Compound adsorption device in pathogen reduction technology removes multiple drugs from plasma components
Bibliographic record
Abstract
BACKGROUND: Blood donor eligibility policies may exclude individuals based on medication use, but the donor health questionnaire may not reliably capture all prescriptions. This study evaluates the capacity of the compound adsorption device (CAD) within the INTERCEPT Blood System Pathogen Reduction Technology (PRT) for plasma to remove exogenous testosterone and high doses of two drugs on the FDA donor deferral list. STUDY DESIGN AND METHODS: Plasma was manufactured from male whole blood donors undergoing testosterone replacement therapy (TRT) or whole blood donors with no medication history to be supplemented with supratherapeutic concentrations of apixaban, a common anticoagulant, and escalating doses of emtricitabine and tenofovir (FTC&TFV) to note any saturating effects of removal. Plasma units were treated with INTERCEPT PRT only using the amotosalen and CAD steps without photochemical activation. Quantitative assays analyzed pre- and post-CAD samples. Statistical analysis was performed using a Wilcoxon signed-rank test and one-way ANOVA. RESULTS: The CAD reduced the mean free and total testosterone by 75% (pre-CAD 107.4, post-CAD 26.8 pg/mL) and 74% (pre-CAD 352.2, post-CAD 85.7 ng/mL), respectively. Apixaban was removed below the limit of detection (pre-CAD mean 420, post-CAD mean <23 ng/mL). Regardless of the initial dose, FTC&TFV concentrations decreased by 100% and 98%, respectively. CONCLUSION: The INTERCEPT PRT plasma kit CAD effectively removed multiple drug classes, suggesting a potential role in mitigating drug contamination in transfusable plasma products. These findings may support the reconsideration of some medication-based donor deferrals where the risk-benefit of donor safety and product efficacy favors collection.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".