An HPLC-UV validated bioanalytical method for measurement of <i>in vitro</i> phase 1 kinetics of α-synuclein binding bifunctional compounds
Bibliographic record
Abstract
Aggregates of the protein α-synuclein are associated with pathophysiology of Parkinson’s disease and are present in Lewy Bodies found in the brains of Parkinson’s patients. We previously demonstrated that bifunctional compounds composed of caffeine linked via a 6 carbon chain to either 1-aminoindan (C8-6-I) or nicotine (C8-6-N) bind α-synuclein and protect yeast cells from α-synuclein mediated toxicity.A critical step in development of Positron Emission Tomography (PET) probes for neurodegenerative diseases is evaluation of their metabolic stability. We determined that C8-6-I, and C8-6-N both undergo phase 1 P450 metabolism in mouse, rat, and human liver microsomes. We utilized this metabolic information to guide the design of fluorinated analogs for use as PET probes and determined that the fluorine in 19F-C8-6-I and 19F-C8-6-N is stable to P450 enzymes.We have developed and validated an analytical HPLC-UV method following FDA and EMA guidelines to measure in vitro Phase 1 kinetics of these compounds and determine their Vmax, KM and CLint,u in mouse liver microsomes. We found that C8-6-I and 19F-C8-6-I have a 2 - 4 fold lower CLint,u than C8-6-N, and 19F-C8-6-N. Our approach shows a simple, specific, and effective step by step system to design and develop compounds as PET probes.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".