Multiscale modeling of protein membrane interactions for nanoparticle targeting in drug delivery

Rational nanoparticle design: Optimization using insights from experiments and mathematical models

Polymeric nanoparticles are highly tunable drug delivery systems that show promise in targeting therapeutics to specific sites within the body. Rational nanoparticle design can make use of mathematical models to organize and extend experimental data, allowing for optimization of nanoparticles for particular drug delivery applications. While rational nanoparticle design is attractive from the standpoint of improving therapy and reducing unnecessary experiments, it has yet to be fully realized. The difficulty lies in the complexity of nanoparticle structure and behavior, which is added to the complexity of the physiological mechanisms involved in nanoparticle distribution throughout the body. In this review, we discuss the most important aspects of rational design of polymeric nanoparticles. Ultimately, we conclude that many experimental datasets are required to fully model polymeric nanoparticle behavior at multiple scales. Further, we suggest ways to consider the limitations and uncertainty of experimental data in creating nanoparticle design optimization schema, which we call quantitative nanoparticle design frameworks.

Multiphysics pharmacokinetic model for targeted nanoparticles

Nanoparticles (NP) are being increasingly explored as vehicles for targeted drug delivery because they can overcome free therapeutic limitations by drug encapsulation, thereby increasing solubility and transport across cell membranes. However, a translational gap exists from animal to human studies resulting in only several NP having FDA approval. Because of this, researchers have begun to turn toward physiologically based pharmacokinetic (PBPK) models to guide in vivo NP experimentation. However, typical PBPK models use an empirically derived framework that cannot be universally applied to varying NP constructs and experimental settings. The purpose of this study was to develop a physics-based multiscale PBPK compartmental model for determining continuous NP biodistribution. We successfully developed two versions of a physics-based compartmental model, models A and B, and validated the models with experimental data. The more physiologically relevant model (model B) had an output that more closely resembled experimental data as determined by normalized root mean squared deviation (NRMSD) analysis. A branched model was developed to enable the model to account for varying NP sizes. With the help of the branched model, we were able to show that branching in vasculature causes enhanced uptake of NP in the organ tissue. The models were solved using two of the most popular computational platforms, MATLAB and Julia. Our experimentation with the two suggests the highly optimized ODE solver package DifferentialEquations.jl in Julia outperforms MATLAB when solving a stiff system of ordinary differential equations (ODEs). We experimented with solving our PBPK model with a neural network using Julia's Flux.jl package. We were able to demonstrate that a neural network can learn to solve a system of ODEs when the system can be made non-stiff via quasi-steady-state approximation (QSSA). Our model incorporates modules that account for varying NP surface chemistries, multiscale vascular hydrodynamic effects, and effects of the immune system to create a more comprehensive and modular model for predicting NP biodistribution in a variety of NP constructs.

Effect of dopamine-functionalization, charge and pH on protein corona formation around TiO2 nanoparticles

Inorganic nanoparticles (NPs) are gaining increasing attention in nanomedicine because of their stimuli responsiveness, which allows combining therapy with diagnosis. However, little information is known about their interaction with intracellular or plasma proteins when they are introduced in a biological environment. Here we present atomistic molecular dynamics (MD) simulations investigating the case study of dopamine-functionalized TiO₂ nanoparticles and two proteins that are overexpressed in cancer cells, i.e. PARP1 and HSP90, since experiments proved them to be the main components of the corona in cell cultures. The mechanism and the nature of the interaction (electrostatic, van der Waals, H-bonds, etc.) is unravelled by defining the protein residues that are more frequently in contact with the NPs, the extent of contact surface area and the variations in the protein secondary structures, at different pH and ionic strength conditions of the solution where they are immersed to simulate a realistic biological environment. The effects of the NP surface functionalization and charge are also considered. Our MD results suggest that less acidic intracellular pH conditions in the presence of cytosolic ionic strength enhance PARP1 interaction with the nanoparticle, whereas the HSP90 contribution is partly weakened, providing a rational explanation to existing experimental observations.

Quantifying and controlling bond multivalency for advanced nanoparticle targeting to cells

Nanoparticles have drawn intense interest as delivery agents for diagnosing and treating various cancers. Much of the early success was driven by passive targeting mechanisms such as the enhanced permeability and retention (EPR) effect, but this has failed to lead to the expected clinical successes. Active targeting involves binding interactions between the nanoparticle and cancer cells, which promotes tumor cell-specific accumulation and internalization. Furthermore, nanoparticles are large enough to facilitate multiple bond formation, which can improve adhesive properties substantially in comparison to the single bond case. While multivalent binding is universally believed to be an attribute of nanoparticles, it is a complex process that is still poorly understood and difficult to control. In this review, we will first discuss experimental studies that have elucidated roles for parameters such as nanoparticle size and shape, targeting ligand and target receptor densities, and monovalent binding kinetics on multivalent nanoparticle adhesion efficiency and cellular internalization. Although such experimental studies are very insightful, information is limited and confounded by numerous differences across experimental systems. Thus, we focus the second part of the review on theoretical aspects of binding, including kinetics, biomechanics, and transport physics. Finally, we discuss various computational and simulation studies of nanoparticle adhesion, including advanced treatments that compare directly to experimental results. Future work will ideally continue to combine experimental data and advanced computational studies to extend our knowledge of multivalent adhesion, as well as design the most powerful nanoparticle-based agents to treat cancer.

Local biomaterial-assisted antitumour immunotherapy for effusions in the pleural and peritoneal cavities caused by malignancies

Malignant pleural effusion (MPE) and malignant ascites (MA), which are common but serious conditions caused by malignancies, are related to poor quality of life and high mortality. Current treatments, including therapeutic thoracentesis and indwelling pleural catheters or paracentesis and catheter drainage, are largely palliative. An effective treatment is urgently needed. MPE and MA are excellent candidates for intratumoural injections that have direct contact with tumour cells and kill tumour cells more effectively and efficiently with fewer side effects, and the fluid environment of MPE and MA can provide a homogeneous area for drug distribution. The immunosuppressive environments within the pleural and peritoneal cavities suggest the feasibility of local immunotherapy. In this review, we introduce the current management of MPE and MA, discuss the latest advances and challenges in utilizing local biomaterial-assisted antitumour therapies for the treatment of MPE and MA, and discuss further opportunities in this field.

Elucidating the rheological implications of adding particles in blood

In the past few decades, nanotechnology has been employed to provide breakthroughs in the diagnosis and treatment of several diseases using drug-carrying particles (DCPs). In such an endeavor, the optimal design of DCPs is paramount, which necessitates the use of an accurate and trustworthy constitutive model in computational fluid dynamics (CFD) simulators. We herein introduce a continuum model for elaborating on the rheological implications of adding particles in blood. The model is developed using non-equilibrium thermodynamics to guarantee thermodynamic admissibility. Red blood cells are modeled as deformed droplets with a constant volume that are able to aggregate, whereas particles are considered rigid spheroids. The model predictions are compared favorably against rheological data for both spherical and non-spherical particles immersed in non-aggregating blood. It is expected that the use of this model will allow for the testing of DCPs in virtual patients and for their tailor-design in treating various diseases.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s00397-021-01289-x.