Genotype-dependence of gabapentin and pregabalin sensitivity: the pharmacogenetic mediation of analgesia is specific to the type of pain being inhibited
Bibliographic record
Abstract
The antiepileptic drug, gabapentin, and another structurally related compound, pregabalin, are increasingly employed in the pharmacotherapy of chronic pain states, although their primary mechanism of action remains a topic of active study. A genomic approach to the study of these drugs may elucidate their potentially novel mechanisms. We examined the heritability of sensitivity to analgesia from gabapentin and pregabalin as a precursor to linkage mapping efforts. Accordingly, 11 inbred mouse strains were tested for inhibition of nociception by gabapentin or pregabalin (50-300 mg/kg, i.p.) in two different preclinical assays of inflammatory pain, the formalin test (5% formalin; 20 microl) and zymosan thermal hyperalgesia on the paw-withdrawal test (3 mg/ml zymosan; 20 microl). Significant strain-dependence of drug action was noted in each case, indicating that sensitivity to these analgesics is heritable. Furthermore, the pattern of strain sensitivities to gabapentin and pregabalin were mostly similar, supporting the notion that they act via similar genetic and physiological mechanisms. However, there was virtually no correlation between strain sensitivities to pregabalin inhibition of formalin nociception and zymosan thermal hyperalgesia. In light of previous data from our laboratory and others regarding morphine analgesia, we now establish and empirically demonstrate the general principle that pharmacogenetic mechanisms underlying analgesic sensitivity are specific to the type of pain being inhibited. This has considerable implications for ongoing pharmacogenetic investigations and, more generally, for the choices of preclinical models of pain used in drug development.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".