Strengthening cardiac therapy pipelines using human pluripotent stem cell-derived cardiomyocytes.

Advances in hiPSC isolation and reprogramming and hPSC-CM differentiation have prompted their therapeutic application and utilization for evaluating potential cardiovascular safety liabilities. In this perspective, we showcase key efforts toward the large-scale production of hiPSC-CMs, implementation of hiPSC-CMs in industry settings, and recent clinical applications of this technology. The key observations are a need for traceable gender and ethnically diverse hiPSC lines, approaches to reduce cost of scale-up, accessible clinical trial datasets, and transparent guidelines surrounding the safety and efficacy of hiPSC-based therapies.

CardioMotion: identification of functional and structural cardiotoxic liabilities in small molecules through brightfield kinetic imaging.

Cardiovascular toxicity is an important cause of drug failures in the later stages of drug development, early clinical safety assessment, and even postmarket withdrawals. Early-stage in vitro assessment of potential cardiovascular liabilities in the pharmaceutical industry involves assessment of interactions with cardiac ion channels, as well as induced pluripotent stem cell-derived cardiomyocyte-based functional assays, such as calcium flux and multielectrode-array assays. These methods are appropriate for the identification of acute functional cardiotoxicity but structural cardiotoxicity, which manifests effects after chronic exposure, is often only captured in vivo. CardioMotion is a novel, label-free, high throughput, in vitro assay and analysis pipeline which records and assesses the spontaneous beating of cardiomyocytes and identifies compounds which impact beating. This is achieved through the acquisition of brightfield images at a high framerate, combined with an optical flow-based python analysis pipeline which transforms the images into waveform data which are then parameterized. Validation of this assay with a large dataset showed that cardioactive compounds with diverse known direct functional and structural mechanisms-of-action on cardiomyocytes are identified (sensitivity = 72.9%), importantly, known structural cardiotoxins also disrupt cardiomyocyte beating (sensitivity = 86%) in this method. Furthermore, the CardioMotion method presents a high specificity of 82.5%.

Prediction of inotropic effect based on calcium transients in human iPSC-derived cardiomyocytes and machine learning.

Functional changes to cardiomyocytes are undesirable during drug discovery and identifying the inotropic effects of compounds is hence necessary to decrease the risk of cardiovascular adverse effects in the clinic. Recently, approaches leveraging calcium transients in human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) have been developed to detect contractility changes, induced by a variety of mechanisms early during drug discovery projects. Although these approaches have been able to provide some predictive ability, we hypothesised that using additional waveform parameters could offer improved insights, as well as predictivity. In this study, we derived 25 parameters from each calcium transient waveform and developed a modified Random Forest method to predict the inotropic effects of the compounds. In total annotated data for 48 compounds were available for modelling, out of which 31 were inotropes. The results show that the Random Forest model with a modified purity criterion performed slightly better than an unmodified algorithm in terms of the Area Under the Curve, giving values of 0.84 vs 0.81 in a cross-validation, and outperformed the ToxCast Pipeline model, for which the highest value was 0.76 when using the best-performing parameter, PW10. Our study hence demonstrates that more advanced parameters derived from waveforms, in combination with additional machine learning methods, provide improved predictivity of cardiovascular risk associated with inotropic effects.

Deriving waveform parameters from calcium transients in human iPSC-derived cardiomyocytes to predict cardiac activity with machine learning.

Human induced pluripotent stem cell-derived cardiomyocytes have been established to detect dynamic calcium transients by fast kinetic fluorescence assays that provide insights into specific aspects of clinical cardiac activity. However, the precise derivation and use of waveform parameters to predict cardiac activity merit deeper investigation. In this study, we derived, evaluated, and applied 38 waveform parameters in a novel Python framework, including (among others) peak frequency, peak amplitude, peak widths, and a novel parameter, shoulder-tail ratio. We then trained a random forest model to predict cardiac activity based on the 25 parameters selected by correlation analysis. The area under the curve (AUC) obtained for leave-one-compound-out cross-validation was 0.86, thereby replicating the predictions of conventional methods and outperforming fingerprint-based methods by a large margin. This work demonstrates that machine learning is able to automate the assessment of cardiovascular liability from waveform data, reducing any risk of user-to-user variability and bias.

Recommended Guidelines for Developing, Qualifying, and Implementing Complex In Vitro Models (CIVMs) for Drug Discovery.

The pharmaceutical industry is continuing to face high research and development (R&D) costs and low overall success rates of clinical compounds during drug development. There is an increasing demand for development and validation of healthy or disease-relevant and physiological human cellular models that can be implemented in early-stage discovery, thereby shifting attrition of future therapeutics to a point in discovery at which the costs are significantly lower. There needs to be a paradigm shift in the early drug discovery phase (which is lengthy and costly), away from simplistic cellular models that show an inability to effectively and efficiently reproduce healthy or human disease-relevant states to steer target and compound selection for safety, pharmacology, and efficacy questions. This perspective article covers the various stages of early drug discovery from target identification (ID) and validation to the hit/lead discovery phase, lead optimization, and preclinical safety. We outline key aspects that should be considered when developing, qualifying, and implementing complex in vitro models (CIVMs) during these phases, because criteria such as cell types (e.g., cell lines, primary cells, stem cells, and tissue), platform (e.g., spheroids, scaffolds or hydrogels, organoids, microphysiological systems, and bioprinting), throughput, automation, and single and multiplexing endpoints will vary. The article emphasizes the need to adequately qualify these CIVMs such that they are suitable for various applications (e.g., context of use) of drug discovery and translational research. The article ends looking to the future, in which there is an increase in combining computational modeling, artificial intelligence and machine learning (AI/ML), and CIVMs.

TNF -308G > a promoter polymorphism (rs1800629) and outcome from critical illness.

BACKGROUND: The susceptibility to adverse outcome from critical illness (occurrence of sepsis, septic shock, organ dysfunction/failure, and mortality) varies dramatically due to different degrees of inflammatory response. An over expression of tumor necrosis factor alpha (TNF-α) can lead to the progression of the inflammatory condition. OBJECTIVE: We assessed the relationship of the genotype distribution of -308G >A TNF-α polymorphism with regard to the development of sepsis, septic shock, higher organ dysfunction or mortality in critically ill patients. METHODS: Observational, hospital-based cohort study of 520 critically ill Caucasian patients from southern Brazil admitted to the general ICU of São Lucas Hospital, Porto Alegre, Brazil. Patients were monitored daily from the ICU admission day to hospital discharge or death, measuring SOFA score, sepsis, and septic shock occurrences. The -308G >A TNF-α SNP effect was analyzed in the entire patient group, in patients with sepsis (349/520), and in those who developed septic shock (248/520). RESULTS: The genotypic and allelic frequencies were -308GG = 0.72; -308GA = 0.27; -308AA = 0.01; -308G = 0.85; -308A = 0.15. No associations were found with sepsis, septic shock, organ dysfunction, and/or mortality rates among the TNF-α genotypes. Our results reveal that the -308G >A TNF-α SNP alone was not predictive of severe outcomes in critically ill patients. CONCLUSION: The principal novel input of this study was the larger sample size in an investigation with -308G > A TNF-α SNP. The presence of -308A allele is not associated with sepsis, septic shock, higher organ dysfunction or mortality in critically ill patients.

TNF -308G > a promoter polymorphism (rs1800629) and outcome from critical illness

BACKGROUND: The susceptibility to adverse outcome from critical illness (occurrence of sepsis, septic shock, organ dysfunction/failure, and mortality) varies dramatically due to different degrees of inflammatory response. An over expression of tumor necrosis factor alpha (TNF-α) can lead to the progression of the inflammatory condition. OBJECTIVE: We assessed the relationship of the genotype distribution of -308G >A TNF-α polymorphism with regard to the development of sepsis, septic shock, higher organ dysfunction or mortality in critically ill patients. METHODS: Observational, hospital-based cohort study of 520 critically ill Caucasian patients from southern Brazil admitted to the general ICU of São Lucas Hospital, Porto Alegre, Brazil. Patients were monitored daily from the ICU admission day to hospital discharge or death, measuring SOFA score, sepsis, and septic shock occurrences. The -308G >A TNF-α SNP effect was analyzed in the entire patient group, in patients with sepsis (349/520), and in those who developed septic shock (248/520). RESULTS: The genotypic and allelic frequencies were -308GG = 0.72; -308GA = 0.27; -308AA = 0.01; -308G = 0.85; -308A = 0.15. No associations were found with sepsis, septic shock, organ dysfunction, and/or mortality rates among the TNF-α genotypes. Our results reveal that the -308G >A TNF-α SNP alone was not predictive of severe outcomes in critically ill patients. CONCLUSION: The principal novel input of this study was the larger sample size in an investigation with -308G > A TNF-α SNP. The presence of -308A allele is not associated with sepsis, septic shock, higher organ dysfunction or mortality in critically ill patients.