Prediction of the effects of drugs on cardiac activity using computer simulations

[ES] Las enfermedades cardiovasculares siguen siendo la principal causa de muerte en Europa. Las arritmias cardíacas son una causa importante de muerte súbita, pero sus mecanismos son complejos. Esto denota la importancia de su estudio y prevención. La investigación sobre electrofisiología cardíaca ha demostrado que las anomalías eléctricas causadas por mutaciones que afectan a canales cardíacos pueden desencadenar arritmias. Sorprendentemente, se ha descubierto una gran variedad de fármacos proarrítmicos, incluidos aquellos que usamos para prevenirlas. Las indicaciones de uso de fármacos actuales intentaron solucionar este problema diseñando una prueba para identificar aquellos fármacos que podían ser peligrosos basado en el bloqueo de un solo canal iónico. El estudio de las interacciones fármaco-canal ha revelado la existencia no sólo de compuestos que bloquean múltiples canales, sino también una gran complejidad en esas interacciones. Esto podría explicar por qué algunos medicamentos pueden mostrar efectos muy diferentes en la misma enfermedad. Existen dos desafíos importantes con respecto a los efectos de los fármacos en la electrofisiología cardíaca. Por un lado, las empresas y entidades reguladoras están buscando una herramienta de alto rendimiento que mejore la detección del potencial proarrítmico durante el desarrollo de fármacos. Por otro lado, los pacientes con anomalías eléctricas a menudo requieren tratamientos personalizados más seguros. Las simulaciones computacionales contienen un poder sin precedentes para abordar fenómenos biofísicos complejos. Deberían ser de utilidad a la hora de determinar las características que definen tanto los efectos beneficiosos como no deseados de los fármacos mediante la reproducción de datos experimentales y clínicos. En esta tesis doctoral, se han utilizado modelos computacionales y simulaciones para dar respuesta a estos dos desafíos. El estudio de los efectos de los fármacos sobre la actividad cardíaca se dividió en el estudio de su seguridad y de su eficacia, respectivamente. Para dar respuesta al primer desafío, se adoptó un enfoque más amplio y se generó un nuevo biomarcador fácil de usar para la clasificación del potencial proarrítmico de los fármacos utilizando modelos del potencial de acción de células y tejidos cardíacos humanos. Se integró el bloqueo de múltiples canales a través de IC50 y el uso de concentraciones terapéuticas con el fin de mejorar el poder predictivo. Luego, se entrenó el biomarcador cuantificando el potencial proarrítmico de 84 fármacos. Los resultados obtenidos sugieren que el biomarcador podría usarse para probar el potencial proarrítmico de nuevos fármacos. Respecto al segundo desafío, se adoptó un enfoque más específico y se buscó mejorar la terapia de pacientes con anomalías eléctricas cardíacas. Por lo tanto, se creó un modelo detallado de la mutación V411M del canal de sodio, causante del síndrome de QT largo, reproduciendo datos clínicos y experimentales. Se evaluaron los posibles efectos beneficiosos de ranolazina, a la par que se aportó información sobre los mecanismos que impulsan la efectividad de la flecainida. Los resultados obtenidos sugieren que, si bien ambos fármacos mostraron diferentes mecanismos de bloqueo de los canales de sodio, un tratamiento con ranolazina podría ser beneficioso en estos pacientes.[CA] Les malalties cardiovasculars continuen sent la principal causa de mort a Europa. Les arrítmies cardíaques són una causa important de mort sobtada, però els seus mecanismes són complexos. Això denota la importància del seu estudi i prevenció. La investigació sobre electrofisiologia cardíaca ha demostrat que les anomalies elèctriques que afecten a canals cardiacs poden desencadenar arrítmies. Sorprenentment, s'ha descobert una gran varietat de fàrmacs proarrítmics, inclosos aquells que utilitzem per a previndre-les. Les indicacions d'ús de fàrmacs actuals van intentar solucionar aquest problema dissenyant una prova per a identificar aquells fàrmacs que podien ser perillosos basada en el bloqueig d'un sol canal iònic. L'estudi de les interaccions fàrmac-canal ha revelat l'existència no sols de compostos que bloquegen múltiples canals, sinó també una gran complexitat en aquestes interaccions. Això podria explicar per què alguns medicaments poden mostrar efectes molt diferents en la mateixa malaltia. Existeixen dos desafiaments importants respecte als efectes dels fàrmacs en la electrofisiologia cardíaca. D'una banda, les empreses i entitats reguladores estan buscant una eina d'alt rendiment que millore la detecció del potencial proarrítmic durant el desenvolupament de fàrmacs. D'altra banda, els pacients amb anomalies elèctriques sovint requereixen tractaments personalitzats més segurs. Les simulacions computacionals contenen un poder sense precedents per a abordar fenòmens biofísics complexos. Haurien de ser d'utilitat a l'hora de determinar les característiques que defineixen tant els efectes beneficiosos com no desitjats dels fàrmacs mitjançant la reproducció de dades experimentals i clíniques. En aquesta tesi doctoral, s'han utilitzat models computacionals i simulacions per a donar resposta a aquests dos desafiaments. L'estudi dels efectes dels fàrmacs sobre l'activitat cardíaca es va dividir en l'estudi de la seva seguretat i la seva eficacia. Per a donar resposta al primer desafiament, es va adoptar un enfocament més ampli i es va generar un nou biomarcador fàcil d'usar per a la classificació del potencial proarrítmic dels fàrmacs utilitzant models del potencial d'acció de cèl·lules i teixits cardíacs humans. Es va integrar el bloqueig de múltiples canals a través d'IC50 i l'ús de concentracions terapèutiques amb la finalitat de millorar el poder predictiu. Després, es va entrenar el biomarcador quantificant el potencial proarrítmic de 84 fàrmacs. Els resultats obtinguts suggereixen que el biomarcador podria usar-se per a provar el potencial proarrítmic de nous fàrmacs. Respecte al segon desafiament, es va adoptar un enfocament més específic i es va buscar millorar la teràpia de pacients amb anomalies elèctriques cardíaques. Per tant, es va crear un model detallat de la mutació V411M del canal de sodi, causant de la síndrome de QT llarg, reproduint dades clíniques i experimentals. Es van avaluar els possibles efectes beneficiosos de ranolazina, a l'una que es va aportar informació sobre els mecanismes que impulsen l'efectivitat de la flecainida. Els resultats obtinguts suggereixen que, si bé tots dos fàrmacs van mostrar diferents mecanismes de bloqueig dels canals de sodi, un tractament amb ranolazina podria ser beneficiós en aquests pacients.[EN] Cardiovascular disease remains the main cause of death in Europe. Cardiac arrhythmias are an important cause of sudden death, but their mechanisms are complex. This denotes the importance of their study and prevention. Research on cardiac electrophysiology has shown that electrical abnormalities caused by mutations in cardiac channels can trigger arrhythmias. Surprisingly, a wide variety of drugs have also shown proarrhythmic potential, including those that we use to prevent arrhythmia. Current guidelines designed a test to identify dangerous drugs by assessing their blocking power on a single ion channel to address this situation. Study of drug-channel interactions has revealed not only compounds that block multiple channels but also a great complexity in those interactions. This could explain why similar drugs can show vastly different effects in some diseases. There are two important challenges regarding the effects of drugs on cardiac electrophysiology. On the one hand, companies and regulators are in search of a high throughput tool that improves proarrhythmic potential detection during drug development. On the other hand, patients with electrical abnormalities often require safer personalized treatments owing to their condition. Computer simulations provide an unprecedented power to tackle complex biophysical phenomena. They should prove useful determining the characteristics that define the drugs' beneficial and unwanted effects by reproducing experimental and clinical observations. In this PhD thesis, we used computational models and simulations to address the two abovementioned challenges. We split the study of drug effects on the cardiac activity into the study of their safety and efficacy, respectively. For the former, we took a wider approach and generated a new easy-to-use biomarker for proarrhythmic potential classification using cardiac cell and tissue human action potential models. We integrated multiple channel block through IC50s and therapeutic concentrations to improve its predictive power. Then, we quantified the proarrhythmic potential of 84 drugs to train the biomarker. Our results suggest that it could be used to test the proarrhythmic potential of new drugs. For the second challenge, we took a more specific approach and sought to improve the therapy of patients with cardiac electrical abnormalities. Therefore, we created a detailed model for the long QT syndrome-causing V411M mutation of the sodium channel reproducing clinical and experimental data. We tested the potential benefits of ranolazine, while giving insights into the mechanisms that drive flecainide's effectiveness. Our results suggest that while both drugs showed different mechanisms of sodium channel block, ranolazine could prove beneficial in these patients.This PhD thesis was developed within the following projects: Ministerio de Economía y Competitividad and Fondo Europeo de Desarrollo Regional (FEDER) DPI2015-69125-R (MINECO/FEDER, UE): Simulación computacional para la predicción personalizada de los efectos de los fármacos sobre la actividad cardiaca. Dirección General de Política Científica de la Generalitat Valenciana (PROMETEU2016/088): “Modelos computacionales personalizados multiescala para la optimización del diagnóstico y tratamiento de arritmias cardiacas (personalised digital heart). Vicerrectorado de Investigación, Innovación y Transferencia de la Universitat Politècnica de València, Ayuda a Primeros Proyectos de Investigación (PAID-06-18), and by Memorial Nacho Barberá. Instituto de Salud Carlos III (La Fe Biobank PT17/0015/0043).Cano García, J. (2021). Prediction of the effects of drugs on cardiac activity using computer simulations [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/164094TESI

A Probabilistic Framework for Statistical Shape Models and Atlas Construction: Application to Neuroimaging

Accurate and reliable registration of shapes and multi-dimensional point sets describing the morphology/physiology of anatomical structures is a pre-requisite for constructing statistical shape models (SSMs) and atlases. Such statistical descriptions of variability across populations (regarding shape or other morphological/physiological quantities) are based on homologous correspondences across the multiple samples that comprise the training data. The notion of exact correspondence can be ambiguous when these data contain noise and outliers, missing data, or significant and abnormal variations due to pathology. But, these phenomena are common in medical image-derived data, due, for example, to inconsistencies in image quality and acquisition protocols, presence of motion artefacts, differences in pre-processing steps, and inherent variability across patient populations and demographics. This thesis therefore focuses on formulating a unified probabilistic framework for the registration of shapes and so-called \textit{generalised point sets}, which is robust to the anomalies and variations described. Statistical analysis of shapes across large cohorts demands automatic generation of training sets (image segmentations delineating the structure of interest), as manual and semi-supervised approaches can be prohibitively time consuming. However, automated segmentation and landmarking of images often result in shapes with high levels of outliers and missing data. Consequently, a robust method for registration and correspondence estimation is required. A probabilistic group-wise registration framework for point-based representations of shapes, based on Student’s t-mixture model (TMM) and a multi-resolution extension to the same (mrTMM), are formulated to this end. The frameworks exploit the inherent robustness of Student’s t-distributions to outliers, which is lacking in existing Gaussian mixture model (GMM)-based approaches. The registration accuracy of the proposed approaches was quantitatively evaluated and shown to outperform the state-of-the-art, using synthetic and clinical data. A corresponding improvement in the quality of SSMs generated subsequently was also shown, particularly for data sets containing high levels of noise. In general, the proposed approach requires fewer user specified parameters than existing methods, whilst affording much improved robustness to outliers. Registration of generalised point sets, which combine disparate features such as spatial positions, directional/axial data, and scalar-valued quantities, was studied next. A hybrid mixture model (HMM), combining different types of probability distributions, was formulated to facilitate the joint registration and clustering of multi-dimensional point sets of this nature. Two variants of the HMM were developed for modelling: (1) axial data; and (2) directional data. The former, based on a combination of Student’s t, Watson and Gaussian distributions, was used to register hybrid point sets comprising magnetic resonance diffusion tensor image (DTI)-derived quantities, such as voxel spatial positions (defining a region/structure of interest), associated fibre orientations, and scalar measures reflecting tissue anisotropy. The latter meanwhile, formulated using a combination of Student’s t and Von-Mises-Fisher distributions, is used for the registration of shapes represented as hybrid point sets comprising spatial positions and associated surface normal vectors. The Watson-variant of the HMM facilitates statistical analysis and group-wise comparisons of DTI data across patient populations, presented as an exemplar application of the proposed approach. The Fisher-variant of the HMM on the other hand, was used to register hybrid representations of shapes, providing substantial improvements over point-based registration approaches in terms of anatomical validity in the estimated correspondences

Quantitative Analysis of Cardiac Magnetic Resonance in Population Imaging

According to the World Health Organisation, cardiovascular diseases are the most prevalent cause of death worldwide and taking nearly 18 million lives each year. Identifying individuals at risk of cardiovascular diseases and ensuring they receive appropriate treatment in time can prevent premature deaths. Early quantitative assessment of cardiac function, structure, and motion support preventive care and early cardiovascular treatment. Therefore, fully automated analysis and interpretation of large-scale population-based cardiovascular magnetic resonance imaging studies become of high importance. This analysis helps to identify patterns and trends across population groups, and accordingly, reveal insights into key risk factors before diseases fully develop. To date, few large-scale population-level cardiac imaging studies have been conducted. UK Biobank (UKB) is currently the world’s most extensive prospective population study, which in addition to various biological and physical measurements, contain cardiovascular magnetic resonance (CMR) images to establish cardiovascular imaging-derived phenotypes. CMR is an essential element of multi-organ multi-modality imaging visits for patients in multiple dedicated UK Biobank imaging centres that will acquire and store imaging data from 100,000 participants by 2023. This thesis introduces CMR image analysis methods that appropriately scales up and can provide a fully automatic 3D analysis of the UKB CMR studies. Without manual user interactions, our pipeline performs end-to-end image analytics from multi-view cine CMR images all the way to anatomical and functional quantification. Besides, our pipelines provide 3D anatomical models of cardiac structures, which enable the extraction of detailed information of the morphodynamics of the cardiac structures for subsequent associations to genetic, omics, lifestyle habits, exposure information, and other available information in population imaging studies. We present the quantification results from 40,000 subjects of the UK Biobank at 50 time-frames, i.e. two million image volumes

Accelerated Quantitative Mapping and Angiography for Cerebral and Cardiovascular Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) produces images with anatomical and functional information. These images can be obtained without the use of contrast agents, which generally require long scan times. This dissertation investigates existing techniques for accelerating such functional MRI methods, contributes novel fast acquisition and reconstruction techniques, and proposes new ways of analyzing real-time MRI data. First, we aim to determine an advantageous approach for accelerating high spatial resolution 3D cardiac T2 relaxometry data by comparing the performance of different data undersampling patterns and reconstruction methods over a range of acceleration rates. Quantitative results on healthy and edematous hearts reveal that the relaxometry maps are more sensitive to undersampling than anatomical images. The 3-fold variable density random undersampling with model-based or joint-sparsity sensitivity encoding (SENSE) is recommended. Second, we develop a rapid T2 mapping protocol using spiral acquisition and novel model-based approach joined with compressed sensing (CS) and model-based reconstruction. We also develop a sequence that suppresses cerebrospinal fluid (CSF). Quantitative evaluation on digital phantoms and healthy volunteers demonstrates the feasibility of T2 quantification with 3D high-resolution and whole-brain coverage in 2-3 min. Third, we propose a Golden Angle (GA) rotated Spiral Sparse Parallel imaging (GASSP) method for high spatial (0.8mm) and high temporal (<21ms) resolution for measuring coronary blood flow in a single breath-hold. We reduce k-space gaps using novel binning and triggered GA schemes. Velocity and flow metrics are validated against two existing methods and show high reproducibility. Fourth, we construct an abdominal non-contrast-enhanced magnetic resonance angiography (MRA) protocol with a large spatial coverage at 3.0T. The protocol uses advanced velocity-selective (VS) pulse trains. MRA with a large spatial coverage is slow and accelerated using CS. The VS-MRA sequences generate high-quality angiograms and arteriograms with high blood contrast. Finally, physiological changes in real-time (RT) MRI (30-100 frames/sec) are explored using Fourier transform (FT), principal component analyses (PCA), and perfusion modeling. We detect spectral patterns in pharyngeal images acquired during speaking and obtain T1-weighted, pulsation-weighted, and respiration-weighted images in healthy volunteers and heart patients with wall motion abnormalities with FT and PCA. RT perfusion maps are estimated from a proposed perfusion model in ongoing work in progress

Thermoresponsive Microgels for Multicellular Spheroids Formation

Multicellular spheroids (MCS) are considered as the most promising three dimensional (3D) in-vitro model which will narrow down the gap between in-vitro two-dimensional culture and in-vivo animal models. They exhibit physiologically relevant cell-cell and cell-matrix interactions, and present similar gene expression, heterogeneity and structural complexity as in-vivo tissues. Multicellular spheroids have been attempted for drug screening and evaluation, mechanical studies on cancer cell invasion and migration, and regeneration medicine. However, fabrication of uniform-sized MCSs at a high throughput platform, and evaluation of MCSs for clinical relevance are two main challenges. In this thesis, thermally responsive microgels were employed as physical supports to culture multicellular spheroids from both tumor cells and stem cells, which are potentially applied in anti-cancer drug evaluation, tissue engineering and regeneration medicine. The thermally reversible poly (N-isopropylacrylamide-co-acrylic acid) (P(NIPAM-AA)) microgels were first employed to fabricate HeLa MCSs. This microgel approach restricted cell mobility at a lower initial cell density due to a large volume in the microgel networks, which resulted in uniform-sized spheroids formation compared to non-adhesive culture. Moreover, because of thermal reversibility of this microgel, spheroids were released from the physical supports via cooling down the system to room temperature. After demonstrating the formation of tumor spheroids in the microgel, HeLa cells were further encapsulated inside microgel-droplets generated from flow focusing microfluidics to obtain controllable uniform-sized spheroids. This approach combined the benefit of using thermal sensitive microgels as physical supports for MCS formation and droplet generation at a high throughput platform. Highly uniform-sized MCSs were obtained through this method. Importantly, the MCSs were easily released from the droplets by reducing the culture temperature to room temperature without using strong chemical or enzyme reagents. This approach may be used for generation of uniform-sized MCSs for drug screening and evaluation. The microenvironment generated from the microgel plays an important role in MCS formation. The key characteristics of the microenvironment, such as surface charge density, hydrophobicity, mechanical strength, and the microstructure of the microgels, were investigated by synthesizing a range of poly(N-isopropylacrylamide) (P(NIPAM)) based microgels, including P(NIPAM), P(NIPAM-co-methacrylic acid) (P(NIPAM-MAA)), P(NIPMAM-co-acrylic acid) (P(NIPAM-AA)), P(NIPAM-co-malic acid) (P(NIPAM-MA)) and P(NIPAM-co-itaconic acid)(P(NIPAM-IA)). It was found that the moderate negatively charged surface with high hydrophilicity P(NIPAM-IA) microgels was beneficial for cellular growth. The high or low charge density resulted in slow cell proliferation. The hydrophobicity of microgels had a negative impact on cell growth. The large pore size of the P(NIPAM-IA) networks also allowed cell migration which promoted MCSs formation. Different cell types (HEK 293, U87, HeLa and mesenchymal stem cells) have been demonstrated to successfully form MCSs within the P(NIPAM-IA) microgel. The thermal sensitive microgels were further applied to form stem cell MCSs. Human cardiac stem cells (hCSCs) were cultured in the P(NIPAM)) based microgel networks including P(NIPAM-co-dimethyl amino ethyl methacrylate) (P(NIPAM-DMAEMA)), P(NIPAM-IA), (P(NIPAM-co-2-hydroxyethyl methacrylate) (P(NIPAM-HEMA)), P(NIPAM-co-poly(ethylene glycol) methyl ether acrylate) (P(NIPAM-PEGA)). These microgels displayed different charges (cationic, anionic, and neutral) and different degrees of hydrophobicity. Through evaluation of hCSCs viability, proliferation and release of regenerative factors, P(NIPAM-IA) was identified as one of the best candidates for forming hCSCs spheroids because of its negatively charged surface with high hydrophilicity. The thermal reversibility of P(NIPAM-IA) renders it as injectable hydrogels. Initial results showed that injection of this microgel into mice did not elicit immune system responses, reduced myocardial apoptosis and promoted angiogenesis in the mice. In summary, we have fabricated MCSs in different types of thermal responsive microgels through either physical control of the uniform size by confining cells in the microgel-droplets generated from microfluidics or fine tune of the microenvironment for MCS formation. The P(NIPAM-IA) microgel with moderated anionic charge and high hydrophilicity was found to promote MCSs formation. This microgel did not elicit any immune response, which indicates the potential of using this microgel for future clinical studies.Thesis (Ph.D.) -- University of Adelaide, School of Chemical Engineering, 201

Proceedings, MSVSCC 2012

Proceedings of the 6th Annual Modeling, Simulation & Visualization Student Capstone Conference held on April 19, 2012 at VMASC in Suffolk, Virginia