Relevance of assessing drug concentration exposure in pharmacogenetic and imaging studies
Bibliographic record
Abstract
Pharmacodynamic differences are difficult to interpret without drug concentration data. In particular, variability in drug exposure may confound the interpretation of pharmacogenetic, therapeutic outcome, and neuroimaging studies. Inter-individual variability in concentrations can be quite high due to variable adherence and pharmacokinetics. For example, clearance may be influenced by genetics, drug interactions, age and illness. We review findings that acute responses to selective serotonin reuptake inhibitors can have a concentration-response relationship using positron emission tomography and neuroendocrine measures. We also present preliminary evidence that the concentration-response relationship for paroxetine is influenced by genotypic differences at the serotonin transporter promoter. In large clinical studies, the accurate assessment of drug exposure can be challenging, with several techniques used to assess exposure. Population pharmacokinetics (Pop PK) is a method that is ideally suited for analysing concentration data from large trials because both patient-specific and population parameters can be determined with only a small number of plasma samples per patient. As opposed to relying on prescribed doses or a single trough level, the ability to determine more accurately exposure with Pop PK reduces the heterogeneity introduced by exposure variability. Pop PK hierarchic Bayesian approaches have been effective for characterizing anticonvulsants, antibiotics, antineoplastics and antiarrhythmics. We have recently successfully incorporated these pop PK analyses into routine assessments of elderly patients in clinical trials of selective serotonin reuptake inhibitors (SSRIs) and second generation antipsychotics. For the design and interpretation of neuroimaging, pharmacogenetic, and behavioural studies, the assessment of drug concentration exposure is therefore feasible and has potentially important ramifications.
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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 | 0.216 | 0.451 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".