Humanization of Drug Metabolism in the <i>Plasmodium berghei</i> Mouse Model for Antimalarial Drug Discovery
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Resistance to artemisinin-based combination therapies (ACTs) is steadily increasing in malaria-endemic countries, and new medicines to treat this disease are urgently needed. Drug discovery efforts are hindered by species differences in drug metabolism as new chemical entities must survive metabolism by diverse enzymes across multiple species, enabling cures in preclinical disease models before progression to the clinic. Here, we show how the use of a mouse line extensively genetically humanized for enzymes of the cytochrome P450 superfamily and their transcriptional regulators, the “8HUM” line, can circumvent this issue and improve the translational accuracy of data generated. Engraftment of human erythrocytes into 8HUM/Rag2 –/–, an immunocompromised version of the 8HUM line lacking mature T and B cells, was insufficient to permit infection with Plasmodium falciparum, and depletion of natural killer cells by antibody treatment did not alter this outcome. However, infection of 8HUM with Plasmodium berghei permitted assessment of drug efficacy against this Plasmodium species. Approved antimalarials were generally more metabolically stable in 8HUM than in wild-type mice. Major species differences between humans and mice in routes of metabolic elimination for quinine derivatives were removed with 8HUM. Therefore, the 8HUM P. berghei model described here will be of value early in the critical path for antimalarial drug discovery, improving alignment of drug metabolism with the clinical situation while bypassing mouse-specific issues of metabolism to facilitate proof-of-concept in vivo demonstration of efficacy, a key requirement for validation of new drug targets and chemical series.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".