Evolution of Multivalent Nanoparticle Adhesion via Specific Molecular Interactions

Abstract

The targeted delivery of nanoparticle carriers holds tremendous potential to transform the detection and treatment of diseases. A major attribute of nanoparticles is the ability to form multiple bonds with target cells, which greatly improves the adhesion strength. However, the multivalent binding of nanoparticles is still poorly understood, particularly from a dynamic perspective. In previous experimental work, we studied the kinetics of nanoparticle adhesion and found that the rate of detachment decreased over time. Here, we have applied the adhesive dynamics simulation framework to investigate binding dynamics between an antibody-conjugated, 200-nm-diameter sphere and an ICAM-1-coated surface on the scale of individual bonds. We found that nano adhesive dynamics (NAD) simulations could replicate the time-varying nanoparticle detachment behavior that we observed in experiments. As expected, this behavior correlated with a steady increase in mean bond number with time, but this was attributed to bond accumulation only during the first second that nanoparticles were bound. Longer-term increases in bond number instead were manifested from nanoparticle detachment serving as a selection mechanism to eliminate nanoparticles that had randomly been confined to lower bond valencies. Thus, time-dependent nanoparticle detachment reflects an evolution of the remaining nanoparticle population toward higher overall bond valency. We also found that NAD simulations precisely matched experiments whenever mechanical force loads on bonds were high enough to directly induce rupture. These mechanical forces were in excess of 300 pN and primarily arose from the Brownian motion of the nanoparticle, but we also identified a valency-dependent contribution from bonds pulling on each other. In summary, we have achieved excellent kinetic consistency between NAD simulations and experiments, which has revealed new insights into the dynamics and biophysics of multivalent nanoparticle adhesion. In future work, we will leverage the simulation as a design tool for optimizing targeted nanoparticle agents.

Quantifying and controlling bond multivalency for advanced nanoparticle targeting to cells

“…As discussed, Haun et al performed a series of flow chamber binding experiments that established relationships between multivalent NP attachment ( k A ) and detachment ( k D ) rate constants and various parameters including receptor/ligand surface densities, flow rate, NP size, and monovalent binding kinetics ( k o f , k o r ) [37,48,54]. To better understand these relationships, and further study the time-dependent detachment rate phenomenon, Wang M et al developed a simulation method that combined Brownian motion, hydrodynamic forces, and a stochastic treatment of bonding [105]. A Monte Carlo method was used to create distributions of NP dynamics to extract the expected rate value for bond formation and rupture ( k f , k r ; Eqs.…”

“…To better understand these relationships, and further study the time-dependent detachment rate phenomenon, Wang M et al . developed a simulation method that combined Brownian motion, hydrodynamic forces, and a stochastic treatment of bonding [ 105 ]. A Monte Carlo method was used to create distributions of NP dynamics to extract the expected rate value for bond formation and rupture ( ; Eqs.…”

“…Based on this insight, it was concluded that it will not be possible to characterize a population of NPs using a single multivalent detachment rate, and for that matter, avidity. Instead, a series of detachment rates co-exist together, each corresponding to sub-populations that possess unique bond numbers and dynamics [ 105 , 106 ]. This simulation is unique because it correctly predicted the power law detachment of NPs in experiment.…”

“…As anticipated, the mean bond number monotonically increased during the first second a NP is bound. Over a longer period of time, an increase in mean bond number was attributed to detachment of NPs with low bond valencies due to low bond strength [ 105 ].

Fig.

…”

Towards Chemotactic Supramolecular Nanoparticles: From Autonomous Surface Motion Following Specific Chemical Gradients to Multivalency-Controlled Disassembly

“…Recent simulations using minimalistic coarse-grained (CG) models allowed to study the adhesion and dynamics of nanoparticles/cells (represented as single spheres) onto ligand-functionalized surfaces. [23][24][25][26] These models permitted to relate the number of interactions between the spherical nanoparticle (NP) and the surface receptors to the surface adhesion 26,27 . Similar CG models also allowed to simulate and monitor the rolling of a deformable (soft) spherical cell model on surfaces under the presence of an external flow.…”