Evidence map›Paper›PMID 38862720›Full record

ArticlePharmaceutical research2024

Laplace Transform Based Modeling of Drug Delivery with Reversible Drug Binding in a Multilayer Tissue.

Ankur Jain, Giuseppe Pontrelli, Sean McGinty

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Article in Pharmaceutical research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Ankur JainMechanical and Aerospace Engineering Department, University of Texas at Arlington, 500 W First St, Rm 211, Arlington, TX, 76019, USA. jaina@uta.edu.
Giuseppe PontrelliIstituto per le Applicazioni del Calcolo - CNR, Via dei Taurini 19, 00185, Rome, Italy.
Sean McGintyDivision of Biomedical Engineering, University of Glasgow, Glasgow, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveDrug delivery from a drug-loaded device into an adjacent tissue is a complicated process involving drug transport through diffusion and advection, coupled with drug binding kinetics responsible for drug uptake in the tissue. This work presents a theoretical model to predict drug delivery from a device into a multilayer tissue, assuming linear reversible drug binding in the tissue layers.

methodsThe governing mass conservation equations based on diffusion, advection and drug binding in a multilayer cylindrical geometry are written, and solved using Laplace transformation. The model is used to understand the impact of various non-dimensional parameters on the amounts of bound and unbound drug concentrations as functions of time.

resultsGood agreement for special cases considered in past work is demonstrated. The effect of forward and reverse binding reaction rates on the multilayer drug binding process is studied in detail. The effect of the nature of the external boundary condition on drug binding and drug loss is also studied. For typical parameter values, results indicate that only a small fraction of drug delivered binds in the tissue. Additionally, the amount of bound drug rises rapidly with time due to early dominance of the forward reaction, reaches a maxima and then decays due to the reverse reaction.

conclusionsThe general model presented here can account for other possible effects such as drug absorption within the device. Besides generalizing past work on drug delivery modeling, this work also offers analytical tools to understand and optimize practical drug delivery devices.

Indexed as

Drug Delivery SystemsModels, BiologicalBiological TransportDiffusionHumansKineticsPharmaceutical PreparationsPharmaceutical Preparationsdrug deliveryLaplace transformationlinear reversible drug bindingtheoretical modeling

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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.