Accelerator Mass Spectrometry in Pharmaceutical Research and Development A New Ultrasensitive Analytical Method for Isotope Measurement
Bibliographic record
Abstract
Accelerator mass spectrometry (AMS) permits the measurement of elemental isotopes at the individual atom level. The main application of AMS in drug discovery and development will be in the analysis of 14-carbon (14C). The principle behind AMS is the separation of individual positively charged atoms through mass, charge and momentum differences. In order to obtain the high-energy charge state required for separation, negative atoms are accelerated through a high voltage field (up to 10 million volts) generated by a tandem Van de Graaff accelerator. In the middle of the accelerator, the outer valency electrons are stripped from the atom and the resulting charged species are separated and counted. For 14C, AMS counts the number of individual atoms rather than measuring radioactive decays. The result is that AMS is up to one million times more sensitive than decay counting. Radioactivity levels as low 0.0001 dpm can be detected using AMS. The exquisite sensitivity of AMS analysis means that much lower amounts of 14C can be used than for conventional counting methods. This makes it easier to use 14C for in vitro, preclinical and clinical research programmes. As 14C poses both a biological and environmental hazard, AMS permits much lower doses to be used. Human drug mass balance studies have been conducted with doses of 50 nanoCuries and below. Radioactive HPLC metabolite profiles of plasma extracts from subjects given nanoCurie doses of 14C-labelled drug have been obtained by injecting as little as 0.25 dpm onto an HPLC column. In studies of biologics, biosynthetically 14C-labelled recombinant protein has been produced with a specific radioactivity sufficient to conduct human clinical studies with AMS analysis. For one human recombinant protein an increase in sensitivity of 2,000-fold over ELISA was obtained with AMS measurement. AMS is an enabling technology that should prove of value in increasing human and environmental safety as well as allowing new research directions to be followed.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.008 |
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".