Modeling IL-1 induced degradation of articular cartilage

The future of in-field sports biomechanics: wearables plus modelling compute real-time in vivo tissue loading to prevent and repair musculoskeletal injuries

This paper explores the use of biomechanics in identifying the mechanistic causes of musculoskeletal tissue injury and degeneration. It appraises how biomechanics has been used to develop training programmes aiming to maintain or recover tissue health. Tissue health depends on the functional mechanical environment experienced by tissues during daily and rehabilitation activities. These environments are the result of the interactions between tissue motion, loading, biology, and morphology. Maintaining health of and/or repairing musculoskeletal tissues requires targeting the "ideal" in vivo tissue mechanics (i.e., loading and deformation), which may be enabled by appropriate real-time biofeedback. Recent research shows that biofeedback technologies may increase their quality and effectiveness by integrating a personalised neuromusculoskeletal modelling driven by real-time motion capture and medical imaging. Model personalisation is crucial in obtaining physically and physiologically valid predictions of tissue biomechanics. Model real-time execution is crucial and achieved by code optimisation and artificial intelligence methods. Furthermore, recent work has also shown that laboratory-based motion capture biomechanical measurements and modelling can be performed outside the laboratory with wearable sensors and artificial intelligence. The next stage is to combine these technologies into well-designed easy to use products to guide training to maintain or recover tissue health in the real-world.

Knee-Loading Predictions with Neural Networks Improve Finite Element Modeling Classifications of Knee Osteoarthritis: Data from the Osteoarthritis Initiative

Physics-based modeling methods have the potential to investigate the mechanical factors associated with knee osteoarthritis (OA) and predict the future radiographic condition of the joint. However, it remains unclear what level of detail is optimal in these methods to achieve accurate prediction results in cohort studies. In this work, we extended a template-based finite element (FE) method to include the lateral and medial compartments of the tibiofemoral joint and simulated the mechanical responses of 97 knees under three conditions of gait loading. Furthermore, the effects of variations in cartilage thickness and failure equation on predicted cartilage degeneration were investigated. Our results showed that using neural network-based estimations of peak knee loading provided classification performances of 0.70 (AUC, p < 0.05) in distinguishing between knees that developed severe OA or mild OA and knees that did not develop OA eight years after a healthy radiographic baseline. However, FE models incorporating subject-specific femoral and tibial cartilage thickness did not improve this classification performance, suggesting there exists an optimal point between personalized loading and geometry for discrimination purposes. In summary, we proposed a modeling framework that streamlines the rapid generation of individualized knee models achieving promising classification performance while avoiding motion capture and cartilage image segmentation.

Advanced application of carbohydrate-based micro/nanoparticles for rheumatoid arthritis

Rheumatoid arthritis (RA) is a kind of synovitis and progressive joint destruction disease. Dysregulated immune cell activation, inflammatory cytokine overproduction, and subsequent reactive oxidative species (ROS) production contribute to the RA process. Carbohydrates, including cellulose, chitosan, alginate and dextran, are among the most abundant and important biomolecules in nature and are widely used in biomedicine. Carbohydrate-based micro/nanoparticles(M/NPs) as functional excipients have the ability to improve the bioavailability, solubility and stability of numerous drugs used in RA therapy. For on-demand therapy, smart reactive M/NPs have been developed to respond to a variety of chemical and physical stimuli, including light, temperature, enzymes, pH and ROS, alternating their physical and macroscopic properties, resulting in innovative new drug delivery systems. In particular, advanced products with targeted dextran or hyaluronic acid are exploiting multiple beneficial properties at the same time. In addition to those that respond, there are promising new derivatives in development with microenvironment and chronotherapy effects. In this review, we provide an overview of these recent developments and an outlook on how this class of agents will further shape the landscape of drug delivery for RA treatment.

Maintaining soldier musculoskeletal health using personalised digital humans, wearables and/or computer vision

OBJECTIVES

The physical demands of military service place soldiers at risk of musculoskeletal injuries and are major concerns for military capability. This paper outlines the development new training technologies to prevent and manage these injuries.

DESIGN

Narrative review.

METHODS

Technologies suitable for integration into next-generation training devices were examined. We considered the capability of technologies to target tissue level mechanics, provide appropriate real-time feedback, and their useability in-the-field.

RESULTS

Musculoskeletal tissues' health depends on their functional mechanical environment experienced in military activities, training and rehabilitation. These environments result from the interactions between tissue motion, loading, biology, and morphology. Maintaining health of and/or repairing joint tissues requires targeting the "ideal" in vivo tissue mechanics (i.e., loading and strain), which may be enabled by real-time biofeedback. Recent research has shown that these biofeedback technologies are possible by integrating a patient's personalised digital twin and wireless wearable devices. Personalised digital twins are personalised neuromusculoskeletal rigid body and finite element models that work in real-time by code optimisation and artificial intelligence. Model personalisation is crucial in obtaining physically and physiologically valid predictions.

CONCLUSIONS

Recent work has shown that laboratory-quality biomechanical measurements and modelling can be performed outside the laboratory with a small number of wearable sensors or computer vision methods. The next stage is to combine these technologies into well-designed easy to use products.

Effects of dexamethasone and dynamic loading on cartilage of human osteochondral explants challenged with inflammatory cytokines

Post-traumatic osteoarthritis (PTOA), characterized by articular cartilage degradation initiated in an inflammatory environment after traumatic joint injury, can lead to alterations in cartilage biomechanical properties. Low dose dexamethasone (Dex) shows chondroprotection in cartilage challenged with inflammatory cytokines, but little is known about the structural biomechanical response of human cartilage to Dex in such a diseased state. This study examined changes in the biomechanical properties and biochemical composition of the cartilage within human osteochondral explants in response to treatment with exogenous cytokines, Dex, and a regimen of cyclic loading at the start and end of culture. Osteochondral explants were harvested from five pairs of human ankle talocrural joints (Collins grade 0-1) and cultured for 10 days with/without exogenous cytokines (100 ng/mL TNFα, 50 ng/mL IL-6, 250 ng/mL sIL-6R) ± Dex (100 nM). Biomechanical testing on day-0 and day-10 enabled estimation of the unconfined compression equilibrium modulus (E_y), dynamic stiffness (E_d) and hydraulic permeability (k_p) of cartilage excised from bone, accompanied by biochemical assessment of media and cartilage tissue. Dex preserved chondrocyte cell viability and decreased sulfated glycosaminoglycan (sGAG) loss and nitric oxide release, but did not alter E_y, E_d and k_p (before or after loading) on day-10. In the cytokine/cytokine+Dex treated groups, sGAG content exhibited a weaker correlation with E_y and E_d than at baseline, suggesting an important role for structural rather than biochemical changes in producing biomechanical alterations in response to cytokines and Dex. These findings aid in forming a more complete profile of potential clinical effects of Dex for use in OA/PTOA treatment regimens.

Surrogate modeling of articular cartilage degradation to understand the synergistic role of MMP-1 and MMP-9: a case study

A characteristic feature of arthritic diseases is cartilage extracellular matrix (ECM) degradation, often orchestrated by the overexpression of matrix metalloproteinases (MMPs) and other proteases. The interplay between fibril level degradation and the tissue-level aggregate response to biomechanical loading was explored in this work by a computational multiscale cartilaginous model. We considered the relative abundance of collagenases (MMP-1) and gelatinases (MMP-9) in surrogate models, where the diffusion (spatial distribution) of these enzymes and the subsequent, co-localized fibrillar damage were spatially randomized with Latin Hypercube Sampling. The computational model was constructed by incorporating the results from prior molecular dynamics simulations (tensile test) of microfibril degradation into a hyper-elastoplastic fibril-reinforced cartilage model. Including MMPs-mediated collagen fibril-level degradation in computational models may help understand the ECM pathomechanics at the tissue level. The mechanics of cartilage tissue and fibril show variations in mechanical integrity depending on the different combinations of MMPs-1 and 9 with a concentration ratio of 1:1, 3:1, and 1:3 in simulated indentation tests. The fibril yield (local failure) was initiated at 20.2 ± 3.0 (%) and at 23.0 ± 2.8 (%) of bulk strain for col 1:gel 3 and col 3: gel 1, respectively. The reduction in failure stress (global response) was 39.8% for col 1:gel 3, 37.5% for col 1:gel 1, and 36.7% for col 3:gel 1 compared with the failure stress of the degradation free tissue. These findings indicate that cartilage's global and local mechanisms of failure largely depend on the relative abundance of the two key enzymes-collagenase (MMP-1) and gelatinase (MMP-9) and the spatial characteristics of diffusion across the layers of the cartilage ECM.

Injury-related cell death and proteoglycan loss in articular cartilage: Numerical model combining necrosis, reactive oxygen species, and inflammatory cytokines

Osteoarthritis (OA) is a common musculoskeletal disease that leads to deterioration of articular cartilage, joint pain, and decreased quality of life. When OA develops after a joint injury, it is designated as post-traumatic OA (PTOA). The etiology of PTOA remains poorly understood, but it is known that proteoglycan (PG) loss, cell dysfunction, and cell death in cartilage are among the first signs of the disease. These processes, influenced by biomechanical and inflammatory stimuli, disturb the normal cell-regulated balance between tissue synthesis and degeneration. Previous computational mechanobiological models have not explicitly incorporated the cell-mediated degradation mechanisms triggered by an injury that eventually can lead to tissue-level compositional changes. Here, we developed a 2-D mechanobiological finite element model to predict necrosis, apoptosis following excessive production of reactive oxygen species (ROS), and inflammatory cytokine (interleukin-1)-driven apoptosis in cartilage explant. The resulting PG loss over 30 days was simulated. Biomechanically triggered PG degeneration, associated with cell necrosis, excessive ROS production, and cell apoptosis, was predicted to be localized near a lesion, while interleukin-1 diffusion-driven PG degeneration was manifested more globally. Interestingly, the model also showed proteolytic activity and PG biosynthesis closer to the levels of healthy tissue when pro-inflammatory cytokines were rapidly inhibited or cleared from the culture medium, leading to partial recovery of PG content. The numerical predictions of cell death and PG loss were supported by previous experimental findings. Furthermore, the simulated ROS and inflammation mechanisms had longer-lasting effects (over 3 days) on the PG content than localized necrosis. The mechanobiological model presented here may serve as a numerical tool for assessing early cartilage degeneration mechanisms and the efficacy of interventions to mitigate PTOA progression.

Computational Modelling for Managing Pathways to Cartilage Failure

Over several decades the perception and therefore description of articular cartilage changed substantially. It has transitioned from being described as a relatively inert tissue with limited repair capacity, to a tissue undergoing continuous maintenance and even adaption, through a range of complex regulatory processes. Even from the narrower lens of biomechanics, the engagement with articular cartilage has changed from it being an interesting, slippery material found in the hostile mechanical environment between opposing long bones, to an intriguing example of mechanobiology in action. The progress revealing this complexity, where physics, chemistry, material science and biology are merging, has been described with increasingly sophisticated computational models. Here we describe how these computational models of cartilage as an integrated system can be combined with the approach of structural reliability analysis. That is, causal, deterministic models placed in the framework of the probabilistic approach of structural reliability analysis could be used to understand, predict, and mitigate the risk of cartilage failure or pathology. At the heart of this approach is seeing cartilage overuse and disease processes as a 'material failure', resulting in failure to perform its function, which is largely mechanical. One can then describe pathways to failure, for example, how homeostatic repair processes can be overwhelmed leading to a compromised tissue. To illustrate this 'pathways to failure' approach, we use the interplay between cartilage consolidation and lubrication to analyse the increase in expected wear rates associated with cartilage defects or meniscectomy.

Polyacrylamide hydrogel lubricates cartilage after biochemical degradation and mechanical injury

Intra-articular injections of hyaluronic acid have been a mainstay of osteoarthritis treatment for decades. However, controversy surrounds the mechanism of action and efficacy of this therapy. As such, there has been recent interest in developing synthetic lubricants that lubricate cartilage. Recently, a synthetic 4 wt% polyacrylamide (pAAm) hydrogel was shown to effectively decrease lameness in horses. However, its mechanism of action and ability to lubricate cartilage is unknown. The goal of this study was to characterize the lubricating ability of this hydrogel and determine its efficacy for healthy and degraded cartilage. The study utilized previously established IL-1β-induced biochemical degradation and mechanical impact injury models to degrade cartilage. The lubricating ability of the hydrogel was then characterized using a custom-built tribometer using a glass counterface and friction was evaluated using the Stribeck framework for articular cartilage. pAAm hydrogel was shown to significantly lower the friction coefficient of cartilage explants from both degradation models (30%-40% reduction in friction relative to controls). A striking finding from this study was the aggregation of the pAAm hydrogel at the articulating surface. The surface aggregation was observed in the histological sections of explants from all treatment groups after tribological evaluation. Using the Stribeck framework, the hydrogel was mapped to higher Sommerfeld numbers and was characterized as a viscous lubricant predominantly in the minimum friction mode. In summary, this study revealed that pAAm hydrogel lubricates native and degraded cartilage explants effectively and may have an affinity for the articulating surface of the cartilage.

Multiscale In Silico Modeling of Cartilage Injuries

Injurious loading of the joint can be accompanied by articular cartilage damage and trigger inflammation. However, it is not well-known which mechanism controls further cartilage degradation, ultimately leading to post-traumatic osteoarthritis. For personalized prognostics, there should also be a method that can predict tissue alterations following joint and cartilage injury. This chapter gives an overview of experimental and computational methods to characterize and predict cartilage degradation following joint injury. Two mechanisms for cartilage degradation are proposed. In (1) biomechanically driven cartilage degradation, it is assumed that excessive levels of strain or stress of the fibrillar or non-fibrillar matrix lead to proteoglycan loss or collagen damage and degradation. In (2) biochemically driven cartilage degradation, it is assumed that diffusion of inflammatory cytokines leads to degradation of the extracellular matrix. When implementing these two mechanisms in a computational in silico modeling workflow, supplemented by in vitro and in vivo experiments, it is shown that biomechanically driven cartilage degradation is concentrated on the damage environment, while inflammation via synovial fluid affects all free cartilage surfaces. It is also proposed how the presented in silico modeling methodology may be used in the future for personalized prognostics and treatment planning of patients with a joint injury.

Shear strain and inflammation-induced fixed charge density loss in the knee joint cartilage following ACL injury and reconstruction: A computational study

Excessive tissue deformation near cartilage lesions and acute inflammation within the knee joint after anterior cruciate ligament (ACL) rupture and reconstruction surgery accelerate the loss of fixed charge density (FCD) and subsequent cartilage tissue degeneration. Here, we show how biomechanical and biochemical degradation pathways can predict FCD loss using a patient-specific finite element model of an ACL reconstructed knee joint exhibiting a chondral lesion. Biomechanical degradation was based on the excessive maximum shear strains that may result in cell apoptosis, while biochemical degradation was driven by the diffusion of pro-inflammatory cytokines. We found that the biomechanical model was able to predict substantial localized FCD loss near the lesion and on the medial areas of the lateral tibial cartilage. In turn, the biochemical model predicted FCD loss all around the lesion and at intact areas; the highest FCD loss was at the cartilage-synovial fluid-interface and decreased toward the deeper zones. Interestingly, simulating a downturn of an acute inflammatory response by reducing the cytokine concentration exponentially over time in synovial fluid led to a partial recovery of FCD content in the cartilage. Our novel numerical approach suggests that in vivo FCD loss can be estimated in injured cartilage following ACL injury and reconstruction. Our novel modeling platform can benefit the prediction of PTOA progression and the development of treatment interventions such as disease-modifying drug testing and rehabilitation strategies.

Poly beta amino ester coated emulsions of NSAIDs for cartilage treatment

Delivering drugs directly into cartilage is still the major challenge in the management and treatment of osteoarthritis (OA) resulting from the aneural, avascular and alymphatic nature of an articular cartilage structure. Progress has been made in the design of drug delivery systems that enhance corticosteroid uptake and retention in cartilage; however also non-steroidal anti-inflammatory drugs (NSAIDs) are prescribed for patients affected by OA and a drug delivery system specifically designed for this drug category is currently unavailable. We developed an approach based on the preparation of NSAID oil-in-water emulsions coated with poly-beta-amino-esters (PBAEs) to exploit the cartilage penetrating ability of such polymers and the high solubility of drugs in oil. These emulsions containing different NSAIDs (indomethacin, ketorolac, diclofenac and naproxen) exhibited enhanced and prolonged drug localisation not only in healthy cartilage tissues but also in early-stage OA samples. The critical role of the PBAE layer on oil droplets was established along with the retained biological activity of the drug as glycosaminoglycan (GAG) and collagen degradation induced by interleukin-1 (IL-1) was prevented by the novel technology. Oil-in-water coated emulsions are very flexible and cost-effective drug delivery systems and such an approach presented here could provide a substantial improvement in the therapeutic treatments of OA and thus patients' outcomes.

Identification of locations susceptible to osteoarthritis in patients with anterior cruciate ligament reconstruction: Combining knee joint computational modelling with follow-up T1ρ and T2 imaging

BACKGROUND

Finite element modelling can be used to evaluate altered loading conditions and failure locations in knee joint tissues. One limitation of this modelling approach has been experimental comparison. The aims of this proof-of-concept study were: 1) identify areas susceptible to osteoarthritis progression in anterior cruciate ligament reconstructed patients using finite element modelling; 2) compare the identified areas against changes in T₂ and T_1ρ values between 1-year and 3-year follow-up timepoints.

METHODS

Two patient-specific finite element models of knee joints with anterior cruciate ligament reconstruction were created. The knee geometry was based on clinical magnetic resonance imaging and joint loading was obtained via motion capture. We evaluated biomechanical parameters linked with cartilage degeneration and compared the identified risk areas against T₂ and T_1ρ maps.

FINDINGS

The risk areas identified by the finite element models matched the follow-up magnetic resonance imaging findings. For Patient 1, excessive values of maximum principal stresses and shear strains were observed in the posterior side of the lateral tibial and femoral cartilage. For Patient 2, high values of maximum principal stresses and shear strains of cartilage were observed in the posterior side of the medial joint compartment. For both patients, increased T₂ and T_1ρ values between the follow-up times were observed in the same areas.

INTERPRETATION

Finite element models with patient-specific geometries and motions and relatively simple material models of tissues were able to identify areas susceptible to post-traumatic knee osteoarthritis. We suggest that the methodology presented here may be applied in large cohort studies.

Activating Nrf2 signalling alleviates osteoarthritis development by inhibiting inflammasome activation

Osteoarthritis (OA), which is characterized by proliferation of subchondral bone and the degeneration of articular cartilage, is the most prevalent human arthritis. Nod-like receptor pyrin domain 3 (NLRP3) inflammasome is a hot spot in recent year and has been reported to be associated with OA synovial inflammation. However, there are few studies on NLRP3 inflammasome in chondrocyte. Licochalcone A (Lico A), a compound extracted from Glycyrrhiza species, has various biological effects such as anti-inflammation, anti-apoptotic, anti-cancer and anti-oxidation. In this study, we investigated the protective effect of Lico A on chondrocytes stimulated by lipopolysaccharide (LPS) and surgically induced OA models. In vitro, Lico A could reduce the expression of NLRP3, apoptosis-associated speck-like protein (ASC), Gasdermin D (GSDMD), caspase-1, interleukin-1beta (IL-1β) and IL-18, which indicated that Lico A attenuates LPS-induced chondrocytes pyroptosis. In addition, Lico A ameliorates the degradation of extracellular matrix (ECM) by enhancing the expression of aggrecan and collagen-II. Meanwhile, we found that Lico A inhibits NLRP3 inflammasome via nuclear factor erythroid-2-related factor 2 (Nrf2)/haeme oxygenase-1(HO-1)/nuclear factor kappa-B (NF-κB) axis. And the Nrf2 small interfering RNA (siRNA) could reverse the anti-pyroptosis effects of Lico A in mouse OA chondrocytes. In vivo, Lico A mitigates progression OA in a mouse model and reduces OA Research Society International (OARSI) scores. Thus, Lico A may have therapeutic potential in OA.

Macroscale mesenchymal condensation to study cytokine-driven cellular and matrix-related changes during cartilage degradation

Understanding the pathophysiological processes of cartilage degradation requires adequate model systems to develop therapeutic strategies towards osteoarthritis (OA). Although different in vitro or in vivo models have been described, further comprehensive approaches are needed to study specific disease aspects. This study aimed to combine in vitro and in silico modeling based on a tissue-engineering approach using mesenchymal condensation to mimic cytokine-induced cellular and matrix-related changes during cartilage degradation. Thus, scaffold-free cartilage-like constructs (SFCCs) were produced based on self-organization of mesenchymal stromal cells (mesenchymal condensation) and (i) characterized regarding their cellular and matrix composition or secondly (ii) treated with interleukin-1β (IL-1β) and tumor necrosis factor α (TNFα) for 3 weeks to simulate OA-related matrix degradation. In addition, an existing mathematical model based on partial differential equations was optimized and transferred to the underlying settings to simulate the distribution of IL-1β, type II collagen degradation and cell number reduction. By combining in vitro and in silico methods, we aimed to develop a valid, efficient alternative approach to examine and predict disease progression and effects of new therapeutics.

Mechanobiological model for simulation of injured cartilage degradation via pro-inflammatory cytokines and mechanical stimulus

Post-traumatic osteoarthritis (PTOA) is associated with cartilage degradation, ultimately leading to disability and decrease of quality of life. Two key mechanisms have been suggested to occur in PTOA: tissue inflammation and abnormal biomechanical loading. Both mechanisms have been suggested to result in loss of cartilage proteoglycans, the source of tissue fixed charge density (FCD). In order to predict the simultaneous effect of these degrading mechanisms on FCD content, a computational model has been developed. We simulated spatial and temporal changes of FCD content in injured cartilage using a novel finite element model that incorporates (1) diffusion of the pro-inflammatory cytokine interleukin-1 into tissue, and (2) the effect of excessive levels of shear strain near chondral defects during physiologically relevant loading. Cytokine-induced biochemical cartilage explant degradation occurs near the sides, top, and lesion, consistent with the literature. In turn, biomechanically-driven FCD loss is predicted near the lesion, in accordance with experimental findings: regions near lesions showed significantly more FCD depletion compared to regions away from lesions (p<0.01). Combined biochemical and biomechanical degradation is found near the free surfaces and especially near the lesion, and the corresponding bulk FCD loss agrees with experiments. We suggest that the presence of lesions plays a role in cytokine diffusion-driven degradation, and also predisposes cartilage for further biomechanical degradation. Models considering both these cartilage degradation pathways concomitantly are promising in silico tools for predicting disease progression, recognizing lesions at high risk, simulating treatments, and ultimately optimizing treatments to postpone the development of PTOA.

Effect of collagenase-gelatinase ratio on the mechanical properties of a collagen fibril: a combined Monte Carlo-molecular dynamics study

Loading in cartilage is supported primarily by fibrillar collagen, and damage will impair the function of the tissue, leading to pathologies such as osteoarthritis. Damage is initiated by two types of matrix metalloproteinases, collagenase and gelatinase, that cleave and denature the collagen fibrils in the tissue. Experimental and modeling studies have revealed insights into the individual contributions of these two types of MMPs, as well as the mechanical response of intact fibrils and fibrils that have experienced random surface degradation. However, no research has comprehensively examined the combined influences of collagenases and gelatinases on collagen degradation nor studied the mechanical consequences of biological degradation of collagen fibrils. Such preclinical examinations are required to gain insights into understanding, treating, and preventing degradation-related cartilage pathology. To develop these insights, we use sequential Monte Carlo and molecular dynamics simulations to probe the effect of enzymatic degradation on the structure and mechanics of a single collagen fibril. We find that the mechanical response depends on the ratio of collagenase to gelatinase—not just the amount of lost fibril mass—and we provide a possible mechanism underlying this phenomenon. Overall, by characterizing the combined influences of collagenases and gelatinases on fibril degradation and mechanics at the preclinical research stage, we gain insights that may facilitate the development of targeted interventions to prevent the damage and loss of mechanical integrity that can lead to cartilage pathology.

Maximum shear strain-based algorithm can predict proteoglycan loss in damaged articular cartilage

Post-traumatic osteoarthritis (PTOA) is a common disease, where the mechanical integrity of articular cartilage is compromised. PTOA can be a result of chondral defects formed due to injurious loading. One of the first changes around defects is proteoglycan depletion. Since there are no methods to restore injured cartilage fully back to its healthy state, preventing the onset and progression of the disease is advisable. However, this is problematic if the disease progression cannot be predicted. Thus, we developed an algorithm to predict proteoglycan loss of injured cartilage by decreasing the fixed charge density (FCD) concentration. We tested several mechanisms based on the local strains or stresses in the tissue for the FCD loss. By choosing the degeneration threshold suggested for inducing chondrocyte apoptosis and cartilage matrix damage, the algorithm driven by the maximum shear strain showed the most substantial FCD losses around the lesion. This is consistent with experimental findings in the literature. We also observed that by using coordinate system-independent strain measures and selecting the degeneration threshold in an ad hoc manner, all the resulting FCD distributions would appear qualitatively similar, i.e., the greatest FCD losses are found at the tissue adjacent to the lesion. The proposed strain-based FCD degeneration algorithm shows a great potential for predicting the progression of PTOA via biomechanical stimuli. This could allow identification of high-risk defects with an increased risk of PTOA progression.

In silico study of principal sex hormone effects on post-injury synovial inflammatory response

Following an anterior cruciate ligament injury, premenopausal females tend to experience poorer outcomes than males, and sex hormones are thought to contribute to the disparity. Evidence seems to suggest that the sex hormones estrogen, progesterone, and testosterone may regulate the inflammation caused by macrophages, which invade the knee after an injury. While the individual effects of hormones on macrophage inflammation have been studied in vitro, their combined effects on post-injury inflammation in the knee have not been examined, even though both males and females have detectable levels of both estrogen and testosterone. In the present work, we developed an in silico kinetic model of the post-injury inflammatory response in the human knee joint and the hormonal influences that may shape that response. Our results indicate that post-injury, sex hormone concentrations observed in females may lead to a more pro-inflammatory, catabolic environment, while the sex hormone concentrations observed in males may lead to a more anti-inflammatory environment. These findings suggest that the female hormonal milieu may lead to increased catabolism, potentially worsening post-injury damage to the cartilage for females compared to males. The model developed herein may inform future in vitro and in vivo studies that seek to uncover the origins of sex differences in outcomes and may ultimately serve as a starting point for developing targeted therapies to prevent or reduce the cartilage damage that results from post-injury inflammation, particularly for females.

Comparison between kinetic and kinetic-kinematic driven knee joint finite element models

Use of knee joint finite element models for diagnostic purposes is challenging due to their complexity. Therefore, simpler models are needed for studies where a high number of patients need to be analyzed, without compromising the results of the model. In this study, more complex, kinetic (forces and moments) and simpler, kinetic-kinematic (forces and angles) driven finite element models were compared during the stance phase of gait. Patella and tendons were included in the most complex model, while they were absent in the simplest model. The greatest difference between the most complex and simplest models was observed in the internal-external rotation and axial joint reaction force, while all other rotations, translations and joint reaction forces were similar to one another. In terms of cartilage stresses and strains, the simpler models behaved similarly with the more complex models in the lateral joint compartment, while minor differences were observed in the medial compartment at the beginning of the stance phase. We suggest that it is feasible to use kinetic-kinematic driven knee joint models with a simpler geometry in studies with a large cohort size, particularly when analyzing cartilage responses and failures related to potential overloads.

A novel mechanobiological model can predict how physiologically relevant dynamic loading causes proteoglycan loss in mechanically injured articular cartilage

Cartilage provides low-friction properties and plays an essential role in diarthrodial joints. A hydrated ground substance composed mainly of proteoglycans (PGs) and a fibrillar collagen network are the main constituents of cartilage. Unfortunately, traumatic joint loading can destroy this complex structure and produce lesions in tissue, leading later to changes in tissue composition and, ultimately, to post-traumatic osteoarthritis (PTOA). Consequently, the fixed charge density (FCD) of PGs may decrease near the lesion. However, the underlying mechanisms leading to these tissue changes are unknown. Here, knee cartilage disks from bovine calves were injuriously compressed, followed by a physiologically relevant dynamic compression for twelve days. FCD content at different follow-up time points was assessed using digital densitometry. A novel cartilage degeneration model was developed by implementing deviatoric and maximum shear strain, as well as fluid velocity controlled algorithms to simulate the FCD loss as a function of time. Predicted loss of FCD was quite uniform around the cartilage lesions when the degeneration algorithm was driven by the fluid velocity, while the deviatoric and shear strain driven mechanisms exhibited slightly discontinuous FCD loss around cracks. Our degeneration algorithm predictions fitted well with the FCD content measured from the experiments. The developed model could subsequently be applied for prediction of FCD depletion around different cartilage lesions and for suggesting optimal rehabilitation protocols.

New algorithm for simulation of proteoglycan loss and collagen degeneration in the knee joint: Data from the osteoarthritis initiative

Osteoarthritis is a harmful joint disease but prediction of disease progression is problematic. Currently, there is only one modeling framework which can be applied to predict the progression of knee osteoarthritis but it only considers degenerative changes in the collagen fibril network. Here, we have developed the framework further by considering all of the major tissue changes (proteoglycan content, fluid flow, and collagen fibril network) occurring in osteoarthritis. While excessive levels of tissue stresses controlled degeneration of the collagen fibril network, excessive levels of tissue strains controlled the decrease in proteoglycan content and the increase in permeability. We created four knee joint models with increasing degrees of complexity based on the depth-wise composition. Models were tested for normal and abnormal, physiologically relevant, loading conditions in the knee. Finally, the predicted depth-wise compositional changes from each model were compared against experimentally observed compositional changes in vitro. The model incorporating the typical depth-wise composition of cartilage produced the best match with experimental observations. Consistent with earlier in vitro experiments, this model simulated the greatest proteoglycan depletion in the superficial and middle zones, while the collagen fibril degeneration was located mostly in the superficial zone. The presented algorithm can be used for predicting simultaneous collagen degeneration and proteoglycan loss during the development of knee osteoarthritis. © 2017 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 36:1673-1683, 2018.

Degradation alters the lubrication of articular cartilage by high viscosity, hyaluronic acid-based lubricants

Hyaluronic acid (HA) is widely injected as a viscosupplement in the treatment of osteoarthritis. Despite its extensive use, it is not currently known if cartilage degradation alters how HA-based solutions lubricate the articular surface. In this study we utilized a model of cartilage degradation by IL-1β along with a recently developed framework to study role of cartilage degradation on lubrication by clinically-approved HA-based lubricants with high viscosities. Cartilage explants were cultured up to 8 days with 10 ng/ml IL-1β. After culture, samples were examined histologically, immunohistochemically, biochemically, mechanically, topographically, and tribologically. The tribological testing analyzed both boundary and mixed lubrication modes to assess individual effects of viscosity and boundary lubricating ability. Friction testing was carried out using PBS and two clinically approved HA-based viscosupplements in a cartilage-glass configuration. After culture with IL-1β, boundary mode friction was elevated after both 4 and 8 days. Additionally, friction in mixed mode lubrication, where HA is most effective as a lubricant, was significantly elevated after 8 days of culture. As cartilage became rougher, softer, and more permeable after culture, the boundary mode plateau was extended, and as a result, significantly increased lubricant viscosities or sliding speeds were necessary to achieve effective mixed lubrication. Overall, this study revealed that lubrication of cartilage by HA is degradation-dependent and coincides with changes in mechanics and roughness. © 2017 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 36:1456-1464, 2018.

Systems Based Study of the Therapeutic Potential of Small Charged Molecules for the Inhibition of IL-1 Mediated Cartilage Degradation

Inflammatory cytokines are key drivers of cartilage degradation in post-traumatic osteoarthritis. Cartilage degradation mediated by these inflammatory cytokines has been extensively investigated using in vitro experimental systems. Based on one such study, we have developed a computational model to quantitatively assess the impact of charged small molecules intended to inhibit IL-1 mediated cartilage degradation. We primarily focus on the simplest possible computational model of small molecular interaction with the IL-1 system-direct binding of the small molecule to the active site on the IL-1 molecule itself. We first use the model to explore the uptake and release kinetics of the small molecule inhibitor by cartilage tissue. Our results show that negatively charged small molecules are excluded from the negatively charged cartilage tissue and have uptake kinetics in the order of hours. In contrast, the positively charged small molecules are drawn into the cartilage with uptake and release timescales ranging from hours to days. Using our calibrated computational model, we subsequently explore the effect of small molecule charge and binding constant on the rate of cartilage degradation. The results from this analysis indicate that the small molecules are most effective in inhibiting cartilage degradation if they are either positively charged and/or bind strongly to IL-1α, or both. Furthermore, our results showed that the cartilage structural homeostasis can be restored by the small molecule if administered within six days following initial tissue exposure to IL-1α. We finally extended the scope of the computational model by simulating the competitive inhibition of cartilage degradation by the small molecule. Results from this model show that small molecules are more efficient in inhibiting cartilage degradation by binding directly to IL-1α rather than binding to IL-1α receptors. The results from this study can be used as a template for the design and development of more pharmacologically effective osteoarthritis drugs, and to investigate possible therapeutic options.