Literature DB >> 28427321

In silico assessment of biomedical products: The conundrum of rare but not so rare events in two case studies.

Marco Viceconti1, Claudio Cobelli2, Tarek Haddad3, Adam Himes3, Boris Kovatchev4, Mark Palmer3.   

Abstract

In silico clinical trials, defined as "The use of individualized computer simulation in the development or regulatory evaluation of a medicinal product, medical device, or medical intervention," 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 recognized 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 University of Virginia/Padova simulator that the Food and Drug Administration has accepted as possible replacement for animal testing in the preclinical 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 patients' phenotypes that are unlikely to appear in the trial cohort, but are still frequent enough to be of concern.

Entities:  

Keywords:  Medical devices; computer modeling and simulation; in silico clinical trials; regulatory science; safety and efficacy

Mesh:

Substances:

Year:  2017        PMID: 28427321     DOI: 10.1177/0954411917702931

Source DB:  PubMed          Journal:  Proc Inst Mech Eng H        ISSN: 0954-4119            Impact factor:   1.617


  11 in total

1.  Regulation Catches Up to Reality.

Authors:  Steven V Edelman
Journal:  J Diabetes Sci Technol       Date:  2016-09-25

Review 2.  Diabetes Technology: Monitoring, Analytics, and Optimal Control.

Authors:  Boris Kovatchev
Journal:  Cold Spring Harb Perspect Med       Date:  2019-06-03       Impact factor: 6.915

Review 3.  Modeling of Diabetes and Its Clinical Impact.

Authors:  Katharina Fritzen; Lutz Heinemann; Oliver Schnell
Journal:  J Diabetes Sci Technol       Date:  2018-07-13

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

Authors:  Philippe Favre; Ghislain Maquer; Adam Henderson; Daniel Hertig; Daniel Ciric; Jeffrey E Bischoff
Journal:  Ann Biomed Eng       Date:  2021-05-10       Impact factor: 3.934

5.  Minimal and Maximal Models to Quantitate Glucose Metabolism: Tools to Measure, to Simulate and to Run in Silico Clinical Trials.

Authors:  Claudio Cobelli; Chiara Dalla Man
Journal:  J Diabetes Sci Technol       Date:  2021-05-25

Review 6.  Next-generation, personalised, model-based critical care medicine: a state-of-the art review of in silico virtual patient models, methods, and cohorts, and how to validation them.

Authors:  J Geoffrey Chase; Jean-Charles Preiser; Jennifer L Dickson; Antoine Pironet; Yeong Shiong Chiew; Christopher G Pretty; Geoffrey M Shaw; Balazs Benyo; Knut Moeller; Soroush Safaei; Merryn Tawhai; Peter Hunter; Thomas Desaive
Journal:  Biomed Eng Online       Date:  2018-02-20       Impact factor: 2.819

7.  Creation and application of virtual patient cohorts of heart models.

Authors:  S A Niederer; Y Aboelkassem; C D Cantwell; C Corrado; S Coveney; E M Cherry; T Delhaas; F H Fenton; A V Panfilov; P Pathmanathan; G Plank; M Riabiz; C H Roney; R W Dos Santos; L Wang
Journal:  Philos Trans A Math Phys Eng Sci       Date:  2020-05-25       Impact factor: 4.226

8.  Development of an Error Model for a Factory-Calibrated Continuous Glucose Monitoring Sensor with 10-Day Lifetime.

Authors:  Martina Vettoretti; Cristina Battocchio; Giovanni Sparacino; Andrea Facchinetti
Journal:  Sensors (Basel)       Date:  2019-12-03       Impact factor: 3.576

9.  A "Slide Rule" to Adjust Insulin Dose Using Trend Arrows in Adults with Type 1 Diabetes: Test in Silico and in Real Life.

Authors:  Daniela Bruttomesso; Federico Boscari; Giuseppe Lepore; Giulia Noaro; Giacomo Cappon; Angela Girelli; Lutgarda Bozzetto; Andrea Tumminia; Giorgio Grassi; Giovanni Sparacino; Luigi Laviola; Andrea Facchinetti
Journal:  Diabetes Ther       Date:  2021-03-16       Impact factor: 2.945

10.  Advancing Regulatory Science With Computational Modeling for Medical Devices at the FDA's Office of Science and Engineering Laboratories.

Authors:  Tina M Morrison; Pras Pathmanathan; Mariam Adwan; Edward Margerrison
Journal:  Front Med (Lausanne)       Date:  2018-09-25
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