In silico clinical trials, defined as ÒThe use of individualised computer simulation in the development or regulatory evaluation of a medicinal product, medical device, or medical interventionÓ, have been proposed as a possible strategy to reduce the regulatory costs of innovation and the time to market for biomedical products. We review some of the the literature on this topic, focusing in particular on those applications where the current practice is recognised as inadequate, as for example the detection of unexpected severe adverse events too rare to be detected in a clinical trial, but still likely enough to be of concern. We then describe with more details two case studies, two successful applications of in silico clinical trial approaches, one relative to the Padova Ð UVA simulator that the FDA has accepted as possible replacement for animal testing in the pre-clinical assessment of artificial pancreas technologies, and the second an investigation of the probability of cardiac lead fracture, where a Bayesian network was used to combine in vivo and in silico observations, suggesting a whole new strategy of in silico-augmented clinical trials, to be used to increase the numerosity where recruitment is impossible, or to explore patientsÕ phenotypes that are unlikely to appear in the trial cohort, but are still frequent enough to be of concern.

Evaluation of medical devices via clinical trial is often a necessary step in the process of bringing a new product to market. In recent years, device manufacturers are increasingly using stochastic engineering models during the product development process. These models have the capability to simulate virtual patient outcomes. This article presents a novel method based on the power prior for augmenting a clinical trial using virtual patient data. To properly inform clinical evaluation, the virtual patient model must simulate the clinical outcome of interest, incorporating patient variability, as well as the uncertainty in the engineering model and in its input parameters. The number of virtual patients is controlled by a discount function which uses the similarity between modeled and observed data. This method is illustrated by a case study of cardiac lead fracture. Different discount functions are used to cover a wide range of scenarios in which the type I error rates and power vary for the same number of enrolled patients. Incorporation of engineering models as prior knowledge in a Bayesian clinical trial design can provide benefits of decreased sample size and trial length while still controlling type I error rate and power.

Segmented polyurethane multiblock polymers containing polydimethylsiloxane and polyether soft segments form tough and easily processed thermoplastic elastomers (PDMS-urethanes). Two commercially available examples, PurSil 35 (denoted as P35) and Elast-Eon E2A (denoted as E2A), were evaluated for abrasion and fatigue resistance after immersion in 85 °C buffered water for up to 80 weeks. We previously reported that water exposure in these experiments resulted in a molar mass reduction, where the kinetics of the hydrolysis reaction is supported by a straight forward Arrhenius analysis over a range of accelerated temperatures (37-85 °C). We also showed that the ultimate tensile properties of P35 and E2A were significantly compromised when the molar mass was reduced. Here, we show that the reduction in molar mass also correlated with a reduction in both the abrasion and fatigue resistance. The instantaneous wear rate of both P35 and E2A, when exposed to the reciprocating motion of an ethylene tetrafluoroethylene (ETFE) jacketed cable, increased with the inverse of the number averaged molar mass (1/Mn). Both materials showed a change in the wear surface when the number-averaged molar mass was reduced to ≈ 16 kg/mole, where a smooth wear surface transitioned to a 'spalling-like' pattern, leaving the wear surface with ≈ 0.3 mm cracks that propagated beyond the contact surface. The fatigue crack growth rate for P35 and E2A also increased in proportion to 1/Mn, after the molar mass was reduced below a critical value of ≈30 kg/mole. Interestingly, this critical molar mass coincided with that at which the single cycle stress-strain response changed from strain hardening to strain softening. The changes in both abrasion and fatigue resistance, key predictors for long term reliability of cardiac leads, after exposure of this class of PDMS-urethanes to water suggests that these materials are susceptible to mechanical compromise in vivo.

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The Transvenous Cardiac Leads Working Group of the Cardiac Rhythm Management Devices Committee of the Association for the Advancement of Medical Instrumentation is developing a fatigue performance standard for cardiac device leads. The proposed standard would calculate a figure-of-merit (FOM) that is based on a life prediction using a Bayesian framework. The framework uses distributions for bending fatigue strength, patient age, patient activity level, and in vivo bending. The benchtop fatigue testing portion of the standard is based on the unsupported bending of the lead at multiple alternating curvature levels to generate fatigue fracture data in low-cycle and high-cycle regimes. To estimate the interlaboratory reproducibility of the benchtop testing methodology, a lead mock-up was constructed from a bifilar MP35N coil in a thin-walled polyurethane tube. Four laboratories each tested 48 specimens and produced fatigue life curves based on the results. To compare the data, the FOM

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