Withdrawal syndrome after antipsychotics discontinuation: an analysis of the WHO database of spontaneous reports (Vigibase) between 2000 and 2022.

RATIONALE: Withdrawal syndrome (WDS) has been described after discontinuation of antipsychotics. WDS could be the consequence of an over-activation of the dopaminergic pathway. Antipsychotics with a higher affinity for dopamine D2 receptors could be associated with a higher risk of WDS. This study aims to address this statement and evaluate the risk difference for withdrawal syndrome between antipsychotics based on pharmacovigilance data. METHODS: We collected individual reports registered in Vigibase® between 01/01/2000 and 31/12/2022 of patients treated with antipsychotics and who had presented WDS. A disproportionality analysis was performed to evaluate the risk of reporting WDS with each antipsychotic compared to all other antipsychotics. We performed a correlation analysis to assess the correlation between the risk of reporting WDS for each antipsychotic in relation with their pKi for D2 and 5HT2A receptors. RESULTS: The most frequent psychiatric withdrawal symptoms after antipsychotic discontinuation were insomnia, anxiety and depression. Tremor, headache and dizziness were among the most frequently reported neurologic withdrawal symptoms. Tiotixene had the highest risk of reporting WDS (ROR 7.08; 95%CI 3.49 - 14.35) followed by pimozide (ROR 4.35; 95%CI 1.93 - 9.77), quetiapine (ROR 4.24; 95%CI 3.87 - 4.64), thioridazine (ROR 4.17; 95%CI 2.50-6.98) and ziprasidone (ROR 2.98; 95%CI 2.41-3.67). We found a poor correlation between D2/5HT2A binding affinity and the risk of reporting withdrawal syndrome (R2 = 0,094). CONCLUSION: Our results suggest that there might be a risk difference for WDS between antipsychotics. Tiotixene, pimozide and quetiapine were associated with a higher risk of reporting a WDS whereas this risk was lower with chlorpromazine, clozapine and fluphenazine. We could not address the issue of withdrawal psychosis, withdrawal dyskinesia, rebound psychosis or supersensitivity psychosis due to the lack of specific WHO medDRA coded terms to identify potential cases.

Drug-induced depressive symptoms: An update through the WHO pharmacovigilance database.

BACKGROUND: Depression is a highly incident condition and some drugs have been described as inducing or worsening depression. However, literature on this topic is rare and possibly outdated. METHODS: We performed disproportionality analyses using VigiBase®, the largest pharmacovigilance database worldwide to identify drugs associated with depression. Then we excluded drugs already known as depressogenic according to American Summary of Product Characteristics (SPC). We then reviewed drug mechanism of action, scientific literature and European SPC for each drug identified to assess a level of plausibility. We measured Reporting Odds Ratio (ROR) statistically significant and superior to 1, suggesting a significant association between a drug and the reporting of depressive symptoms. RESULTS: Out of the 5237 drugs extracted on VigiBase®, we have retained 89 new drugs associated with depression. More than half of drugs of interest are from nervous system. Opicapone (ROR: 20.66 95 %CI: 15.62-27.33), and gadoversetamide (ROR 18.62, 95 %CI 9.63-35.95) were the drugs with the highest ROR. Among the 89 drugs, 38 were considered already described such as suvorexant or ivacaftor, 20 likely associated such as anti-migraines drugs or new antipsychotic drugs and 31 potentially associated. LIMITATIONS: Pharmacovigilance studies have many inherent limitations, such as under-reporting bias, notoriety effect and protopathic bias. These results are not intended to establish a causal link, only a statistical association. CONCLUSION: We found a strong statistical signal and pharmacological plausibility for 58 new depressogenic drugs. This update list of suspected drugs may prove useful for doctors faced with potential cases of drug-induced depression or to stay aware in case. Other studies are needed to confirm the list.

Will artificial intelligence chatbots replace clinical pharmacologists? An exploratory study in clinical practice.

PURPOSE: Recently, there has been a growing interest in using ChatGPT for various applications in Medicine. We evaluated the interest of OpenAI chatbot (GPT 4.0) for drug information activities at Toulouse Pharmacovigilance Center. METHODS: Based on a series of 50 randomly selected questions sent to our pharmacovigilance center by healthcare professionals or patients, we compared the level of responses from the chatbot GPT 4.0 with those provided by specialists in pharmacovigilance. RESULTS: Chatbot answers were globally not acceptable. Responses to inquiries regarding the assessment of drug causality were not consistently precise or clinically meaningful. CONCLUSION: The interest of chatbot assistance needs to be confirmed or rejected through further studies conducted in other pharmacovigilance centers.