In silico clinical trials for pediatric orphan diseases

Carlier, Aurélie; Vasilevich, Aliaksei; Maréchal, Marina; Boer, Jan de; Geris, Liesbet

doi:10.1038/s41598-018-20737-y

Cited by 34 publications

(22 citation statements)

References 38 publications

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“…As never before, the application of modeling and simulation can actively design better vaccine prototypes, support decision making, decrease experimental costs and time, and eventually improve success rates of the trials. To this aim, in silico trials (ISTs) for design and testing medicines [23]- [25] can accelerate and speed-up the vaccine discovery pipeline, predicting any therapeutic failure and minimizing undesired effects.…”

Section: Introductionmentioning

confidence: 99%

In Silico Trial to test COVID-19 candidate vaccines: a case study with UISS platform

Russo

Pennisi

Viceconti

et al. 2020

Preprint

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SARS-CoV-2 is a severe respiratory infection that infects humans. Its outburst entitled it as a pandemic emergence. To get a grip on this outbreak, specific preventive and therapeutic interventions are urgently needed. It must be said that, until now, there are no existing vaccines for coronaviruses. To promptly and rapidly respond to pandemic events, the application of in silico trials can be used for designing and testing medicines against SARS-CoV-2 and speed-up the vaccine discovery pipeline, predicting any therapeutic failure and minimizing undesired effects. Here, we present an in silico platform that showed to be in very good agreement with the latest literature in predicting SARS-CoV-2 dynamics and related immune system host response. Moreover, it has been used to predict the outcome of one of the latest suggested approach to design an effective vaccine, based on monoclonal antibody. Universal Immune System Simulator (UISS) in silico platform is potentially ready to be used as an in silico trial platform to predict the outcome of vaccination strategy against SARS-CoV-2.Index Terms-Agent-Based Model, human monoclonal antibodies, In Silico Trials, SARS-CoV-2, vaccines.

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Section: Introductionmentioning

confidence: 99%

In Silico Trial to test COVID-19 candidate vaccines: a case study with UISS platform

Russo

Pennisi

Viceconti

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…We have listed several examples of how AI has boosted therapeutic development in RDs. These entail the identification of disease biomarkers [70][71][72], the increase of patient recruitment for CTs [67], and the discovery of drugs for repurposing [63]. However, much is still to be done to overturn the low rate of R&D for RDs.…”

Section: Discussionmentioning

confidence: 99%

“…Ward hierarchical clustering was employed to stratify the virtual subject population in adverse responders, non-responders, responders, and asymptomatic. Additionally, an RF-based algorithm predicted potential biomarkers for therapy effectiveness, namely rate of cartilage formation (Pmc) and Y3cb [72]. All in all, AI-based methods can resolve many challenges associated with clinical trials in RDs, particularly in combination with other expanding fields like systems biology.…”

Section: Biomarkersmentioning

confidence: 99%

Artificial Intelligence (AI) in Rare Diseases: Is the Future Brighter?

Brasil

Pascoal

Francisco

et al. 2019

Genes

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The amount of data collected and managed in (bio)medicine is ever-increasing. Thus, there is a need to rapidly and efficiently collect, analyze, and characterize all this information. Artificial intelligence (AI), with an emphasis on deep learning, holds great promise in this area and is already being successfully applied to basic research, diagnosis, drug discovery, and clinical trials. Rare diseases (RDs), which are severely underrepresented in basic and clinical research, can particularly benefit from AI technologies. Of the more than 7000 RDs described worldwide, only 5% have a treatment. The ability of AI technologies to integrate and analyze data from different sources (e.g., multi-omics, patient registries, and so on) can be used to overcome RDs' challenges (e.g., low diagnostic rates, reduced number of patients, geographical dispersion, and so on). Ultimately, RDs' AI-mediated knowledge could significantly boost therapy development. Presently, there are AI approaches being used in RDs and this review aims to collect and summarize these advances. A section dedicated to congenital disorders of glycosylation (CDG), a particular group of orphan RDs that can serve as a potential study model for other common diseases and RDs, has also been included.

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“…Recently, such an approach, which is called "in silico clinical trial" or "computational clinical trial," has been applied for regulatory evaluation in the development of drugs and medical devices. 15,16 In evaluation of ALA-iPDT treatment outcomes for brain tumors, the computational approach does not require the collection of target patients and overcomes the difficulties of direct measurement of treatment outcomes. For a computational clinical trial of ALA-iPDT, a photophysical model based on the mechanism of ALA-iPDT action should be constructed to estimate treatment outcomes.…”

Section: Introductionmentioning

confidence: 99%

Singlet oxygen model evaluation of interstitial photodynamic therapy with 5-aminolevulinic acid for malignant brain tumor

et al. 2019

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Interstitial photodynamic therapy (iPDT) with 5-aminolevulinic acid (ALA) is a possible alternative treatment for malignant brain tumors. Further evaluation is, however, required before it can be clinically applied. Computational simulation of the photophysical process in ALA-iPDT can offer a quantitative tool for understanding treatment outcomes, which depend on various variables related to clinical treatment conditions. We propose a clinical simulation method of ALA-iPDT for malignant brain tumors using a singlet oxygen (1 O 2) model and 1 O 2 threshold to induce cell death. In this method, the amount of 1 O 2 generated is calculated using a photosensitizer photobleaching coefficient and 1 O 2 quantum yield, which have been measured in several previous studies. Results of the simulation using clinical magnetic resonance imaging data show the need to specify the insertion positions of cylindrical light diffusers and the level of light fluence. Detailed analysis with a numerical brain tumor model demonstrates that ALA-iPDT treatment outcomes depend on combinations of photobleaching and threshold values. These results indicate that individual medical procedures, including pretreatment planning and treatment monitoring, will greatly benefit from simulation of ALA-iPDT outcomes. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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In silico clinical trials for pediatric orphan diseases

Cited by 34 publications

References 38 publications

In Silico Trial to test COVID-19 candidate vaccines: a case study with UISS platform

In Silico Trial to test COVID-19 candidate vaccines: a case study with UISS platform

Artificial Intelligence (AI) in Rare Diseases: Is the Future Brighter?

Singlet oxygen model evaluation of interstitial photodynamic therapy with 5-aminolevulinic acid for malignant brain tumor

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