Evidence map›Paper›PMID 36297338›Full record

ArticlePharmaceuticals (Basel, Switzerland)2022

Reservoir-Style Polymeric Drug Delivery Systems: Empirical and Predictive Models for Implant Design.

Linying Li, Chanhwa Lee, Daniela F Cruz, Sai Archana Krovi, Michael G Hudgens, Mackenzie L Cottrell, Leah M Johnson

Abstract read
In one paragraph

Article in Pharmaceuticals (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
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

7 authors.

Linying LiRTI International, 3040 E Cornwallis Road, Research Triangle Park, NC 27709, USA.
Chanhwa LeeDepartment of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27599, USA.ORCID 0000-0003-4828-9416
Daniela F CruzRTI International, 3040 E Cornwallis Road, Research Triangle Park, NC 27709, USA.ORCID 0000-0001-5701-1868
Sai Archana KroviRTI International, 3040 E Cornwallis Road, Research Triangle Park, NC 27709, USA.ORCID 0000-0003-1546-9955
Michael G HudgensDepartment of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27599, USA.
Mackenzie L CottrellDivision of Pharmacotherapy and Experimental Therapeutics, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA.
Leah M JohnsonRTI International, 3040 E Cornwallis Road, Research Triangle Park, NC 27709, USA.

Funding

Virology, Immunology, and Microbiology CoreP30AI050410 · NIAID · UNIV OF NORTH CAROLINA CHAPEL HILL · PI DAVID M. MARGOLIS · 2001 to 2026
$76.9M
Acceptable Multi-user Biodegradable Extended Release (AMBER) Implant System for HIV PreventionR01AI154549 · NIAID · RESEARCH TRIANGLE INSTITUTE · PI JOHNSON, LEAH · 2020 to 2024
$4.8M
LA HIV Treatment in PediatricsR33AI149499 · NIAID · RESEARCH TRIANGLE INSTITUTE · PI COTTRELL, MACKENZIE, JOHNSON, LEAH · 2022 to 2024
$4.3M
Long-Acting Prevention Implantable System (LAPIS) for HIV Pre-ExposureProphylaxis (PrEP)R01AI152713 · NIAID · RESEARCH TRIANGLE INSTITUTE · PI JOHNSON, LEAH · 2020 to 2023
$2.9M
LA HIV Treatment in PediatricsR61AI149499 · NIAID · RESEARCH TRIANGLE INSTITUTE · PI COTTRELL, MACKENZIE, JOHNSON, LEAH · 2020 to 2021
$2.0M
National Institute of Health R61AI149499/4R33AI149499NIAID NIH HHS 1R01AI154549-01NIAID NIH HHS P30 AI050410NIAID NIH HHS R01 AI152713NIAID NIH HHS R01AI152713NIAID NIH HHS R01 AI154549NIAID NIH HHS R33 AI149499NIAID NIH HHS R61 AI149499United States Agency for International Development AID-OAA-A-14-00012United States Agency for International Development AID-OAA-A-17-00011
6 · The paper itself

Abstract

Controlled drug delivery systems can provide sustained release profiles, favorable pharmacokinetics, and improved patient adherence. Here, a reservoir-style implant comprising a biodegradable polymer, poly(ε-caprolactone) (PCL), was developed to deliver drugs subcutaneously. This work addresses a key challenge when designing these implantable drug delivery systems, namely the accurate prediction of drug release profiles when using different formulations or form factors of the implant. The ability to model and predict the release behavior of drugs from an implant based on their physicochemical properties enables rational design and optimization without extensive and laborious in vitro testing. By leveraging experimental observations, we propose a mathematical model that predicts the empirical parameters describing the drug diffusion and partitioning processes based on the physicochemical properties of the drug. We demonstrate that the model enables an adequate fit predicting empirical parameters close to experimental values for various drugs. The model was further used to predict the release performance of new drug formulations from the implant, which aligned with experimental results for implants exhibiting zero-order release kinetics. Thus, the proposed empirical models provide useful tools to inform the implant design to achieve a target release profile.

Indexed as

empirical modelimplantlong-acting drug delivery systempoly(ε-caprolactone)

Identifiers

PMID36297338
PMCPMC9610229

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Registered trials

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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.