An Experimental and Finite Element Protocol to Investigate the Transport of Neutral and Charged Solutes across Articular Cartilage

Osteoarthritis (OA) is a debilitating disease that is associated with degeneration of articular cartilage and subchondral bone. Degeneration of articular ...

Delft University of Technology (TU Delft)

UMC Utrecht

One of the most widely used techniques to determine the mechanical properties of cartilage is based on indentation tests and interpretation of the obtained force-time or displacement-time data. In the current computational approaches, one needs to simulate the indentation test with finite element models and use an optimization algorithm to estimate the mechanical properties of cartilage. The modeling procedure is cumbersome, and the simulations need to be repeated for every new experiment. For the first time, we propose a method for fast and accurate estimation of the mechanical and physical properties of cartilage as a poroelastic material with the aid of artificial neural networks. In our study, we used finite element models to simulate the indentation for poroelastic materials with wide combinations of mechanical and physical properties. The obtained force-time curves are then divided into three parts: the first two parts of the data is used for training and validation of an artificial neural network, while the third part is used for testing the trained network. The trained neural network receives the force-time curves as the input and provides the properties of cartilage as the output. We observed that the trained network could accurately predict the properties of cartilage within the range of properties for which it was trained. The mechanical and physical properties of cartilage could therefore be estimated very fast, since no additional finite element modeling is required once the neural network is trained. The robustness of the trained artificial neural network in determining the properties of cartilage based on noisy force-time data was assessed by introducing noise to the simulated force-time data. We found that the training procedure could be optimized so as to maximize the robustness of the neural network against noisy force-time data.

Determination of the mechanical and physical properties of cartilage by coupling poroelastic-based finite element models of indentation with artificial neural networks.

Charged and uncharged solutes penetrate through cartilage to maintain the metabolic function of chondrocytes and to possibly restore or further breakdown the cartilage tissue in different stages of osteoarthritis. In this study the transport of charged solutes across the various zones of cartilage was quantified, taken into account the physicochemical interactions between the solute and the cartilage constituents. A multiphasic finite-bath finite element (FE) model was developed to simulate equine cartilage diffusion experiments that used a negatively charged contrast agent (ioxaglate) in combination with serial micro-computed tomography (micro-CT) to measure the diffusion. By comparing the FE model with the experimental data both the diffusion coefficient of ioxaglate and the fixed charge density (FCD) were obtained. In the multiphasic model, cartilage was divided into multiple (three) zones to help understand how diffusion coefficient and FCD vary across cartilage thickness. The direct effects of charged solute-FCD interaction on diffusion were investigated by comparing the diffusion coefficients derived from the multiphasic and biphasic-solute models. We found a relationship between the FCD obtained by the multiphasic model and ioxaglate partitioning obtained from micro-CT experiments. Using our multi-zone multiphasic model, diffusion coefficient of the superficial zone was up to ten-fold higher than that of the middle zone, while the FCD of the middle zone was up to almost two-fold higher than that of the superficial zone. In conclusion, the developed finite-bath multiphasic model provides us with a non-destructive method by which we could obtain both diffusion coefficient and FCD of different cartilage zones. The outcomes of the current work will also help understand how charge of the bath affects the diffusion of a charged molecule and also predict the diffusion behavior of a charged solute across articular cartilage.

Multiphasic modeling of charged solute transport across articular cartilage: Application of multi-zone finite-bath model.

Analytical and numerical methods have been used to extract essential engineering parameters such as elastic modulus, Poisson׳s ratio, permeability and diffusion coefficient from experimental data in various types of biological tissues. The major limitation associated with analytical techniques is that they are often only applicable to problems with simplified assumptions. Numerical multi-physics methods, on the other hand, enable minimizing the simplified assumptions but require substantial computational expertise, which is not always available. In this paper, we propose a novel approach that combines inverse and forward artificial neural networks (ANNs) which enables fast and accurate estimation of the diffusion coefficient of cartilage without any need for computational modeling. In this approach, an inverse ANN is trained using our multi-zone biphasic-solute finite-bath computational model of diffusion in cartilage to estimate the diffusion coefficient of the various zones of cartilage given the concentration-time curves. Robust estimation of the diffusion coefficients, however, requires introducing certain levels of stochastic variations during the training process. Determining the required level of stochastic variation is performed by coupling the inverse ANN with a forward ANN that receives the diffusion coefficient as input and returns the concentration-time curve as output. Combined together, forward-inverse ANNs enable computationally inexperienced users to obtain accurate and fast estimation of the diffusion coefficients of cartilage zones. The diffusion coefficients estimated using the proposed approach are compared with those determined using direct scanning of the parameter space as the optimization approach. It has been shown that both approaches yield comparable results.

Combined inverse-forward artificial neural networks for fast and accurate estimation of the diffusion coefficients of cartilage based on multi-physics models.

Transport of solutes helps to regulate normal physiology and proper function of cartilage in diarthrodial joints. Multiple studies have shown the effects of characteristic parameters such as concentration of proteoglycans and collagens and the orientation of collagen fibrils on the diffusion process. However, not much quantitative information and accurate models are available to help understand how the characteristics of the fluid surrounding articular cartilage influence the diffusion process. In this study, we used a combination of micro-computed tomography experiments and biphasic-solute finite element models to study the effects of three parameters of the overlying bath on the diffusion of neutral solutes across cartilage zones. Those parameters include bath size, degree of stirring of the bath, and the size and concentration of the stagnant layer that forms at the interface of cartilage and bath. Parametric studies determined the minimum of the finite bath size for which the diffusion behavior reduces to that of an infinite bath. Stirring of the bath proved to remarkably influence neutral solute transport across cartilage zones. The well-stirred condition was achieved only when the ratio of the diffusivity of bath to that of cartilage was greater than ≈1000. While the thickness of the stagnant layer at the cartilage-bath interface did not significantly influence the diffusion behavior, increase in its concentration substantially elevated solute concentration in cartilage. Sufficient stirring attenuated the effects of the stagnant layer. Our findings could be used for efficient design of experimental protocols aimed at understanding the transport of molecules across articular cartilage.

Application of multiphysics models to efficient design of experiments of solute transport across articular cartilage.

The metabolic function of cartilage primarily depends on transport of solutes through diffusion mechanism. In the current study, we use contrast enhanced micro-computed tomography to determine equilibrium concentration of solutes through different cartilage zones and solute flux in the cartilage, using osteochondral plugs from equine femoral condyles. Diffusion experiments were performed with two solutes of different charge and approximately equal molecular weight, namely iodixanol (neutral) and ioxaglate (charge=-1) in order to isolate the effects of solute's charge on diffusion. Furthermore, solute concentrations as well as bath osmolality were changed to isolate the effects of steric hindrance on diffusion. Bath concentration and bath osmolality only had minor effects on the diffusion of the neutral solute through cartilage at the surface, middle and deep zones, indicating that the diffusion of the neutral solute was mainly Fickian. The negatively charged solute diffused considerably slower through cartilage than the neutral solute, indicating a large non-Fickian contribution in the diffusion of charged molecules. The numerical models determined maximum solute flux in the superficial zone up to a factor of 2.5 lower for the negatively charged solutes (charge=-1) as compared to the neutral solutes confirming the importance of charge-matrix interaction in diffusion of molecules across cartilage.

Isolated effects of external bath osmolality, solute concentration, and electrical charge on solute transport across articular cartilage.

Investigation of the solute transfer across articular cartilage and subchondral bone plate could nurture the understanding of the mechanisms of osteoarthritis (OA) progression. In the current study, we approached the transport of neutral solutes in human (slight OA) and equine (healthy) samples using both computed tomography and biphasic-solute finite element modeling. We developed a multi-zone biphasic-solute finite element model (FEM) accounting for the inhomogeneity of articular cartilage (superficial, middle and deep zones) and subchondral bone plate. Fitting the FEM model to the concentration-time curves of the cartilage and the equilibrium concentration of the subchondral plate/calcified cartilage enabled determination of the diffusion coefficients in the superficial, middle and deep zones of cartilage and subchondral plate. We found slightly higher diffusion coefficients for all zones in the human samples as compared to the equine samples. Generally the diffusion coefficient in the superficial zone of human samples was about 3-fold higher than the middle zone, the diffusion coefficient of the middle zone was 1.5-fold higher than that of the deep zone, and the diffusion coefficient of the deep zone was 1.5-fold higher than that of the subchondral plate/calcified cartilage. Those ratios for equine samples were 9, 2 and 1.5, respectively. Regardless of the species considered, there is a gradual decrease of the diffusion coefficient as one approaches the subchondral plate, whereas the rate of decrease is dependent on the type of species.

Neutral solute transport across osteochondral interface: A finite element approach.

Cross-talk of subchondral bone and articular cartilage could be an important aspect in the etiology of osteoarthritis. Previous research has provided some evidence of transport of small molecules (~370Da) through the calcified cartilage and subchondral bone plate in murine osteoarthritis models. The current study, for the first time, uses a neutral diffusing computed tomography (CT) contrast agent (iodixanol, ~1550Da) to study the permeability of the osteochondral interface in equine and human samples. Sequential CT monitoring of diffusion after injecting a finite amount of contrast agent solution onto the cartilage surface using a micro-CT showed penetration of the contrast molecules across the cartilage-bone interface. Moreover, diffusion through the cartilage-bone interface was affected by thickness and porosity of the subchondral bone as well as the cartilage thickness in both human and equine samples. Our results revealed that porosity of the subchondral plate contributed more strongly to the diffusion across osteochondral interface compared to other morphological parameters in healthy equine samples. However, thickness of the subchondral plate contributed more strongly to the diffusion in slightly osteoarthritic human samples.

Vahid Arbabi

Department of Biomechanical Engineering,
Faculty of Mechanical,
Maritime,
and Materials Engineering,
Department of Biomechanical Engineering, Faculty of Mechanical, Maritime, and Materials Engineering

Delft University of Technology (TU Delft)

An Experimental and Finite Element Protocol to Investigate the Transport of Neutral and Charged Solutes across Articular Cartilage

Vahid Arbabi

.css-o250i3{font-size:18px;font-family:Open Sans,sans-serif;line-height:21.6px;}Department of Biomechanical Engineering,Faculty of Mechanical,Maritime,and Materials Engineering,Department of Biomechanical Engineering, Faculty of Mechanical, Maritime, and Materials Engineering

Delft University of Technology (TU Delft)

An Experimental and Finite Element Protocol to Investigate the Transport of Neutral and Charged Solutes across Articular Cartilage

Department of Biomechanical Engineering,
Faculty of Mechanical,
Maritime,
and Materials Engineering,
Department of Biomechanical Engineering, Faculty of Mechanical, Maritime, and Materials Engineering