High Throughput Screening Method for Systematic Surveillance of Drugs of Abuse by Multisegment Injection–Capillary Electrophoresis–Mass Spectrometry
Bibliographic record
Abstract
New technologies are urgently required for reliable drug screening given a worldwide epidemic of prescription drug abuse and its devastating socioeconomic impacts on public health. Primary screening of drugs of abuse (DoA) currently relies on immunoassays that are prone to bias and are not applicable to detect an alarming array of psychoactive stimulants, tranquilizers, and synthetic opioids. These limitations impact patient safety when monitoring for medication compliance, drug substitution, or misuse/abuse and require follow-up confirmatory testing by more specific yet lower throughput instrumental methods. Herein, we introduce a high throughput platform for nontargeted screening of a broad spectrum of DoA and their metabolites based on multisegment injection-capillary electrophoresis-mass spectrometry (MSI-CE-MS). We demonstrate that MSI-CE-MS enables serial injections of 10 samples within a single run (<3 min/sample) where multiplexed electrophoretic separations are coupled to high resolution MS with full-scan data acquisition. Unambiguous drug identification was achieved by four or more independent parameters, including comigration with a deuterated internal standard or in silico prediction of electromigration behavior together with accurate mass, most likely molecular formula, as well as MS/MS as required for confirmation testing. Acceptable precision was demonstrated for over 50 DoA at 3 concentration levels over 4 days (median coefficient of variance = 13%, n = 117) with minimal ion suppression, isobaric interferences, and sample carry-over (<1%). This approach offers a rapid yet accurate method for simultaneous detection and identification of DoA at their recommended screening cutoff levels in human urine while allowing for systematic surveillance, specimen verification, and retrospective testing of designer drugs that elude conventional drug tests.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".