Pharmacokinetics, Tissue Distribution, and Formulation Study of a Small-molecule Inhibitor of MKLP2, LG157
Bibliographic record
Abstract
LG157 is a recently identified small-molecule inhibitor of mitotic kinesin-like protein 2 (MKLP2), an overlooked oncology target. This study aims to explore the drug developability of LG157, by assessing its druglike properties, determining plasma drug exposure in various oral formulations, and exploring the self-emulsifying drug delivery system (SEDDS). Solubility of LG157 ranges from 175 to 228 μM across pH 1.0 to 13.0, with a LogD of 2.41 at pH 7.4. It showed a high protein binding rate of 92.58% in mouse plasma and 90.30% in human serum. The bioavailability radar plot aligns with experimental data (69-85%), indicating good bioavailability. In line with the computation prediction, preclinical formulation studies in mice reveal that all five formulations tested offer decent plasma LG157 exposure, with the highest level of LG157 exposure in the PEG300-based formulation. Subsequent tissue distribution studies in rats indicated that the compound is widely distributed with the highest concentration of LG157 in the liver and the lowest level in the brain. The optimal SEDDS formulation, SEDDS-F14, consists of 65% Oleic acid, 26.25% Tween 20, and 8.75% PEG400 as oil, surfactant, and co-surfactant, respectively. SEDDS optimization, based on the central composite design, has achieved the maximum loading of 188.7 mg/mL for LG157. These findings support the developability of LG157 and encourage continued exploration and refinement of formulations for improved therapeutic efficacy.
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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) | 0.000 | 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) | 0.001 | 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".