Translation of nondopaminergic treatments for levodopa‐induced dyskinesia from MPTP‐lesioned nonhuman primates to phase IIa clinical studies: Keys to success and roads to failure
Bibliographic record
Abstract
Studies in MPTP-lesioned nonhuman primates have demonstrated the potential of nondopaminergic drugs in reducing the problems of levodopa-induced dyskinesia (LID). Here we review the process of translating findings from the monkey to man. Agents targeting glutamate, adensosine, noradrenaline, 5-hydroxytryptamine, cannabinoid, and opioid transmitter systems have been assessed for antidyskinetic potential in human studies. Eleven nondopaminergic drugs with antidyskinetic efficacy in the MPTP primate have been advanced to proof-of-concept phase IIa trials in PD patients (amantadine, istradefylline, idazoxan, fipamezole, sarizotan, quetiapine, clozapine, nabilone, rimonabant, naloxone, and naltrexone). For all six nondopaminergic transmitter systems reviewed, the MPTP-lesioned primate correctly predicted phase II efficacy of at least one drug. Of the 11 specific molecules tested in both monkeys and humans, 8 showed clear antidyskinetic properties in both human and monkey. In the instances where the primate studies did not, or did not consistently, predict the outcome of the human studies, the discrepancy may reflect limitations in the validity of the model or limitations in the design of either the clinical or the preclinical studies. We find that the major determinant of success in predicting efficacy is to ensure that primate studies are conducted in a statistically rigorous way and incorporate designs and outcome measures with clinical applicability. On the other hand, phase IIa trials should strive to replicate the preclinical study, especially in terms of protocol, drug dose equivalence, and outcome measure, so as to test the same hypothesis. Failure to meet these criteria carries the risk of false negative conclusions in phase IIa trials.
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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 | 0.020 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".