Department of Biomedical Engineering, National University of Singapore, 4 Engineering Drive 3, #04-08, Singapore 117583, Singapore.
Bioinformatics Institute, ASTAR, 30 Biopolis St, Singapore 138671, Singapore.
Lab Chip. 2022 May 17;22(10):1890-1904. doi: 10.1039/d1lc01140e.
Adverse cutaneous reactions are potentially life-threatening skin side effects caused by drugs administered into the human body. The availability of a human-specific platform that can prospectively screen drugs and predict this risk is therefore of great importance to drug safety. However, since adverse cutaneous drug reactions are mediated by at least 2 distinct mechanisms, both involving systemic interactions between liver, immune and dermal tissues, existing skin models have not been able to comprehensively recapitulate these complex, multi-cellular interactions to predict the skin-sensitization potential of drugs. Here, we report a novel drug screening platform, which comprises a microfluidic multicellular coculture array (MCA) to model different mechanisms-of-action using a collection of simplistic cellular assays. The resultant readouts are then integrated with a machine-learning algorithm to predict the skin sensitizing potential of systemic drugs. The MCA consists of 4 cell culture compartments connected by diffusion microchannels to enable crosstalk between hepatocytes that generate drug metabolites, antigen-presenting cells (APCs) that detect the immunogenicity of the drug metabolites, and keratinocytes and dermal fibroblasts, which collectively determine drug metabolite-induced FasL-mediated apoptosis. A single drug screen using the MCA can simultaneously generate 5 readouts, which are integrated using support vector machine (SVM) and principal component analysis (PCA) to classify and visualize the drugs as skin sensitizers or non-skin sensitizers. The predictive performance of the MCA and SVM classification algorithm is then validated through a pilot screen of 11 drugs labelled by the US Food and Drug Administration (FDA), including 7 skin-sensitizing and 4 non-skin sensitizing drugs, using stratified 4-fold cross-validation (CV) on SVM. The predictive performance of our model achieves an average of 87.5% accuracy (correct prediction rate), 75% specificity (prediction rate of true negative drugs), and 100% sensitivity (prediction rate of true positive drugs). We then employ the MCA and the SVM training algorithm to prospectively identify the skin-sensitizing likelihood and mechanism-of-action for obeticholic acid (OCA), a farnesoid X receptor (FXR) agonist which has undergone clinical trials for non-alcoholic steatohepatitis (NASH) with well-documented cutaneous side effects.
皮肤不良反应是药物在人体中引起的潜在危及生命的皮肤副作用。因此，拥有一个可以前瞻性筛选药物并预测这种风险的人体特异性平台对药物安全性非常重要。然而，由于皮肤药物不良反应是由至少 2 种不同机制介导的，这两种机制都涉及肝脏、免疫和皮肤组织之间的全身相互作用，现有的皮肤模型尚未能够全面再现这些复杂的、多细胞相互作用，以预测药物的皮肤致敏潜力。在这里，我们报告了一种新的药物筛选平台，该平台由一个微流控多细胞共培养阵列（MCA）组成，该阵列使用一系列简单的细胞测定来模拟不同的作用机制。然后，将所得结果与机器学习算法集成，以预测系统性药物的皮肤致敏潜力。MCA 由 4 个细胞培养室组成，通过扩散微通道连接，使生成药物代谢物的肝细胞、检测药物代谢物免疫原性的抗原呈递细胞（APC）以及角质形成细胞和真皮成纤维细胞之间能够相互作用，这些细胞共同决定药物代谢物诱导的 FasL 介导的细胞凋亡。使用 MCA 进行单次药物筛选可以同时产生 5 个结果，然后使用支持向量机（SVM）和主成分分析（PCA）对这些结果进行集成，以对药物进行分类并将其可视化，将其归类为皮肤致敏剂或非皮肤致敏剂。然后，通过对美国食品和药物管理局（FDA）标记的 11 种药物（包括 7 种皮肤致敏药物和 4 种非皮肤致敏药物）进行分层 4 倍交叉验证（CV）的 pilot 筛选，对 MCA 和 SVM 分类算法的预测性能进行验证。我们的模型的预测性能平均达到 87.5%的准确率（正确预测率）、75%的特异性（真阴性药物的预测率）和 100%的灵敏度（真阳性药物的预测率）。然后，我们使用 MCA 和 SVM 训练算法来前瞻性地识别法尼醇 X 受体（FXR）激动剂奥贝胆酸（OCA）的皮肤致敏可能性和作用机制，奥贝胆酸已在临床试验中用于非酒精性脂肪性肝炎（NASH），并伴有明确的皮肤副作用。
