Design principles for precision targeting

Author(s):  
Giuseppe Battaglia ◽  
Stefano Agioletti-Uberti

<div> <div> <div> <p>Tight control on the selectivity of nanoparticles’ interaction with biological systems is paramount for the development of targeted therapies. However, the large number of synthetically tuneable parameters makes it difficult to identify optimal design “sweet spots” without rational guiding principles. Here we address this problem combining super-selectivity theory (SST) with analytical models from soft matter and polymer physics into a unified theoretical framework. Starting from an archetypal system, a polymer-stabilized nanoparticle function- alised with targeting ligands, we use our model to identify the most selective combination of parameters in terms of particle size, brush polymerization degree and grafting density, as well as tether length, binding affinity and ligands number. </p> </div> </div> </div>

2019 ◽  
Author(s):  
Xiaohe Tian ◽  
Stefano Agioletti-Uberti ◽  
Giuseppe Battaglia

<div> <div> <p>Tight control on the selectivity of nanoparticles' interaction with biological systems is paramount for the development of targeted therapies. However, the large number of synthetically tunable parameters makes it difficult to identify optimal design ``sweet spots'' without rational guiding principles. Here we address this problem combining super-selectivity theory (SST) with analytical models from soft matter and polymer physics into a unified theoretical framework. Starting from an archetypal system, a polymersome functionalized with targeting ligands, we use our model to identify the most selective combination of parameters in terms of particle size, brush polymerization degree and grafting density, as well as tether length, binding affinity and ligands number. We further show how to combine multivalent interactions into multiplexed systems which act holistically as a function of the density of more than one receptor type, so as to achieve binding only when multiple receptors are expressed above a threshold density. We show that theory can be used to effectively fit experimental data and, hence confirming its suitability. We thus propose the design of “bar-coding" targeting approach that can be tailor-made to unique cell populations enabling personalized therapies. </p> </div> </div>


2019 ◽  
Author(s):  
Xiaohe Tian ◽  
Stefano Agioletti-Uberti ◽  
Giuseppe Battaglia

<div> <div> <p>Tight control on the selectivity of nanoparticles' interaction with biological systems is paramount for the development of targeted therapies. However, the large number of synthetically tunable parameters makes it difficult to identify optimal design ``sweet spots'' without rational guiding principles. Here we address this problem combining super-selectivity theory (SST) with analytical models from soft matter and polymer physics into a unified theoretical framework. Starting from an archetypal system, a polymersome functionalized with targeting ligands, we use our model to identify the most selective combination of parameters in terms of particle size, brush polymerization degree and grafting density, as well as tether length, binding affinity and ligands number. We further show how to combine multivalent interactions into multiplexed systems which act holistically as a function of the density of more than one receptor type, so as to achieve binding only when multiple receptors are expressed above a threshold density. We show that theory can be used to effectively fit experimental data and, hence confirming its suitability. We thus propose the design of “bar-coding" targeting approach that can be tailor-made to unique cell populations enabling personalized therapies. </p> </div> </div>


2017 ◽  
Author(s):  
Giuseppe Battaglia ◽  
Stefano Angioletti-Umberti

<div> <div> <div> <p>Tight control on the selectivity of nanoparticles’ interaction with biological systems is paramount for the development of targeted therapies. However, the large number of synthetically tuneable parameters makes it difficult to identify optimal design “sweet spots” without rational guiding principles. Here we address this problem combining super-selectivity theory (SST) with analytical models from soft matter and polymer physics into a unified theoretical framework. Starting from an archetypal system, a polymer-stabilized nanoparticle function- alised with targeting ligands, we use our model to identify the most selective combination of parameters in terms of particle size, brush polymerization degree and grafting density, as well as tether length, binding affinity and ligands number. We further show how to combine mul- tivalent interactions into multiplexed systems which act holistically as a function of the den- sity of more than one receptor type, so as to achieve binding only when multiple receptors are expressed above a threshold density. We thus propose the design of ”bar-coding” targeting approach that can be tailor-made to unique cell populations enabling personalized therapies. </p> </div> </div> </div>


2019 ◽  
Author(s):  
Xiaohe Tian ◽  
Stefano Agioletti-Uberti ◽  
Giuseppe Battaglia

<div> <div> <p>Tight control on the selectivity of nanoparticles' interaction with biological systems is paramount for the development of targeted therapies. However, the large number of synthetically tunable parameters makes it difficult to identify optimal design ``sweet spots'' without rational guiding principles. Here we address this problem combining super-selectivity theory (SST) with analytical models from soft matter and polymer physics into a unified theoretical framework. Starting from an archetypal system, a polymersome functionalized with targeting ligands, we use our model to identify the most selective combination of parameters in terms of particle size, brush polymerization degree and grafting density, as well as tether length, binding affinity and ligands number. We further show how to combine multivalent interactions into multiplexed systems which act holistically as a function of the density of more than one receptor type, so as to achieve binding only when multiple receptors are expressed above a threshold density. We show that theory can be used to effectively fit experimental data and, hence confirming its suitability. We thus propose the design of “bar-coding" targeting approach that can be tailor-made to unique cell populations enabling personalized therapies. </p> </div> </div>


2020 ◽  
Vol 6 (4) ◽  
pp. eaat0919 ◽  
Author(s):  
Xiaohe Tian ◽  
Stefano Angioletti-Uberti ◽  
Giuseppe Battaglia

Tight control on the selectivity of nanoparticles’ interaction with biological systems is paramount for the development of targeted therapies. However, the large number of tunable parameters makes it difficult to identify optimal design “sweet spots” without guiding principles. Here, we combine superselectivity theory with soft matter physics into a unified theoretical framework and we prove its validity using blood brain barrier cells as target. We apply our approach to polymersomes functionalized with targeting ligands to identify the most selective combination of parameters in terms of particle size, brush length and density, as well as tether length, affinity, and ligand number. We show that the combination of multivalent interactions into multiplexed systems enable interaction as a function of the cell phenotype, that is, which receptors are expressed. We thus propose the design of a “bar-coding” targeting approach that can be tailor-made to unique cell populations enabling personalized therapies.


2019 ◽  
Author(s):  
Andrew McCluskey ◽  
Tom Arnold ◽  
Joshaniel F. K. Cooper ◽  
Tim Snow

The analysis of neutron and X-ray reflectometry data is important for the study of interfacial soft matter structures. However, there is still substantial discussion regarding the analytical models<br>that should be used to rationalise relflectometry data. In this work, we outline a robust and generic framework for the determination of the evidence for a particular model given experimental data, by<br>applying Bayesian logic. We apply this framework to the study of Langmuir-Blodgett monolayers by considering three possible analytical models from a recently published investigation [Campbell et al., J. Colloid Interface Sci, 2018, 531, 98]. From this, we can determine which model has the most evidence given the experimental data, and show the effect that different isotopic contrasts of neutron reflectometry will have on this. We believe that this general framework could become an important component of neutron and X-ray reflectometry data analysis, and hope others more regularly consider the relative evidence for their analytical models.<br>


Author(s):  
Pinaki Kumar ◽  
Roberto Benzi ◽  
Jeannot Trampert ◽  
Federico Toschi

Using a multi-component lattice Boltzmann (LB) model, we perform fluid kinetic simulations of confined and concentrated emulsions. The system presents the phenomenology of soft-glassy materials, including a Herschel–Bulkley rheology, yield stress, ageing and long relaxation time scales. Shearing the emulsion in a Couette cell below the yield stress results in plastic topological re-arrangement events which follow established empirical seismic statistical scaling laws, making this system a good candidate to study the physics of earthquakes. One characteristic of this model is the tendency for events to occur in avalanche clusters, with larger events, triggering subsequent re-arrangements. While seismologists have developed statistical tools to study correlations between events, a process to confirm causality remains elusive. We present here, a modification to our LB model, involving small, fast vibrations applied to individual droplets, effectively a macroscopic forcing, which results in the arrest of the topological plastic re-arrangements. This technique provides an excellent tool for identifying causality in plastic event clusters by examining the evolution of the dynamics after ‘stopping’ an event, and then checking which subsequent events disappear. This article is part of the theme issue ‘Fluid dynamics, soft matter and complex systems: recent results and new methods’.


Author(s):  
S. Caleb Douglas

Union Pacific Railroad has prioritized optimizing the life span of ballast material utilized across the system. A multi-faceted approach is presented which addresses (1) obtaining hard, durable ballast; (2) ensuring that the ballast delivered satisfies the specification; and (3) evaluating the resistance of ballast to breakdown during transportation. The initial focus included a quality management system to verify that properly graded and clean ballast is loaded into the railcars. Periodic testing and unannounced quarry visits are in place for each ballast source. Petrographic testing was completed for each source to identify potential problematic mineralogical issues. The second focus is ballast degradation during transport. The study included three railcars shipped to a central location where a gradation analysis on each railcar was completed in its entirety. Various sampling methods were utilized at the ballast sources during loading of the railcar and are compared to the unloaded results. The variability of gradation results from different sampling methods was a significant outcome of the study. The test results indicate that the half inch particle size increased during transport. The outcomes of these series of studies are constant evaluation of ballast suppliers is warranted, obtaining representative samples of ballast for gradation quality control is difficult, and transportation results in a minor increase of the half-inch particle size.


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