Assessing Computational Model Credibility Using a Risk-Based Framework: Application to Hemolysis in Centrifugal Blood Pumps

Advancing cancer drug development with mechanistic mathematical modeling: bridging the gap between theory and practice

Quantitative predictive modeling of cancer growth, progression, and individual response to therapy is a rapidly growing field. Researchers from mathematical modeling, systems biology, pharmaceutical industry, and regulatory bodies, are collaboratively working on predictive models that could be applied for drug development and, ultimately, the clinical management of cancer patients. A plethora of modeling paradigms and approaches have emerged, making it challenging to compile a comprehensive review across all subdisciplines. It is therefore critical to gauge fundamental design aspects against requirements, and weigh opportunities and limitations of the different model types. In this review, we discuss three fundamental types of cancer models: space-structured models, ecological models, and immune system focused models. For each type, it is our goal to illustrate which mechanisms contribute to variability and heterogeneity in cancer growth and response, so that the appropriate architecture and complexity of a new model becomes clearer. We present the main features addressed by each of the three exemplary modeling types through a subjective collection of literature and illustrative exercises to facilitate inspiration and exchange, with a focus on providing a didactic rather than exhaustive overview. We close by imagining a future multi-scale model design to impact critical decisions in oncology drug development.

Clinical applications of deep learning in neuroinflammatory diseases: A scoping review

BACKGROUND

Deep learning (DL) is an artificial intelligence technology that has aroused much excitement for predictive medicine due to its ability to process raw data modalities such as images, text, and time series of signals.

OBJECTIVES

Here, we intend to give the clinical reader elements to understand this technology, taking neuroinflammatory diseases as an illustrative use case of clinical translation efforts. We reviewed the scope of this rapidly evolving field to get quantitative insights about which clinical applications concentrate the efforts and which data modalities are most commonly used.

METHODS

We queried the PubMed database for articles reporting DL algorithms for clinical applications in neuroinflammatory diseases and the radiology.healthairegister.com website for commercial algorithms.

RESULTS

The review included 148 articles published between 2018 and 2024 and five commercial algorithms. The clinical applications could be grouped as computer-aided diagnosis, individual prognosis, functional assessment, the segmentation of radiological structures, and the optimization of data acquisition. Our review highlighted important discrepancies in efforts. The segmentation of radiological structures and computer-aided diagnosis currently concentrate most efforts with an overrepresentation of imaging. Various model architectures have addressed different applications, relatively low volume of data, and diverse data modalities. We report the high-level technical characteristics of the algorithms and synthesize narratively the clinical applications. Predictive performances and some common a priori on this topic are finally discussed.

CONCLUSION

The currently reported efforts position DL as an information processing technology, enhancing existing modalities of paraclinical investigations and bringing perspectives to make innovative ones actionable for healthcare.

Establishing finite element model credibility of a pedicle screw system under compression-bending: An end-to-end example of the ASME V&V 40 standard

Computational modeling and simulation (CM&S) is a key tool in medical device design, development, and regulatory approval. For example, finite element analysis (FEA) is widely used to understand the mechanical integrity and durability of orthopaedic implants. The ASME V&V 40 standard and supporting FDA guidance provide a framework for establishing model credibility, enabling deeper reliance on CM&S throughout the total product lifecycle. Examples of how to apply the principles outlined in the ASME V&V 40 standard are important to facilitating greater adoption by the medical device community, but few published examples are available that demonstrate best practices. Therefore, this paper outlines an end-to-end (E2E) example of the ASME V&V 40 standard applied to an orthopaedic implant. The objective of this study was to illustrate how to establish the credibility of a computational model intended for use as part of regulatory evaluation. In particular, this study focused on whether a design change to a spinal pedicle screw construct (specifically, the addition of a cannulation to an existing non-cannulated pedicle screw) would compromise the rod-screw construct mechanical performance. This question of interest (?OI) was addressed by establishing model credibility requirements according to the ASME V&V 40 standard. Experimental testing to support model validation was performed using spinal rods and non-cannulated pedicle screw constructs made with medical grade titanium (Ti-6Al-4V ELI). FEA replicating the experimental tests was performed by three independent modelers and validated through comparisons of common mechanical properties such as stiffness and yield force. The validated model was then used to simulate F1717 compression-bending testing on the new cannulated pedicle screw design to answer the ?OI, without performing any additional experimental testing. This E2E example provides a realistic scenario for the application of the ASME V&V 40 standard to orthopedic medical device applications.

A risk and credibility framework for in silico clinical trials of medical devices

BACKGROUND AND OBJECTIVE

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.

METHODS

Expanding on guidance currently in place for assessing the risk of traditional modeling applications of medical devices and demonstrating model credibility through benchtop validation activities, a framework is proposed to (1) evaluate the model risk for ISCT applications based on the independent factors of scope, coverage, and severity, and (2) assess the credibility of clinical validation activities based on consideration of the clinical comparator, the validation model, the agreement between the two, and the applicability of the clinical validation activities to the ISCT application.

RESULTS

The resulting framework spans across the range of ISCT applications that may be envisioned, as well as the variety of clinical datasets that can be used to demonstrate model credibility. Credibility factors reflect the expected clinical variability in the validation comparator and validation model, the statistical power of the comparator, the rigor of agreement between the comparator and model in terms of both inputs and outputs, and the overall similarity of the device in the validation activities to the device within the intended ISCT. When applied to a high-risk case study, the framework reveals that planned clinical validation activities require additional rigor in order to achieve the credibility targets, enabling an assessment of the validation effort relative to the potential benefit prior to investing in the validation studies.

DISCUSSION

An objective and risk-based framework for establishing credibility requirements for ISCT applications is a critical step in advancing ISCT from theory to practice. The proposed framework enforces that appropriate validation of ISCT applications requires evidence that the intended clinical environment is accurately represented. The framework will contribute to reducing uncertainty amongst technical, clinical, and regulatory constituents on ISCT applications, and promote rational adoption.

Credibility assessment of computational models according to ASME V&V40: Application to the Bologna Biomechanical Computed Tomography solution

BACKGROUND AND OBJECTIVE

When a computational model aims to be adopted beyond research purposes, e.g. to inform a clinical or regulatory decision, trust must be placed in its predictive accuracy. This practically translates into the need to demonstrate its credibility. In fact, prior to its adoption for regulatory purposes, an in silico methodology should be proven credible enough for the scope. This has become especially relevant as, although evidence of the safety and efficacy of new medical products or interventions has been traditionally provided to the regulator experimentally, i.e., in vivo or ex vivo, recently the idea to inform a regulatory decision in silico has made its way in the regulatory scenario. While a harmonised technical standard is currently missing in the EU regulatory system, in 2018 the ASME issued V&V40-2018, where a risk-based framework to assess the credibility of a computational model through the performance of predefined credibility activities is provided. The credibility framework is here applied to Bologna Biomechanical Computed Tomography (BBCT) solution, which predicts the absolute risk of fracture at the femur for a subject. BBCT has recently been the object of a qualification advice request to the European Medicine Agency.

METHODS

The full implementation of ASME V&V40-2018 framework on BBCT is shown. Starting from BBCT proposed context of use the whole credibility plan is presented and the credibility activities (Verification, Validation, Applicability) described together with the achieved credibility levels.

RESULTS

BBCT risk is judged medium, and the credibility levels achieved considered acceptable. The uncertainties intrinsically present in the material properties assignment affected BBCT predictions to the highest extent.

CONCLUSIONS

This work provides the practical application of the ASME V&V40-2018 risk-based credibility assessment framework, which could be applied to demonstrate model credibility in any field and support future regulatory submissions and foster the adoption of In Silico Trials.

Recommendations for finite element modelling of nickel-titanium stents-Verification and validation activities

The objective of this study is to present a credibility assessment of finite element modelling of self-expanding nickel-titanium (Ni-Ti) stents through verification and validation (VV) activities, as set out in the ASME VV-40 standard. As part of the study, the role of calculation verification, model input sensitivity, and model validation is examined across three different application contexts (radial compression, stent deployment in a vessel, fatigue estimation). A commercially available self-expanding Ni-Ti stent was modelled, and calculation verification activities addressed the effects of mesh density, element integration and stable time increment on different quantities of interests, for each context of use considered. Sensitivity analysis of the geometrical and material input parameters and validation of deployment configuration with in vitro comparators were investigated. Results showed similar trends for global and local outputs across the contexts of use in response to the selection of discretization parameters, although with varying sensitivities. Mesh discretisation showed substantial variability for less than 4 × 4 element density across the strut cross-section in radial compression and deployment cases, while a finer grid was deemed necessary in fatigue estimation for reliable predictions of strain/stress. Element formulation also led to substantial variation depending on the chosen integration options. Furthermore, for explicit analyses, model results were highly sensitive to the chosen target time increment (e.g., mass scaling parameters), irrespective of whether quasistatic conditions were ensured (ratios of kinetic and internal energies below 5%). The higher variability was found for fatigue life simulation, with the estimation of fatigue safety factor varying up to an order of magnitude depending on the selection of discretization parameters. Model input sensitivity analysis highlighted that the predictions of outputs such as radial force and stresses showed relatively low sensitivity to Ni-Ti material parameters, which suggests that the calibration approaches used in the literature to date appear reasonable, but a higher sensitivity to stent geometry, namely strut thickness and width, was found. In contrast, the prediction of vessel diameter following deployment was least sensitive to numerical parameters, and its validation with in vitro comparators offered a simple and accurate (error ~ 1-2%) method when predicting diameter gain, and lumen area, provided that the material of the vessel is appropriately characterized and modelled.

Relevant Choices Affecting the Fatigue Analysis of Ni-Ti Endovascular Devices

Ni-Ti alloys are widely used for biomedical applications due to their superelastic properties, which are especially convenient for endovascular devices that require minimally invasive insertion and durable effects, such as peripheral/carotid stents and valve frames. After crimping and deployment, stents undergo millions of cyclic loads imposed by heart/neck/leg movements, causing fatigue failure and device fracture that can lead to possibly severe consequences for the patient. Standard regulations require experimental testing for the preclinical assessment of such devices, which can be coupled with numerical modeling to reduce the time and costs of such campaigns and to obtain more information regarding the local state of stress and strain in the device. In this frame, this review aimed to enlighten the relevant choices that can affect the outcome of the fatigue analysis of Ni-Ti devices, both from experimental and numerical perspectives.

Applicability assessment for in-silico patient-specific TEVAR procedures

Thoracic Endovascular Aortic Repair (TEVAR) is a minimally invasive technique to treat thoracic aorta pathologies and consists of placing a self-expandable stent-graft into the pathological region to restore the vessel lumen and recreate a more physiological condition. Exhaustive computational models, namely the finite element analysis, can be implemented to reproduce the clinical procedure. In this context, numerical models, if used for clinical applications, must be reliable and the simulation credibility should be proved to predict clinical procedure outcomes or to build in-silico clinical trials. This work aims first at applying a previously validated TEVAR methodology to a patient-specific case. Then, defining the TEVAR procedure performed on a patient population as the context of use, the overall applicability of the TEVAR modeling is assessed to demonstrate the reliability of the model itself following a step-by-step method based on the ASME V&V40 protocol. Validation evidence sources are identified for the specific context of use and adopted to demonstrate the applicability of the numerical procedure, thereby answering a question of interest that evaluates the deployed stent-graft configuration in the vessel.

Towards a comprehensive assessment of QSP models: what would it take?

Quantitative Systems Pharmacology (QSP) has emerged as a powerful ensemble of approaches aiming at developing integrated mathematical and computational models elucidating the complex interactions between pharmacology, physiology, and disease. As the field grows and matures its applications expand beyond the boundaries of research and development and slowly enter the decision making and regulatory arenas. However, widespread acceptance and eventual adoption of a new modeling approach requires assessment criteria and quantifiable metrics that establish credibility and increase confidence in model predictions. QSP aims to provide an integrated understanding of pathology in the context of therapeutic interventions. Because of its ambitious nature and the fact that QSP emerged in an uncoordinated manner as a result of activities distributed across organizations and academic institutions, high entropy characterizes the tools, methods, and computational methodologies and approaches used. The eventual acceptance of QSP model predictions as supporting material for an application to a regulatory agency will require that two key aspects are considered: (1) increase confidence in the QSP framework, which drives standardization and assessment; and (2) careful articulation of the expectations. Both rely heavily on our ability to rigorously and consistently assess QSP models. In this manuscript, we wish to discuss the meaning and purpose of such an assessment in the context of QSP model development and elaborate on the differentiating features of QSP that render such an endeavor challenging. We argue that QSP establishes a conceptual, integrative framework rather than a specific and well-defined computational methodology. QSP elicits the use of a wide variety of modeling and computational methodologies optimized with respect to specific applications and available data modalities, which exceed the data structures employed by chemometrics and PK/PD models. While the range of options fosters creativity and promises to substantially advance our ability to design pharmaceutical interventions rationally and optimally, our expectations of QSP models need to be clearly articulated and agreed on, with assessment emphasizing the scope of QSP studies rather than the methods used. Nevertheless, QSP should not be considered an independent approach, rather one of many in the broader continuum of computational models.

Integration of Heterogeneous Biological Data in Multiscale Mechanistic Model Calibration: Application to Lung Adenocarcinoma

Mechanistic models are built using knowledge as the primary information source, with well-established biological and physical laws determining the causal relationships within the model. Once the causal structure of the model is determined, parameters must be defined in order to accurately reproduce relevant data. Determining parameters and their values is particularly challenging in the case of models of pathophysiology, for which data for calibration is sparse. Multiple data sources might be required, and data may not be in a uniform or desirable format. We describe a calibration strategy to address the challenges of scarcity and heterogeneity of calibration data. Our strategy focuses on parameters whose initial values cannot be easily derived from the literature, and our goal is to determine the values of these parameters via calibration with constraints set by relevant data. When combined with a covariance matrix adaptation evolution strategy (CMA-ES), this step-by-step approach can be applied to a wide range of biological models. We describe a stepwise, integrative and iterative approach to multiscale mechanistic model calibration, and provide an example of calibrating a pathophysiological lung adenocarcinoma model. Using the approach described here we illustrate the successful calibration of a complex knowledge-based mechanistic model using only the limited heterogeneous datasets publicly available in the literature.

Promoting Sustainability through Next-Generation Biologics Drug Development

The fourth industrial revolution in 2011 aimed to transform the traditional manufacturing processes. As part of this revolution, disruptive innovations in drug development and data science approaches have the potential to optimize CMC (chemistry, manufacture, and control). The real-time simulation of processes using “digital twins” can maximize efficiency while improving sustainability. As part of this review, we investigate how the World Health Organization’s 17 sustainability goals can apply toward next-generation drug development. We analyze the state-of-the-art laboratory leadership, inclusive personnel recruiting, the latest therapy approaches, and intelligent process automation. We also outline how modern data science techniques and machine tools for CMC help to shorten drug development time, reduce failure rates, and minimize resource usage. Finally, we systematically analyze and compare existing approaches to our experiences with the high-throughput laboratory KIWI-biolab at the TU Berlin. We describe a sustainable business model that accelerates scientific innovations and supports global action toward a sustainable future.

Reliable Numerical Models of Nickel-Titanium Stents: How to Deduce the Specific Material Properties from Testing Real Devices

The current interest of those dealing with medical research is the preparation of digital twins. In this frame, the first step to accomplish is the preparation of reliable numerical models. This is a challenging task since it is not common to know the exact device geometry and material properties unless in studies performed in collaboration with the manufacturer. The particular case of modeling Ni-Ti stents can be highlighted as a worst-case scenario due to both the complex geometrical features and non-linear material response. Indeed, if the limitations in the description of the geometry can be overcome, many difficulties still exist in the assessment of the material, which can vary according to the manufacturing process and requires many parameters for its description. The purpose of this work is to propose a coupled experimental and computational workflow to identify the set of material properties in the case of commercially-resembling Ni-Ti stents. This has been achieved from non-destructive tensile tests on the devices compared with results from Finite Element Analysis (FEA). A surrogate modeling approach is proposed for the identification of the material parameters, based on a minimization problem on the database of responses of Ni-Ti materials obtained with FEA with a series of different parameters. The reliability of the final result was validated through the comparison with the output of additional experiments.

Use of Real-World Data for Enhancing Model Credibility: Applications to Medical Device Development

Evaluating the credibility of computational models used in medical device development is increasingly important as medical devices become more complex and modeling takes on a more critical role in the device development process. While bench-testing based comparisons are common for assessing model credibility and have many advantages, such as control over test specimens and the ability to quantify outputs, the credibility assessments performed with bench tests often do not evaluate the clinical relevance of key aspects of model form (such as boundary conditions, constitutive models/properties, and geometries) selected when simulating in vivo conditions.

Real-world data (outcomes data generated through clinical use of a device) offer an opportunity to assess the applicability and clinical relevance of a computational model. Although real-world data are frequently less controlled and more qualitative than benchtop data, real-world data are often a direct assessment of a particular clinical complication and therefore of high clinical relevance. Further, real-world data have the potential to reveal failure modes not previously identified in pre-clinical failure modes analysis, thereby motivating testing advancements. To review the use of clinical data in medical device modeling, this paper presents a series of examples related to tibial tray fracture that incorporate varying levels of benchtop data and real world data when evaluating model credibility. The merits and drawbacks of the credibility assessment for each example are discussed in order to provide practical and actionable guidance on the use of real world data for establishing and demonstrating model credibility.

Bayesian Augmented Clinical Trials in TB Therapeutic Vaccination

We propose a Bayesian hierarchical method for combining in silico and in vivo data onto an augmented clinical trial with binary end points. The joint posterior distribution from the in silico experiment is treated as a prior, weighted by a measure of compatibility of the shared characteristics with the in vivo data. We also formalise the contribution and impact of in silico information in the augmented trial. We illustrate our approach to inference with in silico data from the UISS-TB simulator, a bespoke simulator of virtual patients with tuberculosis infection, and synthetic physical patients from a clinical trial.

How to Validate in silico Deployment of Coronary Stents: Strategies and Limitations in the Choice of Comparator

This study aims at proposing and discussing useful indications to all those who need to validate a numerical model of coronary stent deployment. The proof of the reliability of a numerical model is becoming of paramount importance in the era of in silico trials. Recently, the ASME V&V Standard Committee for medical devices prepared the V&V 40 standard document that provides a framework that guides users in establishing and assessing the relevance and adequacy of verification and validation activities performed for proving the credibility of models. To the knowledge of the authors, only a few examples of the application of the V&V 40 framework to medical devices are available in the literature, but none about stents. Specifically, in this study, the authors wish to emphasize the choice of a relevant set of experimental activities to provide data for the validation of computational models aiming to predict coronary stent deployment. Attention is focused on the use of ad hoc 3D-printed mock vessels in the validation plan, which could allow evaluating aspects of clinical relevance in a representative but controlled environment.

Distribution and history of extensional stresses on vWF surrogate molecules in turbulent flow

The configuration of proteins is critical for their biochemical behavior. Mechanical stresses that act on them can affect their behavior leading to the development of decease. The von Willebrand factor (vWF) protein circulating with the blood loses its efficacy when it undergoes non-physiological hemodynamic stresses. While often overlooked, extensional stresses can affect the structure of vWF at much lower stress levels than shear stresses. The statistical distribution of extensional stress as it applies on models of the vWF molecule within turbulent flow was examined here. The stress on the molecules of the protein was calculated with computations that utilized a Lagrangian approach for the determination of the molecule trajectories in the flow filed. The history of the stresses on the proteins was also calculated. Two different flow fields were considered as models of typical flows in cardiovascular mechanical devises, one was a Poiseuille flow and the other was a Poiseuille–Couette flow field. The data showed that the distribution of stresses is important for the design of blood flow devices because the average stress can be below the critical value for protein damage, but tails of the distribution can be outside the critical stress regime.

Large eddy simulation as a fast and accurate engineering approach for the simulation of rotary blood pumps

An accurate representation of the flow field in blood pumps is important for the design and optimization of blood pumps. The primary turbulence modeling methods applied to blood pumps have been the Reynolds-averaged Navier-Stokes (RANS) or URANS (unsteady RANS) method. Large eddy simulation (LES) method has been introduced to simulate blood pumps. Nonetheless, LES has not been widely used to assist in the design and optimization of blood pumps to date due to its formidable computational cost. The purpose of this study is to explore the potential of the LES technique as a fast and accurate engineering approach for the simulation of rotary blood pumps. The performance of "Light LES" (using the same time and spatial resolutions as the URANS) and LES in two rotary blood pumps was evaluated by comparing the results with the URANS and extensive experimental results. This study showed that the results of both "Light LES" and LES are superior to URANS, in terms of both performance curves and key flow features. URANS could not predict the flow separation and recirculation in diffusers for both pumps. In contrast, LES is superior to URANS in capturing these flows, performing well for both design and off-design conditions. The differences between the "Light LES" and LES results were relatively small. This study shows that with less computational cost than URANS, "Light LES" can be considered as a cost-effective engineering approach to assist in the design and optimization of rotary blood pumps.

Computational evaluation of inferior vena cava filters through computational fluid dynamics methods

Numerical simulation is growing in its importance toward the design, testing and evaluation of medical devices. Computational fluid dynamics and finite element analysis allow improved calculation of stress, heat transfer, and flow to better understand the medical device environment. Current research focuses not only on improving medical devices, but also on improving the computational tools themselves. As methods and computer technology allow for faster simulation times, iterations and trials can be performed faster to collect more data. Given the adverse events associated with long-term inferior vena cava (IVC) filter placement, IVC filter design and device evaluation are of paramount importance. This work reviews computational methods used to develop, test, and improve IVC filters to ultimately serve the needs of the patient.

Validation of the computational model of a coronary stent: a fundamental step towards in silico trials

The proof of the reliability of a numerical model is becoming of paramount importance in the era of in silico clinical trials. When dealing with a coronary stenting procedure, the virtual scenario should be able to replicate the real device, passing through the different stages of the procedure, which has to maintain the atherosclerotic vessel opened. Nevertheless, most of the published studies adopted commercially resembling geometries and generic material parameters, without a specific validation of the employed numerical models. In this work, a workflow for the generation and validation of the computational model of a coronary stent was proposed. Possible sources of variability in the results, such as the inter-batches variability in the material properties and the choice of proper simulation strategies, were accounted for and discussed. Then, a group of in vitro tests, representative of the device intended use was used as a comparator to validate the model. The free expansion simulation, which is the most used simulation in the literature, was shown to be only partially useful for stent model validation purposes. On the other hand, the choice of proper additional experiments, as the suggested uniaxial tensile tests on the stent and deployment tests into a deformable tube, could provide further suitable information to prove the efficacy of the numerical approach.

Hemolysis estimation in turbulent flow for the FDA critical path initiative centrifugal blood pump

Hemolysis in medical devices and implants has been a primary concern over the past fifty years. Turbulent flow in particular can cause cell trauma and hemolysis in such devices. In this work, the effects of turbulence on red blood cell (RBC) damage are examined by simulating the flow field through a centrifugal blood pump that has been identified as a case study through the critical path initiative of the US Food and Drug Administration (FDA). In this study, a new model was employed to predict hemolysis in the turbulent flow environment in the pump selected for the FDA critical path initiative. The operating conditions for a centrifugal blood pump were specified by the FDA for rotational speeds of 2500 and 3500 rpm. The model is based on the analysis of the smaller eddies within the turbulent flow field, since it is assumed that turbulent flow eddies with sizes comparable to the dimensions of the RBCs lead to cell trauma. The Kolmogorov length scale of the velocity field is used to identify such small eddies. Using model parameters obtained in prior work through comparisons to capillary and jet flow, it is found that hemolysis for the 2500-rpm pump was predicted well, while hemolysis for the 3500-rpm pump was overpredicted. Results indicate refinement of the model and empirical constants with better experimental data is needed.

From the real device to the digital twin: A coupled experimental-numerical strategy to investigate a novel bioresorbable vascular scaffold

The purpose of this work is to propose a workflow that couples experimental and computational activities aimed at developing a credible digital twin of a commercial coronary bioresorbable vascular scaffold when direct access to data about material mechanical properties is not possible. Such a situation is be faced when the manufacturer is not involved in the study, thus directly investigating the actual device is the only source of information available. The object of the work is the Fantom® Encore polymeric stent (REVA Medical) made of Tyrocore™. Four devices were purchased and used in mechanical tests that are easily reproducible in any mechanical laboratory, i.e. free expansion and uniaxial tension testing, the latter performed with protocols that emphasized the rate-dependent properties of the polymer. Given the complexity of the mechanical behaviour observed experimentally, it was chosen to use the Parallel Rehological Framework material model, already used in the literature to describe the behaviour of other polymers, such as PLLA. Calibration of the material model was based on simulations that replicate the tensile test performed on the device. Given the high number of material parameters, a plan of simulations was done to find the most suitable set, varying each parameter value in a feasible range and considering a single repetitive unit of the stent, neglecting residual stresses generated by crimping and expansion. This strategy resulted in a significant reduction of computational cost. The performance of the set of parameters thus identified was finally evaluated considering the whole delivery system, by comparing the experimental results with the data collected simulating free expansion and uniaxial tension testing. Moreover, radial force testing was numerically performed and compared with literature data. The obtained results demonstrated the effectiveness of the digital twin development pipeline, a path applicable to any commercial device whose geometric structure is based on repetitive units.

In Silico Assessment of Class I Antiarrhythmic Drug Effects on Pitx2-Induced Atrial Fibrillation: Insights from Populations of Electrophysiological Models of Human Atrial Cells and Tissues

Electrical remodelling as a result of homeodomain transcription factor 2 (Pitx2)-dependent gene regulation was linked to atrial fibrillation (AF) and AF patients with single nucleotide polymorphisms at chromosome 4q25 responded favorably to class I antiarrhythmic drugs (AADs). The possible reasons behind this remain elusive. The purpose of this study was to assess the efficacy of the AADs disopyramide, quinidine, and propafenone on human atrial arrhythmias mediated by Pitx2-induced remodelling, from a single cell to the tissue level, using drug binding models with multi-channel pharmacology. Experimentally calibrated populations of human atrial action po-tential (AP) models in both sinus rhythm (SR) and Pitx2-induced AF conditions were constructed by using two distinct models to represent morphological subtypes of AP. Multi-channel pharmaco-logical effects of disopyramide, quinidine, and propafenone on ionic currents were considered. Simulated results showed that Pitx2-induced remodelling increased maximum upstroke velocity (dVdtmax), and decreased AP duration (APD), conduction velocity (CV), and wavelength (WL). At the concentrations tested in this study, these AADs decreased dVdtmax and CV and prolonged APD in the setting of Pitx2-induced AF. Our findings of alterations in WL indicated that disopyramide may be more effective against Pitx2-induced AF than propafenone and quinidine by prolonging WL.

Verification and Validation of Computational Models Used in Biopharmaceutical Manufacturing: Potential Application of the ASME Verification and Validation 40 Standard and FDA Proposed AI/ML Model Life Cycle Management Framework

A wide variety of computational models covering statistical, mechanistic, and machine learning (locked and adaptive) methods are explored for use in biopharmaceutical manufacturing. Limited discussion exists on how to establish the credibility of a computational model for application in biopharmaceutical manufacturing. In this work, we tried to use the American Society of Mechanical Engineers (ASME) Verification and Validation 40 (V&V 40) standard and FDA proposed AI/ML model life cycle management framework for Software as a Medical Device (SaMD) in biopharmaceutical manufacturing use cases, by applying to a set of curated hypothetical examples. We discussed the need for standardized frameworks to facilitate consistent decision making to enable efficient adoption of computational models in biopharmaceutical manufacturing and alignment of existing good practices with existing frameworks. In the study of our examples, we anticipate existing frameworks like V&V 40 can be adopted.

Mechanisms underlying pro-arrhythmic abnormalities arising from Pitx2-induced electrical remodelling: an in silico intersubject variability study

Background

Electrical remodelling as a result of the homeodomain transcription factor 2 (Pitx2)-dependent gene regulation induces atrial fibrillation (AF) with different mechanisms. The purpose of this study was to identify Pitx2-induced changes in ionic currents that cause action potential (AP) shortening and lead to triggered activity.

Methods

Populations of computational atrial AP models were developed based on AP recordings from sinus rhythm (SR) and AF patients. Models in the AF population were divided into triggered and untriggered AP groups to evaluate the relationship between each ion current regulated by Pitx2 and triggered APs. Untriggered AP models were then divided into shortened and unshortened AP groups to determine which Pitx2-dependent ion currents contribute to AP shortening.

Results

According to the physiological range of AP biomarkers measured experimentally, populations of 2,885 SR and 4,781 AF models out of the initial pool of 30,000 models were selected. Models in the AF population predicted AP shortening and triggered activity observed in experiments in Pitx2-induced remodelling conditions. The AF models included 925 triggered AP models, 1,412 shortened AP models and 2,444 unshortened AP models. Intersubject variability in I_Ks and I_CaL primarily modulated variability in AP duration (APD) in all shortened and unshortened AP models, whereas intersubject variability in I_K1 and SERCA mainly contributed to the variability in AP morphology in all triggered and untriggered AP models. The incidence of shortened AP was positively correlated with I_Ks and I_K1 and was negatively correlated with I_Na , I_CaL and SERCA, whereas the incidence of triggered AP was negatively correlated with I_Ks and I_K1 and was positively correlated with I_Na , I_CaL and SERCA.

Conclusions

Electrical remodelling due to Pitx2 upregulation may increase the incidence of shortened AP, whereas electrical remodelling arising from Pitx2 downregulation may favor to the genesis of triggered AP.

Biomechanical modelling of the facet joints: a review of methods and validation processes in finite element analysis

There is an increased interest in studying the biomechanics of the facet joints. For in silico studies, it is therefore important to understand the level of reliability of models for outputs of interest related to the facet joints. In this work, a systematic review of finite element models of multi-level spinal section with facet joints output of interest was performed. The review focused on the methodology used to model the facet joints and its associated validation. From the 110 papers analysed, 18 presented some validation of the facet joints outputs. Validation was done by comparing outputs to literature data, either computational or experimental values; with the major drawback that, when comparing to computational values, the baseline data was rarely validated. Analysis of the modelling methodology showed that there seems to be a compromise made between accuracy of the geometry and nonlinearity of the cartilage behaviour in compression. Most models either used a soft contact representation of the cartilage layer at the joint or included a cartilage layer which was linear elastic. Most concerning, soft contact models usually did not contain much information on the pressure-overclosure law. This review shows that to increase the reliability of in silico model of the spine for facet joints outputs, more needs to be done regarding the description of the methods used to model the facet joints, and the validation for specific outputs of interest needs to be more thorough, with recommendation to systematically share input and output data of validation studies.

Quantitative Systems Pharmacology Approaches for Immuno-Oncology: Adding Virtual Patients to the Development Paradigm

Drug development in oncology commonly exploits the tools of molecular biology to gain therapeutic benefit through reprograming of cellular responses. In immuno‐oncology (IO) the aim is to direct the patient’s own immune system to fight cancer. After remarkable successes of antibodies targeting PD1/PD‐L1 and CTLA4 receptors in targeted patient populations, the focus of further development has shifted toward combination therapies. However, the current drug‐development approach of exploiting a vast number of possible combination targets and dosing regimens has proven to be challenging and is arguably inefficient. In particular, the unprecedented number of clinical trials testing different combinations may no longer be sustainable by the population of available patients. Further development in IO requires a step change in selection and validation of candidate therapies to decrease development attrition rate and limit the number of clinical trials. Quantitative systems pharmacology (QSP) proposes to tackle this challenge through mechanistic modeling and simulation. Compounds’ pharmacokinetics, target binding, and mechanisms of action as well as existing knowledge on the underlying tumor and immune system biology are described by quantitative, dynamic models aiming to predict clinical results for novel combinations. Here, we review the current QSP approaches, the legacy of mathematical models available to quantitative clinical pharmacologists describing interaction between tumor and immune system, and the recent development of IO QSP platform models. We argue that QSP and virtual patients can be integrated as a new tool in existing IO drug development approaches to increase the efficiency and effectiveness of the search for novel combination therapies.

Computational Model Validation of Contact Mechanics in Total Ankle Arthroplasty

Revision rates in total ankle arthroplasty (TAA) are nearly double compared with hip or knee arthroplasty procedures. Contact mechanics for metal-polyethylene articulation in TAA is critical due to the reduced size of the implant and higher expected load, compared with a hip or knee joint. This study was focused on developing a validated computational model to predict contact area in a polyethylene tibial bearing articulating with a metallic talar component in a bicondylar TAA design. Contact area was evaluated at five different flexion angles in an experimental test and in a computational model, per ASTM F2665. The overall contact area values predicted in the computational model matched closely (within 8%) with that measured in the comparator; well within the range reported in the literature. The credibility of the model to sufficiently predict the outputs relative to the experimental data was discussed using the guidelines provided by the recently published ASME V&V 40-2018 standard. Various sensitivities associated with both the model and the comparator, were explored. It was concluded that the validated modeling approach presented in this study demonstrated sufficient accuracy to support the use of modeling for evaluation of contact area of TAA designs. © 2019 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 38:1063-1069, 2020.

Computational modeling of the Food and Drug Administration's benchmark centrifugal blood pump

In order to simulate hemodynamics within centrifugal blood pumps and to predict pump hemolysis, CFD simulations must be thoroughly validated against experimental data. They must also account for and accurately model the specific working fluid in the pump, whether that is a blood-analog solution to match an experimental PIV study or animal blood in a hemolysis experiment. Therefore, the Food and Drug Administration (FDA) benchmark centrifugal blood pump and its database of experimental PIV and hemolysis data were used to thoroughly validate CFD simulations of the same blood pump. A Newtonian blood model was first used to compare to the PIV data with a blood analog fluid while hemolysis data were compared using a power-law hemolysis model fit to porcine blood data. A viscoelastic blood model was then incorporated into the CFD solver to investigate the importance of modeling blood's viscoelasticity in centrifugal pumps. The established computational framework, including a dynamic rotating mesh, animal blood-specific fluid properties and hemolysis modeling, and a k-ω SST turbulence model, was shown to more accurately predict pump pressure heads, velocity fields, and hemolysis compared to previously published CFD studies of the FDA centrifugal pump. The CFD simulations were able to match the FDA pressure and hemolysis data for multiple pump operating conditions, with the CFD results being within the standard deviations of the experimental results. While CFD radial velocity profiles between the impeller blades also compared well to the PIV velocity results, more work is still needed to address the large variability among both experimental and computational predictions of velocity in the diffuser outlet jet. Small differences were observed between the Newtonian and viscoelastic blood models in pressure head and hemolysis at the higher flow rate cases (FDA Conditions 4 and 5) but were more significant at lower flow rate and pump impeller speeds (FDA Condition 1). These results suggest that the importance of accounting for blood's viscoelasticity may be dependent on the specific blood pump operating conditions. This detailed computational framework with improved modeling techniques and an extensive validation procedure will be used in future CFD studies of centrifugal blood pumps to aid in device design and predictions of their biological responses.

In silico trials: Verification, validation and uncertainty quantification of predictive models used in the regulatory evaluation of biomedical products

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Regulators now consider also evidences produced in silico.

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We need accepted methods to evaluate the credibility of models.

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In this paper we describe the use of the ASME V&V-40 technical standard.

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We also discuss its application to various types of modelling methods.

Historically, the evidences of safety and efficacy that companies provide to regulatory agencies as support to the request for marketing authorization of a new medical product have been produced experimentally, either in vitro or in vivo. More recently, regulatory agencies started receiving and accepting evidences obtained in silico, i.e. through modelling and simulation. However, before any method (experimental or computational) can be acceptable for regulatory submission, the method itself must be considered “qualified” by the regulatory agency. This involves the assessment of the overall “credibility” that such a method has in providing specific evidence for a given regulatory procedure. In this paper, we describe a methodological framework for the credibility assessment of computational models built using mechanistic knowledge of physical and chemical phenomena, in addition to available biological and physiological knowledge; these are sometimes referred to as “biophysical” models. Using guiding examples, we explore the definition of the context of use, the risk analysis for the definition of the acceptability thresholds, and the various steps of a comprehensive verification, validation and uncertainty quantification process, to conclude with considerations on the credibility of a prediction for a specific context of use. While this paper does not provide a guideline for the formal qualification process, which only the regulatory agencies can provide, we expect it to help researchers to better appreciate the extent of scrutiny required, which should be considered early on in the development/use of any (new) in silico evidence.

Consideration of a Credibility Assessment Framework in Model-Informed Drug Development: Potential Application to Physiologically-Based Pharmacokinetic Modeling and Simulation

The use of computational models in drug development has grown during the past decade. These model‐informed drug development (MIDD) approaches can inform a variety of drug development and regulatory decisions. When used for regulatory decision making, it is important to establish that the model is credible for its intended use. Currently, there is no consensus on how to establish and assess model credibility, including the selection of appropriate verification and validation activities. In this article, we apply a risk‐informed credibility assessment framework to physiologically‐based pharmacokinetic modeling and simulation and hypothesize this evidentiary framework may also be useful for evaluating other MIDD approaches. We seek to stimulate a scientific discussion around this framework as a potential starting point for uniform assessment of model credibility across MIDD. Ultimately, an overarching framework may help to standardize regulatory evaluation across therapeutic products (i.e., drugs and medical devices).