Simulation enabled search for explanatory mechanisms of the fracture healing process

Utilizing virtual experiments to increase understanding of discrepancies involving in vitro-to-in vivo predictions of hepatic clearance

Predictions of xenobiotic hepatic clearance in humans using in vitro-to-in vivo extrapolation methods are frequently inaccurate and problematic. Multiple strategies are being pursued to disentangle responsible mechanisms. The objective of this work is to evaluate the feasibility of using insights gained from independent virtual experiments on two model systems to begin unraveling responsible mechanisms. The virtual culture is a software analog of hepatocytes in vitro, and the virtual human maps to hepatocytes within a liver within an idealized model human. Mobile objects (virtual compounds) map to amounts of xenobiotics. Earlier versions of the two systems achieved quantitative validation targets for intrinsic clearance (virtual culture) and hepatic clearance (virtual human). The major difference between the two systems is the spatial organization of the virtual hepatocytes. For each pair of experiments (virtual culture, virtual human), hepatocytes are configured the same. Probabilistic rules govern virtual compound movements and interactions with other objects. We focus on highly permeable virtual compounds and fix their extracellular unbound fraction at one of seven values (0.05–1.0). Hepatocytes contain objects that can bind and remove compounds, analogous to metabolism. We require that, for a subset of compound properties, per-hepatocyte compound exposure and removal rates during culture experiments directly predict corresponding measures made during virtual human experiments. That requirement serves as a cross-system validation target; we identify compound properties that enable achieving it. We then change compound properties, ceteris paribus, and provide model mechanism-based explanations for when and why measures made during culture experiments under- (or over-) predict corresponding measures made during virtual human experiments. The results show that, from the perspective of compound removal, the organization of hepatocytes within virtual livers is more efficient than within cultures, and the greater the efficiency difference, the larger the underprediction. That relationship is noteworthy because most in vitro-to-in vivo extrapolation methods abstract away the structural organization of hepatocytes within a liver. More work is needed on multiple fronts, including the study of an expanded variety of virtual compound properties. Nevertheless, the results support the feasibility of the approach and plan.

[Numerical simulation of fracture healing]

Fracture is a common physical injury. Its healing process involves complex biological activities at tissue, cellular and molecular levels and is affected by mechanical and biological factors. Over recent years, numerical simulation methods have been widely used to explore the mechanisms of fracture healing, design fixators and develop novel treatment strategies, etc. This paper mainly recommend the numerical methods used for simulating fracture healing and their latest research progress, which helps people better understand the mechanism of fracture healing, and also provides direction and guidance for the numerical simulation research of fracture healing in the future. First, the fracture healing process and its relationship with mechanical stimulation and biological factors are described. Then, the numerical models used for simulating fracture healing (including mechano-regulatory model, biological regulatory model and mechano-biological regulatory model) and corresponding modeling techniques (mainly including agent-based techniques and fuzzy logic controlling method) were summarized in particular. Finally, the future research directions in numerical simulation of fracture healing were preliminarily prospected.

Biomedical research models in the science of fracture healing - Pitfalls & promises

Development of intervention strategies to stimulate fracture healing has long been a focus of musculoskeletal research. Considerable investment in empirical research has led to the discovery of several genes and signaling pathways that are involved in skeletal development and regeneration. However, there are currently very few biologic interventions that can efficiently be used to enhance fracture healing in clinical practice. This translational barrier is due in part to experimental barriers to mechanism discovery. Animal models, biomechanical models, finite element models, and mathematical models are a few examples of models that aid in the discovery of mechanisms. Understanding the advantages, limitations, and specialized uses of each model type is critical to our ability to interpret mechanistic insights from such research and to help bridge the translation gap between pre-clinical research and clinical practice. In this review, we look at specific modeling methods used in the study of the fracture healing mechanism. We also discuss the strength and limitations to translation of each method, hopefully leading to a better understanding of how we can use models to advance the study of fracture healing.