Science Pool

Cardiotoxicity is defined as toxicity that affects the heart. Drug-induced cardiotoxicity remains an important cause of pre-clinical and clinical drug failure. At Cyprotex, we are developing cutting-edge strategies to effectively predict toxicities at an early stage in the drug development process to guarantee the progression of safe novel pharmaceuticals and reduce later-stage attrition.

The mechanisms by which drugs can induce cardiotoxicity are diverse, ranging from functional (acute alteration of the mechanical function of the myocardium) to structural impairment (morphological damage to cardiomyocytes), and the clinical manifestations are wide-ranging, spanning from arrhythmia to myocardial dysfunction, to terminal heart failure. Cardiotoxicity generally results from the disruption of key cardiomyocyte processes affecting contractility, electrophysiology (ion channel trafficking), mitochondrial function, growth factors and cytokine regulation. Consequently, our assays have been developed to cover various readouts by combining multiple approaches to obtain a complete understanding of toxic effects.

At the 58^th Congress of the European Societies of Toxicology (Copenhagen, Eurotox 2024), we presented our latest work in cardiotoxicity prediction in the form of a poster, titled: “High-throughput transcriptomics combined with in vitro assays for cardiotoxicity risk assessment and mechanistic understanding”. Here, we investigated the effects of 148 reference compounds, (including structural and/or functional cardiotoxicants as well as non-cardiotoxicants) on human induced pluripotent stem cell derived cardiomyocytes (hiPSC-CMs) using high-throughput transcriptomics assessing the entire transcriptome (HT-transcriptomics), high-content imaging (HCI) and kinetic monitoring of calcium transients (CaT). Our compound set covered a broad range of mechanisms of action including ion channel inhibitors (Na⁺, K⁺, Ca²⁺), receptor modulators (adrenergic, dopamine, serotonin, histamine, acetylcholine, glucocorticoid, sulfonylurea), enzyme activities (COX, phosphodiesterase) and DNA metabolism.

Calcium transients, assessed by fast kinetic fluorescent readings, allowed a series of Ca²⁺ peak parameters to be studied including amplitude, frequency, full rise and decay time and peak width, which taken together revealed the effects of compounds on cardiomyocyte contraction. Since the calcium transients are closely associated with muscle contraction and ventricular action potentials, they can help us understand the in vivo cardiotoxicity effects of some compounds including electrocardiogram alterations such as QT interval prolongation. Additionally, HCI was used to assess any structural damage to the cardiomyocytes upon analysis of nuclei impairment, calcium homeostasis and mitochondrial function. Finally, HT-transcriptomics shed light on the transcriptional responses triggered upon compound treatment, which were further analysed for pathway enrichment and differential gene expression.

This multi-parametric approach allowed the identification of the readout showing the lowest minimum effective concentration (MEC). Compounds were then classified as cardiotoxic if the MEC value was below a specific maximum plasma concentration (C_max) threshold calculated using in vivo literature cardiotox classifications. Additionally, AI/Machine Learning (ML) models were developed to predict cardiotoxicity using a 20x C_max threshold and a tox score threshold. This allowed the classification of compounds as cardiotoxic if the true C_max (historical in vivo response) was above the predicted safe C_max, giving excellent prediction metrics with 78.7% sensitivity, 86.7% specificity and 81.4% accuracy.

Finally, testing dynamic C_max thresholds and different assay combinations proved useful to effectively predicting cardiotoxicity risk with excellent accuracy, whilst assigning more weight to specificity over sensitivity to avoid losing valuable drug candidates due to false-positive risks. The best predictions were achieved by combining HT-transcriptomics AI/ML modelling (20x C_max), HCI (1x C_max) and CaT (2x C_max) endpoints, with 85.9% sensitivity, 84.1% specificity and 85.3% accuracy.

Future work will involve further expansion of our reference compound list to cover an even larger range of mechanisms and chemical space, and to explore the transcriptomics pathway endpoints by performing a Point of Departure (PoD) using Benchmark Dose (BMD) analysis approach, for both hiPSC-CMs and organotypic 3D models which are likely to be more representative of the in vivo tissue structurally and functionally.

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Drug-induced liver injury (DILI) is a major cause of drug attrition - responsible for 18% of drug withdrawals from the market.

Learn more about how Cyprotex can support you in understanding potential DILI risk of your test article.

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Cardiotoxicity is a major cause of drug attrition accounting for 45% of all drugs taken off the market between 1994 and 2006.

Learn more about how Cyprotex can support you in understanding the potential risk of cardiotoxicity of your test article.

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Cell painting analyses phenotypic changes in cells following dosing with test articles. It can be used to assess potential toxicity risks.

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Cardiotoxicity is one of the leading causes of drug attrition. To address this, there is a need for improved predictive screens which can be applied at an early stage in drug development to ensure only safe compounds progress to the clinic.

Drug-induced cardiotoxicity can manifest itself via various different mechanisms which makes detection challenging. Either functional or structural changes can occur, and these effects can be direct or indirect. Functional cardiotoxicity results from acute alteration in the heart function often as a consequence of electrophysiological effects. Structural cardiotoxicity is associated with alterations in cell and tissue morphology and can manifest itself as cell death, inflammatory changes and fibrosis. These morphological changes can take time to present themselves clinically and, therefore, sensitive techniques which can detect early molecular changes are valuable from a predictive perspective.

Combining transcriptomics with artificial intelligence is showing great potential in providing this transformative improvement in predictive safety assessment. In fact, the value of this approach in drug-induced liver injury (DILI) has been demonstrated in a recent poster presented by Cyprotex. At SOT in Salt Lake City from 10-14 March 2024, Cyprotex showcased how this technique can also be applied to cardiotoxicity. The research evaluated 42 compounds (33 cardiotoxicants and 9 non-cardiotoxicants) used in a variety of therapeutic indications. The compounds were assessed at 2 time points and 8 concentrations in a beating cardiac organ model using human iPSC-derived cardiomyocytes. At the end of the incubation period, three different analytical methods were compared; high content screening (HCS) (cell count, cellular ATP, mitochondrial mass, mitochondrial membrane potential, cellular calcium levels, DNA structure and nuclear size), calcium transience (wave amplitude, frequency, full peak width and full decay time) and transcriptomics (high-throughput RNA sequencing).

The HCS and calcium transience assays were valuable in detecting structural and functional cardiotoxicants, respectively. However, the synergism of combining these assays with transcriptomics was demonstrated by an overall improved cardiotoxicity risk prediction. Additionally, transcriptomics analysis provided detailed mechanistic information and identified specific pathway responses. Combined, the three approaches (HCS, calcium transience and transcriptomics analysis) gave excellent cardiotoxicity prediction metrics of 100% specificity, 82% sensitivity and 86% accuracy at 10x C_max and 89% specificity, 91% sensitivity and 90% accuracy at 25x C_max. The transcriptomics analysis improved the overall sensitivity by identifying various molecular mechanisms of structural toxicity such as alterations in cardiac pathways, genotoxicity, ER stress and mitochondrial toxicity.

It is not just cardiomyocytes which can be affected by drug induced toxicity, non-cardiomyocytes such as fibroblasts and endothelial cells may also be impacted. Organotypic models developed using different cell types, therefore, are likely to be more representative both structurally and functionally. In the future, Cyprotex is extending its research to evaluate a number of different cell-based models and different organ-specific toxicities using the transcriptomics approach. Our cardiac safety database is also growing and we now have identified approximately 140 compounds for testing in our models. The use of transcriptomics and artificial intelligence is accelerating the development of new cell-based models in the field of drug-induced toxicity leading to a new paradigm in in vitro safety testing.

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The endocrine system comprises of a network of glands and organs that produce and release hormones to control various bodily functions. Endocrine disruptors, either natural or man-made, can affect the endocrine system by interfering with the normal actions of hormones. This can lead to serious health problems such as cancer, birth defects and developmental disorders. Many of the traditional endocrine disruption testing approaches use radiolabelled material or involve animal models and so have high cost and low efficiency, in addition to ethical concerns. Accurate low-cost screening models are required to flag up chemicals which are potential endocrine disruptors at an early stage.

At the Society of Toxicology (SOT) conference on March 10-14, 2024, Cyprotex presented a poster titled, ‘Establishment of a High Throughput Endocrine Disruptor Screening Panel of Assays for Rapid Screening of Chemicals’. The research developed a panel of 384 well high throughput cell-based hormone activation assays and in vitro hormone receptor binding assays with a 24-48 hr turnaround time. A range of chemicals with different potencies for the endocrine receptors were assessed.

Cell-based endocrine receptor activation assays expressing the human androgen receptor (AR), the human estrogen receptor (ERα or ERβ) or the the human thyroid hormone receptor (TR) were used to assess the chemicals at a range of concentrations at 37°C over 24 hr. Upon ligand activation, the hormone receptor binds to the promoter sequence linked to a luciferase reporter gene and the activation is quantified by luminescence. Cell viability was also assessed in parallel.

Hormone receptor competitor binding assays were also used to assess potential AR or ER ligands by measuring fluorescence polarization. A fluorescently-tagged ligand is added to the AR or ER in the presence of the competitor test chemical. If the test chemical binds then it prevents the formation of the fluorescent ligand/receptor complex and a decrease in polarisation is observed. The extent of the shift in polarization was used to determine the relative affinity of the test chemical for the hormone receptor.

The results observed for the known set of chemicals were consistent with literature values and data corresponded well between the two assays. Additional steroidogenesis assays are being developed to detect hormone levels of AR and ER in H295R cell culture supernatants following exposure to endocrine disruptors.

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Endocrine disruptors, which can be either natural or man-made, interfere with the normal actions of hormones in the body. This can result in serious health issues such as cancer, birth defects and developmental disorders. Low cost screening models are needed to flag chemicals as potential endocrine disruptors at an early stage.

In this poster, we focus on:

• the development of a new high throughput screening panel for chemicals to detect potential endocrine disruptors
• the comparison between endocrine receptor activation assays expressing the human androgen receptor, the human estrogen receptor or the human thyroid receptor, and hormone receptor competitor binding assays using fluorescence polarisation.
• the relationship between the literature and the data generated in the assays.

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Identifying and developing safer and more effective food additives are essential for a healthy growing population. Regulators such as the FDA and EFSA are responsible for monitoring the safety of these food additives. Current in vitro approaches for assessing genotoxicity of these additives present a lack of consistency in the literature regarding incubation time and analysis.

At the Society of Toxicology (SOT) conference on March 10-14, 2024, Cyprotex presented a poster titled, ‘Validation of a new genotoxicity pre-screening package for food additives’. The research evaluates a high content screening approach with robust data analysis which would be suitable as an early stage genotoxicity screening package for de-risking food additives.

Genotoxins are chemicals that cause DNA or chromosomal damage. This can be assessed using in vitro assays such as the phosphorylation of histone H2AX (pH2AX) and histone H3 (pH3), and the micronucleus test (MNT; OECD guideline 487). By assessing both pH2AX and pH3, it allows for assessment of clastogens (pH2AX) and aneugens (pH3). Clastogens are substances that result in structural damage to the chromosome through DNA double strand breaks. Aneugens are substances which result in the daughter cell having an abnormal number of chromosomes due to deletion or insertion of a whole chromosome. The in vitro MNT detects micronuclei which are formed from the misincorporation of chromosomal material that might be structurally and/or genetically damaged, due to interactions with clastogens and/or aneugens interactions. It is an approach recommended by the regulatory authorities.

For the pH2AX and pH3 assays, HepG2 cells were dosed with the food additives over 24hr. For the in vitro MNT, CHO-K1 cells were dosed with the food additives over 24hr. All assays used automated high content screening with robust data analysis to identify potential genotoxicity. From the 12 food additives assessed, 83% were correctly identified in at least one of the methods. Both of the false negatives (benzoic acid and tartrazine) have been reported to induce DNA damage under certain conditions but not others. This disparity in the literature may explain our results.

In summary, Cyprotex have developed an early stage high throughput screening approach to assess the genotoxic potential of food additives using a panel of assays to determine clastogenic and aneugenic potential in addition to micronucleus formation. As well as genotoxicity markers, the assays provide valuable additional information on cell survival, membrane integrity and cell cycle information.

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Food additive testing is essential to ensure human safety during consumption. Early stage genotoxicity screening is an efficient way of triaging additives to ensure only the safest additives are taken forward for more rigorous testing.

In this poster, we focus on:

• the development and validation of a new early stage genotoxicity approach for food additives
• the use of a panel of in vitro assays for detecting clastogens (pH2AX) and aneugens (pH3) as well as micronuclei formation (MNT)

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Predicting DILI (drug-induced liver injury) is challenging compared to other organ-specific toxicities. Translation from animals to humans is poor and, mechanistically, DILI can be complex. As a consequence, DILI continues to be one of the leading causes of attrition during drug development. Better human relevant models are required to improve early stage DILI prediction. Cyprotex is committed to researching and developing approaches to improve the prediction of DILI using human cell-based models in combination with novel techniques such as toxicogenomics and artificial intelligence (AI).

At the Society of Toxicology (SOT) conference on March 10-14, 2024, Cyprotex presented a poster titled, ‘An AI Approach to Drug-Induced Liver Injury Risk: Prediction of Safe Maximum Doses from Toxicogenomics Profiles’. The research evaluated 128 compounds from the FDA Liver Toxicity Knowledge Base – 68 of these compounds were associated with DILI and 60 of these compounds were not associated with DILI. Transcriptomics profiles were generated after dosing primary human hepatocytes in triplicate at 8 concentrations over 24 hr.

Machine learning is a subset of artificial intelligence which is used to find patterns, make decisions and optimise outcomes. In this study, the high throughput transcriptomics profiles of a set of known DILI-positive and DILI-negative compounds were used to train a supervised machine learning model to predict a safe maximum C_max for novel compounds. When interpreting the results, a compound was predicted as DILI-positive if the true C_max was above the predicted safe C_max, and a compound was predicted as DILI-negative if the true C_max was below the predicted safe Cmax. The model achieved the following metrics on the test set (assuming 40x C_max level and 90% DILI score threshold):

The poster provides a detailed insight into two DILI-positive (TAK-875 and bosentan) and two DILI-negative compounds (dopamine and caffeine) to demonstrate the power of the transcriptomics and AI in predicting DILI as well as identifying specific mechanisms of toxicity. The model was able to capture the importance of cholestasis-associated genes in DILI.

To learn more about the use of transcriptomics and AI in DILI prediction:

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