Facilitating the Clinical Integration of Nanomedicines: The Roles of

Sep 8, 2016 - This armamentarium represents an ideal tool for maximizing the therapeutic efficacy of nanomedicines, thus facilitating their integratio...
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Facilitating the Clinical Integration of Nanomedicines: The Roles of Theoretical and Computational Scientists Paolo Decuzzi* Laboratory of Nanotechnology for Precision Medicine, Fondazione Istituto Italiano di Tecnologia Via Morego 30, Genoa 16163, Italy ABSTRACT: Since the launch of multiple research initiatives on nanotechnology applied to medicine in the early 2000s, a plethora of nanomedicines have been developed that exhibit great therapeutic efficacy in preclinical models but yet minimal impact in daily clinical practice. The successful and complete clinical fruition of nanomedicines requires addressing three major technical challenges: improving loading efficacy and on-command release, modulating recognition and sequestration by immune cells, and maximizing accumulation at biological targets. In this Perspective, I describe how theoretical and computational models can help address each of these challenges. This armamentarium represents an ideal tool for maximizing the therapeutic efficacy of nanomedicines, thus facilitating their integration into daily clinical operations.

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In this issue of ACS Nano, Sengupta and colleagues present a computational framework for optimizing the design of lipidbased nanoparticles for the delivery of anticancer molecules. The framework enables one to analyze in silico the interaction between therapeutic molecules and lipid chains and to predict the stability of the resulting lipid-based nanocarrier. Taxanes are used as model drugs, and the computational framework is employed to identify therapeutic supramolecular units, consisting of taxanes linked to hydrophobic tails, providing optimal loading and stability of the lipid bilayers. In preclinical models of ovarian and breast cancers, the resulting nanomedicines demonstrate enhanced antitumor efficacy as compared to the reference drug. The work of Sengupta and colleagues highlights the relevance and potential of in silico simulations in boosting the therapeutic efficacy of nanomedicines. Model building and systematic theoretical and computational analyses will become important tools for the advancement and clinical fruition of nanomedicines. Theoretical and computational scientists should focus their efforts on three major technical challenges that are severely limiting the clinical integration of nanomedicines: improving the loading and stable encapsulation of therapeutic agents while diminishing uncontrolled release within the bloodstream and facilitating oncommand release; controlling the adsorption of blood proteins while limiting recognition by cells of the immune system and adverse reactions and improving molecular targeting; and maximizing accumulation at disease sites and modulating blood

anoparticles encapsulating therapeutic agentsnanomedicineshave been developed for the treatment of cancer, atherosclerosis, thrombosis, neurodegeneration, and a variety of chronic inflammatory diseases.1 Compared to conventional drug molecules, nanomedicines facilitate the deployment of multiple therapeutic agents with high spatial and temporal specificity, thus enabling de facto combination therapy; protection of therapeutic molecules from enzymatic degradations and rapid renal and hepatic clearance, enhancing blood longevity and bioavailability of drugs; and coloading of imaging agents, facilitating therapeutic monitoring and on-command drug release via image-guided interventions.2,3 Since the early 2000s, multiple research initiatives on nanotechnology applied to medicine have been launched worldwide, and material scientists, chemists, pharmacologists, and engineers have been developing a plethora of different nanomedicines. Typically, these comprise a core, encapsulating the therapeutic agents, and a surface coating, exposing specific chemical moieties. Lipids, polymers, inorganic materials, and combinations thereof are used as the constituent materials. Nanomedicines range in size from a few tens to several hundreds of nanometers and exhibit a variety of shapes including spherical, discoidal, cylindrical, stellar, and more. All this activity has led to over 40 nanomedicines currently undergoing clinical trials and a few nanomedicines that have already been approved for daily clinical use.4,5 The higher complexity of nanomedicines provides opportunities for more effective therapies, but it also comes with bigger challenges for clinical integration.6−8 © XXXX American Chemical Society

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Figure 1. Technical challenges for theoretical and computational scientists in nanomedicine. Loading and release: Nanomedicines can deliver multiple drug molecules enabling de facto combination therapies. Molecular simulations can be used to maximize drug-loading efficiency while ensuring controlled drug release at the diseased site. Protein corona formation: Upon injection into the bloodstream, nanomedicines are covered by a variety of proteins (protein corona). In silico designed protein corona could minimize recognition and sequestration by immune cells with the potential to increase targeting efficiency. Accumulation at the biological target: Hierarchical computational models, integrating particle-based methods and continuum mechanics approaches, can be developed to optimize the size, shape, surface properties, and mechanical stiffness of nanomedicines for maximal accumulation at the diseased site and minimal sequestration by filtering organs of the reticulo-endothelial system.

longevity while minimizing sequestration by filtering organs within the reticulo-endothelial system. This set of targets is schematically illustrated in Figure 1.

selecting materials, solubilizing media, and nanoparticle architecture augments encapsulation and loading efficiencies, resulting in increased therapeutic efficacy and reduced off-site toxicity.9,17,24 Loading efficiencies of nanomedicines can be further improved by opting for chemical encapsulation where therapeutic agents are directly linked through cleavable chemical bonds to nanoparticle constituents. Generally, this strategy requires the formation of a lipid or polymer prodrug that is eventually assembled to form the nanomedicine. With this approach, loading efficiencies as high as 80−90% can be realized. For instance, MD simulations enabled the optimization of docetaxel-lauroyl conjugates, linked through a single hydrolyzable ester bond, which form lipid-polyethylene glycol (PEG) nanoemulsion and reach drug entrapment efficiencies over 90%.12 Similar approaches have been followed by other authors using lipid-based25,26 complexes, including the work of Sengupta and collaborators,9 or polymer-based19,27 supramolecular units. Release of the loaded cargo can be triggered by endogenous and exogenous stimuli. In particular, local pH, concentration of enzymes, oxygen tension, as well as ultrasound, optical radiation, and magnetic fields have been exploited to destabilize the structure of micelles and nanoparticles and thus trigger the release of therapeutic molecules.3 Computational models are being developed to understand and to optimize on-command drug release via pH,20 concentration of activating molecules,25,28,29 as well as exogenous energy deposition methods.30−32

IMPROVING LOADING, STABLE ENCAPSULATION, AND ON-COMMAND RELEASE Currently, most nanomedicines encapsulate their therapeutic agents within porous cores. Encapsulation and retention of these molecular agents is regulated by colloidal interactions arising at the interface between the agent itself and the surrounding environment, i.e., the structure of the nanomedicine (Figure 1). Typically, this physical encapsulation enables loading efficiencies on the order of 10−20%, implying that at least 80% of the injected nanomedicine mass provides no therapeutic contribution. Clearly, the objective should be to increase the mass of encapsulated drugs over the total mass of the nanomedicine (i.e., loading efficiency) while ensuring controlled drug release at the disease sites. Mathematically speaking, this is an optimization problem with an objective function to be maximized and specific constraints. In such a context, molecular simulations can be employed to analyze the so-called ‘nanoparticle−drug compatibility’ and predict encapsulation efficiency, loading efficiency, retention, and release profiles as a function of the loaded molecules and nanoparticle structures.10,11 Molecular dynamics (MD), Monte Carlo (MC), and coarse-grained (CG) simulations provide useful tools for analyzing and optimizing nanoparticle loading efficiency and stability and have been used for a variety of nanomedicines, including nanoemulsions,12,13 dendrimers,14−16 polymeric micelles,17−20 and liposomes.21−23 These studies have shown that enhancing nanoparticle−drug compatibility by properly B

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smaller than 1%.49 Although this number might be on the low side compared to some recent work,50−52 it emphasizes one of the current limitations of nanomedicines. Incidentally, it must be noted that conventional drug molecules achieve tumor accumulations well below 0.1% of the injected dose. Currently, two modus operandi are employed for delivering nanomedicines to tumor tissues: the most commonly used method exploits the hyperpermeability of the tumor vasculature, often referred to as enhanced permeability and retention effect (EPR), whereas the other method aims at targeting the tumor vasculature and subsequent release of therapeutic agents, known as the multistage approach. In the first case, nanomedicines should be smaller than 200 nm to cross intercellular openings occurring within the tumor endothelium passively.53 The second approach mimics leukocyte vascular margination and adhesion and was inspired by a number of theoretical and computational models.54−56 The vascular behavior of molecules, nanoparticles, and cells within a complex network of blood vessels can be predicted using continuum mechanics approaches57,58 and particle-based methods, such as dissipative particle dynamics59,60 and the lattice Boltzmann method.61−63 For instance, it has been shown that submicron particles can efficiently navigate next to vessel walls sensing for vascular abnormalities,57 nonspherical nanoparticles adhere more avidly to diseased endothelial cells,55,62 and deformable capsules tend to move away from vessel walls.59,63 At the cellular level, continuum models as well as molecular simulations have confirmed that spherical nanomedicines tend to be more avidly internalized by cells of the immune system.64−68 At the tissue level, computational models have just started to be used in analyzing the effect of tumor size and vascularity on the spatiotemporal deposition of bloodborne molecules and nanomedicines.69−73

CONTROLLING THE ADSORPTION OF BLOOD PROTEINS Any materials exposed to blood tend to be rapidly covered by different molecules forming a multilayered corona (Figure 1). It is well documented that the formation of a protein corona may dramatically affect the bioavailability, organ-specific distribution, and therapeutic performance of nanomedicines.33−35 Atomistic, molecular, and particle-based simulations can help in developing spatiotemporal models of the protein corona as a function of the nanomedicine surface properties, predicting the density, structural organization, and adsorption rates of multiple blood molecules. For instance, MD and CG models have been used to elucidate the surface adsorption of ubiquitin on silver and gold nanoparticles, demonstrating, in agreement with experimental observations, the formation of a multilayered corona with specific structural changes of ubiquitin.36,37 Other studies have modeled the interaction of albumin,38 fibrinogen,39 and mixtures of blood proteins with differently treated surfaces in order to predict conditions favoring or avoiding the adsorption of blood molecules onto nanomedicines. It should also be noted that large proteins, such as fibrinogen (340 kDa) or albumin (66.5 kDa), once adsorbed on nanomedicines, can fully or partially cover any other chemical moiety originally exposed on the surface. This corona tends to affect the targeting efficacy of nanomedicines decorated with small peptides and ligands (∼1 kDa).40 Another crucial and somewhat controversial topic in the design of nanomedicines is the role of nanoparticle decoration with PEG chains and surface adsorption of molecules of the complement system.41,42 Molecular simulations can predict the interaction of complement fragments, such as C 3d and C 5d, and nanoparticles.43−45 These simulations help in elucidating the basic biochemical mechanisms regulating nanoparticle opsonization and provide information for properly redesigning nanoparticle surfaces.

Hierarchical computational models, spanning multiple temporal and spatial scales, sustained by theoretical calculations would help identify new strategies for enhancing nanomedicine accumulation within diseased tissues.

Modeling and systematic simulations of blood protein adsorption can be used to design surfaces with specific chemical features that would support the formation of an “ideal” protein corona. Modeling and systematic simulations of blood protein adsorption can be used to design surfaces with specific chemical features that would support the formation of an “ideal” protein corona. This in silico designed protein corona could be exploited to modulate recognition and sequestration by cells of the immune system with the potential to increase the targeting efficiency of nanomedicines.46−48

Hierarchical computational models, spanning multiple temporal and spatial scales, sustained by theoretical calculations would help identify new strategies for enhancing nanomedicine accumulation within diseased tissues. These models should be able to account for specific features of nanomedicines, such as the size, shape, surface properties, and mechanical stiffness, as well as the biophysical properties of the target tissue, such as vascular density and permeability, blood perfusion, density of specific cell receptors, intracellular molecular diffusivity, and cell population.

MAXIMIZING ACCUMULATION AT DISEASE SITES AND MODULATING BLOOD LONGEVITY Nanomedicines are administered intravascularly and, as a result of blood-borne transport, can reach distant locations within the vascular network (Figure 1). However, the journey is hindered by a multitude of biological barriers, and often only a small portion of the injected nanomedicine accumulates at disease sites, whereas the remaining portion deposits nonspecifically within organs of the reticulo-endothelial system. A recent analysis conducted retrospectively on a large number of nanomedicines has shown absolute tumor accumulations

CONCLUSIONS AND PROSPECTS Increased loading of therapeutic molecules and improved oncommand release, minimal recognition and sequestration by immune cells, and maximal accumulation at the biological target are three crucial issues for the successful development of nanomedicines. Theoretical and computational scientists should continue the development of models and simulation tools focusing specifically on these issues. C

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Model building and systematic theoretical and computational studies are not only useful for cost-effective in silico screening of nanomedicines, but, perhaps most importantly, they also could help in elucidating the mechanisms regulating the biological behavior and therapeutic performance of nanomedicines. Indeed, it is well-accepted that there may be no successful clinical integration of nanomedicines without a precise understanding of the scientific principles underlying their interaction with biological entities over multiple length scales, from small circulating opsonins to cells, tissues, and the whole organism. This need calls for concerted action by scientists and policy makers. Engineers and theoretical and computational scientists should come together with pharmacologists, chemists, materials scientists, and clinical investigators forming a common research front to address these three major challenges in nanomedicine. In the short term, this collaboration could be fostered by developing thematic workshops and special issues in scientific journals. On a longer time scale, policy makers should continue investing in interdisciplinary and basic research in nanotechnologies applied to medicine, using as a reference the fact that clinical utilization of conventional drugs generally requires several years of research and development by consolidated enterprises, billions of dollars in upfront investments, and an immeasurable number of failures. Nanomedicines can revolutionize the way medicine will be practiced for many generations to come, with unpredictable benefits for humankind. Theoretical and computational scientists have sharp tools for facilitating this revolution.

AUTHOR INFORMATION Corresponding Author

*E-mail: [email protected]. Notes

The author declares no competing financial interest.

ACKNOWLEDGMENTS This project was partially supported by the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement no. 616695 and by AIRC (Italian Association for Cancer ) the individual investigator grant no. 17664. The author acknowledges the contribution of Amy N. Thomas, Visual Designer at the Radiology Department of Stanford University, for all graphical works. REFERENCES (1) Peer, D.; Karp, J. M.; Hong, S.; Farokhzad, O. C.; Margalit, R.; Langer, R. Nanocarriers as an Emerging Platform for Cancer Therapy. Nat. Nanotechnol. 2007, 2, 751−760. (2) Lee, D. E.; Koo, H.; Sun, I. C.; Ryu, J. H.; Kim, K.; Kwon, I. C. Multifunctional Nanoparticles for Multimodal Imaging and Theragnosis. Chem. Soc. Rev. 2012, 41, 2656−2572. (3) Mura, S.; Nicolas, J.; Couvreur, P. Stimuli-Responsive Nanocarriers for Drug Delivery. Nat. Mater. 2013, 12, 991−1003. (4) Anselmo, A. C.; Mitragotri, S. An Overview of Clinical and Commercial Impact of Drug Delivery Systems. J. Controlled Release 2014, 190, 15−28. (5) Min, Y.; Caster, J. M.; Eblan, M. J.; Wang, A. Z. Clinical Translation of Nanomedicine. Chem. Rev. 2015, 115, 11147−11190. (6) Park, K. Facing the Truth about Nanotechnology in Drug Delivery. ACS Nano 2013, 7, 7442−7447. (7) Duncan, R.; Gaspar, R. Nanomedicine(s) Under the Microscope. Mol. Pharmaceutics 2011, 8, 2101−2141. D

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DOI: 10.1021/acsnano.6b05536 ACS Nano XXXX, XXX, XXX−XXX