural changes of the cardiovascular system due to possible exposure to medicines. This phenomenon extends beyond cardiovascular drugs to include non-cardiovascular drugs including anticancer drugs such as tyrosine kinase inhibitors, anthracyclines and immune checkpoint inhibitors (ICIs), as well as various antipsychotics, venlafaxine, and even some antibiotics (such as macrolides). Cardiac ADRs comprise an array of effects, ranging from heart failure and myocardial ischemia to valvular disease, thrombosis, myocarditis, pericarditis, arrhythmias, and conduction abnormalities. The underlying mechanisms may include disturbances of ionic processes, induction of cellular damage via impaired mitochondrial function, and even hypercoagulability. To mitigate the impact of drug-induced cardiotoxicity, multi-stage evaluation guidelines have been established, following the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) guidelines for in vitro and in vivo testing. Despite preclinical safeguards, post-marketing surveillance remains critical, as certain cardiotoxic drugs may escape initial scrutiny. Indeed, historical data show that cardiovascular ADRs contribute to almost 10% of market withdrawals. The impact of drug-induced cardiotoxicity on cardiac issues, particularly heart failure, is often underestimated, with incidence rates ranging from 11.0% to over 20.0%. We here comprehensively examine different patterns of drug-induced cardiotoxicity, highlighting current concerns and emerging pharmacovigilance signals. Understanding the underlying mechanisms and the associated risk factors is critical in order to promptly identify, effectively manage, and proactively prevent drug-induced cardiac adverse events. Collaborative efforts between physicians and cardiologists, coupled with thorough assessment and close monitoring, are essential to ensuring patient safety in the face of potential drug-induced cardiotoxicity., (Copyright © 2023 Société française de pharmacologie et de thérapeutique. Published by Elsevier Masson SAS. All rights reserved.)

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·kg 1 ·12 h -1 for the CYP3A5 expressor)., Conclusion: The MAP-BE developed should facilitate estimation of tacrolimus AUC in heart transplant patients., (© 2023 British Pharmacological Society.)

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.)

Eating disorders, characterized by abnormal eating, weight control behaviors or both include anorexia nervosa (AN) and bulimia nervosa (BN). We investigated their potential iatrogenic triggers, using real-world data from the WHO safety database (VigiBase®). VigiBase® was queried for all AN and BN reports. The reports were classified as `pediatric' or `adult' according to age. Disproportionality analyses relied on the Information Component (IC), in which a 95% confidence interval lower-end positivity was required to suspect a signal. Our queries yielded 309 AN and 499 BN reports. Isotretinoin was disproportionately reported in pediatric AN (IC 3.6; [2.6-4.3]), adult AN (IC 3.1; [1.7-4.0]), and pediatric BN (IC 3.9; [3.0-4.7]). Lamivudine (IC 4.2; [3.2-4.9]), nevirapine (IC 3.7; [2.6-4.6]), and zidovudine (IC 3.4; [2.0-4.3]) had the highest ICs in adult AN. AN was associated with isotretinoin, anticonvulsants in minors, and antiretroviral drugs in adults. In adults, BN was related to psychotropic and hormonally active drugs. Before treatment initiation, an anamnesis should seek out mental health conditions, allowing the identification of patients at risk of developing or relapsing into AN or BN. In addition to misuse, the hypothesis of iatrogenic triggers for AN and BN should also be considered., Competing Interests: Declaration of Competing Interest None., (Copyright © 2023. Published by Elsevier B.V.)

Migraine constitutes the world's second-leading cause of disability. Triptans, as serotonin 5-HT 1B/1D receptor agonists, remain the first-line treatment, despite discouraged use in individuals at high cardiovascular risk. Lasmiditan, a selective lipophilic 5-HT 1F agonist without vasoconstrictive effects, is an emerging option. We aimed to investigate the safety profile of lasmiditan in the WHO pharmacovigilance database (VigiBase ® ) using a comparative disproportionality analysis with triptans. VigiBase ® was queried for all reports involving lasmiditan and triptans. Disproportionality analyses relied on the calculation of the information component (IC), for which 95% confidence interval (CI) lower bound positivity was required for signal detection. We obtained 826 reports involving lasmiditan. Overall, 10 adverse drug reaction classes were disproportionately reported with triptans, while only neurological (IC 1.6; 95% CI 1.5-1.7) and psychiatric (IC 1.5; 95% CI 1.3-1.7) disorders were disproportionately reported with lasmiditan. Sedation, serotonin syndrome, euphoric mood, and autoscopy had the strongest signals. When compared with triptans, 19 out of 22 neuropsychiatric signals persisted. The results of our analysis provide a more precise semiology of the neuropsychiatric effects of lasmiditan, with symptoms such as autoscopy and panic attacks. The cardiovascular adverse drug reaction risk with triptans was confirmed. In contrast, caution is warranted with lasmiditan use in patients with neurological or psychiatric comorbidities or serotonin syndrome risk. Our study was hindered by pharmacovigilance flaws, and further studies should help in validating these results. Our findings suggest that lasmiditan is a safe alternative for migraine treatment, especially when the neuropsychiatric risk is outweighed by the cardiovascular burden., (© 2023. The American Society for Experimental Neurotherapeutics, Inc.)

Background: Chimeric antigen receptor T (CAR-T) cells have proven to be a game changer for treating several hematologic malignancies. Randomized controlled trials have highlighted potential life-threatening adverse drug reactions (ADRs), including cytokine release syndrome (CRS). Acute renal failure (ARF) has also been reported in 20% of the patients treated. However, an analysis of renal safety supported by large-scale real-life data seems warranted., Patients and Methods: We queried VigiBase® for all reports of the Standardised MedDRA Query "acute renal failure" (ARF) involving a CAR-T cell, registered until 24 July 2022. Disproportionality for this ADR was analyzed through calculation of the Information Component [IC (95% confidence interval)]. A positive lower end of the 95% confidence interval of the IC is the threshold used in statistical signal detection in VigiBase®. The same analysis was carried out for various hydroelectrolytic disorders., Results: We gathered 224 reports of ARF, and 125 reports of hydroelectrolytic disorders involving CAR-T cells. CAR-T cells were disproportionately reported with ARF [IC 1.5 (1.3-1.7)], even after excluding reports mentioning CRS. A significant disproportionate reporting was also found for hypernatremia [IC 3.1 (2.2-3.8)], hyperphosphatemia [IC 3.1 (1.8-3.9)], hypophosphatemia [IC 2.0 (0.6-2.9)], metabolic acidosis [IC 1.8 (1.2-2.2)], hyponatremia [IC 1.6 (1.1-2.0)], and hypercalcemia [IC 1.4 (0.5-2.1)]. There was no disproportionate reporting of dyskalemia., Conclusions: This study is limited by the inherent flaws of pharmacovigilance approaches. Nonetheless, our findings suggest that ARF and an array of hydroelectrolytic disorders are potential ADRs of CAR-T cell therapy, in real-life settings and in a nonselected population., (© 2023. The Author(s).)

Dalbavancin, a long-acting lipoglycopeptide antibiotic targeting susceptible Gram-positive bacteria, is WHO critically important antibiotic, increasingly used in critical situations such as osteoarticular infections. To ensure its effectiveness and its safety, the therapeutic drug monitoring (TDM) of dalbavancin is strongly recommended. In the absence of an available minimum inhibitory concentration (MIC), the European Committee on Antimicrobial Susceptibility Testing (EUCAST) recommends a breakpoint of 0.125 mg/L for Staphylococcus aureus, corresponding to a trough target concentration of 25 mg/L. Nowadays, the TDM is usually performed using a high-performance liquid chromatography (HPLC) method coupled with a tandem mass spectrometry. However, this expensive and specialized equipment and reagents may be difficult to acquire for non-specialized laboratories. The use of HPLC coupled with diode array detector (DAD) facilitates TDM with a lower cost, while preserving the reliability of the results. Our aim was to provide a sensitive and specific method, relying on HPLC-DAD for extending the TDM of dalbavancin beyond non-specialized labs, therefore maximizing its efficiency and Benefit/risk ratio. Our method complied with the European Medicines Agency guidelines of bioanalytical validation. Irrespective of the concentrations of dalbavancin, the coefficient of variation < 10% confirmed the reliability of this analytical method, with a calibration curve ranging from 5 to 100 mg/L. No interferences nor carryover was observed. Our HPLC-DAD method, combined with a simple extraction, provides a widely usable, inexpensive and easy-to-implement new asset for the TDM of Dalbavancin., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

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., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Pharmacovigilance and pharmacoepidemiology studies regarding the sex difference in adverse drug reactions are numerous, and it is now a challenge to take them into account in order to increase drug safety. Here, we present an overview of this topic through data on epidemiology, mechanisms, and methods used for assessing sex differences in drug safety. Because the literature is extensive, we choose to expose a few examples of studies for cardiovascular drugs, anti-infectious, psychotropics, antidiabetics, anticancer drugs and some specific drugs to illustrate our purpose. Many studies show a higher risk in women for most of drugs involving in sex differences. However, physiological, methodological and subjective points have to be taken into account to interpret these results. Clinical trials must also enroll more women to better evaluate sex differences both in efficacy and pharmacovigilance. Nevertheless, when there is a pharmacological rationale underlying the observed association between sex and drug safety profile, it is now unavoidable to think about its consideration for a personalized prescription., (Copyright © 2022 Société française de pharmacologie et de thérapeutique. Published by Elsevier Masson SAS. All rights reserved.)

Purpose: Tacrolimus is an immunosuppressant widely used in transplantations requiring mandatory concentration-controlled dosing to prevent acute rejection or adverse effects, including new-onset diabetes mellitus (NODM). However, no relationship between NODM and tacrolimus exposure has been established. This study aimed to evaluate the relationship between cumulative tacrolimus exposure and NODM occurrence., Methods: A total of 452 kidney transplant patients were included in this study. Sixteen patients developed NODM during the first 3 months after transplant. We considered all tacrolimus concentration (C0) values collected until the diagnosis of NODM in these patients and until 3 months after transplant in the others. New tacrolimus cumulative exposure metrics were derived from the time profile of the tacrolimus morning predose concentration, C0: the percentage of C0 values > cutoff, the average of C0 values above the cutoff, and the percentage of the area under C0 versus time curve, AUCC0, above the cutoff. The cutoff chosen was 15 ng/mL, corresponding to the higher end of the therapeutic range for the early post-transplant period. The influence of these metrics on NODM and other clinical and biological characteristics was investigated using the Cox models., Results: The percentage of C0 > 15 mcg/L was statistically different between patients with and without NODM (P = 0.01). Only these tacrolimus C0-derived metrics were significantly associated with an increased risk of NODM [HR: 1.73 (1.43-2.10, P < 0.001)]., Conclusion: This study shows that tacrolimus concentrations >15 mcg/L affect the incidence of NODM., Competing Interests: The authors declare no conflict of interest., (Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.)

Antipsychotic drugs (APs) aim to treat schizophrenia, bipolar mania, and behavioral symptoms. In child psychiatry, despite limited evidence regarding their efficacy and safety, APs are increasingly subject to off-label use. Studies investigating addictology-related symptoms in young people being scarce, we aimed to characterize the different patterns of AP misuse and withdrawal in children and adolescents relying on the WHO pharmacovigilance database (VigiBase ® , Uppsala Monitoring Centre, Sweden). Using the standardized MedDRA Query 'drug abuse, dependence and withdrawal', disproportionality for each AP was assessed with the reporting odds ratio and the information component. A signal was detected when the lower end of the 95% confidence interval of the information component was positive. Results revealed mainly withdrawal symptoms in infants (under 2 years), intentional misuse in children (2 to 11 years), and abuse in adolescents (12 to 17 years). Olanzapine, risperidone, aripiprazole, and quetiapine were disproportionately reported in all age groups, with quetiapine being subject to a specific abuse signal in adolescents. Thus, in adolescents, the evocation of possible recreational consumption may lead to addiction-appropriate care. Further, in young patients with a history of AP treatment, a careful anamnesis may allow one to identify misuse and its role in the case of new-onset symptoms.

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., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Parosmia is a qualitative distortion of smell perception. Resulting from central causes, sinonasal diseases, and infections, parosmia has also been associated with medications. Therefore, we aimed to investigate potential signals for drugs associated with parosmia. VigiBase ® (the WHO pharmacovigilance database) was queried for all reports of "Parosmia" (MedDRA Preferred Term), registered up to 23 January 2022. Disproportionality analysis relied on the reporting odds ratio and the information component. A signal is detected when the lower end of the 95% confidence interval of the information component is positive. We found 14,032 reports of parosmia, with a median patient age of 53 years. Most reported drugs were antiinfectives, among which COVID-19 vaccines accounted for 27.1% of reports. Antibiotics and corticosteroids were involved in 6.8% and 4.6% of reports. Significant disproportionate reporting was detected for corticosteroids, antibiotics, drugs used in nicotine dependence, COVID-19 and HPV vaccines, serotonin-norepinephrine reuptake inhibitors (SNRI), and incretin mimetics. We suggest potential safety signals involving nicotine replacement therapies and vaccines. We also highlight the potential role of less suspected classes, such as SNRIs and incretin mimetics. An iatrogenic etiology should be evoked when parosmia occurs, especially in the elderly.

Background: Maximum a posteriori Bayesian estimation (MAP-BE) based on a limited sampling strategy and a population pharmacokinetic model is frequently used to estimate pharmacokinetic parameters in individuals, however with some uncertainty (bias). Recent works have shown that the performance in individual estimation or pharmacokinetic parameters can be improved by combining population pharmacokinetic and machine learning algorithms., Objective: The objective of this work was to investigate the use of a hybrid machine learning/population pharmacokinetic approach to improve individual iohexol clearance estimation., Methods: The reference iohexol clearance values were derived from 500 simulated profiles (samples collected between 0.1 and 24.7 h) using a population pharmacokinetic model we recently developed in Monolix and obtained using all the concentration timepoints available. Xgboost and glmnet algorithms able to predict the error of MAP-BE clearance estimates based on a limited sampling strategy (0.1 h, 1 h, and 9 h) versus reference values were developed in a training subset (75%) and were evaluated in a testing subset (25%) and in 36 real patients., Results: The MAP-BE limited sampling strategy estimated clearance was corrected by the machine learning predicted error leading to a decrease in root mean squared error by 29% and 24%, and in the percentage of profiles with the mean prediction error out of the ± 20% bias by 60% and 40% in the external validation dataset for the glmnet and Xgboost machine learning algorithms, respectively. These results were attributable to a decrease in the eta-shrinkage (shrinkage for a MAP-BE limited sampling strategy = 32.4%, glmnet = 18.2%, and Xgboost = 19.4% in the external dataset)., Conclusions: In conclusion, this hybrid algorithm represents a significant improvement in comparison to MAP-BE estimation alone., (© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Sleep-related eating disorder (SRED) is a parasomnia with recurrent, involuntary, amnestic eating episodes during sleep. There is growing evidence of the association between SRED and medications. Therefore, we aimed to rank drugs showing the strongest association. VigiBase® (WHO pharmacovigilance database) was queried for all reports of “Sleep-related eating disorder”. Disproportionality analysis relied on the Reporting Odds Ratio, with its 95% Confidence Interval (CI), and the Information Component. Our VigiBase® query yielded 676 cases of drug-associated SRED. Reports mostly involved zolpidem (243, 35.9%), sodium oxybate (185, 27.4%), and quetiapine (97, 14.3%). Significant disproportionality was found for 35 medications, including zolpidem (387.6; 95%CI 331.2−453.7), sodium oxybate (204.2; 95%CI 172.4−241.8), suvorexant (67.3; 95%CI 38.0−119.2), quetiapine (53.3; 95%CI 43.0−66.1), and several psychostimulants and serotonin-norepinephrine reuptake inhibitors (SNRIs). Patients treated with nonbenzodiazepines or SNRIs were significantly older (mean age: 49.0 vs. 37.5; p < 0.001) and their SRED were more likely to be serious (62.6% vs. 51.4%; p = 0.014) than patients treated with sodium oxybate or psychostimulants. Psychotropic drugs are involved in almost all reports. In patients with SRED, an iatrogenic trigger should be searched for.

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., (© 2022 British Pharmacological Society.)

Coronavirus disease 2019 (COVID-19) spread rapidly, resulting in a global pandemic for which vaccines were quickly developed. As their safety continues to be monitored, cases of transient global amnesia (TGA) following mRNA vaccination with elasomeran have been reported. TGA is characterized by sudden onset of anterograde amnesia with preservation of other cognitive functions and resolution within 24 h. We aimed to investigate the potential link of TGA with COVID-19 vaccines. We queried the World Health Organization VigiBase ® for all reports of "Transient global amnesia", up to 6 December 2021. Disproportionality analysis relied on the Reporting Odds Ratio (ROR) with its 95% Confidence Interval (CI) and the Information Component (IC). A positive lower end of the 95% CI of the IC (IC025) is used to statistically detect a signal. Of all TGA cases, 289 were associated with a COVID-19 vaccine, representing the most frequent association. Tozinameran was mostly represented (147, 50.8%), followed by AZD1222 (69, 23,8%), elasomeran (60, 20.8%), and JNJ-78436735 (12, 4.2%). With an IC025 > 0, COVID-19 vaccines showed a significant ROR (5.1; 95%CI 4.4-6.0). Tozinameran reached the strongest ROR (4.6; 95%CI 3.9-5.0), followed by elasomeran (4.4; 95%CI 3.4-6.0), AZD1222 (3.8; 95%CI 3.0-5.0), and JNJ-78436735 (3.7; 95%CI 2.1-6.0). Our analysis of COVID-19 vaccines-related TGA reports shows significant disproportionality. Cerebrovascular, inflammatory, or migrainous mechanisms may underlie this association. Yet, numerous confounding factors cannot be tackled with this approach, and causality cannot be ascertained. The identification of this trigger of TGA may help the clinician in his etiological research., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Merino, Gérard, Van Obberghen, Ben Othman, Ettore, Giordana, Viard, Rocher, Destere, Benoit and Drici.)

Background: Migraine is responsible for significant disability and societal burden. Recently, drugs targeting the calcitonin gene-related peptide (CGRP) pathway raised new hopes. CGRP, a potent vasodilator, plays a key role in the pathogenesis of migraine attacks. The deficiency of CGRP is involved in Raynaud's phenomenon, which consists of abnormal vasoconstriction of the digits. We aimed to assess the potential association of Raynaud's phenomenon with CGRP-targeting drugs, analyzing real-world data from the World Health Organization (VigiBase®)., Methods: We queried all reports of Raynaud's phenomenon involving a CGRP-targeting drug. We sought disproportionate reporting of Raynaud's phenomenon with these drugs. For this purpose, we relied on the calculation of the Information Component (IC). A positive lower end of the 95% confidence interval (CI) of the IC defines a statistically significant association. As migraine patients are prone to Raynaud's phenomenon, we also calculated the IC of Raynaud's phenomenon with CGRP-targeting drugs compared to 5HT1 B/D agonists (triptans), and beta-blockers used in the treatment of migraine., Results: Overall, 99 reports of Raynaud's phenomenon involving CGRP-targeting drugs have been yielded in VigiBase®. The most reported CGRP-targeting drug was erenumab, with 56 reports (56.6%). The median time to onset was 84 days. No fatality was notified, but one patient suffered from gangrene and extremity necrosis. As a whole, CGRP-targeting drugs were significantly associated with Raynaud's phenomenon, with an IC of 3.3 (95%CI: 3.0-3.5). There was a disproportionate reporting of Raynaud's phenomenon with CGRP-targeting drugs compared to triptans (IC 0.4; 95%CI: 0.1-0.6) and to beta-blockers (IC 0.5; 95%CI: 0.2-0.7) as well., Conclusions: There is a significant disproportionality signal of Raynaud's phenomenon with CGRP-targeting. This signal stands out when CGRP-targeting drugs are compared to other drugs used in patients with migraine. This study is limited by missing data in pharmacovigilance reports. CGRP-targeting drugs may be subject to Weber effect and reporting bias. Nonetheless, CGRP blockade might be the last straw that disrupts the physiological balance of vascular response in patients at-risk of Raynaud's phenomenon. Pending further data regarding vascular safety of CGRP-targeting drugs, caution is warranted in these patients., (© 2022. The Author(s).)

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 r 2 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.

To study the antikinetoplastid 3-nitroimidazo[1,2-a]pyridine pharmacophore, a structure-activity relationship study was conducted through the synthesis of 26 original derivatives and their in vitro evaluation on both Leishmania spp and Trypanosoma brucei brucei. This SAR study showed that the antitrypanosomal pharmacophore was less restrictive than the antileishmanial one and highlighted positions 2, 6 and 8 of the imidazopyridine ring as key modulation points. None of the synthesized compounds allowed improvement in antileishmanial activity, compared to previous hit molecules in the series. Nevertheless, compound 8, the best antitrypanosomal molecule in this series (EC 50  = 17 nM, SI = 2650 & E° = -0.6 V), was not only more active than all reference drugs and previous hit molecules in the series but also displayed improved aqueous solubility and better in vitro pharmacokinetic characteristics: good microsomal stability (T 1/2  > 40 min), moderate albumin binding (77%) and moderate permeability across the blood brain barrier according to a PAMPA assay. Moreover, both micronucleus and comet assays showed that nitroaromatic molecule 8 was not genotoxic in vitro. It was evidenced that bioactivation of molecule 8 was operated by T. b. brucei type 1 nitroreductase, in the same manner as fexinidazole. Finally, a mouse pharmacokinetic study showed that 8 displayed good systemic exposure after both single and repeated oral administrations at 100 mg/kg (NOAEL) and satisfying plasmatic half-life (T 1/2  = 7.7 h). Thus, molecule 8 appears as a good candidate for initiating a hit to lead drug discovery program., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Masson SAS. All rights reserved.)

An antikinetoplastid pharmacomodulation study was done at position 8 of a previously identified pharmacophore in 3-nitroimidazo[1,2-a]pyridine series. Twenty original derivatives bearing an alkynyl moiety were synthesized via a Sonogashira cross-coupling reaction and tested in vitro, highlighting 3 potent (40 nM ≤ EC 50 blood stream form≤ 70 nM) and selective (500 ≤ SI ≤ 1800) anti-T. brucei brucei molecules (19, 21 and 22), in comparison with four reference drugs. Among these hit molecules, compound 19 also showed the same level of activity against T. cruzi (EC 50 amastigotes = 1.2 μM) as benznidazole and fexinidazole. An in vitro comet assay showed that nitroaromatic derivative 19 was not genotoxic. It displayed a low redox potential value (-0.68 V/NHE) and was shown to be bioactivated by type 1 nitroreductases both in Leishmania and Trypanosoma. The SAR study indicated that an alcohol function improved aqueous solubility while maintaining good activity and low cytotoxicity when the hydroxyl group was at position beta of the alkyne triple bond. Hit-compound 19 was also evaluated regarding in vitro pharmacokinetic data: 19 is BBB permeable (PAMPA assay), has a 16 min microsomal half-life and a high albumin binding (98.5%). Moreover, compound 19 was orally absorbed and was well tolerated in mouse after both single and repeated administrations at 100 mg/kg. Its mouse plasma half-life (10 h) is also quite encouraging, paving the way toward further efficacy evaluations in parasitized mouse models, looking for a novel antitrypanosomal lead compound., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Masson SAS. All rights reserved.)

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.)

Glucocorticoids (GCs) are amongst the most widely used and effective treatments to control inflammatory and autoimmune diseases. In obese subjects, drug dosing adjusted by body weight is problematic, all the more so as patients are at higher risk of GC metabolic side effects. We propose here to describe the determinants of drug pharmacokinetics (PK) in obese subjects and GC pharmacology, and to identify the existing PK studies that may help discussing the best size descriptor for GC dosing in obese subjects. A clinician and a pharmacist screened PubMed using the MeSH Terms: "glucocorticoids" OR "steroidal agents" AND "pharmacokinetics" AND "obesity" OR "overweight". The search was limited to the publications written in English language and to those performed in humans. A systematic search using the MeSH terms was performed until August 31st, 2017. Only three such PK studies have been published so far that compare dexamethasone, prednisolone and methylprednisolone in obese and normal weight subjects. The studies concur that GC partially distribute in the excess of body weight and that adjustment by total body weight (TBW) or by body weight (BW) excess would increase the initial plasma GC concentration after a loading dose and would thus be inappropriate. Contradictory results are observed regarding GC exposure or clearance according to the GC studied. Behind this overwhelming lack of conclusive evidence for adjusting GC by body weight, further PK studies are clearly needed for guiding their dosing. Furthermore, studies demonstrated an increased sensibility to GC, even when GC exposure was reduced, suggesting that adjustment by body weight may not only be unnecessary but also dangerous., (Copyright © 2019. Published by Elsevier Masson SAS.)