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Membranes
Special Issues
Membrane Carriers for Drug Delivery Purposes
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Membrane Carriers for Drug Delivery Purposes

A Special Issue of Membranes (ISSN 2077-0375) belonging to the section "Membrane Applications for Other Areas".

Deadline for manuscript submissions: closed (31 July 2026) | Viewed by 1149

Editor

Dr. Yosuke Senju

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Guest Editor
Research Institute for Interdisciplinary Science (RIIS), Okayama University, Okayama 700-8530, Japan
Interests: biophysics; synthetic biology; structural biology; liposome; protein-lipid interaction; drug delivery system (DDS); drug discovery; biomimetics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to invite you to contribute to the Special Issue “Membrane Carriers for Drug Delivery Purposes.” Plasma and intracellular membranes are characterized by different lipid compositions that enable proteins to localize to distinct subcellular compartments. Many proteins, including transmembrane proteins (e.g., ion channels, transmembrane receptors, and transporters), have been shown to interact with cell and subcellular membranes. These protein–lipid interactions determine protein conformations and precisely regulate the activation and localization of molecular complexes at their respective membranes. Furthermore, these signaling pathways play vital roles in various cellular processes such as membrane trafficking, signal transduction, and extracellular vesicle (EV) formation. Membrane proteins are implicated in many diseases, including cancer and Alzheimer’s; thus, their potential applications in drug design need to be investigated. However, effective and controlled targeted subcellular delivery and release, and minimizing side effects in the development of drug delivery systems (DDS), such as lipid nanoparticles (LNPs), are yet to be achieved.

This Special Issue aims to present recent advances in membrane carriers for drug delivery purposes from various perspectives. Studies will illuminate the structural and physiological functions of membrane proteins and provide new insights into the fundamental principles of membrane carriers for drug delivery. Both original research articles and reviews are welcome. Research areas may include (but are not limited to) cell biology, biochemistry, and biophysics, including structural biology and molecular dynamics (MD) simulations. Multidisciplinary approaches are also welcome. We look forward to receiving your contributions.

Dr. Yosuke Senju
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Membranes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • lipid–protein interactions
  • extracellular vesicles (EVs)
  • signal transduction
  • membrane traffic
  • organelle
  • model membranes and liposomes
  • receptors
  • channels
  • transporters
  • drug delivery system (DDS)
  • lipid nanoparticles (LNPs)
  • drug discovery
  • synthetic biology
  • biomimetics

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Published Papers (1 paper)

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Research

35 pages, 11009 KB  
Article
Machine Learning–Driven Surrogate Modeling and Operating-Point Selection for a Microfluidic Diffusion-Membrane Platform for Transdermal Drug Delivery
by Tara Torabi, Mahsa Jafar Harasy, Jafar Tahmoresnezhad, Samira Malekmohammadi, Adolfo Iulianelli and Kamran Ghasemzadeh
Membranes 2026, 16(7), 239; https://doi.org/10.3390/membranes16070239 - 15 Jul 2026
Viewed by 624
Abstract
Microfluidic diffusion systems provide a powerful in vitro platform for evaluating transdermal drug delivery (TDD), yet their predictive capability is often constrained by limited experimental datasets and nonlinear transport behavior across membrane—device configurations. This study integrates machine learning (ML) with microfluidic experimentation to [...] Read more.
Microfluidic diffusion systems provide a powerful in vitro platform for evaluating transdermal drug delivery (TDD), yet their predictive capability is often constrained by limited experimental datasets and nonlinear transport behavior across membrane—device configurations. This study integrates machine learning (ML) with microfluidic experimentation to develop accurate and generalizable surrogate models for cumulative drug permeation under different hydrodynamic and membrane conditions. This work presents an ML-augmented microfluidic TDD framework for predicting cumulative drug permeation from small experimental datasets. Caffeine cream permeation was examined across twelve device—membrane configurations (sMDC, mMDC, and LiveBox2 paired with PET, CA, rat skin, and alginate) at three perfusion flow rates. For each configuration, SVR, MLP, RFR, GBR, XGB, and KNN models were trained and cross-validated using only 33 experimental measurements. SVR showed the strongest overall performance among the evaluated models, achieving test R2 values typically above 0.97 and RMSE values of <1–3 µg/cm2, accurately capturing the nonlinear time-flow-cumulative mass behavior of TDD profiles. A domain-bounded Gaussian-noise augmentation strategy was used to increase local sampling density while keeping augmented values within the experimentally observed time and cumulative-mass ranges. Polynomial equations were obtained from the predictions of SVR to capture the interaction between inputs and outputs. The trained SVR surrogates were then used for automated steady-state identification and surrogate-based operating-point selection, revealing the dependence of the selected flow rate on membrane permeability and device geometry. Alginate consistently delivered the highest steady-state cumulative mass across all systems (up to ~448 µg/cm2), establishing it as the most efficient TDD membrane among those evaluated. Finally, compact third-degree polynomial equations were derived from the SVR predictions, enabling explicit analytical prediction and rapid design-space exploration. Overall, these ML-derived models and analytical equations provide a fast, low-cost tool for predictive design, enabling rapid microfluidic system evaluation and operating-condition selection, and significantly accelerating the development and screening of next-generation TDD platforms. Full article
(This article belongs to the Special Issue Membrane Carriers for Drug Delivery Purposes)
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