In the literature: January 2024 highlights

Click here to read some interesting recently published papers from our community. If you have published an article in the field of in silico medicine, send it to us: we will include it in this section of the newsletter!

Computer Methods and Programs in Biomedicine: A risk and credibility framework for in silico clinical trials of medical devices

Jeffrey E Bischoff et al

Abstract

The use of in silico clinical trials (ISCTs) to generate clinically-relevant data on new medical devices is an emerging area of regulatory research. Interest in ISCTs stems from recognized challenges in acquiring sufficient clinical data and the continued maturation of in silico technologies. There is currently no guidance in place for evaluating the credibility of ISCT applications. The objective of this work was to adapt an existing risk-based credibility framework specifically for ISCT applications, and demonstrate its utility on a contemporary case study.

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Front Digit Health: CONNECTED: leveraging digital twins and personal knowledge graphs in healthcare digitalization

Antonella Carbonaro et al

Abstract

Healthcare has always been a strategic domain in which innovative technologies can be applied to increase the effectiveness of services and patient care quality. Recent advancements have been made in the adoption of Digital Twins (DTs) and Personal Knowledge Graphs (PKGs) in this field. Despite this, their introduction has been hindered by the complex nature of the context itself which leads to many challenges both technical and organizational. In this article, we reviewed the literature about these technologies and their integrations, identifying the most critical requirements for clinical platforms. These latter have been used to design CONNECTED (COmpreheNsive and staNdardized hEalth-Care plaTforms to collEct and harmonize clinical Data), a conceptual framework aimed at defining guidelines to overcome the crucial issues related to the development of healthcare applications. It is structured in a multi-layer shape, in which heterogeneous data sources are first integrated, then standardized, and finally used to realize general-purpose DTs of patients backed by PKGs and accessible through dedicated APIs. These DTs will be the foundation on which smart applications can be built.

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Front Digit Health: A digital twin model for evidence-based clinical decision support in multiple myeloma treatment

Nora Grieb et al

Abstract

The treatment landscape for multiple myeloma (MM) has experienced substantial progress over the last decade. Despite the efficacy of new substances, patient responses tend to still be highly unpredictable. With increasing cognitive burden that is introduced through a complex and evolving treatment landscape, data-driven assistance tools are becoming more and more popular. Model-based approaches, such as digital twins (DT), enable simulation of probable responses to a set of input parameters based on retrospective observations. In the context of treatment decision-support, those mechanisms serve the goal to predict therapeutic outcomes to distinguish a favorable option from a potential failure. In the present work, we propose a similarity-based multiple myeloma digital twin (MMDT) that emphasizes explainability and interpretability in treatment outcome evaluation. We've conducted a requirement specification process using scientific literature from the medical and methodological domains to derive an architectural blueprint for the design and implementation of the MMDT. In a subsequent stage, we've implemented a four-layer concept where for each layer, we describe the utilized implementation procedure and interfaces to the surrounding DT environment. We further specify our solutions regarding the adoption of multi-line treatment strategies, the integration of external evidence and knowledge, as well as mechanisms to enable transparency in the data processing logic. Furthermore, we define an initial evaluation scenario in the context of patient characterization and treatment outcome simulation as an exemplary use case for our MMDT. Our derived MMDT instance is defined by 475 unique entities connected through 438 edges to form a MM knowledge graph. Using the MMRF CoMMpass real-world evidence database and a sample MM case, we processed a complete outcome assessment. The output shows a valid selection of potential treatment strategies for the integrated medical case and highlights the potential of the MMDT to be used for such applications. DT models face significant challenges in development, including availability of clinical data to algorithmically derive clinical decision support, as well as trustworthiness of the evaluated treatment options. We propose a collaborative approach that mitigates the regulatory and ethical concerns that are broadly discussed when automated decision-making tools are to be included into clinical routine.

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Methods Mol Biol.: In Silico Clinical Trials: Is It Possible?

Simon Arsène et al

Abstract

Modeling and simulation (M&S), including in silico (clinical) trials, helps accelerate drug research and development and reduce costs and have coined the term “model-informed drug development (MIDD).” Data-driven, inferential approaches are now becoming increasingly complemented by emerging complex physiologically and knowledge-based disease (and drug) models, but differ in setup, bottlenecks, data requirements, and applications (also reminiscent of the different scientific communities they arose from). At the same time, and within the MIDD landscape, regulators and drug developers start to embrace in silico trials as a potential tool to refine, reduce, and ultimately replace clinical trials. Effectively, silos between the historically distinct modeling approaches start to break down. Widespread adoption of in silico trials still needs more collaboration between different stakeholders and established precedence use cases in key applications, which is currently impeded by a shattered collection of tools and practices. In order to address these key challenges, efforts to establish best practice workflows need to be undertaken and new collaborative M&S tools devised, and an attempt to provide a coherent set of solutions is provided in this chapter. First, a dedicated workflow for in silico clinical trial (development) life cycle is provided, which takes up general ideas from the systems biology and quantitative systems pharmacology space and which implements specific steps toward regulatory qualification. Then, key characteristics of an in silico trial software platform implementation are given on the example of jinkō.ai (nova’s end-to-end in silico clinical trial platform). Considering these enabling scientific and technological advances, future applications of in silico trials to refine, reduce, and replace clinical research are indicated, ranging from synthetic control strategies and digital twins, which overall shows promise to begin a new era of more efficient drug development.

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Springer Link.:Comparison between EMG-based and optimisation-based approaches for back-muscle forces and intervertebral efforts

Simon Hinnekens et al

Abstract

In biomechanics, computing muscle forces and joint efforts with mathematical optimisation copes with the muscle-redundancy problem, i.e. an infinity of possible muscle forces for a unique configuration. Achievements have been made to develop cost functions that reflect physiologically more correct muscle strategies and to validate them with experiments. It has also been proposed to use experimental input such as electromyography (EMG) in the model to guide the optimisation computation. In line with that, the present study proposes an EMG-based approach to compute back-muscle forces and the resulting intervertebral efforts in a horizontal static configuration of the trunk. This approach is based on EMG signals of three back muscles, lumbar and thoracic paravertebral muscles and the quadratus lumborum (QL), recorded on 19 healthy male subjects. Results of this approach were compared with those from optimisation computations involving four cost functions, classically used in the literature for the trunk and the spine. Our approach showed that muscle forces and intervertebral efforts were in line with these computed by mathematical optimisation, but muscle forces obtained with our approach were more representative of the measured EMG signals compared to muscle forces computed by optimisation. Indeed, three of the four cost functions completely missed to recruit the QL, while the latter was clearly activated during the experiment. This result highlights that EMG and experimental input should be more considered when using a musculoskeletal model and optimisation tools. Since the EMG-based approach used in this study was based on a pure deterministic distribution of a global equivalent force, future work will focus on involving EMG input in the optimisation process to guide its solution in a more physiological manner.

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Journal of Biomechanics: Predicted vs. measured paraspinal muscle activity in adolescent idiopathic scoliosis patients: EMG validation of optimization-based musculoskeletal simulations

Cedric Rauber et al

Abstract

Musculoskeletal (MSK) models offer great potential for predicting the muscle forces required to inform more detailed simulations of vertebral endplate loading in adolescent idiopathic scoliosis (AIS). In this work, simulations based on static optimization were compared with in vivo measurements in two AIS patients to determine whether computational approaches alone are sufficient for accurate prediction of paraspinal muscle activity during functional activities.

We used biplanar radiographs and marker-based motion capture, ground reaction force, and electromyography (EMG) data from two patients with mild and moderate thoracolumbar AIS (Cobb angles: 21° and 45°, respectively) during standing while holding two weights in front (reference position), walking, running, and object lifting. Using a fully automated approach, 3D spinal shape was extracted from the radiographs. Geometrically personalized OpenSim-based MSK models were created by deforming the spine of pre-scaled full-body models of children/adolescents. Simulations were performed using an experimentally controlled backward approach. Differences between model predictions and EMG measurements of paraspinal muscle activity (both expressed as a percentage of the reference position values) at three different locations around the scoliotic main curve were quantified by root mean square error (RMSE) and cross-correlation (XCorr).

Predicted and measured muscle activity correlated best for mild AIS during object lifting (XCorr’s ≥ 0.97), with relatively low RMSE values. For moderate AIS as well as the walking and running activities, agreement was lower, with XCorr reaching values of 0.51 and comparably high RMSE values.

This study demonstrates that static optimization alone seems not appropriate for predicting muscle activity in AIS patients, particularly in those with more than mild deformations as well as when performing upright activities such as walking and running.

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Date: 26/01/2024 | Tag: | News: 1539 of 1597
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