107 Evaluation of the efficacy, safety and utilization of racemic mixtures versus single enantiomers in British Columbia primary care: a comprehensive review
Bibliographic record
Abstract
Objectives The primary objective of this focused literature review was to determine whether clinically significant differences exist in the efficacy and safety of commonly prescribed racemic mixtures compared to their corresponding single enantiomers in British Columbia (BC). We aimed to assess whether the theoretical pharmacological advantages of single enantiomers translate into meaningful clinical benefits in practice. As a secondary objective, we analyzed drug utilization patterns and associated expenditures within primary care settings in BC, with a focus on evaluating the cost differences between single enantiomers and their racemic counterparts. Furthermore, we critically evaluated the credibility of manufacturers’ claims regarding the clinical superiority of single enantiomers. By addressing these objectives, we aimed to provide a comprehensive evaluation of the clinical outcomes associated with racemic mixtures and single enantiomers, ensuring that any additional financial burden placed on patients is justified by meaningful clinical benefit. Method The top 200 most prescribed drugs in BC, from January 1 to May 31, 2024, based on patient count and prescription volume, were reviewed to identify currently marketed racemic mixture and single enantiomer drug pairs. To be eligible, at least one drug had to appear among the top 200 list. A systematic literature search was conducted using MEDLINE, Embase, PubMed, Epistemonikos, and Cochrane CENTRAL from their inception dates to June 2024. We included head-to-head randomized trials comparing the efficacy and safety of equipotent doses of racemic mixtures versus single enantiomers. Studies conducted solely in healthy participants were excluded. Two authors independently screened all search results for eligibility and conducted a critical appraisal of included studies using the Cochrane RoB2 tool. The Fragility Index was calculated for reported dichotomous outcomes. Additionally, we reviewed US FDA and Health Canada drug assessments, BC Ministry of Health expenditure data, and Canadian product monographs. Results After reviewing the top 200 most prescribed drugs in BC, we identified five racemic mixture and single enantiomer pairs: omeprazole/esomeprazole, citalopram/escitalopram, lansoprazole/dexlansoprazole, zopiclone/eszopiclone, and mixed amphetamine salts/dextroamphetamine. 26 studies comparing these pairs at equipotent doses were iden]fied. All studies were assessed to have a high risk of bias. 24 studies reported on efficacy; only seven reported statistically significant differences favouring the enantiomer, though fragility index calculations revealed five were statistically fragile. 24 studies assessed safety; 23 found no difference in adverse events risk, and one favoured the racemic mixture. Manufacturers’ claims regarding enantiomers’ superiority were not supported by robust evidence. Additionally, our assessment of drug expenditures in BC demonstrated that residents incurred substan]ally higher costs for single enantiomer drugs. For instance, in 2024 private payers spent more than $16 million and $14 million on esomeprazole and escitalopram, respectively, compared to their less expensive racemic counterparts. Conclusions Claims regarding the superiority of single enantiomers are primarily based on pharmacodynamic and pharmacokinetic studies, as well as studies comparing drug pairs at non-equivalent doses. Our study indicated that the proposed pharmacological advantages of single enan]omers do not translate into superior clinical efficacy or improved safety profiles. Although our financial assessment exclusively compared drug expenditures in the province of British Columbia, single enantiomer are often considered clinically superior to their racemic mixtures worldwide. It should be noted that the findings of this study could lead to significant cost savings for governments, private payers, and individuals, and could support the development of optimized prescribing plaeorms for primary care providers to promote cost-effective prescribing.
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 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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".