The emerging role of in vitro electrophysiological methods in CNS safety pharmacology
Bibliographic record
Abstract
Adverse CNS effects account for a sizeable proportion of all drug attrition cases. These adverse CNS effects are mediated predominately by off-target drug activity on neuronal ion-channels, receptors, transporters and enzymes - altering neuronal function and network communication. In response to these concerns, there is growing support within the pharmaceutical industry for the requirement to perform more comprehensive CNS safety testing prior to first-in-human trials. Accordingly, CNS safety pharmacology commonly integrates several in vitro assay methods for screening neuronal targets in order to properly assess therapeutic safety. One essential assay method is the in vitro electrophysiological technique - the 'gold standard' ion channel assay. The in vitro electrophysiological method is a useful technique, amenable to a variety of different tissues and cell configurations, capable of assessing minute changes in ion channel activity from the level of a single receptor to a complex neuronal network. Recent advances in automated technology have further expanded the usefulness of in vitro electrophysiological methods into the realm of high-throughput, addressing the bottleneck imposed by the manual conduct of the technique. However, despite a large range of applications, manual and automated in vitro electrophysiological techniques have had a slow penetrance into the field of safety pharmacology. Nevertheless, developments in throughput capabilities and in vivo applicability have led to a renewed interest in in vitro electrophysiological techniques that, when complimented by more traditional safety pharmacology methods, often increase the preclinical predictability of potential CNS liabilities.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.005 | 0.003 |
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".