Biology subjects

Zingaro, A.

Publications and source records attributed to Zingaro, A..

2 recordsLinked to original sources

bioRxiv2024

Real-time prediction of drug-induced proarrhythmic risk with sex-specific cardiac emulators

In silico trials for drug safety assessment require a large number of high-fidelity 3D cardiac electrophysiological simulations to predict drug-induced QT interval prolongation, making the process computationally expensive and time-consuming. These simulations, while necessary to accurately model the complex physiological conditions of the human heart, are often cost-prohibitive when scaled to large populations or diverse conditions. To overcome this challenge, we develop sex-specific emulators for the real-time prediction of QT interval prolongation, with separate models for each sex. Building an extensive dataset from 900 simulations allows us to show the superior sensitivity of 3D models over 0D single-cell models in detecting abnormal electrical propagation in response to drug effects as the risk level increases. The resulting emulators trained on this dataset showed high accuracy level, with an average relative error of 4% compared to simulation results. This enables global sensitivity analysis and the replication of in silico cardiac safety clinical trials with accuracy comparable to that of simulations when validated against in vivo data. With our emulators, we carry out in silico clinical trials in seconds on a standard laptop, drastically reducing computational time compared to traditional high-performance computing methods. This efficiency enables the rapid testing of drugs across multiple concentration ranges without additional computational cost. This approach directly addresses several key challenges faced by the biopharmaceutical industry: optimizing trial designs, accounting for variability in biological assays, and enabling rapid, cost-effective drug safety evaluations. By integrating these emulators into the drug development process, we can enhance the reliability of preclinical assessments, streamline regulatory submissions, and advance the practical application of digital twins in biomedicine. This work represents a significant step toward more efficient and personalized drug development, ultimately benefiting patient safety and accelerating the path to market.

pharmacology and toxicology↗

bioRxiv2024

A comprehensive stroke risk assessment by combining atrial computational fluid dynamics simulations and functional patient data

Stroke, a major global health concern often rooted in cardiac dynamics, demands precise risk evaluation for targeted intervention. Current risk models, like the CHA2DS2-VASc score, often lack the granularity required for personalized predictions. In this study, we present a nuanced and thorough stroke risk assessment by integrating functional insights from cardiac magnetic resonance (CMR) with patient-specific computational fluid dynamics (CFD) simulations. Our cohort, evenly split between control and stroke groups, comprises eight patients. Utilizing CINE CMR, we compute kinematic features, revealing smaller left atrial volumes for stroke patients. The incorporation of patient-specific atrial displacement into our hemodynamic simulations unveils the influence of atrial compliance on the flow fields, emphasizing the importance of LA motion in CFD simulations and challenging the conventional rigid wall assumption in hemodynamics models. Standardizing hemodynamic features with functional metrics enhances the differentiation between stroke and control cases. While standalone assessments provide limited clarity, the synergistic fusion of CMR-derived functional data and patient-informed CFD simulations offers a personalized and mechanistic understanding, distinctly segregating stroke from control cases. Specifically, our investigation reveals a crucial clinical insight: normalizing hemodynamic features based on ejection fraction fails to differentiate between stroke and control patients. Differently, when normalized with stroke volume, a clear and clinically significant distinction emerges and this holds true for both the left atrium and its appendage, providing valuable implications for precise stroke risk assessment in clinical settings. This work introduces a novel framework for seamlessly integrating hemodynamic and functional metrics, laying the groundwork for improved predictive models, and highlighting the significance of motion-informed, personalized risk assessments.

bioengineering↗