In silico assessment of the reliability and performance of artificial sphincter for urinary incontinence.

BACKGROUND: The standard artificial urinary sphincter (AUS) is an implantable device for the treatment of urinary incontinence by applying a pressure loading around the urethra through an inflatable cuff, often inducing no-physiological stimulation up to tissue degenerative phenomena. A novel in silico approach is proposed to fill the gap of the traditional procedures by providing tools to quantitatively assess AUS reliability and performance based on AUS-urethra interaction. METHODS: The approach requires the development of 3D numerical models of AUS and urethra, and experimental investigations to define their mechanical behaviors. Computational analyses are performed to simulate the urethral lumen occlusion by AUS inflation under different pressures, and the lumen opening by applying an intraluminal pressure progressively increased under the AUS action (Abaqus Explicit solver). The AUS reliability is evaluated in terms of tissue stimulation by the mechanical fields potentially responsible for vasoconstriction and tissue damage, while the performance by the intraluminal pressure that causes the lumen opening for a specific occlusive pressure, showing the maximum urethral pressure for which continence is guaranteed. RESULTS: The present study implemented the procedure considering the gold standard AMS 800 and a novel patented AUS. Results provided the comparison between two sphincteric devices and the evaluation of the influence of different building materials and geometrical features on the AUS functionality. CONCLUSIONS: The approach was developed for the AUS, but it could be adapted also to artificial sphincters for the treatment of other anatomical dysfunctions, widening the analyzable device configurations and reducing experimental and ethical efforts.

Validation of Modeling and Simulation Methods in Computational Biology.

In recent years, a highly sophisticated array of modeling and simulation tools in all areas of biological and biomedical research has been developed. These tools have the potential to provide new insights into biological mechanisms integrating subcellular, cellular, tissue, organ, and potentially whole organism levels. Current research is focused on how to use these methods for translational medical research, such as for disease diagnosis and understanding, as well as drug discovery. In addition, these approaches enhance the ability to use human-derived data and to contribute to the refinement of high-cost experimental-based research. Additionally, the conflicting conceptual frameworks and conceptions of modeling and simulation methods from the broad public of users could have a significant impact on the successful implementation of aforementioned applications. This in turn could result in successful collaborations across academic, clinical, and industrial sectors. To that end, this study provides an overview of the frameworks and disciplines used for validation of computational methodologies in biomedical sciences.

Reconfigurable MRI technology for low-SAR imaging of deep brain stimulation at 3T: Application in bilateral leads, fully-implanted systems, and surgically modified lead trajectories.

Patients with deep brain stimulation devices highly benefit from postoperative MRI exams, however MRI is not readily accessible to these patients due to safety risks associated with RF heating of the implants. Recently we introduced a patient-adjustable reconfigurable coil technology that substantially reduced local SAR at tips of single isolated DBS leads during MRI at 1.5 T in 9 realistic patient models. This contribution extends our work to higher fields by demonstrating the feasibility of scaling the technology to 3T and assessing its performance in patients with bilateral leads as well as fully implanted systems. We developed patient-derived models of bilateral DBS leads and fully implanted DBS systems from postoperative CT images of 13 patients and performed finite element simulations to calculate SAR amplification at electrode contacts during MRI with a reconfigurable rotating coil at 3T. Compared to a conventional quadrature body coil, the reconfigurable coil system reduced the SAR on average by 83% for unilateral leads and by 59% for bilateral leads. A simple surgical modification in trajectory of implanted leads was demonstrated to increase the SAR reduction efficiency of the rotating coil to >90% in a patient with a fully implanted bilateral DBS system. Thermal analysis of temperature-rise around electrode contacts during typical brain exams showed a 15-fold heating reduction using the rotating coil, generating <1°C temperature rise during ∼4-min imaging with high-SAR sequences where a conventional CP coil generated >10°C temperature rise in the tissue for the same flip angle.

Predicting bone strength from CT data: Clinical applications.

In this review we summarise over 15 years of research and development around the prediction of whole bones strength from Computed Tomography data, with particular reference to the prediction of the risk of hip fracture in osteoporotic patients. We briefly discuss the theoretical background, and then provide a summary of the laboratory and clinical validation of these modelling technologies. We then discuss the three current clinical applications: in clinical research, in clinical trials, and in clinical practice. On average the strength predicted with finite element models (QCT-FE) based on computed tomography is 7% more accurate that that predicted with areal bone mineral density from Dual X-ray Absorptiometry (DXA-aBMD), the current standard of care, both in term of laboratory validation on cadaver bones and in terms of stratification accuracy on clinical cohorts of fractured and non-fractured women. This improved accuracy makes QCT-FE superior to DXA-aBMD in clinical research and in clinical trials, where the its use can cut in half the number of patients to be enrolled to get the same statistical power. For routine clinical use to decide who to treat with antiresorptive drugs, QCT-FE is more accurate but less cost-effective than DXA-aBMD, at least when the decision is on first line treatment like bisphosphonates. But the ability to predict skeletal strength from medical imaging is now opening a number of other applications, for example in paediatrics and oncology.

The Virtual Physiological Human: Ten Years After.

Biomedical research and clinical practice are struggling to cope with the growing complexity that the progress of health care involves. The most challenging diseases, those with the largest socioeconomic impact (cardiovascular conditions; musculoskeletal conditions; cancer; metabolic, immunity, and neurodegenerative conditions), are all characterized by a complex genotype-phenotype interaction and by a "systemic" nature that poses a challenge to the traditional reductionist approach. In 2005 a small group of researchers discussed how the vision of computational physiology promoted by the Physiome Project could be translated into clinical practice and formally proposed the term Virtual Physiological Human. Our knowledge about these diseases is fragmentary, as it is associated with molecular and cellular processes on the one hand and with tissue and organ phenotype changes (related to clinical symptoms of disease conditions) on the other. The problem could be solved if we could capture all these fragments of knowledge into predictive models and then compose them into hypermodels that help us tame the complexity that such systemic behavior involves. In 2005 this was simply not possible-the necessary methods and technologies were not available. Now, 10 years later, it seems the right time to reflect on the original vision, the results achieved so far, and what remains to be done.

In Silico clinical trial to predict the efficacy of hip protectors for preventing hip fractures.

Osteoporosis is characterized by loss of bone mineral density and increased fracture risk. Reduction of hip fracture incidence is of major clinical importance. Hip protectors aim to attenuate the impact force transmitted to the femur upon falling, however different conclusions on their efficacy have been reported; some authors suggest this may be due to differences in compliance. The aim of this study was to apply an In Silico trial methodology to predict the effectiveness of hip protectors and its dependence on compliance. A cohort of 1044 virtual patients (Finite Element models of proximal femur) were generated. A Markov chain process was implemented to predict fracture incidence with and without hip protectors, by simulating different levels of compliance. At each simulated follow-up year, a Poisson distribution was randomly sampled to determine the number of falls sustained by each patient. Impact direction and force were stochastically sampled from a range of possible scenarios. The effect of wearing a hip protector was simulated by applying attenuation coefficients to the impact force (12.9 %, 19 % and 33.8 %, as reported for available devices). A patient was considered fractured when impact force exceeded the femur strength. Without hip protector, virtual patients experienced 66 ± 5 fractures in 10 years. Wearing the three devices, fracture incidence was reduced to 43 ± 4, 35 ± 4 and 17 ± 2 respectively, at full compliance. As expected, effectiveness was dependent on compliance. This In Silico trial technology can be applied in the future to test multiple interventions, optimise intervention strategies, improve clinical trial design and drug development.

How does sutures pattern influence stomach motility after endoscopic sleeve gastroplasty? A computational study.

The relatively recent adoption of Endoscopic Sleeve Gastroplasty (ESG) amongst obese patients has gained approval within the surgical community due to its notable benefits, including significant weight loss, safety, feasibility, repeatability, and potential reversibility. However, despite its promising clinical outcomes and reduced invasiveness, there is still a lack of standardised procedures for performing ESG. Multiple suture patterns and stitching methods have been proposed over time, yet rational tools to quantify and compare their effects on gastric tissues are absent. To address this gap, this study proposed a computational approach. The research involved a case study analyzing three distinct suture patterns (C-shaped, U-shaped and Z-shaped) using a patient-specific computational stomach model generated from magnetic resonance imaging. Simulations mimicked food intake by placing wire features in the intragastric cavity to replicate sutures, followed by applying a linearly increasing internal pressure up to 15 mmHg. The outcomes facilitated comparisons between suture configurations based on pressure-volume behaviours and the distribution of maximum stress on biological tissues, revealing the U-shaped as the more effective in terms of volume reduction, even if with reduced elongation strains and increased tissues stresses, whereas the Z-shaped is responsible of the greatest stomach shortness after ESG. In summary, computational biomechanics methods serve as potent tools in clinical and surgical settings, offering insights into aspects that are challenging to explore in vivo, such as tissue elongation and stress. These methods allow for mechanical comparisons between different configurations, although they might not encompass crucial clinical outcomes.

Post-angioplasty remodeling of coronary arteries investigated via a chemo-mechano-biological in silico model.

This work presents the application of a chemo-mechano-biological constitutive model of soft tissues for describing tissue inflammatory response to damage in collagen constituents. The material model is implemented into a nonlinear finite element formulation to follow up a coronary standard balloon angioplasty for one year. Numerical results, compared with available in vivo clinical data, show that the model reproduces the temporal dynamics of vessel remodeling associated with subintimal damage. Such dynamics are bimodular, being characterized by an early tissue resorption and lumen enlargement, followed by late tissue growth and vessel constriction. Applicability of the modeling framework in retrospective studies is demonstrated, and future extension towards prospective applications is discussed.

Patient-specific in silico 3D coronary model in cardiac catheterisation laboratories.

Coronary artery disease is caused by the buildup of atherosclerotic plaque in the coronary arteries, affecting the blood supply to the heart, one of the leading causes of death around the world. X-ray coronary angiography is the most common procedure for diagnosing coronary artery disease, which uses contrast material and x-rays to observe vascular lesions. With this type of procedure, blood flow in coronary arteries is viewed in real-time, making it possible to detect stenoses precisely and control percutaneous coronary interventions and stent insertions. Angiograms of coronary arteries are used to plan the necessary revascularisation procedures based on the calculation of occlusions and the affected segments. However, their interpretation in cardiac catheterisation laboratories presently relies on sequentially evaluating multiple 2D image projections, which limits measuring lesion severity, identifying the true shape of vessels, and analysing quantitative data. In silico modelling, which involves computational simulations of patient-specific data, can revolutionise interventional cardiology by providing valuable insights and optimising treatment methods. This paper explores the challenges and future directions associated with applying patient-specific in silico models in catheterisation laboratories. We discuss the implications of the lack of patient-specific in silico models and how their absence hinders the ability to accurately predict and assess the behaviour of individual patients during interventional procedures. Then, we introduce the different components of a typical patient-specific in silico model and explore the potential future directions to bridge this gap and promote the development and utilisation of patient-specific in silico models in the catheterisation laboratories.

Optimizing inferior vena cava filter design: A computational fluid dynamics study on strut configuration for enhanced hemodynamic performance and thrombosis reduction.

Background and objective: Inferior vena cava filters have been shown to be effective in preventing deep vein thrombosis and its secondary complication, pulmonary embolism, thereby reducing the high mortality rate. Although inferior vena cava filters have evolved, specific complications like inferior vena cava thrombosis-induced deep vein thrombosis worsening and recurrent pulmonary embolism continue to pose challenges. This study analyzes the effects of geometric parameter variations of inferior vena cava filters, which have a significant impact on the thrombus formation inside the filter, the capture, dissolution, and hemodynamic flow of thrombus, as well as the shear stress on the filter and vascular wall. Methods: This study used computational fluid dynamic simulations with the carreau model to investigate the impact of varying inferior vena cava filter design parameters (number of struts, strut arm length, and tilt angle) on hemodynamics. Results: Recirculation and stagnation areas due to flow velocity and pressure, along with wall shear stress values, were identified as key factors. It is important to find a balance between wall shear stress high enough to aid thrombolysis and low enough to prevent platelet activation. The results of this paper show that the risk of platelet activation and thrombus filtration may be lowest when the wall shear stress of the filter ranges from 0 to 4 [Pa], minimizing stress concentration within the filter. Conclusion: 16 arm struts with a length of 20 mm and a tilt angle of 0° provide the best balance between thrombus capture and minimization of hemodynamic disturbance. This configuration minimizes the size of the stagnation and recirculation zones while maintaining sufficient wall shear stress for thrombus dissolution.

Fluid-structure interaction simulation of mechanical aortic valves: a narrative review exploring its role in total product life cycle.

Over the last years computer modelling and simulation has emerged as an effective tool to support the total product life cycle of cardiovascular devices, particularly in the device preclinical evaluation and post-market assessment. Computational modelling is particularly relevant for heart valve prostheses, which require an extensive assessment of their hydrodynamic performance and of risks of hemolysis and thromboembolic complications associated with mechanically-induced blood damage. These biomechanical aspects are typically evaluated through a fluid-structure interaction (FSI) approach, which enables valve fluid dynamics evaluation accounting for leaflets movement. In this context, the present narrative review focuses on the computational modelling of bileaflet mechanical aortic valves through FSI approach, aiming to foster and guide the use of simulations in device total product life cycle. The state of the art of FSI simulation of heart valve prostheses is reviewed to highlight the variety of modelling strategies adopted in the literature. Furthermore, the integration of FSI simulations in the total product life cycle of bileaflet aortic valves is discussed, with particular emphasis on the role of simulations in complementing and potentially replacing the experimental tests suggested by international standards. Simulations credibility assessment is also discussed in the light of recently published guidelines, thus paving the way for a broader inclusion of in silico evidence in regulatory submissions. The present narrative review highlights that FSI simulations can be successfully framed within the total product life cycle of bileaflet mechanical aortic valves, emphasizing that credible in silico models evaluating the performance of implantable devices can (at least) partially replace preclinical in vitro experimentation and support post-market biomechanical evaluation, leading to a reduction in both time and cost required for device development.

A Multidisciplinary Hyper-Modeling Scheme in Personalized In Silico Oncology: Coupling Cell Kinetics with Metabolism, Signaling Networks, and Biomechanics as Plug-In Component Models of a Cancer Digital Twin.

The massive amount of human biological, imaging, and clinical data produced by multiple and diverse sources necessitates integrative modeling approaches able to summarize all this information into answers to specific clinical questions. In this paper, we present a hypermodeling scheme able to combine models of diverse cancer aspects regardless of their underlying method or scale. Describing tissue-scale cancer cell proliferation, biomechanical tumor growth, nutrient transport, genomic-scale aberrant cancer cell metabolism, and cell-signaling pathways that regulate the cellular response to therapy, the hypermodel integrates mutation, miRNA expression, imaging, and clinical data. The constituting hypomodels, as well as their orchestration and links, are described. Two specific cancer types, Wilms tumor (nephroblastoma) and non-small cell lung cancer, are addressed as proof-of-concept study cases. Personalized simulations of the actual anatomy of a patient have been conducted. The hypermodel has also been applied to predict tumor control after radiotherapy and the relationship between tumor proliferative activity and response to neoadjuvant chemotherapy. Our innovative hypermodel holds promise as a digital twin-based clinical decision support system and as the core of future in silico trial platforms, although additional retrospective adaptation and validation are necessary.

Mapping the use of computational modelling and simulation in clinics: A survey.

In silico medicine describes the application of computational modelling and simulation (CM&S) to the study, diagnosis, treatment or prevention of a disease. Tremendous research advances have been achieved to facilitate the use of CM&S in clinical applications. Nevertheless, the uptake of CM&S in clinical practice is not always timely and accurately reflected in the literature. A clear view on the current awareness, actual usage and opinions from the clinicians is needed to identify barriers and opportunities for the future of in silico medicine. The aim of this study was capturing the state of CM&S in clinics by means of a survey toward the clinical community. Responses were collected online using the Virtual Physiological Human institute communication channels, engagement with clinical societies, hospitals and individual contacts, between 2020 and 2021. Statistical analyses were done with R. Participants (n = 163) responded from all over the world. Clinicians were mostly aged between 35 and 64 years-old, with heterogeneous levels of experience and areas of expertise (i.e., 48% cardiology, 13% musculoskeletal, 8% general surgery, 5% paediatrics). The CM&S terms "Personalised medicine" and "Patient-specific modelling" were the most well-known within the respondents. "In silico clinical trials" and "Digital Twin" were the least known. The familiarity with different methods depended on the medical specialty. CM&S was used in clinics mostly to plan interventions. To date, the usage frequency is still scarce. A well-recognized benefit associated to CM&S is the increased trust in planning procedures. Overall, the recorded level of trust for CM&S is high and not proportional to awareness level. The main barriers appear to be access to computing resources, perception that CM&S is slow. Importantly, clinicians see a role for CM&S expertise in their team in the future. This survey offers a snapshot of the current situation of CM&S in clinics. Although the sample size and representativity could be increased, the results provide the community with actionable data to build a responsible strategy for accelerating a positive uptake of in silico medicine. New iterations and follow-up activities will track the evolution of responses over time and contribute to strengthen the engagement with the medical community.

A novel computer modeling and simulation technique for bronchi motion tracking in human lungs under respiration.

In this work, we proposed a novel computer modeling and simulation technique for motion tracking of lung bronchi (or tumors) under respiration using 9 cases of computed tomography (CT)-based patient-specific finite element (FE) models and Ogden's hyperelastic model. In the fabrication of patient-specific FE models for the respiratory system, various organs such as the mediastinum, diaphragm, and thorax that could affect the lung motions during breathing were considered. To describe the nonlinear material behavior of lung parenchyma, the comparative simulation for biaxial tension-compression of lung parenchyma was carried out using several hyperelastic models in ABAQUS, and then, Ogden's model was adopted as an optimal model. Based on the aforementioned FE models and Ogden's material model, the 9 cases of respiration simulation were carried out from exhalation to inhalation, and the motion of lung bronchi (or tumors) was tracked. In addition, the changes in lung volume, lung cross-sectional area on the axial plane during breathing were calculated. Finally, the simulation results were quantitatively compared to the inhalation/exhalation CT images of 9 subjects to validate the proposed technique. Through the simulation, it was confirmed that the average relative errors of simulation to clinical data regarding to the displacement of 258 landmarks in the lung bronchi branches of total subjects were 1.10%~2.67%. In addition, the average relative errors of those with respect to the lung cross-sectional area changes and the volume changes in the superior-inferior direction were 0.20%~5.00% and 1.29 ~ 9.23%, respectively. Hence, it was considered that the simulation results were coincided well with the clinical data. The novelty of the present study is as follows: (1) The framework from fabrication of the human respiratory system to validation of the bronchi motion tracking is provided step by step. (2) The comparative simulation study for nonlinear material behavior of lung parenchyma was carried out to describe the realistic lung motion. (3) Various organs surrounding the lung parenchyma and restricting its motion were considered in respiration simulation. (4) The simulation results such as landmark displacement, lung cross-sectional area/volume changes were quantitatively compared to the clinical data of 9 subjects.

Model-Based Design to Enhance Neotissue Formation in Additively Manufactured Calcium-Phosphate-Based Scaffolds.

In biomaterial-based bone tissue engineering, optimizing scaffold structure and composition remains an active field of research. Additive manufacturing has enabled the production of custom designs in a variety of materials. This study aims to improve the design of calcium-phosphate-based additively manufactured scaffolds, the material of choice in oral bone regeneration, by using a combination of in silico and in vitro tools. Computer models are increasingly used to assist in design optimization by providing a rational way of merging different requirements into a single design. The starting point for this study was an in-house developed in silico model describing the in vitro formation of neotissue, i.e., cells and the extracellular matrix they produced. The level set method was applied to simulate the interface between the neotissue and the void space inside the scaffold pores. In order to calibrate the model, a custom disk-shaped scaffold was produced with prismatic canals of different geometries (circle, hexagon, square, triangle) and inner diameters (0.5 mm, 0.7 mm, 1 mm, 2 mm). The disks were produced with three biomaterials (hydroxyapatite, tricalcium phosphate, and a blend of both). After seeding with skeletal progenitor cells and a cell culture for up to 21 days, the extent of neotissue growth in the disks' canals was analyzed using fluorescence microscopy. The results clearly demonstrated that in the presence of calcium-phosphate-based materials, the curvature-based growth principle was maintained. Bayesian optimization was used to determine the model parameters for the different biomaterials used. Subsequently, the calibrated model was used to predict neotissue growth in a 3D gyroid structure. The predicted results were in line with the experimentally obtained ones, demonstrating the potential of the calibrated model to be used as a tool in the design and optimization of 3D-printed calcium-phosphate-based biomaterials for bone regeneration.

A Hybrid Particle-Flow CFD Modeling Approach in Truncated Hepatic Arterial Trees for Liver Radioembolization: A Patient-specific Case Study.

Hepatocellular carcinoma (HCC) is the most common form of primary liver cancer. At its intermediate, unresectable stage, HCC is typically treated by local injection of embolizing microspheres in the hepatic arteries to selectively damage tumor tissue. Interestingly, computational fluid dynamics (CFD) has been applied increasingly to elucidate the impact of clinically variable parameters, such as injection location, on the downstream particle distribution. This study aims to reduce the computational cost of such CFD approaches by introducing a novel truncation algorithm to simplify hepatic arterial trees, and a hybrid particle-flow modeling approach which only models particles in the first few bifurcations. A patient-specific hepatic arterial geometry was pruned at three different levels, resulting in three trees: Geometry 1 (48 outlets), Geometry 2 (38 outlets), and Geometry 3 (17 outlets). In each geometry, 1 planar injection and 3 catheter injections (each with different tip locations) were performed. For the truncated geometries, it was assumed that, downstream of the truncated outlets, particles distributed themselves proportional to the blood flow. This allowed to compare the particle distribution in all 48 "outlets" for each geometry. For the planar injections, the median difference in outlet-specific particle distribution between Geometry 1 and 3 was 0.21%; while the median difference between outlet-specific flow and particle distribution in Geometry 1 was 0.40%. Comparing catheter injections, the maximum median difference in particle distribution between Geometry 1 and 3 was 0.24%, while the maximum median difference between particle and flow distribution was 0.62%. The results suggest that the hepatic arterial tree might be reliably truncated to estimate the particle distribution in the full-complexity tree. In the resulting hybrid particle-flow model, explicit particle modeling was only deemed necessary in the first few bifurcations of the arterial tree. Interestingly, using flow distribution as a surrogate for particle distribution in the entire tree was considerably less accurate than using the hybrid model, although the difference was much higher for catheter injections than for planar injections. Future work should focus on replicating and experimentally validating these results in more patient-specific geometries.

Health Technology Assessment for In Silico Medicine: Social, Ethical and Legal Aspects.

The application of in silico medicine is constantly growing in the prevention, diagnosis, and treatment of diseases. These technologies allow us to support medical decisions and self-management and reduce, refine, and partially replace real studies of medical technologies. In silico medicine may challenge some key principles: transparency and fairness of data usage; data privacy and protection across platforms and systems; data availability and quality; data integration and interoperability; intellectual property; data sharing; equal accessibility for persons and populations. Several social, ethical, and legal issues may consequently arise from its adoption. In this work, we provide an overview of these issues along with some practical suggestions for their assessment from a health technology assessment perspective. We performed a narrative review with a search on MEDLINE/Pubmed, ISI Web of Knowledge, Scopus, and Google Scholar. The following key aspects emerge as general reflections with an impact on the operational level: cultural resistance, level of expertise of users, degree of patient involvement, infrastructural requirements, risks for health, respect of several patients' rights, potential discriminations for access and use of the technology, and intellectual property of innovations. Our analysis shows that several challenges still need to be debated to allow in silico medicine to express all its potential in healthcare processes.

In silico approaches for transcatheter aortic valve replacement inspection.

Introduction: Increasing applications of transcatheter aortic valve replacement (TAVR) to treat high- or medium-risk patients with aortic diseases have been proposed in recent years. Despite its increasing use, many influential factors are still to be understood. Furthermore, innovative applications of TAVR such as in bicuspid aortic valves or in low-risk patients are emerging in clinical use. Numerical analyses are increasingly used to reproduce clinical treatments. The future trends in this area are foreseen for in silico trials and personalized medicine. Areas covered: This review paper analyzes the recent years (Jan 2018 - Aug 2020) of in silico studies simulating the behavior of transcatheter aortic valves with emphasis on the addressed clinical question and the used modeling strategies. The manuscripts are firstly classified based on their clinical hypothesis. A second classification is based on the adopted modeling approach in terms of patient domain, device modeling, and inclusion or exclusion of the fluid domain. Expert opinion: The TAVR can be virtually performed in numerous vessel geometries and with different devices. This versatility allows a rapid evaluation of the feasibility of different implantation approaches for specific patients, and patient populations, resulting in faster and safer introduction or optimization of new treatments or devices.

Phytochemicals for Drug Discovery in Alzheimer's Disease: In Silico Advances.

The search for novel drugs that can prevent or control Alzheimer's disease has attracted a lot of attention from researchers across the globe. Phytochemicals are increasingly being used to provide scaffolds to design drugs for AD. In silico techniques have proven to be a game-changer in this drug design and development process. In this review, the authors have focussed on current advances in the field of in silico medicine, applied to phytochemicals, to discover novel drugs to prevent or cure AD. After giving a brief context of the etiology and available drug targets for AD, authors have discussed the latest advances and techniques in computational drug design of AD from phytochemicals. Some of the prototypical studies in this area are discussed in detail. In silico phytochemical analysis is a tool of choice for researchers all across the globe and helps integrate chemical biology with drug design.

Non-invasive Stenotic Renal Artery Haemodynamics by in silico Medicine.

Background: Measuring the extent to which renal artery stenosis (RAS) alters renal haemodynamics may permit precision medicine by physiologically guided revascularization. This currently requires invasive intra-arterial pressure measurement with associated risks and is rarely performed. The present proof-of-concept study investigates an in silico approach that uses computational fluid dynamic (CFD) modeling to non-invasively estimate renal artery haemodynamics from routine anatomical computed tomography (CT) imaging of RAS. Methods: We evaluated 10 patients with RAS by CT angiography. Intra-arterial renal haemodynamics were invasively measured by a transducing catheter under resting and hyperaemic conditions, calculating the translesional ratio of distal to proximal pressure (Pd/Pa). The diagnostic and quantitative accuracy of the CFD-derived virtual Pd/Pa ratio (vPd/Pa) was evaluated against the invasively measured Pd/Pa ratio (mPd/Pa). Results: Hyperaemic haemodynamics was infeasible and CT angiography in 4 patients had insufficient image resolution. Resting flow data is thus reported for 7 stenosed arteries from 6 patients (one patient had bilateral RAS). The comparison showed a mean difference of 0.015 (95% confidence intervals of ± 0.08), mean absolute error of 0.064, and a Pearson correlation coefficient of 0.6, with diagnostic accuracy for a physiologically significant Pd/Pa of ≤ 0.9 at 86%. Conclusion: We describe the first in silico estimation of renal artery haemodynamics from CT angiography in patients with RAS, showing it is feasible and diagnostically accurate. This provides a methodological framework for larger prospective studies to ultimately develop non-invasive precision medicine approaches for studies and interventions of RAS and resistant hypertension.