Evans Conor L, Hermsmeier Maiko, Yamamoto Akira, Chan Kin F
Massachusetts General Hospital, Wellman Center for Photomedicine, Boston 02114, USA.
BioPharmX, Inc., 115 Nicholson Ln, San Jose, CA 95134, USA.
Biomed Opt Express. 2020 Nov 4;11(12):6864-6880. doi: 10.1364/BOE.405502. eCollection 2020 Dec 1.
Mapping the uptake of topical drugs and quantifying dermal pharmacokinetics (PK) presents numerous challenges. Though high resolution and high precision methods such as mass spectrometry offer the means to quantify drug concentration in tissue, these tools are complex and often expensive, limiting their use in routine experiments. For the many topical drugs that are naturally fluorescent, tracking fluorescence emission can be a means to gather critical PK parameters. However, skin autofluorescence can often overwhelm drug fluorescence signatures. Here we demonstrate the combination of standard epi-fluorescence imaging with deep learning for the visualization and quantification of fluorescent drugs in human skin. By training a U-Net convolutional neural network on a dataset of annotated images, drug uptake from both high "infinite" dose and daily clinical dose regimens can be measured and quantified. This approach has the potential to simplify routine topical product development in the laboratory.
绘制局部用药的摄取情况并量化皮肤药代动力学（PK）存在诸多挑战。尽管诸如质谱等高分辨率和高精度方法提供了量化组织中药物浓度的手段，但这些工具复杂且往往成本高昂，限制了它们在常规实验中的应用。对于许多具有天然荧光的局部用药而言，追踪荧光发射可以作为获取关键药代动力学参数的一种方法。然而，皮肤自身荧光常常会掩盖药物的荧光信号。在此，我们展示了将标准落射荧光成像与深度学习相结合，用于可视化和量化人体皮肤中的荧光药物。通过在注释图像数据集上训练U-Net卷积神经网络，可以测量和量化高“无限”剂量和日常临床剂量方案下的药物摄取情况。这种方法有可能简化实验室中的常规局部用药产品开发。
