Advancing transdermal drug delivery through 4D bioprinting and dynamic skin modelling

TL;DR

This paper explores how 4D bioprinting and dynamic skin modeling can improve transdermal drug delivery by simulating drug permeation and skin responses over time.

Contribution

The paper introduces the integration of time as a dimension in transdermal drug delivery models using 4D bioprinting and dynamic modeling.

Findings

4D modeling captures drug diffusion and skin responses over time, improving prediction accuracy.

4D bioprinting allows fabrication of stimuli-responsive skin constructs for realistic drug delivery simulations.

Integration of AI could enhance 4D models for personalized medicine applications.

Abstract

Transdermal drug delivery (TDD) provides a non-invasive approach for sustained drug release. However, traditional models present limitations in capturing the dynamic interactions between drugs, skin and the environmental factors over time. The incorporation of time as a critical dimension alongside three-dimensional (3D) structures in four-dimensional (4D) modelling offers a promising solution by simulating the temporal evolution of drug diffusion and skin responses. In this review, 4D modelling refers to the computational and material-based systems that incorporate time-dependent changes whereas 4D bioprinting specifically involves fabrication of dynamic, stimuli-responsive skin constructs. Together, these approaches create temporally adaptive models which are ideal for simulating drug permeation and skin behaviour. This review will explore the potential application of 4D modelling in TDD, primarily focusing on and emphasising its capacity to predict drug permeation, release kinetics and skin interactions in response to variables such as hydration, temperature and mechanical impact. 4D bioprinting provides a more accurate depiction of real-world scenarios, enabling researchers to optimise drug formulations whilst minimising reliance on empirical testing. Despite challenges associated with cost and complexity, 4D modelling presents considerable opportunities, particularly in the advancement of personalised medicine. The integration of artificial intelligence could further enhance these models, resulting in more accurate predictions. By addressing both spatial and temporal dimensions, 4D constructs will continue to evolve and have the potential to transform TDD; particularly in the context of individualised treatment where dynamic patient-specific variables can be integrated to develop more effective and tailored treatments.

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