Key Learning Outcomes for Clinical Pharmacology and Therapeutics Education in Europe: A Modified Delphi Study.

Harmonizing clinical pharmacology and therapeutics (CPT) education in Europe is necessary to ensure that the prescribing competency of future doctors is of a uniform high standard. As there are currently no uniform requirements, our aim was to achieve consensus on key learning outcomes for undergraduate CPT education in Europe. We used a modified Delphi method consisting of three questionnaire rounds and a panel meeting. A total of 129 experts from 27 European countries were asked to rate 307 learning outcomes. In all, 92 experts (71%) completed all three questionnaire rounds, and 33 experts (26%) attended the meeting. 232 learning outcomes from the original list, 15 newly suggested and 5 rephrased outcomes were included. These 252 learning outcomes should be included in undergraduate CPT curricula to ensure that European graduates are able to prescribe safely and effectively. We provide a blueprint of a European core curriculum describing when and how the learning outcomes might be acquired

Unsupervised Text Clusterisation to characterize Adverse Drug Reactions from hospitalization reports

International audienceThe detection of Adverse Drug Reactions (ADRs) in clinical records plays a pivotal role in pharmacovigilance (PhV). Achieving nearideal practice relies on well-trained health professionals, who are trained to identify, assess and report to health authorities ADRs occurring after drug marketing approval, including those that are infrequent. However, the number of experts trained in this practice is low and despite reporting ADRs being mandatory for healthcare professionals, pharmacovigilance still suffers from a significant under-reporting, accounting for only 5-10% of all ADRs. Yet, drug safety is crucial for assessing the benefit/risk ratio of a given drug. It is therefore important to circumvent under-reporting and to be able to collect ADRs automatically from medical reports. The most natural approach would be to train a model in a supervised manner, which requires annotation of a large volume of data, but this is unfortunately not possible. We therefore propose here an unsupervised approach to distinguish between ADRs-related and non-related reports. From a more formal point of view, we address this problem as a clustering task aiming at distinguishing medical reports containing the description of an ADR from those without

Unsupervised Text Clusterisation to characterize Adverse Drug Reactions from hospitalization reports

International audienceThe detection of Adverse Drug Reactions (ADRs) in clinical records plays a pivotal role in pharmacovigilance (PhV). Achieving near-ideal practice relies on well-trained health professionals, who are trained to identify, assess, and report to health authorities ADRs occurring after drug marketing approval, including those that are infrequent. However, the number of experts trained in this practice is low and despite reporting ADRs being mandatory for healthcare professionals, pharmacovigilance still suffers from a significant under-reporting, accounting for only 5-10% of all ADRs. Yet, drug safety is crucial for assessing the benefit/risk ratio of a given drug. It is therefore important to circumvent under-reporting and to be able to collect ADRs automatically from medical reports. The most natural approach would be to train a model in a supervised manner, which requires annotation of a large volume of data, but this is unfortunately not possible. We therefore propose here an unsupervised approach to distinguish between ADRs-related and non-related reports. From a more formal point of view, we address this problem as a clustering task aiming at distinguishing medical reports containing the description of an ADR from those without

Contrastive Clustering of medical reports to characterise Adverse Drug Reactions

National audienc

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

International audienceObjectives 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

Response to the editorial “COVID-19 in patients with cardiovascular diseases”

International audienc

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

French Pharmacovigilance Public System and COVID-19 Pandemic

International audienceThe current COVID-19 pandemic is an exceptional health situation including for drug use. As there was no known effective drug for COVID-19 at the beginning of the pandemic, different candidates were proposed. In this short article, we present the French public pharmacovigilance activities during this health crisis. Although COVID-19 is a confounding factor per se, owing to its potential for multi-organ damage including the heart and kidney, the quality of the transmitted data in adverse drug reaction reports, the timeliness of feedback from clinicians, and the real-time pharmacological and medical analysis by the French network of the regional pharmacovigilance centers made it possible to swiftly identify relevant safety signals. The French National Agency of Medicine was thus able to validate the data and convey their findings very early. This decentralized organization based on medical and pharmacological evaluation of case reports has proven to be efficient and responsive in this unique and challenging healthcare emergency

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

International audienceMigraine 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