Targeted drug delivery using functionalized nanocarriers (NCs) is a strategy in therapeutic and diagnostic applications. In this paper we review the recent development of models at multiple length and time scales and their applications to targeting of antibody functionalized nanocarriers to antigens (receptors) on the endothelial cell (EC) surface. Our mesoscale (100 nm-1 µ m) model is based on phenomenological interaction potentials for receptor-ligand interactions, receptor-flexure and resistance offered by glycocalyx. All free parameters are either directly determined from independent biophysical and cell biology experiments or estimated using molecular dynamics simulations. We employ a Metropolis Monte Carlo (MC) strategy in conjunction with the weighted histogram analysis method (WHAM) to compute the free energy landscape (potential of mean force or PMF) associated with the multivalent antigen-antibody interactions mediating the NC binding to EC. The binding affinities (association constants) are then derived from the PMF by computing absolute binding free energy of binding of NC to EC, taking into account the relevant translational and rotational entropy losses of NC and the receptors. We validate our model predictions by comparing the computed binding affinities and PMF to a wide range of experimental measurements, including in vitro cell culture, in vivo endothelial targeting, atomic force microscopy (AFM), and flow chamber experiments. The model predictions agree quantitatively with all types experimental measurements. On this basis, we conclude that our computational protocol represents a quantitative and predictive approach for model driven design and optimization of functionalized NCs in targeted vascular drug delivery.

使用功能化纳米载体（NC）进行靶向药物递送是治疗和诊断应用中的一种策略。在本文中，我们回顾了多个长度和时间尺度模型的最新发展及其在将抗体功能化纳米载体靶向内皮细胞（EC）表面抗原（受体）方面的应用。我们的中尺度（100 nm-1 µ m）模型基于受体-配体相互作用、受体-弯曲和糖萼提供的阻力的现象学相互作用势。所有自由参数要么直接从独立的生物物理和细胞生物学实验确定，要么使用分子动力学模拟估计。我们采用 Metropolis Monte Carlo (MC) 策略结合加权直方图分析方法 (WHAM) 来计算与介导 NC 与 EC 结合的多价抗原-抗体相互作用相关的自由能景观（平均力或 PMF 的潜力）。然后通过计算 NC 与 EC 结合的绝对结合自由能，并考虑 NC 和受体的相关平移和旋转熵损失，从 PMF 导出结合亲和力（缔合常数）。我们通过将计算的结合亲和力和 PMF 与广泛的实验测量进行比较来验证我们的模型预测，包括体外细胞培养、体内内皮靶向、原子力显微镜 (AFM) 和流动室实验。模型预测在数量上与所有类型的实验测量一致。在此基础上，我们得出的结论是，我们的计算协议代表了一种定量和预测方法，用于模型驱动设计和靶向血管药物输送中功能化 NC 的优化。