Mechanistic Systems Modeling to Improve Understanding and Prediction of Cardiotoxicity Caused by Targeted Cancer Therapeutics

Quantitative Systems Toxicology Modeling Informed Safe Dose Selection of Emvododstat in Acute Myeloid Leukemia Patients

Clinical investigation of emvododstat for the treatment of solid tumors was halted after two patients who were heavily treated with other anticancer therapies experienced drug-induced liver failure. However, preclinical investigations supported that emvododstat at lower doses might be effective in treating acute myeloid leukemia (AML) and against severe acute respiratory syndrome-coronavirus 2 as a dihydroorotate dehydrogenase inhibitor. Therefore, a quantitative systems toxicology model, DILIsym, was used to predict liver safety of the proposed dosing of emvododstat in AML clinical trials. In vitro mechanistic toxicity data of emvododstat and its desmethyl metabolite were integrated with in vivo exposure within DILIsym to predict hepatotoxicity responses in a simulated human population. DILIsym simulations predicted alanine aminotransferase elevations observed in prior emvododstat clinical trials in patients with solid tumors, but not in the prospective AML clinical trial with the proposed dosing regimens. Exposure predictions based on physiologically-based pharmacokinetic modeling suggested that reduced doses of emvododstat would produce clinical exposures that would be efficacious to treat AML. In the AML clinical trial, only eight patients experienced aminotransferase elevations, all of which were mild (grade 1), all resolving within a short period of time, and no patient showed symptoms of hepatotoxicity, confirming the prospective prediction of liver safety. Overall, retrospective DILIsym simulations adequately predicted the liver safety liabilities of emvododstat in solid tumor trials and prospective simulations predicted the liver safety of reduced doses in an AML clinical trial. The modeling was critical to enabling regulatory approval to proceed with the AML clinical trial wherein the predicted liver safety was confirmed.

Mathematical modeling of cardio-oncology: Modeling the systemic effects of cancer therapeutics on the cardiovascular system

Cardiotoxicity is a common side-effect of many cancer therapeutics; however, to-date there has been very little push to understand the mechanisms underlying this group of pathologies. This has led to the emergence of cardio-oncology, a field of medicine focused on understanding the effects of cancer and its treatment on the human heart. Here, we describe how mechanistic modeling approaches have been applied to study open questions in the cardiovascular system and how these approaches are being increasingly applied to advance knowledge of the underlying effects of cancer treatments on the human heart. A variety of mechanistic, mathematical modeling techniques have been applied to explore the link between common cancer treatments, such as chemotherapy, radiation, targeted therapy, and immunotherapy, and cardiotoxicity, nevertheless there is limited coverage in the different types of cardiac dysfunction that may be associated with these treatments. Moreover, cardiac modeling has a rich heritage of mathematical modeling and is well suited for the further development of novel approaches for understanding the cardiotoxicities associated with cancer therapeutics. There are many opportunities to combine mechanistic, bottom-up approaches with data-driven, top-down approaches to improve personalized, precision oncology to better understand, and ultimately mitigate, cardiac dysfunction in cancer patients.

Adverse effects of tyrosine kinase inhibitors in cancer therapy: pathophysiology, mechanisms and clinical management

Since their invention in the early 2000s, tyrosine kinase inhibitors (TKIs) have gained prominence as the most effective pathway-directed anti-cancer agents. TKIs have shown significant utility in the treatment of multiple hematological malignancies and solid tumors, including chronic myelogenous leukemia, non-small cell lung cancers, gastrointestinal stromal tumors, and HER2-positive breast cancers. Given their widespread applications, an increasing frequency of TKI-induced adverse effects has been reported. Although TKIs are known to affect multiple organs in the body including the lungs, liver, gastrointestinal tract, kidneys, thyroid, blood, and skin, cardiac involvement accounts for some of the most serious complications. The most frequently reported cardiovascular side effects range from hypertension, atrial fibrillation, reduced cardiac function, and heart failure to sudden death. The potential mechanisms of these side effects are unclear, leading to critical knowledge gaps in the development of effective therapy and treatment guidelines. There are limited data to infer the best clinical approaches for the early detection and therapeutic modulation of TKI-induced side effects, and universal consensus regarding various management guidelines is yet to be reached. In this state-of-the-art review, we examine multiple pre-clinical and clinical studies and curate evidence on the pathophysiology, mechanisms, and clinical management of these adverse reactions. We expect that this review will provide researchers and allied healthcare providers with the most up-to-date information on the pathophysiology, natural history, risk stratification, and management of emerging TKI-induced side effects in cancer patients.

Association between Cardiovascular Response and Inflammatory Cytokines in Non-Small Cell Lung Cancer Patients

Cardiovascular disease is an essential comorbidity in patients with non-small cell lung cancer (NSCLC) and represents an independent risk factor for increased mortality. Therefore, careful monitoring of cardiovascular disease is crucial in the healthcare of NSCLC patients. Inflammatory factors have previously been associated with myocardial damage in NSCLC patients, but it remains unclear whether serum inflammatory factors can be utilized to assess the cardiovascular health status in NSCLC patients. A total of 118 NSCLC patients were enrolled in this cross-sectional study, and their baseline data were collected through a hospital electronic medical record system. Enzyme-linked immunosorbent assay (ELISA) was used to measure the serum levels of leukemia inhibitory factor (LIF), interleukin (IL)-18, IL-1β, transforming growth factor-β1 (TGF-β1), and connective tissue growth factor (CTGF). Statistical analysis was performed using the SPSS software. Multivariate and ordinal logistic regression models were constructed. The data revealed an increased serum level of LIF in the group using tyrosine kinase inhibitor (TKI)-targeted drugs compared to non-users (p < 0.001). Furthermore, serum TGF-β1 (area under the curve, AUC: 0.616) and cardiac troponin T (cTnT) (AUC: 0.720) levels were clinically evaluated and found to be correlated with pre-clinical cardiovascular injury in NSCLC patients. Notably, the serum levels of cTnT and TGF-β1 were found to indicate the extent of pre-clinical cardiovascular injury in NSCLC patients. In conclusion, the results suggest that serum LIF, as well as TGFβ1 together with cTnT, are potential serum biomarkers for the assessment of cardiovascular status in NSCLC patients. These findings offer novel insights into the assessment of cardiovascular health and underscore the importance of monitoring cardiovascular health in the management of NSCLC patients.

Prospects of Novel and Repurposed Immunomodulatory Drugs against Acute Respiratory Distress Syndrome (ARDS) Associated with COVID-19 Disease

Acute respiratory distress syndrome (ARDS) is intricately linked with SARS-CoV-2-associated disease severity and mortality, especially in patients with co-morbidities. Lung tissue injury caused as a consequence of ARDS leads to fluid build-up in the alveolar sacs, which in turn affects oxygen supply from the capillaries. ARDS is a result of a hyperinflammatory, non-specific local immune response (cytokine storm), which is aggravated as the virus evades and meddles with protective anti-viral innate immune responses. Treatment and management of ARDS remain a major challenge, first, because the condition develops as the virus keeps replicating and, therefore, immunomodulatory drugs are required to be used with caution. Second, the hyperinflammatory responses observed during ARDS are quite heterogeneous and dependent on the stage of the disease and the clinical history of the patients. In this review, we present different anti-rheumatic drugs, natural compounds, monoclonal antibodies, and RNA therapeutics and discuss their application in the management of ARDS. We also discuss on the suitability of each of these drug classes at different stages of the disease. In the last section, we discuss the potential applications of advanced computational approaches in identifying reliable drug targets and in screening out credible lead compounds against ARDS.

Berberine attenuates sunitinib-induced cardiac dysfunction by normalizing calcium regulation disorder via SGK1 activation

Sunitinib (SNT)-induced cardiotoxicity is associated with abnormal calcium regulation caused by phosphoinositide 3 kinase inhibition in the heart. Berberine (BBR) is a natural compound that exhibits cardioprotective effects and regulates calcium homeostasis. We hypothesized that BBR ameliorates SNT-induced cardiotoxicity by normalizing the calcium regulation disorder via serum and glucocorticoid-regulated kinase 1 (SGK1) activation. Mice, neonatal rat cardiomyocytes (NRVMs), and human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) were used to study the effects of BBR-mediated SGK1 activity on the calcium regulation disorder caused by SNT as well as the underlying mechanism. BBR offered prevention against SNT-induced cardiac systolic dysfunction, QT interval prolongation, and histopathological changes in mice. After the oral administration of SNT, the Ca²⁺ transient and contraction of cardiomyocytes was significantly inhibited, whereas BBR exhibited an antagonistic effect. In NRVMs, BBR was significantly preventive against the SNT-induced reduction of calcium transient amplitude, prolongation of calcium transient recovery, and decrease in SERCA2a protein expression; however, SGK1 inhibitors resisted the preventive effects of BBR. In hiPSC-CMs, BBR pretreatment significantly prevented SNT from inhibiting the contraction, whereas coincubation with SGK1 inhibitors antagonized the effects of BBR. These findings indicate that BBR attenuates SNT-induced cardiac dysfunction by normalizing the calcium regulation disorder via SGK1 activation.

Identification of significant gene expression changes in multiple perturbation experiments using knockoffs

Large-scale multiple perturbation experiments have the potential to reveal a more detailed understanding of the molecular pathways that respond to genetic and environmental changes. A key question in these studies is which gene expression changes are important for the response to the perturbation. This problem is challenging because (i) the functional form of the nonlinear relationship between gene expression and the perturbation is unknown and (ii) identification of the most important genes is a high-dimensional variable selection problem. To deal with these challenges, we present here a method based on the model-X knockoffs framework and Deep Neural Networks to identify significant gene expression changes in multiple perturbation experiments. This approach makes no assumptions on the functional form of the dependence between the responses and the perturbations and it enjoys finite sample false discovery rate control for the selected set of important gene expression responses. We apply this approach to the Library of Integrated Network-Based Cellular Signature data sets which is a National Institutes of Health Common Fund program that catalogs how human cells globally respond to chemical, genetic and disease perturbations. We identified important genes whose expression is directly modulated in response to perturbation with anthracycline, vorinostat, trichostatin-a, geldanamycin and sirolimus. We compare the set of important genes that respond to these small molecules to identify co-responsive pathways. Identification of which genes respond to specific perturbation stressors can provide better understanding of the underlying mechanisms of disease and advance the identification of new drug targets.

Successful Treatment with Low-dose Crizotinib in a Patient with ROS1-rearranged Lung Cancer Who Developed Crizotinib-induced Heart Failure

Crizotinib shows antitumor activity against C-ros oncogene 1-rearranged non-small-cell lung cancer (NSCLC). While corrected QT interval (QTc) prolongation and bradycardia are known as cardiac adverse effects, little is known about crizotinib-related heart failure. Our patient with C-ros oncogene 1-rearranged NSCLC on a reduced dose of crizotinib (200 mg twice daily) after initially experiencing bradycardia and QTc prolongation developed crizotinib-induced heart failure. With further dose reduction (250 mg once daily), there was no recurrence of any cardiac adverse effects, and the patient achieved a long-term response. Although crizotinib can cause heart failure, continuation of crizotinib at a low dose may be an effective treatment option.

Predicting individual-specific cardiotoxicity responses induced by tyrosine kinase inhibitors

Introduction: Tyrosine kinase inhibitor drugs (TKIs) are highly effective cancer drugs, yet many TKIs are associated with various forms of cardiotoxicity. The mechanisms underlying these drug-induced adverse events remain poorly understood. We studied mechanisms of TKI-induced cardiotoxicity by integrating several complementary approaches, including comprehensive transcriptomics, mechanistic mathematical modeling, and physiological assays in cultured human cardiac myocytes. Methods: Induced pluripotent stem cells (iPSCs) from two healthy donors were differentiated into cardiac myocytes (iPSC-CMs), and cells were treated with a panel of 26 FDA-approved TKIs. Drug-induced changes in gene expression were quantified using mRNA-seq, changes in gene expression were integrated into a mechanistic mathematical model of electrophysiology and contraction, and simulation results were used to predict physiological outcomes. Results: Experimental recordings of action potentials, intracellular calcium, and contraction in iPSC-CMs demonstrated that modeling predictions were accurate, with 81% of modeling predictions across the two cell lines confirmed experimentally. Surprisingly, simulations of how TKI-treated iPSC-CMs would respond to an additional arrhythmogenic insult, namely, hypokalemia, predicted dramatic differences between cell lines in how drugs affected arrhythmia susceptibility, and these predictions were confirmed experimentally. Computational analysis revealed that differences between cell lines in the upregulation or downregulation of particular ion channels could explain how TKI-treated cells responded differently to hypokalemia. Discussion: Overall, the study identifies transcriptional mechanisms underlying cardiotoxicity caused by TKIs, and illustrates a novel approach for integrating transcriptomics with mechanistic mathematical models to generate experimentally testable, individual-specific predictions of adverse event risk.

Combinatorial approaches for novel cardiovascular drug discovery: a review of the literature

INTRODUCTION

In this article, authors report an inclusive discussion about the combinatorial approach for the treatment of cardiovascular diseases (CVDs) and for counteracting the cardiovascular risk factors. The mentioned strategy was demonstrated to be useful for improving the efficacy of pharmacological treatments and in CVDs showed superior efficacy with respect to the classical monotherapeutic approach.

AREAS COVERED

According to this topic, authors analyzed the combinatorial treatments that are available on the market, highlighting clinical studies that demonstrated the efficacy of combinatorial drug strategies to cure CVDs and related risk factors. Furthermore, the review gives an outlook on the future perspective of this therapeutic option, highlighting novel drug targets and disease models that could help the future cardiovascular drug discovery.

EXPERT OPINION

The use of specifically designed and increasingly rational and effective drug combination therapies can therefore be considered the evolution of polypharmacy in cardiometabolic and CVDs. This approach can allow to intervene on multiple etiopathogenetic mechanisms of the disease or to act simultaneously on different pathologies/risk factors, using the combinations most suitable from a pharmacodynamic, pharmacokinetic, and toxicological perspective, thus finding the most appropriate therapeutic option.

Welson N, Mostafa-Hedeab G, Qasem AH, Conte-Junior CA. Combination of Panax ginseng C. A. Mey and Febuxostat Boasted Cardioprotective Effects Against Doxorubicin-Induced Acute Cardiotoxicity in Rats

Doxorubicin (DOX) is an anticancer agent for treating solid and soft tissue malignancies. However, the clinical use of DOX is restricted by cumulative, dose-dependent cardiotoxicity. Therefore, the present study aimed to assess the cardioprotective effects of P. ginseng C. A. Mey, febuxostat, and their combination against DOX-induced cardiotoxicity. Thirty-five Sprague Dawley male rats were used in this study. The animals were randomly divided into five groups, with seven rats per group. The control group received normal saline, the induced group received DOX only, and the treated group received P. ginseng, febuxostat, and their combination before DOX treatment. Biomarkers of acute cardiac toxicity were assessed in each group. Results showed that treatment with the combination of febuxostat and P. ginseng before DOX led to a significant improvement in the biomarkers of acute DOX-induced cardiotoxicity. In conclusion, the combination of P. ginseng and febuxostat produced more significant cardioprotective effects against DOX-induced cardiotoxicity when compared to either P. ginseng or febuxostat when used alone. The potential mechanism of this combination was mainly mediated by the anti-inflammatory and antioxidant effects of P. ginseng and febuxostat.

A novel cardiovascular systems model to quantify drugs effects on the inter‐relationship between contractility and other hemodynamic variables

The use of systems‐based pharmacological modeling approaches to characterize mode‐of‐action and concentration‐effect relationships for drugs on specific hemodynamic variables has been demonstrated. Here, we (i) expand a previously developed hemodynamic system model through integration of cardiac output (CO) with contractility (CTR) using pressure‐volume loop theory, and (ii) evaluate the contribution of CO data for identification of system‐specific parameters, using atenolol as proof‐of‐concept drug. Previously collected experimental data was used to develop the systems model, and included measurements for heart rate (HR), CO, mean arterial pressure (MAP), and CTR after administration of atenolol (0.3–30 mg/kg) from three in vivo telemetry studies in conscious Beagle dogs. The developed cardiovascular (CVS)‐contractility systems model adequately described the effect of atenolol on HR, CO, dP/dtmax, and MAP dynamics and allowed identification of both system‐ and drug‐specific parameters with good precision. Model parameters were structurally identifiable, and the true mode of action can be identified properly. Omission of CO data did not lead to a significant change in parameter estimates compared to a model that included CO data. The newly developed CVS‐contractility systems model characterizes short‐term drug effects on CTR, CO, and other hemodynamic variables in an integrated and quantitative manner. When the baseline value of total peripheral resistance is predefined, CO data was not required to identify drug‐ and system‐specific parameters. Confirmation of the consistency of system‐specific parameters via inclusion of data for additional drugs and species is warranted. Ultimately, the developed model has the potential to be of relevance to support translational CVS safety studies.

Does the Heart Want What It Wants? A Case for Self-Adapting, Mechano-Sensitive Therapies After Infarction

There is a critical need for interventions to control the development and remodeling of scar tissue after myocardial infarction. A significant hurdle to fibrosis-related therapy is presented by the complex spatial needs of the infarcted ventricle, namely that collagenous buildup is beneficial in the ischemic zone but detrimental in the border and remote zones. As a new, alternative approach, we present a case to develop self-adapting, mechano-sensitive drug targets in order to leverage local, microenvironmental mechanics to modulate a therapy's pharmacologic effect. Such approaches could provide self-tuning control to either promote fibrosis or reduce fibrosis only when and where it is beneficial to do so.

Nonclinical Safety Profile of Sotorasib, a KRASG12C-Specific Covalent Inhibitor for the Treatment of KRAS p.G12C-Mutated Cancer

Sotorasib is a first-in-class KRAS^G12C covalent inhibitor in clinical development for the treatment of tumors with the KRAS p.G12C mutation. A comprehensive nonclinical safety assessment package, including secondary/safety pharmacology and toxicology studies, was conducted to support the marketing application for sotorasib. Sotorasib was negative in a battery of genotoxicity assays and negative in an in vitro phototoxicity assay. Based on in vitro assays, sotorasib had no off-target effects against various receptors, enzymes (including numerous kinases), ion channels, or transporters. Consistent with the tumor-specific target distribution (ie, KRAS^G12C), there were no primary pharmacology-related on-target effects identified. The kidney was identified as a target organ in the rat but not the dog. Renal toxicity in the rat was characterized by tubular degeneration and necrosis restricted to a specific region suggesting that the toxicity was attributed to the local formation of a putative toxic reactive metabolite. In the 3-month dog study, adaptive changes of hepatocellular hypertrophy due to drug metabolizing enzyme induction were observed in the liver that was associated with secondary effects in the pituitary and thyroid gland. Sotorasib was not teratogenic and had no direct effect on embryo-fetal development in the rat or rabbit. Human, dog, and rat circulating metabolites, M24, M10, and M18, raised no clinically relevant safety concerns based on the general toxicology studies, primary/secondary pharmacology screening, an in vitro human ether-à-go-go-related gene assay, or mutagenicity assessment. Overall, the results of the nonclinical safety program support a high benefit/risk ratio of sotorasib for the treatment of patients with KRAS p.G12C-mutated tumors.

Mitochondrial dynamics imbalance and mitochondrial dysfunction contribute to the molecular cardiotoxic effects of lenvatinib

Lenvatinib is a tyrosine kinase inhibitor (TKI) approved for the treatment of resistant differentiated thyroid cancer, advanced renal cell carcinoma, unresectable hepatocellular carcinoma, and endometrial carcinoma. Although it is successful in cancer treatment, it can cause life-threatening side effects such as cardiotoxicity. The molecular mechanism of cardiotoxicity caused by lenvatinib is not fully known. In this study, the molecular mechanism of lenvatinib's cardiotoxicity was investigated focusing on mitochondrial toxicity in the H9c2 cardiomyoblastic cell line. Lenvatinib inhibited cell viability at 48 and 72 h exposure with three selected concentrations (1.25 μM, 5 μM and 10 μM); and inhibited intracellular ATP after 72 h exposure compared to the control group. Mitochondrial membrane potential was decreased after 48 h and did not show significant changes after 72 h exposure. Evaluated with real-time PCR, mitochondrial dynamics (Mfn1, Mfn2, OPA1, DRP1, Fis1) expression levels after lenvatinib treatment significantly changed. Lenvatinib triggered the tendency from fusion to fission in mitochondria after 48 h exposure, and increased both fusion and fission after 72 h. The mtDNA ratio increased after 48 h and decreased after 72 h. ASK1, JNK and AMPKα2 increased. UCP2 showed downregulation, SOD2 level showed upregulation and Cat levels decreased after drug treatment. Nrf1 and Nrf2 also changed concentration-dependently. Protein carbonyl levels increased significantly after lenvatinib treatments indicating oxidative stress. The protein levels of the electron transport chain complexes, LONP1, UCP2, and P21 showed significant differences after lenvatinib treatment. The outcome of our study is expected to be a contribution to the understanding of the molecular mechanisms of TKI-induced cardiotoxicity.

Network model-based screen for FDA-approved drugs affecting cardiac fibrosis

Cardiac fibrosis is a significant component of pathological heart remodeling, yet it is not directly targeted by existing drugs. Systems pharmacology approaches have the potential to provide mechanistic frameworks with which to predict and understand how drugs modulate biological systems. Here, we combine network modeling of the fibroblast signaling network with 36 unique drug-target interactions from DrugBank to predict drugs that modulate fibroblast phenotype and fibrosis. Galunisertib was predicted to decrease collagen and α-SMA expression, which we validated in human cardiac fibroblasts. In vivo fibrosis data from the literature validated predictions for 10 drugs. Further, the model was used to identify network mechanisms by which these drugs work. Arsenic trioxide was predicted to induce fibrosis by AP1-driven TGFβ expression and MMP2-driven TGFβ activation. Entresto (valsartan/sacubitril) was predicted to suppress fibrosis by valsartan suppression of ERK signaling and sacubitril enhancement of PKG activity, both of which decreased Smad3 activity. Overall, this study provides a framework for integrating drug-target mechanisms with logic-based network models, which can drive further studies both in cardiac fibrosis and other conditions.

Computational model of cardiomyocyte apoptosis identifies mechanisms of tyrosine kinase inhibitor-induced cardiotoxicity

Despite clinical observations of cardiotoxicity among cancer patients treated with tyrosine kinase inhibitors (TKIs), the molecular mechanisms by which these drugs affect the heart remain largely unknown. Mechanistic understanding of TKI-induced cardiotoxicity has been limited in part due to the complexity of tyrosine kinase signaling pathways and the multi-targeted nature of many of these drugs. TKI treatment has been associated with reactive oxygen species generation, mitochondrial dysfunction, and apoptosis in cardiomyocytes. To gain insight into the mechanisms mediating TKI-induced cardiotoxicity, this study constructs and validates a computational model of cardiomyocyte apoptosis, integrating intrinsic apoptotic and tyrosine kinase signaling pathways. The model predicts high levels of apoptosis in response to sorafenib, sunitinib, ponatinib, trastuzumab, and gefitinib, and lower levels of apoptosis in response to nilotinib and erlotinib, with the highest level of apoptosis induced by sorafenib. Knockdown simulations identified AP1, ASK1, JNK, MEK47, p53, and ROS as positive functional regulators of sorafenib-induced apoptosis of cardiomyocytes. Overexpression simulations identified Akt, IGF1, PDK1, and PI3K among the negative functional regulators of sorafenib-induced cardiomyocyte apoptosis. A combinatorial screen of the positive and negative regulators of sorafenib-induced apoptosis revealed ROS knockdown coupled with overexpression of FLT3, FGFR, PDGFR, VEGFR, or KIT as a particularly potent combination in reducing sorafenib-induced apoptosis. Network simulations of combinatorial treatment with sorafenib and the antioxidant N-acetyl cysteine (NAC) suggest that NAC may protect cardiomyocytes from sorafenib-induced apoptosis.

Adverse cardiac effects of cancer therapies: cardiotoxicity and arrhythmia

Remarkable progress has been made in the development of new therapies for cancer, dramatically changing the landscape of treatment approaches for several malignancies and continuing to increase patient survival. Accordingly, adverse effects of cancer therapies that interfere with the continuation of best-possible care, induce life-threatening risks or lead to long-term morbidity are gaining increasing importance. Cardiovascular toxic effects of cancer therapeutics and radiation therapy are the epitome of such concerns, and proper knowledge, interpretation and management are needed and have to be placed within the context of the overall care of individual patients with cancer. Furthermore, the cardiotoxicity spectrum has broadened to include myocarditis with immune checkpoint inhibitors and cardiac dysfunction in the setting of cytokine release syndrome with chimeric antigen receptor T cell therapy. An increase in the incidence of arrhythmias related to inflammation such as atrial fibrillation can also be expected, in addition to the broadening set of cancer therapeutics that can induce prolongation of the corrected QT interval. Therefore, cardiologists of today have to be familiar not only with the cardiotoxicity associated with traditional cancer therapies, such as anthracycline, trastuzumab or radiation therapy, but even more so with an ever-increasing repertoire of therapeutics. This Review provides this information, summarizing the latest developments at the juncture of cardiology, oncology and haematology.

Cardiovascular disease models: A game changing paradigm in drug discovery and screening

Cardiovascular disease is the leading cause of death worldwide. Although investment in drug discovery and development has been sky-rocketing, the number of approved drugs has been declining. Cardiovascular toxicity due to therapeutic drug use claims the highest incidence and severity of adverse drug reactions in late-stage clinical development. Therefore, to address this issue, new, additional, replacement and combinatorial approaches are needed to fill the gap in effective drug discovery and screening. The motivation for developing accurate, predictive models is twofold: first, to study and discover new treatments for cardiac pathologies which are leading in worldwide morbidity and mortality rates; and second, to screen for adverse drug reactions on the heart, a primary risk in drug development. In addition to in vivo animal models, in vitro and in silico models have been recently proposed to mimic the physiological conditions of heart and vasculature. Here, we describe current in vitro, in vivo, and in silico platforms for modelling healthy and pathological cardiac tissues and their advantages and disadvantages for drug screening and discovery applications. We review the pathophysiology and the underlying pathways of different cardiac diseases, as well as the new tools being developed to facilitate their study. We finally suggest a roadmap for employing these non-animal platforms in assessing drug cardiotoxicity and safety.

A demonstration of modularity, reuse, reproducibility, portability and scalability for modeling and simulation of cardiac electrophysiology using Kepler Workflows

Multi-scale computational modeling is a major branch of computational biology as evidenced by the US federal interagency Multi-Scale Modeling Consortium and major international projects. It invariably involves specific and detailed sequences of data analysis and simulation, often with multiple tools and datasets, and the community recognizes improved modularity, reuse, reproducibility, portability and scalability as critical unmet needs in this area. Scientific workflows are a well-recognized strategy for addressing these needs in scientific computing. While there are good examples if the use of scientific workflows in bioinformatics, medical informatics, biomedical imaging and data analysis, there are fewer examples in multi-scale computational modeling in general and cardiac electrophysiology in particular. Cardiac electrophysiology simulation is a mature area of multi-scale computational biology that serves as an excellent use case for developing and testing new scientific workflows. In this article, we develop, describe and test a computational workflow that serves as a proof of concept of a platform for the robust integration and implementation of a reusable and reproducible multi-scale cardiac cell and tissue model that is expandable, modular and portable. The workflow described leverages Python and Kepler-Python actor for plotting and pre/post-processing. During all stages of the workflow design, we rely on freely available open-source tools, to make our workflow freely usable by scientists.

We present a computational workflow as a proof of concept for integration and implementation of a reusable and reproducible cardiac multi-scale electrophysiology model that is expandable, modular and portable. This framework enables scientists to create intuitive, user-friendly and flexible end-to-end automated scientific workflows using a graphical user interface. Kepler is an advanced open-source platform that supports multiple models of computation. The underlying workflow engine handles scalability, provenance, reproducibility aspects of the code, performs orchestration of data flow, and automates execution on heterogeneous computing resources. One of the main advantages of workflow utilization is the integration of code written in multiple languages Standardization occurs at the interfaces of the workflow elements and allows for general applications and easy comparison and integration of code from different research groups or even multiple programmers coding in different languages for various purposes from the same group. A workflow driven problem-solving approach enables domain scientists to focus on resolving the core science questions, and delegates the computational and process management burden to the underlying Workflow. The workflow driven approach allows scaling the computational experiment with distributed data-parallel execution on multiple computing platforms, such as, HPC resources, GPU clusters, Cloud etc. The workflow framework tracks software version information along with hardware information to allow users an opportunity to trace any variation in workflow outcome to the system configurations.

Systems Pharmacology: Defining the Interactions of Drug Combinations

The majority of diseases are associated with alterations in multiple molecular pathways and complex interactions at the cellular and organ levels. Single-target monotherapies therefore have intrinsic limitations with respect to their maximum therapeutic benefits. The potential of combination drug therapies has received interest for the treatment of many diseases and is well established in some areas, such as oncology. Combination drug treatments may allow us to identify synergistic drug effects, reduce adverse drug reactions, and address variability in disease characteristics between patients. Identification of combination therapies remains challenging. We discuss current state-of-the-art systems pharmacology approaches to enable rational identification of combination therapies. These approaches, which include characterization of mechanisms of disease and drug action at a systems level, can enable understanding of drug interactions at the molecular, cellular, physiological, and organismal levels. Such multiscale understanding can enable precision medicine by promoting the rational development of combination therapy at the level of individual patients for many diseases.

Bridging the Data Gap From in vitro Toxicity Testing to Chemical Safety Assessment Through Computational Modeling

Chemical toxicity testing is moving steadily toward a human cell and organoid-based in vitro approach for reasons including scientific relevancy, efficiency, cost, and ethical rightfulness. Inferring human health risk from chemical exposure based on in vitro testing data is a challenging task, facing various data gaps along the way. This review identifies these gaps and makes a case for the in silico approach of computational dose-response and extrapolation modeling to address many of the challenges. Mathematical models that can mechanistically describe chemical toxicokinetics (TK) and toxicodynamics (TD), for both in vitro and in vivo conditions, are the founding pieces in this regard. Identifying toxicity pathways and in vitro point of departure (PoD) associated with adverse health outcomes requires an understanding of the molecular key events in the interacting transcriptome, proteome, and metabolome. Such an understanding will in turn help determine the sets of sensitive biomarkers to be measured in vitro and the scope of toxicity pathways to be modeled in silico. In vitro data reporting both pathway perturbation and chemical biokinetics in the culture medium serve to calibrate the toxicity pathway and virtual tissue models, which can then help predict PoDs in response to chemical dosimetry experienced by cells in vivo. Two types of in vitro to in vivo extrapolation (IVIVE) are needed. (1) For toxic effects involving systemic regulations, such as endocrine disruption, organism-level adverse outcome pathway (AOP) models are needed to extrapolate in vitro toxicity pathway perturbation to in vivo PoD. (2) Physiologically-based toxicokinetic (PBTK) modeling is needed to extrapolate in vitro PoD dose metrics into external doses for expected exposure scenarios. Linked PBTK and TD models can explore the parameter space to recapitulate human population variability in response to chemical insults. While challenges remain for applying these modeling tools to support in vitro toxicity testing, they open the door toward population-stratified and personalized risk assessment.