ReviewClinical and translational science2022
Adapting regulatory drug-drug interaction guidance to design clinical pharmacokinetic natural product-drug interaction studies: A NaPDI Center recommended approach.
Review in Clinical and translational science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- Microbiota-mediated mechanisms of natural products in atherosclerosis: focus on metabolic and inflammatory pathways.Frontiers in endocrinology · 2026Review
- A gut response: Application of human enteroid monolayers to probe the mechanism of the goldenseal-mediated inhibition of metformin intestinal absorption.The Journal of pharmacology and experimental therapeutics · 2025Article
- Intrasubject Variability in Intravenous and Oral Probes for Hepatic and First-Pass CYP3A Activity.Clinical pharmacokinetics · 2024Article
- Translating Kratom-Drug Interactions: From Bedside to Bench and Back.Drug metabolism and disposition: the biological fate of chemicals · 2023Review
- Developing a Knowledge Graph for Pharmacokinetic Natural Product-Drug Interactions.Journal of biomedical informatics · 2023Article
- Adapting regulatory drug-drug interaction guidance to design clinical pharmacokinetic natural product-drug interaction studies: A NaPDI Center recommended approach.Clinical and translational science · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Pharmacokinetic drug interactions precipitated by botanical and other natural products (NPs) remain critically understudied. Investigating these complex interactions is fraught with difficulties due to the methodologic and technical challenges associated with the inherently complex chemistries and product variability of NPs. This knowledge gap is perpetuated by a continuing absence of a harmonized framework regarding the design of clinical pharmacokinetic studies of NPs and NP-drug interactions. Accordingly, this Recommended Approach, the fourth in a series of Recommended Approaches released by the Center of Excellence for Natural Product Drug Interaction Research (NaPDI Center), provides recommendations for the design of clinical pharmacokinetic studies involving NPs. Building on prior Recommended Approaches and data generated from the NaPDI Center, such a framework is presented for the design of (1) phase 0 studies to assess the pharmacokinetics of an NP and (2) clinical pharmacokinetic NP-drug interaction studies. Suggestions for NP sourcing, dosing, study design, participant selection, sampling periods, and data analysis are presented. With the intent to begin addressing the gap between regulatory agencies' guidance documents about drug-drug interactions and contemporary NPDI research, the objective of this Recommended Approach is to propose methods for the design of clinical pharmacokinetic studies of NPs and NP-drug interactions.
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What OpenQuestion 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 OpenQuestion graph.