Abstract
Molecular imaging agent design involves simultaneously optimizing multiple probe properties. While several desired characteristics are straightforward, including high affinity and low non-specific background signal, in practice there are quantitative trade-offs between these properties. These include plasma clearance, where fast clearance lowers background signal but can reduce target uptake, and binding, where high affinity compounds sometimes suffer from lower stability or increased non-specific interactions. Further complicating probe development, many of the optimal parameters vary depending on both target tissue and imaging agent properties, making empirical approaches or previous experience difficult to translate. Here, we focus on low molecular weight compounds targeting extracellular receptors, which have some of the highest contrast values for imaging agents. We use a mechanistic approach to provide a quantitative framework for weighing trade-offs between molecules. Our results show that specific target uptake is well-described by quantitative simulations for a variety of targeting agents, whereas non-specific background signal is more difficult to predict. Two in vitro experimental methods for estimating background signal in vivo are compared – non-specific cellular uptake and plasma protein binding. Together, these data provide a quantitative method to guide probe design and focus animal work for more cost-effective and time-efficient development of molecular imaging agents.
Highlights
The trade-offs in molecular properties can be quantitatively compared for selection of the most promising agents to move forward with imaging probe development
Assuming a high affinity binder with no blood flow limitations, first-order uptake in background tissues, and probe degradation rates that are slower than plasma clearance and internalization (SI), the maximum target-to-background ratio (TBR) can be expressed as TBR = V target εkint,ns where P is the vascular permeability of the target tissue, S/V is the tissue vessel surface area, ε is the tissue interstitial void volume, and kint,ns is the non-specific internalization rate in background tissue
Non-fenestrated vessels suggested pore radii of 0.7 and 60 nm with fractional area to thickness ratios of 10 and 2 cm−1 for small and large pores, respectively. Using these permeability fits and a 50% extraction fraction cutoff, the prediction for tumor targeting indicates a molecule below 2200 Da will effectively extravasate out of the blood vessel into the tumor tissue (Fig. 2)
Summary
While a qualitative understanding of in vivo conditions offers insight on which parameters play a role in targeting, a quantitative model that is both mechanistic and predictive would allow for much more efficient design of imaging probes. To help guide the design of novel imaging agents against extracellular targets, we present a quantitative and mechanistic model and in vitro experimental approaches for describing target tissue uptake and background signal. The simulations build on previously published models that incorporate the time-varying and spatially heterogeneous tissue concentrations present under non-equilibrium conditions following a bolus dose. The trade-offs in molecular properties can be quantitatively compared for selection of the most promising agents to move forward with imaging probe development
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