Bringing computational models of bone regeneration to the clinic

Abstract

Although the field of bone regeneration has experienced great advancements in the last decades, integrating all the relevant, patient‐specific information into a personalized diagnosis and optimal treatment remains a challenging task due to the large number of variables that affect bone regeneration. Computational models have the potential to cope with this complexity and to improve the fundamental understanding of the bone regeneration processes as well as to predict and optimize the patient‐specific treatment strategies. However, the current use of computational models in daily orthopedic practice is very limited or inexistent. We have identified three key hurdles that limit the translation of computational models of bone regeneration from bench to bed side. First, there exists a clear mismatch between the scope of the existing and the clinically required models. Second, most computational models are confronted with limited quantitative information of insufficient quality thereby hampering the determination of patient‐specific parameter values. Third, current computational models are only corroborated with animal models, whereas a thorough (retrospective and prospective) assessment of the computational model will be crucial to convince the health care providers of the capabilities thereof. These challenges must be addressed so that computational models of bone regeneration can reach their true potential, resulting in the advancement of individualized care and reduction of the associated health care costs. WIREs Syst Biol Med 2015, 7:183–194. doi: 10.1002/wsbm.1299 This article is categorized under: Analytical and Computational Methods > Computational Methods Models of Systems Properties and Processes > Organ, Tissue, and Physiological Models

Bone fracture healing in mechanobiological modeling: A review of principles and methods

“…The limitations involve a scope mismatch between existing models and clinical requirements, limited quantitative experimental data, and lack of human case studies for clinical applications (Carlier et al, 2015a). A model should be predictive, case sensitive, and adjustable for different individuals (Khayyeri et al, 2011, Khayyeri et al, 2015).…”

“…They reported that a decrease in distraction rate led to longer healing time. Ultimately all simulation results need to be verified by experiments and clinical observations (Carlier et al, 2015a). …”

“…biological or mechanobiological models) is developed and validated. Different scenarios of injection, seeding, and production of growth factors or cells are simulated through the validated model (Carlier et al, 2015a, Geris et al, 2010a), which allow the study of different aspects such as dosage and density effects (Geris et al, 2008, Checa and Prendergast, 2010, Geris et al, 2006), injection location, mode of seeding (i.e. uniform or peripheral seeding) (Checa and Prendergast, 2010), type of growth factors (i.e.…”

“…Nevertheless, computational modeling approaches have their own limitations in clinical applications. Thus, these models need to be further developed and validated in order to achieve clinically relevant results (Pivonka and Dunstan, 2012, Carlier et al, 2015a). These models help us better understand the mechanism of bone healing by emphasizing the known correlation between the mechanical environment and the bone healing process (Claes and Heigele, 1999, Prendergast et al, 1997).…”

Theories of mechanically induced tissue differentiation and adaptation in the musculoskeletal system

“…Several works have reviewed the processes of tissue differentiation and adaptation in musculoskeletal tissues from different perspectives [16]- [18]. However, to the knowledge of the author, the literature that reviews their mathematical fundamentals and implementation is scarce.…”