To be or not to be, when synthetic data meet clinical pharmacology: A focused study on pharmacogenetics.

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.

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

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

A Plasma Pyrophosphate Cutoff Value for Diagnosing Pseudoxanthoma Elasticum.

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.

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

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

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

BACKGROUND AND OBJECTIVES: Ganciclovir (GCV) and valganciclovir (VGCV) show large interindividual pharmacokinetic variability, particularly in children. The objectives of this study were (1) to develop machine learning (ML) algorithms trained on simulated pharmacokinetics profiles obtained by Monte Carlo simulations to estimate the best ganciclovir or valganciclovir starting dose in children and (2) to compare its performances on real-world profiles to previously published equation derived from literature population pharmacokinetic (POPPK) models achieving about 20% of profiles within the target. MATERIALS AND METHODS: The pharmacokinetic parameters of four literature POPPK models in addition to the World Health Organization (WHO) growth curve for children were used in the mrgsolve R package to simulate 10,800 pharmacokinetic profiles. ML algorithms were developed and benchmarked to predict the probability to reach the steady-state, area-under-the-curve target (AUC0-24 within 40-60 mg × h/L) based on demographic characteristics only. The best ML algorithm was then used to calculate the starting dose maximizing the target attainment. Performances were evaluated for ML and literature formula in a test set and in an external set of 32 and 31 actual patients (GCV and VGCV, respectively). RESULTS: A combination of Xgboost, neural network, and random forest algorithms yielded the best performances and highest target attainment in the test set (36.8% for GCV and 35.3% for the VGCV). In actual patients, the best GCV ML starting dose yielded the highest target attainment rate (25.8%) and performed equally for VGCV with the Franck model formula (35.3% for both). CONCLUSION: The ML algorithms exhibit good performances in comparison with previously validated models and should be evaluated prospectively.

An artificial intelligence algorithm for co-clustering to help in pharmacovigilance before and during the COVID-19 pandemic.

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.

Cardiac and metabolic safety profile of antipsychotics in youths: A WHO safety database analysis.

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.

Optimization of Rituximab Therapy in Adult Patients With PLA2R1-Associated Membranous Nephropathy With Artificial Intelligence.

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.

Drug-induced cardiac toxicity and adverse drug reactions, a narrative review.

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.

Iatrogenic triggers for anorexia nervosa and bulimia nervosa: A WHO safety database disproportionality analysis.

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.

The Neuropsychiatric Safety Profile of Lasmiditan: A Comparative Disproportionality Analysis with Triptans.

Migraine constitutes the world's second-leading cause of disability. Triptans, as serotonin 5-HT1B/1D receptor agonists, remain the first-line treatment, despite discouraged use in individuals at high cardiovascular risk. Lasmiditan, a selective lipophilic 5-HT1F 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.

Tacrolimus population pharmacokinetics in adult heart transplant patients.

INTRODUCTION: Tacrolimus is an immunosuppressant largely used in heart transplantation. However, the calculation of its exposure based on the area under the curve (AUC) requires the use of a population pharmacokinetic (PK) model. The aims of this work were (i) to develop a population PK model for tacrolimus in heart transplant patients, (ii) to derive a maximum a posteriori Bayesian estimator (MAP-BE) based on a limited sampling strategy (LSS) and (iii) to estimate probabilities of target attainment (PTAs) for AUC and trough concentration (C0). MATERIAL AND METHODS: Forty-seven PK profiles (546 concentrations) of 18 heart transplant patients of the Pharmacocinétique des Immunosuppresseurs chez les patients GREffés Cardiaques study receiving tacrolimus (Prograf®) were included. The database was split into a development (80%) and a validation (20%) set. PK parameters were estimated in MONOLIX® and based on this model a Bayesian estimator using an LSS was built. Simulations were performed to calculate the PTA for AUC and C0. RESULTS: The best model to describe the tacrolimus PK was a two-compartment model with a transit absorption and a linear elimination. Only the CYP3A5 covariate was kept in the final model. The derived MAP-BE based on the LSS (0-1-2 h postdose) yielded an AUC bias ± SD = 2.7 ± 10.2% and an imprecision of 9.9% in comparison to the reference AUC calculated using the trapezoidal rule. PTAs based on AUC or C0 allowed new recommendations to be proposed for starting doses (0.11 mg·kg-1 ·12 h-1 for the CYP3A5 nonexpressor and 0.22 mg·kg1 ·12 h-1 for the CYP3A5 expressor). CONCLUSION: The MAP-BE developed should facilitate estimation of tacrolimus AUC in heart transplant patients.

CAR-T Cells and the Kidney: Insights from the WHO Safety Database.

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.

Safety Signal Generation for Sudden Sensorineural Hearing Loss Following Messenger RNA COVID-19 Vaccination: Postmarketing Surveillance Using the French Pharmacovigilance Spontaneous Reporting Database.

BACKGROUND: The World Health Organization recently described sudden sensorineural hearing loss (SSNHL) as a possible adverse effect of COVID-19 vaccines. Recent discordant pharmacoepidemiologic studies invite robust clinical investigations of SSNHL after COVID-19 messenger RNA (mRNA) vaccines. This postmarketing surveillance study, overseen by French public health authorities, is the first to clinically document postvaccination SSNHL and examine the role of potential risk factors. OBJECTIVE: This nationwide study aimed to assess the relationship between SSNHL and exposure to mRNA COVID-19 vaccines and estimate the reporting rate (Rr) of SSNHL after mRNA vaccination per 1 million doses (primary outcome). METHODS: We performed a retrospective review of all suspected cases of SSNHL after mRNA COVID-19 vaccination spontaneously reported in France between January 2021 and February 2022 based on a comprehensive medical evaluation, including the evaluation of patient medical history, side and range of hearing loss, and hearing recovery outcomes after a minimum period of 3 months. The quantification of hearing loss and assessment of hearing recovery outcomes were performed according to a grading system modified from the Siegel criteria. A cutoff of 21 days was used for the delay onset of SSNHL. The primary outcome was estimated using the total number of doses of each vaccine administered during the study period in France as the denominator. RESULTS: From 400 extracted cases for tozinameran and elasomeran, 345 (86.3%) spontaneous reports were selected. After reviewing complementary data, 49.6% (171/345) of documented cases of SSNHL were identified. Of these, 83% (142/171) of SSNHL cases occurred after tozinameran vaccination: Rr=1.45/1,000,000 injections; no difference for the rank of injections; complete recovery in 22.5% (32/142) of cases; median delay onset before day 21=4 days (median age 51, IQR 13-83 years); and no effects of sex. A total of 16.9% (29/171) of SSNHL cases occurred after elasomeran vaccination: Rr=1.67/1,000,000 injections; rank effect in favor of the first injection (P=.03); complete recovery in 24% (7/29) of cases; median delay onset before day 21=8 days (median age 47, IQR 33-81 years); and no effects of sex. Autoimmune, cardiovascular, or audiovestibular risk factors were present in approximately 29.8% (51/171) of the cases. SSNHL was more often unilateral than bilateral for both mRNA vaccines (P<.001 for tozinameran; P<.003 for elasomeran). There were 13.5% (23/142) of cases of profound hearing loss, among which 74% (17/23) did not recover a serviceable ear. A positive rechallenge was documented for 8 cases. CONCLUSIONS: SSNHL after COVID-19 mRNA vaccines are very rare adverse events that do not call into question the benefits of mRNA vaccines but deserve to be known given the potentially disabling impact of sudden deafness. Therefore, it is essential to properly characterize postinjection SSNHL, especially in the case of a positive rechallenge, to provide appropriate individualized recommendations.

A HPLC-DAD method to facilitate large-scale therapeutic drug monitoring of dalbavancin.

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.

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

OBJECTIVES: Maximum a posteriori Bayesian estimation (MAP-BE) based on a limited sampling strategy and a population pharmacokinetic (POPPK) model is used to estimate individual pharmacokinetic parameters. Recently, we proposed a methodology that combined population pharmacokinetic and machine learning (ML) to decrease the bias and imprecision in individual iohexol clearance prediction. The aim of this study was to confirm the previous results by developing a hybrid algorithm combining POPPK, MAP-BE and ML that accurately predicts isavuconazole clearance. METHODS: A total of 1727 isavuconazole rich PK profiles were simulated using a POPPK model from the literature, and MAP-BE was used to estimate the clearance based on: (i) the full PK profiles (refCL); and (ii) C24h only (C24h-CL). Xgboost was trained to correct the error between refCL and C24h-CL in the training dataset (75%). C24h-CL as well as ML-corrected C24h-CL were evaluated in a testing dataset (25%) and then in a set of PK profiles simulated using another published POPPK model. RESULTS: A strong decrease in mean predictive error (MPE%), imprecision (RMSE%) and the number of profiles outside ± 20% MPE% (n-out20%) was observed with the hybrid algorithm (decreased in MPE% by 95.8% and 85.6%; RMSE% by 69.5% and 69.0%; n-out20% by 97.4% and 100% in the training and testing sets, respectively. In the external validation set, the hybrid algorithm decreased MPE% by 96%, RMSE% by 68% and n-out20% by 100%. CONCLUSION: The hybrid model proposed significantly improved isavuconazole AUC estimation over MAP-BE based on the sole C24h and may improve dose adjustment.

Longitudinal Exposure to Tacrolimus and New-Onset Diabetes Mellitus in Renal Transplant Patients.

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.

Sex differences in adverse drug reactions: Are women more impacted?

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.

Antipsychotic Abuse, Dependence, and Withdrawal in the Pediatric Population: A Real-World Disproportionality Analysis.

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.