Tandem mass spectrometric analysis of novel caffeine scaffold‐based bifunctional compounds for Parkinson's disease
Bibliographic record
Abstract
Rationale Novel bifunctional compounds composed of a caffeine scaffold attached to nicotine (C8‐6‐N), 1‐aminoindan (C8‐6‐I), or caffeine (C8‐6‐C8) were designed as therapeutics or diagnostics for Parkinson's disease (PD). In order to probe their pharmacological and toxicological profile, an appropriate analytical method is required. The goal of this study is to establish a tandem mass spectrometric fingerprint for the development of quantitative and qualitative methods that will aid future assessment of the in vitro and in vivo absorption, distribution, metabolism, excretion (ADME) and pharmacokinetic properties of these lead bifunctional compounds for PD. Methods Accurate mass measurement was performed using a hybrid quadrupole orthogonal time‐of‐flight mass spectrometer while multistage MS/MS and MS3 analyses were conducted using a triple quadrupole linear ion trap mass spectrometer. Both instruments are equipped with an electrospray ionization (ESI) source and were operated in the positive ion mode. The source and compound parameters were optimized for all three tested bifunctional compounds. Results The MS/MS analysis indicates that the fragmentation of C8‐6‐N and C8‐6‐I is driven by the dissociation of the nicotine and 1‐aminoindan moieties, respectively, but not caffeine. A significant observation in the MS/MS fragmentation of C8‐6‐C8 suggests that a previously reported loss of acetaldehyde during caffeine dissociation is instead a loss of CO2. Conclusions The collision‐induced tandem mass spectrometry (CID‐MS/MS) analysis of these novel bifunctional compounds revealed compound‐specific diagnostic product ions and neutral losses for all three tested bifunctional compounds. The established MS/MS fingerprint will be applied to the future development of qualitative and quantitative methods.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".