Update and trends on pharmacokinetic studies in patients with impaired renal function: practical insight into application of the FDA and EMA guidelines
Bibliographic record
Abstract
INTRODUCTION: The incidence of kidney dysfunction increases with age and is highly prevalent among patients with hypertension. Since many therapeutic compounds are primarily eliminated through the kidneys, impaired renal function can have negative consequences on drug disposition, efficacy and safety. Therefore, regulatory agencies such as the Food and Drug Administration (FDA) and European Medicines Agency (EMA) have issued detailed guidelines for new drug applications to determine posology requirements for patients with renal impairment. Areas covered: The current review highlights and contrasts agency requirements for pharmacokinetic renal impairment clinical studies. While many of the guidelines are similar among the two agencies, glomerular filtration rate (GFR) determination and reporting differ. Design considerations for a reduced, full or dialysis renal impairment study, as well as modifications to the FDA's draft guidance are discussed. Furthermore, scenarios where pharmacokinetic modelling analysis can benefit a drug development program are also reviewed. Moreover, practical solutions for patient recruitment challenges are addressed. Expert commentary: We summarize how 'one size does not fit all' for GFR assessment, and recommend when to use certain modalities. Finally, we highlight the need for the pharmaceutical industry to engage therapeutic experts to assist in protocol development for renal impairment studies, as these experts understand the nuances of this special population and recommended guidelines.
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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 | 0.019 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.005 | 0.007 |
| 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".