In Silico Clinical Trials in the Orthopedic Device Industry: From Fantasy to Reality?

Abstract

The orthopedic device industry relies heavily on clinical evaluation to confirm the safety, performance, and clinical benefits of its implants. Limited sample size often prevents these studies from capturing the full spectrum of patient variability and real-life implant use. The device industry is accustomed to simulating benchtop tests with numerical methods and recent developments now enable virtual "in silico clinical trials" (ISCT). In this article, we describe how the advancement of computer modeling has naturally led to ISCT; outline the potential benefits of ISCT to patients, healthcare systems, manufacturers, and regulators; and identify how hurdles associated with ISCT may be overcome. In particular, we highlight a process for defining the relevant patient risks to address with ISCT, the utility of a versatile software pipeline, the necessity to ensure model credibility, and the goal of limiting regulatory uncertainty. By complementing-not replacing-traditional clinical trials with computational evidence, ISCT provides a viable technical and regulatory strategy for characterizing the full spectrum of patients, clinical conditions, and configurations that are embodied in contemporary orthopedic implant systems.

Keywords: Clinical application; Finite element; Modeling and simulation; Orthopedics; Regulatory submission; Virtual clinical trials.

Figure 1

Safety and performance are first established by pre-clinical testing through computer modeling and bench testing, provided by Research and Development (R&D) (green). A pre-CE clinical study is often necessary, when a medical device or its use are deemed novel. When conducting clinical evaluations for a device, four traditional sources can be included (blue): 1. Clinical studies (pre-CE clinical studies or post-market clinical follow-up studies—PMCF), 2. Literature, 3. Complaints, and 4. Registries. After receipt of the CE mark by the regulator, the product can be launched to market provided a clear plan for collecting clinical data is available. In parallel with market launch, PMCF studies are established to proactively collect data. The scientific literature, in particular peer-reviewed publications, are screened to ensure that the device offers the intended benefits and to detect potential adverse events. All implant-related complaints directly received by the manufacturer or reported in internal clinical studies or published literature studies are analyzed in post-market surveillance (PMS). Registries are also a great source of long-term data as they follow large cohorts and cover several implant designs. Despite these potential sources, data may be lacking for rare implants, demographics, and indications. ISCT may fill these gaps, and may become the fifth data source.

Figure 2

Traditional use of modeling to identify the worst case from multiple configurations (a) and component positions (b). Variability in loading is also typically considered. The worst case can then be physically tested (c). All test conditions are very controlled.

Figure 3

Modeling a physical benchtop test performed on a cadaver. Such modeling includes patient characteristics (bone density, bone geometry).

Figure 4

Modeling to simulate a whole population allowing statistical comparison of an outcome between two groups (pink and blue).

Figure 5

Classification of risks depending on relevance, ability to simulate and impact from design.

Figure 6

Software architecture to permit modularity of the ISCT platform. The model configuration contains the parameters specific to the implant at hand. The core contains the generic steps common to the simulations of any implant type.