SynthesisDaru : journal of Faculty of Pharmacy, Tehran University of Medical Sciences2022
Assessment of the predictive capability of modelling and simulation to determine bioequivalence of inhaled drugs: A systematic review.
Synthesis in Daru : journal of Faculty of Pharmacy, Tehran University of Medical Sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed, 3 citations in OpenAlex.
- Understanding Pulmonary and Systemic Pharmacokinetics of Inhaled Fluticasone Propionate in Healthy Subjects Using Population PK Analysis.European journal of drug metabolism and pharmacokinetics · 2026Trial
- Prediction of site-specific drug deposition via dry powder inhaler using non-invasive real-time particle emission signal monitoring system.Frontiers in pharmacology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThere are a multitude of different modelling techniques that have been used for inhaled drugs. The main objective of this review was to conduct an exhaustive survey of published mathematical models in the area of asthma and chronic obstructive pulmonary disease (COPD) for inhalation drugs. Additionally, this review will attempt to assess the applicability of these models to assess bioequivalence (BE) of orally inhaled products (OIPs). EVIDENCE ACQUISITION: PubMed, Science Direct, Web of Science, and Scopus databases were searched from 1996 to 2020, to find studies that described mathematical models used for inhaled drugs in asthma/COPD.
results50 articles were finally included in this systematic review. This research identified 22 articles on in silico aerosol deposition models, 20 articles related to population pharmacokinetics and 8 articles on physiologically based pharmacokinetic modelling (PBPK) modelling for inhaled drugs in asthma/COPD. Among all the aerosol deposition models, computational fluid dynamics (CFD) simulations are more likely to predict regional aerosol deposition pattern in human respiratory tracts. Across the population PK articles, body weight, gender, age and smoking status were the most common covariates that were found to be significant. Further, limited published PBPK models reported approximately 29 parameters relevant for absorption and distribution of inhaled drugs. The strengths and weaknesses of each modelling technique has also been reviewed.
conclusionOverall, while there are different modelling techniques that have been used for inhaled drugs in asthma and COPD, there is very limited application of these models for assessment of bioequivalence of OIPs. This review also provides a ready reference of various parameters that have been considered in various models which will aid in evaluation if one model or hybrid in silico models need to be considered when assessing bioequivalence of OIPs.
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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.