ReviewJournal of pharmacokinetics and pharmacodynamics2016
Methods and strategies for assessing uncontrolled drug-drug interactions in population pharmacokinetic analyses: results from the International Society of Pharmacometrics (ISOP) Working Group.
Review in Journal of pharmacokinetics and pharmacodynamics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 27 citations in OpenAlex.
- Article
- Comparing the applications of machine learning, PBPK, and population pharmacokinetic models in pharmacokinetic drug-drug interaction prediction.CPT: pharmacometrics & systems pharmacology · 2022Review
- Leveraging a Previously Published Population Pharmacokinetic Model to Predict Rivaroxaban Exposure in Real-World Patients.Journal of clinical pharmacology · 2022Article
- Pediatric Drug-Drug Interaction Evaluation: Drug, Patient Population, and Methodological Considerations.Journal of clinical pharmacology · 2021Review
- Capturing the Magic Bullet: Pharmacokinetic Principles and Modeling of Antibody-Drug Conjugates.The AAPS journal · 2020Review
- The guard changes.Journal of pharmacokinetics and pharmacodynamics · 2020Article
- Pharmacological plasticity-How do you hit a moving target?Pharmacology research & perspectives · 2019Review
- Clinical Studies on Drug-Drug Interactions Involving Metabolism and Transport: Methodology, Pitfalls, and Interpretation.Clinical pharmacology and therapeutics · 2019Review
- Increased Exposure of Tacrolimus by Co-administered Mycophenolate Mofetil: Population Pharmacokinetic Analysis in Healthy Volunteers.Scientific reports · 2018Article
- Population Pharmacokinetic Analysis of Ixazomib, an Oral Proteasome Inhibitor, Including Data from the Phase III TOURMALINE-MM1 Study to Inform Labelling.Clinical pharmacokinetics · 2017Article
- Combining "Bottom-up" and "Top-down" Approaches to Assess the Impact of Food and Gastric pH on Pictilisib (GDC-0941) Pharmacokinetics.CPT: pharmacometrics & systems pharmacology · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 8 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The purpose of this work was to present a consolidated set of guidelines for the analysis of uncontrolled concomitant medications (ConMed) as a covariate and potential perpetrator in population pharmacokinetic (PopPK) analyses. This white paper is the result of an industry-academia-regulatory collaboration. It is the recommendation of the working group that greater focus be given to the analysis of uncontrolled ConMeds as part of a PopPK analysis of Phase 2/3 data to ensure that the resulting outcome in the PopPK analysis can be viewed as reliable. Other recommendations include: (1) collection of start and stop date and clock time, as well as dose and frequency, in Case Report Forms regarding ConMed administration schedule; (2) prespecification of goals and the methods of analysis, (3) consideration of alternate models, other than the binary covariate model, that might more fully characterize the interaction between perpetrator and victim drug, (4) analysts should consider whether the sample size, not the percent of subjects taking a ConMed, is sufficient to detect a ConMed effect if one is present and to consider the correlation with other covariates when the analysis is conducted, (5) grouping of ConMeds should be based on mechanism (e.g., PGP-inhibitor) and not drug class (e.g., beta-blocker), and (6) when reporting the results in a publication, all details related to the ConMed analysis should be presented allowing the reader to understand the methods and be able to appropriately interpret the results.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.