m+Medelevator.RU
← Поиск публикаций
Drug delivery · 2026

Numerical simulation of PDRN iontophoresis: comparison with passive diffusion and key parameter analysis.

СтатьяСравнительное исследованиеНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…

История · было → стало

Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.

Cho Jongho, Han Dongjun, Lee Hyemi, Park Dong-Wook

Аннотация

Polydeoxyribonucleotide (PDRN) is a bio-derived therapeutic agent known to exhibit regenerative, angiogenic, and anti-inflammatory effects. However, its relatively large molecular size limits penetration across the skin barrier, thereby reducing the feasibility of noninvasive transdermal delivery. Iontophoresis is a promising approach for enhancing the transport of charged macromolecules, yet quantitative understanding of PDRN iontophoresis remains limited. Here, we present an in silico framework for cathodal PDRN iontophoresis based on the Nernst-Planck equation, focusing on passive diffusion and electromigration. The PDRN diffusion coefficient was determined from Franz diffusion cell experiments using the lag-time method, and the effective charge was estimated from prior experimental reports on dsDNA. A three-layer skin model comprising the stratum corneum, epidermis, and dermis was employed, and PDRN transport was analyzed as a function of applied current density, initial PDRN concentration, delivery time, and PDRN diffusion coefficient. Transport was quantified using molar flux and cumulative permeation at the epidermis-dermis interface and mid-dermis. The results clarify parameter-dependent regulation of delivery and reveal a depth-dependent shift in the dominant transport mechanism, highlighting the importance of iontophoresis for macromolecular delivery. Using calibrated outputs, we assessed agreement with experimental permeation data using the root-mean-square deviation (RMSD), mean absolute percentage error (MAPE), and the coefficient of determination (R2). Overall, this work provides a computational baseline to predict PDRN iontophoresis outcomes and proposes practical iontophoresis strategies for PDRN to guide device design.

Предметные рубрики MeSH

ИП Урсолов Владлен Сергеевич

ИНН 500118482352 · ОГРНИП 314500117700049

Реквизиты и контакты