Safety and Efficacy Concerns of Lopinavir/Ritonavir in COVID-19 Affected Patients: A Retrospective Series
Bibliographic record
Abstract
Abstract Context Originally developed for the treatment of human immunodeficiency virus (HIV), the antiviral combination lopinavir/ritonavir (LPV/r) is being investigated for use against coronavirus disease (COVID-19). We present a case series raising safety and efficacy concerns in COVID-19 affected patients. Methods We measured LPV trough concentrations in 12 patients treated at our center and reviewed their clinical charts for side effects known to occur in HIV patients. Results Compared to established LPV trough concentrations in HIV treated patients, concentrations in COVID-19 affected patients were 3-fold greater (20.64 +/- 10.14 mcg/mL versus 6.25 mcg/mL). In addition, cholestasis and dyslipidemia toxicity thresholds were exceeded in 12/12 and 11/12 patients respectively. No patients achieved the presumed therapeutic concentration. The side effects noted were mainly gastrointestinal symptoms (5/12, 42%), electrolytes imbalances (4/12, 33%), liver enzyme disturbances (5/12, 42%), and triglyceride elevations (2/12, 17%). Conclusion None of our patients reached presumed therapeutic LPV concentrations despite experiencing side effects and exceeding cholestasis and dyslipidemia toxicity thresholds. This raises concerns for the safety and efficacy of LPV/r. Clinicians should consider closely monitoring for side effects and not necessarily attribute them to COVID-19 itself.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".