Considerations and Pitfalls in Selecting the Drug Vehicles for Evaluation of New Drug Candidates: Focus on in vivo Pharmaco-Toxicological Assays Based on the Rotarod Performance Test
Bibliographic record
Abstract
Purpose - During the discovery and development of new drugs, compounds with low aqueous solubility pose special challenges in their pharmacological evaluation and, therefore, the selection of appropriate vehicles to administer the compounds of interest is determinant for the quality of the results generated during the in vivo non-clinical studies. This work aimed to evaluate the motor deficit (as a surrogate of neurotoxicity) of several administration/delivery vehicles through the rotarod performance test. Methods - Trained male CD-1 mice were intraperitoneally administered with the following vehicles: dimethyl sulfoxide (DMSO), aqueous sodium chloride (NaCl) 0.9%, aqueous carboxymethylcellulose (CMC) 0.5%, polyethylene glycol (PEG)-400, propylene glycol (PG), and solutions of these vehicles containing 5% and 10% DMSO. Results - It was observed that the aqueous vehicles (NaCl 0.9% and CMC 0.5%) did not affect the performance of the animals on the rod. On the other hand, a vehicle consisting solely of DMSO led to significant motor impairment and only a small improvement was recorded over time. Additionally, a strong neuromotor toxicity was observed in the early evaluation points of the experiment using vehicles constituted by PG and PEG-400 or by mixtures of PG/DMSO (5% and 10%) and PEG-400/DMSO (5% and 10%). Conclusion - This study provides useful data about the neurotoxicity inherent to several vehicles frequently used in non-clinical pharmaco-toxicological assays, aiming to draw especial attention to the need of a careful selection of drug vehicles in order to avoid the impact of such confounding variables on the accuracy of the results and in decision-making processes. This article is open to POST-PUBLICATION REVIEW. Registered readers (see "For Readers") may comment by clicking on ABSTRACT on the issue's contents page.
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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 | 0.008 | 0.002 |
| 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 | 0.001 |
| 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".