Gaps in the detection of drug-drug interactions between antipsychotic and cardiometabolic medications: a multisource analysis
Bibliographic record
Abstract
BACKGROUND: Individuals with severe mental illness (SMI) are frequently prescribed both antipsychotic medications and cardiometabolic medications, placing them at increased risk of drug-drug interactions (DDIs). However, evidence guiding the identification and management of these interactions remains fragmented. To address this research gap, in this study, we systematically summarize potential DDIs between antipsychotic and cardiometabolic medications and evaluate the performance of commonly used online DDI checkers in identifying these interactions. METHODS: A systematic review was conducted using PubMed, Embase, PsycINFO, and Web of Science to identify studies reporting DDIs between antipsychotic and cardiometabolic medications up to March 20, 2024. Disproportionality analysis was performed using data from the Canada Vigilance Adverse Reaction Online Database (1965-2024) and the FDA Adverse Event Reporting System (FAERS, 2004-2024) to identify DDI signals. Four online DDI checkers-Drugs.com, Medscape, ddinter, and ANSM Thesaurus-were used to evaluate their ability to identify the observed interactions. RESULTS: Across all sources, 1776 unique potential DDIs were identified. Clozapine was the most frequently implicated antipsychotic medication in a systematic review, often associated with musculoskeletal and connective tissue disorders. DDI signals associated with aripiprazole and quetiapine were also frequently observed. Except for nervous system disorders and cardiometabolic disorders, the adverse outcomes of DDIs involving aripiprazole or quetiapine were most commonly associated with musculoskeletal and connective tissue disorders and gastrointestinal disorders. Quetiapine interactions, especially with lipid-lowering agents such as simvastatin, were also commonly linked to musculoskeletal and connective tissue disorders. Notably, 45.4% of identified DDIs were not flagged by any of the four DDI checkers. Drugs.com detected the most interactions. Combinations of clozapine and metformin, ziprasidone and metformin, and risperidone and clonidine were consistently identified by at least three of the checkers. CONCLUSIONS: This systematic review and disproportionality analysis identified potential DDIs between antipsychotic medications and cardiometabolic medications, many of which were not captured by commonly used DDI checkers. These findings underscore the need for clinicians to consult multiple sources and apply clinical judgment when prescribing these medications. Improved integration of pharmacovigilance data into DDI checkers may enhance the identification and prevention of harmful interactions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".