Drug–Drug Interactions in Outpatient Psychiatry: From Interaction Profiles to Smart Monitoring

firstₚage Download PDF settings Order Article Reprints Font Type: Arial Georgia Verdana Font Size: Aa Aa Aa Line Spacing:    Column Width:    Background: Open AccessAbstract Drug–Drug Interactions in Outpatient Psychiatry: From Interaction Profiles to Smart Monitoring † by Florina-Diana GoldișFlorina-Diana Goldiș SciProfiles Scilit Preprints. org Google Scholar 1, *, Răzvan PăiușanRăzvan Păiușan SciProfiles Scilit Preprints. org Google Scholar 2, Mihai UdrescuMihai Udrescu SciProfiles Scilit Preprints. org Google Scholar 2 and Lucreția UdrescuLucreția Udrescu SciProfiles Scilit Preprints. org Google Scholar 1, 3 1 Center for Drug Data Analysis, Cheminformatics, and the Internet of Medical Things, "Victor Babeș" University of Medicine and Pharmacy, 300041 Timișoara, Romania 2 Department of Computer and Information Technology, University Politehnica of Timişoara, 300223 Timişoara, Romania 3 Department I-Drug Analysis, Faculty of Pharmacy, "Victor Babeș" University of Medicine and Pharmacy, 300041 Timișoara, Romania * Author to whom correspondence should be addressed. † Presented at the International Conference on Interdisciplinary Approaches and Emerging Trends in Pharmaceutical Doctoral Research: Innovation and Integration, Timisoara, Romania, 7–9 July 2025. Proceedings 2025, 127 (1), 18; https: //doi. org/10. 3390/proceedings2025127018 (registering DOI) Published: 26 September 2025 (This article belongs to the Proceedings of International Conference on Interdisciplinary Approaches and Emerging Trends in Pharmaceutical Doctoral Research: Innovation and Integration) Download keyboardₐrrowdown Download PDF Download XML Download Epub Versions Notes Keywords: psychopharmacotherapy; drug interactions; internet of medical things Objective. To characterize drug–drug interactions (DDIs) within outpatient psychiatric prescriptions and identify high-risk combinations, optimize therapy, and minimize adverse effects. Methods. We retrospectively reviewed 662 psychiatric prescriptions (October–December 2023), recording patient sex, age, diagnoses, and medication. Inclusion criteria required ≥2 drugs per drug regimen and informed consent signed by the patient. DDIs were identified via DrugBank 6. 0 1 and then classified as major, moderate, or minor, and no interaction was found. Results and Discussion. Of the 662 prescriptions, 458 were for female patients, and 204 were for male patients. Regarding the age category, the most represented is the age category over 65 years (n = 311 prescriptions), more susceptible to the development of adverse effects following drug interactions due to comorbidities, polypharmacy, and physiological changes. Of the 662 prescriptions, 327 contained a number greater than or equal to 5 drugs (polypharmacy) 2, 3. Of the total pairs analyzed, 4. 84% are major DDIs, 32. 18% are moderate DDIs, 19. 08% are minor DDIs, and the rest show no interactions. The most prescribed benzodiazepine (BZD) was alprazolam (n = 146 prescriptions), followed by lorazepam (n = 113), zolpidem (n = 77), and zopiclone (n = 69). Among thymic stabilizers, valproic acid was the most used (n = 129 prescriptions), followed by carbamazepine (n = 54). In the case of antipsychotics, quetiapine is the most frequent (n = 120), followed by risperidone (n = 59). The most prescribed antidepressant is trazodone (n = 98 prescriptions), followed by escitalopram (n = 67), tianeptine (n = 63), and mirtazapine (n = 61). The benzodiazepine involved in the most major DDIs was zopiclone (n = 22), followed by lorazepam (n = 8) and alprazolam (n = 7). In our cohort, zopiclone was involved in the most major DDIs (n = 22), mostly with carbamazepine (n = 9) and clopidogrel (n = 6), reflecting CYP450-mediated pharmacokinetic interactions 4, 5, 6. Less-frequent major DDIs occur with clarithromycin (n = 2), salmeterol (n = 2), amiodarone (n = 1), azelastine (n = 1), and mometasone furoate (n = 1), underscoring the need to review any concurrent anti-infectives, antiarrhythmics, and even intranasal therapies. By contrast, moderate interactions, driven by additive CNS depression or mild metabolic shifts, were common with sertraline (n = 25), trazodone (n = 22), escitalopram (n = 18), quetiapine (n = 15), and valproic acid (n = 14), underscoring frequent zopiclone co-prescription alongside SSRIs and mood stabilizers and the consequent risk of excessive sedation and cognitive impairment 4, 6, 7. To mitigate the overlap of sedative effects when administering SSRIs together with BZD and BZD-related drugs, we piloted a closed-loop Internet of Medical Things (IoMT) system using Empatica's Embrace Plus wearable 8. By tracking accelerometry, heart rate, SpO2, skin temperature, and galvanic skin response—combined with pharmacokinetic modeling—we aim to identify the optimal moment to administer the personalized SSRI dosing. Conclusions. Major DDIs, primarily metabolic interactions between zopiclone and CYP-modulating agents, require dose adjustments or alternative therapies/monitoring. The high prevalence of moderate CNS–depressant interactions, especially between BZD and SSRIs/mood stabilizers, calls for vigilant monitoring, patient education on sedation risk, and scheduling strategies 8, 9. By integrating wearable physiology monitoring with simple pharmacokinetic modeling, we can conduct a data-driven analysis of the optimal moment for personalized SSRIs administration with BZD. Author ContributionsConceptualization, F. -D. G. and L. U. ; Methodology, F. -D. G. , L. U. and M. U. ; Formal Analysis, F. -D. G. and R. P. ; Investigation, F. -D. G. ; Data Curation, F. -D. G. and R. P. ; Writing—Original Draft Preparation, F. -D. G. ; Writing—Review and Editing, F. -D. G. and L. U. ; Supervision, L. U. All authors have read and agreed to the published version of the manuscript. FundingThis research received no external funding. Institutional Review Board StatementThe study was conducted in accordance with the Declaration of Helsinki, and approved by the Scientific Research Ethics Committee of the "Victor Babeș" University of Medicine and Pharmacy Timișoara (study protocol no. 59/16. 12. 2022). Informed Consent StatementInformed consent was obtained from all subjects involved in the study. Data Availability StatementThe data presented in this study are available upon request from the corresponding author due to the ethical restrictions. Conflicts of InterestThe authors declare no conflict of interest. ReferencesKnox, C. ; Wilson, M. ; Klinger, C. M. ; Franklin, M. ; Oler, E. ; Wilson, A. ; Pon, A. ; Cox, J. ; Chin, N. E. ; Strawbridge, S. A. ; et al. DrugBank 6. 0: The DrugBank knowledgebase for 2024. Nucleic Acids Res. 2024, 52, D1265–D1275. Google Scholar CrossRef PubMedMaher, R. L. ; Hanlon, J. ; Hajjar, E. R. Clinical consequences of polypharmacy in elderly. Expert Opin. Drug Saf. 2014, 13, 57–65. 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This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https: //creativecommons. org/licenses/by/4. 0/). Share and Cite MDPI and ACS Style Goldiș, F. -D. ; Păiușan, R. ; Udrescu, M. ; Udrescu, L. Drug–Drug Interactions in Outpatient Psychiatry: From Interaction Profiles to Smart Monitoring. Proceedings 2025, 127, 18. https: //doi. org/10. 3390/proceedings2025127018 AMA Style Goldiș F-D, Păiușan R, Udrescu M, Udrescu L. Drug–Drug Interactions in Outpatient Psychiatry: From Interaction Profiles to Smart Monitoring. Proceedings. 2025; 127 (1): 18. https: //doi. org/10. 3390/proceedings2025127018 Chicago/Turabian Style Goldiș, Florina-Diana, Răzvan Păiușan, Mihai Udrescu, and Lucreția Udrescu. 2025. "Drug–Drug Interactions in Outpatient Psychiatry: From Interaction Profiles to Smart Monitoring" Proceedings 127, no. 1: 18. https: //doi. org/10. 3390/proceedings2025127018 APA Style Goldiș, F. -D. , Păiușan, R. , Udrescu, M. , & Udrescu, L. (2025). Drug–Drug Interactions in Outpatient Psychiatry: From Interaction Profiles to Smart Monitoring. Proceedings, 127 (1), 18. https: //doi. org/10. 3390/proceedings2025127018 Article Metrics No No Article Access Statistics Multiple requests from the same IP address are counted as one view.