ReviewCPT: pharmacometrics & systems pharmacology2019
In Silico Methods for Development of Generic Drug-Device Combination Orally Inhaled Drug Products.
Review in CPT: pharmacometrics & systems pharmacology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed, 35 citations in OpenAlex.
- Exploring the challenges faced by generic version of complex drugs: a scoping review.Systematic reviews · 2025Article
- Impact of Mechanistic Modeling and Simulation Methodologies on Product-Specific Guidance Development for Non-Orally Administered Drug Products.CPT: pharmacometrics & systems pharmacology · 2025Review
- Lung Deposition of Extrafine Versus Nonextrafine Aerosols at Low Inhalation Flow Rates in Adult Asthma Patients: A Composition Study.Journal of aerosol medicine and pulmonary drug delivery · 2025Article
- A Critical Analysis of the CFD-DEM Simulation of Pharmaceutical Aerosols Deposition in Upper Intra-Thoracic Airways: Considerations on Aerosol Transport and Deposition.Pharmaceutics · 2024Article
- Application of Advanced Modeling Approaches Supporting Generic Product Development Under GDUFA for Fiscal Year 2023.The AAPS journal · 2024Article
- Increasing impact of quantitative methods and modeling in establishment of bioequivalence and characterization of drug delivery.CPT: pharmacometrics & systems pharmacology · 2023Article
- Regulatory utility of mechanistic modeling to support alternative bioequivalence approaches: A workshop overview.CPT: pharmacometrics & systems pharmacology · 2023Review
- Mechanistic modeling of generic orally inhaled drug products: A workshop summary report.CPT: pharmacometrics & systems pharmacology · 2023Review
- A multiplex inhalation platform to modelFrontiers in pharmacology · 2023Article
- Combined in Vitro-in Silico Approach to Predict Deposition and Pharmacokinetics of Budesonide Dry Powder Inhalers.Pharmaceutical research · 2020Article
- American Society for Clinical Pharmacology and Therapeutics 2019 Annual Meeting Pre-Conferences.CPT: pharmacometrics & systems pharmacology · 2019Article
- Lung deposition of inhaled once-daily long-acting muscarinic antagonistsTherapeutic advances in respiratory diseaseArticle
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
The development of generic, single-entity, drug-device combination products for orally inhaled drug products is challenging in part because of the complex nature of device design characteristics and the difficulties associated with establishing bioequivalence for a locally acting drug product delivered to the site of action in the lung. This review examines in silico models that may be used to support the development of generic orally inhaled drug products and how model credibility may be assessed.
Indexed as
Identifiers
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.