ArticleMedical devices (Auckland, N.Z.)2018
Development of an advanced injection time model for an autoinjector.
Article in Medical devices (Auckland, N.Z.), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
What it found
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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
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic Review of Device Parameters and Design of Studies Bridging Biologic-Device Combination Products Using Prefilled Syringes and Autoinjectors.The AAPS journal · 2020Pooled it
- A modeling framework for spring-driven autoinjectors with dual-chamber cartridges.Drug delivery and translational research · 2026Article
- Clinical Qualification of Subcutaneous Injection Devices for Monoclonal Antibodies.BioDrugs : clinical immunotherapeutics, biopharmaceuticals and gene therapy · 2026Review
- Microfluidic Delivery of High Viscosity Liquids Using Piezoelectric Micropumps for Subcutaneous Drug Infusion Applications.IEEE open journal of engineering in medicine and biology · 2024Article
- Framework for creating a qualified medical device development tool of autoinjectors.Frontiers in medical technology · 2023Article
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis work describes an advanced physics-based mathematical model that accurately predicts autoinjector injection time. Autoinjectors are a well-established technology for parenteral drug delivery and quantifying the probability to achieve a given injection time is critical to the successful development and commercial launch of the autoinjector.
methodEach parameter that can influence injection time was treated as a statistical variable with an appropriate distribution function. Monte Carlo simulation was used to obtain the probability of achieving the required injection time. Sensitivity analyses were performed to identify those parameters most critical in contributing to the overall injection time. To validate the model, a number of experiments were conducted on autoinjectors, with key contributors to injection time measured and characterized.
resultsThe results showed excellent agreement between modeled and measured injection time. The modeling error for all investigated device configurations was smaller than 12% and the error range was less than 6%. The consistent over-estimation of injection time suggests a small bias in the model which could be accounted for by reducing internal friction.
conclusionThis work provides evidence that the selected modeling approach, which aims for a simple yet computationally inexpensive model, is accurate and enables running comprehensive statistical simulations to determine the full range of expected injection times due to component variability.
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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.