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!
Cancer Research Communications: A Pilot Study on Patient-specific Computational Forecasting of Prostate Cancer Growth during Active Surveillance Using an Imaging-informed Biomechanistic Model
Guillermo Lorenzo et al
Abstract
Active surveillance (AS) is a suitable management option for newly diagnosed prostate cancer, which usually presents low to intermediate clinical risk. Patients enrolled in AS have their tumor monitored via longitudinal multiparametric MRI (mpMRI), PSA tests, and biopsies. Hence, treatment is prescribed when these tests identify progression to higher-risk prostate cancer. However, current AS protocols rely on detecting tumor progression through direct observation according to population-based monitoring strategies. This approach limits the design of patient-specific AS plans and may delay the detection of tumor progression. Here, we present a pilot study to address these issues by leveraging personalized computational predictions of prostate cancer growth. Our forecasts are obtained with a spatiotemporal biomechanistic model informed by patient-specific longitudinal mpMRI data (T2-weighted MRI and apparent diffusion coefficient maps from diffusion-weighted MRI). Our results show that our technology can represent and forecast the global tumor burden for individual patients, achieving concordance correlation coefficients from 0.93 to 0.99 across our cohort (n = 7). In addition, we identify a model-based biomarker of higher-risk prostate cancer: the mean proliferation activity of the tumor (P = 0.041). Using logistic regression, we construct a prostate cancer risk classifier based on this biomarker that achieves an area under the ROC curve of 0.83. We further show that coupling our tumor forecasts with this prostate cancer risk classifier enables the early identification of prostate cancer progression to higher-risk disease by more than 1 year. Thus, we posit that our predictive technology constitutes a promising clinical decision-making tool to design personalized AS plans for patients with prostate cancer.
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American Heart Journal: Preoperative computed tomography-imaging with patient-specific computer simulation in transcatheter aortic valve implantation: Design and rationale of the GUIDE-TAVI trial
Romy R M J J Hegeman et al
Abstract
Background: Transcatheter aortic valve implantation (TAVI) is an established treatment option for patients with severe aortic valve stenosis, but is still associated with relatively high rates of pacemaker implantation and paravalvular regurgitation. Routine preoperative computed tomography (CT) combined with patient-specific computer modelling can predict the interaction between the TAVI device and the patient's unique anatomy, allowing physicians to assess the risk for paravalvular regurgitation and conduction disorders in advance to the procedure. The aim of this trial is to assess potential improvement in the procedural outcome of TAVI by applying CT-based patient-specific computer simulations in patients with suitable anatomy for TAVI.
Methods: The GUIDE-TAVI trial is an international multicenter randomized controlled trial including patients accepted for TAVI by the Heart Team. Patients enrolled in the study will be randomized into 2 arms of each 227 patients. In patients randomized to the use of FEops HEARTGuide (FHG), patient-specific computer simulation with FHG is performed in addition to routine preoperative CT imaging and results of the FHG are available to the operator(s) prior to the scheduled intervention. In patients randomized to no use of FHG, only routine preoperative CT imaging is performed. The primary objective is to evaluate whether the use of FHG will reduce the incidence of mild to severe PVR, according to the Valve Academic Research Consortium 3. Secondary endpoints include the incidence of new conduction disorders requiring permanent pacemaker implantation, the difference between preoperative and final selected valve size, the difference between target and final implantation depth, change of preoperative decision, failure to implant valve, early safety composite endpoint and quality of life.
Conclusions: The GUIDE-TAVI trial is the first multicenter randomized controlled trial to evaluate the value of 3-dimensional computer simulations in addition to standard preprocedural planning in TAVI procedures.
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Expert Opinion on Drug Metabolism & Toxicology: Predictive Modelling in pharmacokinetics: from in-silico simulations to personalized medicine
Ajita Paliwal et al
Abstract
Introduction: Pharmacokinetic parameters assessment is a critical aspect of drug discovery and development, yet challenges persist due to limited training data. Despite advancements in machine learning and in-silico predictions, scarcity of data hampers accurate prediction of drug candidates' pharmacokinetic properties.
Areas covered: The study highlights current developments in human pharmacokinetic prediction, talks about attempts to apply synthetic approaches for molecular design, and searches several databases, including Scopus, PubMed, Web of Science, and Google Scholar. The article stresses importance of rigorous analysis of machine learning model performance in assessing progress and explores molecular modeling (MM) techniques, descriptors, and mathematical approaches. Transitioning to clinical drug development, article highlights AI (Artificial Intelligence) based computer models optimizing trial design, patient selection, dosing strategies, and biomarker identification. In-silico models, including molecular interactomes and virtual patients, predict drug performance across diverse profiles, underlining the need to align model results with clinical studies for reliability. Specialized training for human specialists in navigating predictive models is deemed critical. Pharmacogenomics, integral to personalized medicine, utilizes predictive modeling to anticipate patient responses, contributing to more efficient healthcare system. Challenges in realizing potential of predictive modeling, including ethical considerations and data privacy concerns, are acknowledged.
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IEEE Transactions on Bio-medical Engineering: Verification Study of in Silico Computed Intracardiac Blood Flow With 4D Flow MRI
L Obermeier et al
Abstract
Objective: Major challenges for clinical applications of in silico medicine are limitations in time and computational resources. Computational approaches should therefore be tailored to specific applications with relatively low complexity and must be verified and validated against clinical gold standards.
Methods: This study performed computational fluid dynamics simulations of left ventricular hemodynamics of different complexity based on shape reconstruction from steady state gradient echo magnetic resonance imaging (MRI) data. Computed flow results of a rigid wall model (RWM) and a prescribed motion fluid-structure interaction (PM-FSI) model were compared against phase-contrast MRI measurements for three healthy subjects.
Results: Extracted boundary conditions from the steady state MRI sequences as well as computed metrics, such as flow rate, valve velocities, and kinetic energy show good agreement with in vivo flow measurements. Regional flow analysis reveals larger differences.
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STEM CELLS Translational Medicine: Robotics-Driven Manufacturing of Cartilaginous Microtissues for Skeletal Tissue Engineering Applications
Isaak Decoene et al
Abstract
Automated technologies are attractive for enhancing the robust manufacturing of tissue-engineered products for clinical translation. In this work, we present an automation strategy using a robotics platform for media changes, and imaging of cartilaginous microtissues cultured in static microwell platforms. We use an automated image analysis pipeline to extract microtissue displacements and morphological features as noninvasive quality attributes. As a result, empty microwells were identified with a 96% accuracy, and dice coefficient of 0.84 for segmentation. Design of experiment are used for the optimization of liquid handling parameters to minimize empty microwells during long-term differentiation protocols. We found no significant effect of aspiration or dispension speeds at and beyond manual speed. Instead, repeated media changes and time in culture were the driving force or microtissue displacements. As the ovine model is the preclinical model of choice for large skeletal defects, we used ovine periosteum-derived cells to form cartilage-intermediate microtissues. Increased expression of COL2A1 confirms chondrogenic differentiation and RUNX2 shows no osteogenic specification. Histological analysis shows an increased secretion of cartilaginous extracellular matrix and glycosaminoglycans in larger microtissues. Furthermore, microtissue-based implants are capable of forming mineralized tissues and bone after 4 weeks of ectopic implantation in nude mice. We demonstrate the development of an integrated bioprocess for culturing and manipulation of cartilaginous microtissues and anticipate the progressive substitution of manual operations with automated solutions for the manufacturing of microtissue-based living implants.
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Medicine - Computational and Systems Biology: Clinical phenotypes in acute and chronic infarction explained through human ventricular electromechanical modelling and simulations
Xin Zhou et al
Abstract
Sudden death after myocardial infarction (MI) is associated with electrophysiological heterogeneities and ionic remodelling, which are reflected as variable phenotypes. Low ejection fraction (EF) is used in risk stratification, but its mechanistic links with the post-MI pro-arrhythmic heterogeneities are unknown. We aim to provide a mechanistic explanation of clinical phenotypes in acute and chronic MI, from ionic remodeling to ECG and EF, using human electromechanical modelling and simulation to augment experimental and clinical investigations.
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Medical Image Analysis: Digital twinning of the human ventricular activation sequence to Clinical 12-lead ECGs and magnetic resonance imaging using realistic Purkinje networks for in silico clinical trials.
Julia Camps et al
Abstract
Cardiac in silico clinical trials can virtually assess the safety and efficacy of therapies using human-based modelling and simulation. These technologies can provide mechanistic explanations for clinically observed pathological behaviour. Designing virtual cohorts for in silico trials requires exploiting clinical data to capture the physiological variability in the human population. The clinical characterisation of ventricular activation and the Purkinje network is challenging, especially non-invasively. Our study aims to present a novel digital twinning pipeline that can efficiently generate and integrate Purkinje networks into human multiscale biventricular models based on subject-specific clinical 12-lead electrocardiogram and magnetic resonance recordings. Essential novel features of the pipeline are the human-based Purkinje network generation method, personalisation considering ECG R wave progression as well as QRS morphology, and translation from reduced-order Eikonal models to equivalent biophysically-detailed monodomain ones. We demonstrate ECG simulations in line with clinical data with clinical image-based multiscale models with Purkinje in four control subjects and two hypertrophic cardiomyopathy patients (simulated and clinical QRS complexes with Pearson's correlation coefficients > 0.7). Our methods also considered possible differences in the density of Purkinje myocardial junctions in the Eikonal-based inference as regional conduction velocities. These differences translated into regional coupling effects between Purkinje and myocardial models in the monodomain formulation. In summary, we demonstrate a digital twin pipeline enabling simulations yielding clinically consistent ECGs with clinical CMR image-based biventricular multiscale models, including personalised Purkinje in healthy and cardiac disease conditions.
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