Open-Access Adverse Drug Reaction Databases: Applicability for Safety Assessment of the Developed Drugs (Review)
Bibliographic record
Abstract
INTRODUCTION. Evaluating safety data on already registered drugs is an important stage in medicine development. Databases (DBs) of adverse drug reactions (ADRs) have been created and maintained by regulatory authorities and individual research groups. The data aggregated in such DBs can help create reference and training datasets for experimental and in silico studies. A preliminary assessment and systematisation of information from these databases can facilitate the selection of DBs relevant to a specific study. AIM. This study aimed to assess applicability of public ADR databases for safety studies of medicinal products. DISCUSSION. Eleven public DBs that provide information on ADRs of approved drugs were studied: FAERS, DAEN, MedEffect Canada, EudraVigilance, VigiBase, SIDER, MetaADEDB, ADReCS-Target, T-ARDIS, OnSIDES, and WWAD. The differences between these databases are primarily due to the variety of sources they use: spontaneous reports (FAERS, DAEN, MEDEFFECT, EudraVigilance, VigiBase), patient information leaflets and other official documents (SIDER, OnSIDES, WWAD), scientific publications (ADReCS-Target), and other open-access web resources (MetaADEDB, T-ARDIS). All the reviewed databases can be used for informational support and analysis of drug safety profiles. SIDER, MetaADEDB, ADReCS-Target, OnSIDES, and WWAD are useful in preclinical studies, particularly while developing training sets for in silico methods. Hypotheses for possible ADR mechanisms and search for new drug repurposing vectors can be arranged using ADReCS-Target, WWAD, and T-ARDIS, since these DBs provide additional data on active pharmaceutical substances or target molecules. Using computer-aided methods without thorough hands-on search in the DBs such as ADReCS-Target, T-ARDIS, OnSIDES, and MetaADEDB, limits their applicability in tasks requiring accurately analysed information. CONCLUSIONS. The DBs reviewed can serve as a valuable tool for addressing a wide range of biomedical issues. To select a DB relevant for a specific study, it is important to consider the underlying principles, since varying sources and annotation methods can affect the reliability of results.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.021 | 0.018 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.004 |
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 source (direct Gemma or distilled Codex), 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".