Kwong Gabriel A, Dudani Jaideep S, Carrodeguas Emmanuel, Mazumdar Eric V, Zekavat Seyedeh M, Bhatia Sangeeta N
Institute for Medical Engineering and Science, Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA 02139; Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139;
Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139; Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139;
Proc Natl Acad Sci U S A. 2015 Oct 13;112(41):12627-32. doi: 10.1073/pnas.1506925112. Epub 2015 Sep 28.
Advances in nanomedicine are providing sophisticated functions to precisely control the behavior of nanoscale drugs and diagnostics. Strategies that coopt protease activity as molecular triggers are increasingly important in nanoparticle design, yet the pharmacokinetics of these systems are challenging to understand without a quantitative framework to reveal nonintuitive associations. We describe a multicompartment mathematical model to predict strategies for ultrasensitive detection of cancer using synthetic biomarkers, a class of activity-based probes that amplify cancer-derived signals into urine as a noninvasive diagnostic. Using a model formulation made of a PEG core conjugated with protease-cleavable peptides, we explore a vast design space and identify guidelines for increasing sensitivity that depend on critical parameters such as enzyme kinetics, dosage, and probe stability. According to this model, synthetic biomarkers that circulate in stealth but then activate at sites of disease have the theoretical capacity to discriminate tumors as small as 5 mm in diameter-a threshold sensitivity that is otherwise challenging for medical imaging and blood biomarkers to achieve. This model may be adapted to describe the behavior of additional activity-based approaches to allow cross-platform comparisons, and to predict allometric scaling across species.
纳米医学的进展正在提供复杂的功能，以精确控制纳米级药物和诊断工具的行为。在纳米颗粒设计中，利用蛋白酶活性作为分子触发器的策略变得越来越重要，然而，如果没有一个定量框架来揭示非直观的关联，这些系统的药代动力学就很难理解。我们描述了一个多室数学模型，用于预测使用合成生物标志物进行癌症超灵敏检测的策略，合成生物标志物是一类基于活性的探针，可将癌症衍生信号放大到尿液中，作为一种非侵入性诊断方法。使用由与蛋白酶可裂解肽缀合的聚乙二醇核心组成的模型公式，我们探索了一个广阔的设计空间，并确定了提高灵敏度的指导原则，这些原则取决于酶动力学、剂量和探针稳定性等关键参数。根据该模型，在体内循环时保持隐形但在疾病部位激活的合成生物标志物理论上能够区分直径小至5毫米的肿瘤——这是一个阈值灵敏度，否则对于医学成像和血液生物标志物来说很难实现。该模型可以进行调整，以描述其他基于活性的方法的行为，从而实现跨平台比较，并预测不同物种之间的异速生长比例。
