Different pharmacokinetics of lithium orotate inform why it is more potent, effective, and less toxic than lithium carbonate in a mouse model of mania
Bibliographic record
Abstract
Lithium carbonate (LiCO) is a mainstay therapeutic for the prevention of mood-episode recurrences in bipolar disorder (BD). Unfortunately, its narrow therapeutic index is associated with complications that may lead to treatment non-compliance. Intriguingly, lithium orotate (LiOr) is suggested to possess unique uptake characteristics that would allow for reduced dosing and mitigation of toxicity concerns. We hypothesized that due to differences in pharmacokinetics, LiOr is more potent with reduced adverse effects. Dose responses were established for LiOr and LiCO in male and female mice using an amphetamine-induced hyperlocomotion (AIH) model; AIH captures manic elements of BD and is sensitive to a dose-dependent lithium blockade. LiCO induced a partial block of AIH at doses of 15 mg/kg in males and 20 mg/kg in females. In contrast, LiOr elicited a near complete blockade at concentrations of just 1.5 mg/kg in both sexes, indicating improved efficacy and potency. Prior application of organic anion transport inhibitors, or inhibition of orotate uptake into the pentose pathway, completely blocked the effects of LiOr on AIH while sparing LiCO effects, confirming differences in transport and compartmentalization between the two compounds. Next, the relative toxicities of LiOr and LiCO were contrasted after 14 consecutive daily administrations. LiCO, but not LiOr, elicited polydipsia in both sexes, elevated serum creatinine levels in males, and increased serum TSH expression in females. LiOr demonstrates superior efficacy, potency, and tolerability to LiCO in both male and female mice because of select transport-mediated uptake and pentose pathway incorporation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".