Physicochemical properties provide an insight into the structure of a molecule and its physical behavior within a system, and they can influence its dissolution, absorption, distribution, metabolism, elimination, protein affinity and toxicity. Early assessment of a molecule's physicochemical properties supports rational compound design within drug discovery.
Read our fact sheet to learn more about our physicochemical profiling services.
Tags: Fact Sheets, ADME/DMPK, Cyprotex, Modelling and Simulation
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 Cmax and 89% specificity, 91% sensitivity and 90% accuracy at 25x Cmax. 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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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 Cmax for novel compounds. When interpreting the results, a compound was predicted as DILI-positive if the true Cmax was above the predicted safe Cmax, and a compound was predicted as DILI-negative if the true Cmax was below the predicted safe Cmax. The model achieved the following metrics on the test set (assuming 40x Cmax 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:
Prediction of drug-induced liver injury (DILI) is challenging. Translation from animals to humans is poor and manifestation of DILI can be complex mechanistically.
In this poster, we focus on:
Read our poster to learn more about our research!
Tags: Posters, Toxicology & Safety, Modelling and Simulation
The 13th edition of our Drug Discovery Update (DDup) provides insights into our unique PanHunter next generation multi-omics data analysis platform.
In this edition, it covers:
Tags: Articles & Whitepapers, Proteomics, Metabolomics & Biomarkers, Modelling and Simulation
Bioinformatics supporting Infectious Disease: Creating knowledge for efficient discovery and development of better medicines.
Pathogen functional genomics analyses for unbiased, multidimensional small molecule effect characterisation (RNA-, DNA-seq analyses to characterise pathogen genome and transcriptome)
Genome assembly and annotation with a focus on resistance and virulence (Illumina, ONT, hybrid)
Tags: Fact Sheets, Anti-Infectives, Modelling and Simulation
Featured in Nature, this article interviews experts, Paul Walker, Rüdiger Fritsch, and Carla Tameling, and provides the latest insights into transcriptomics and how the technology is transforming toxicology prediction.
It includes:
Tags: Articles & Whitepapers, Toxicology & Safety, Modelling and Simulation
According to a study from 2020, a total of 133 drugs were withdrawn from the market due to safety reasons between 1990 and 2010. Major causes were hepatotoxicity (27.1%), cardiac disorders (18.8%), hypersensitivity (12.8%), and nephrotoxicity (9.8%), accounting for 69.2% of all drugs withdrawn. In most cases, these withdrawals were initiated because of spontaneous reports and/or case reports. Another study looking into drug withdrawals between 1953 and 2013 revealed that 18% of drug withdrawals from the market in this period were due to liver damage.
Add to these withdrawals of marketed drugs the attrition rate of drug candidates in clinical trials: 90 percent of all drug candidates fail in clinical trials, and 30 percent of these failures are due to unmanageable toxicity issues.
These failures occur despite thorough preclinical work and intensive animal studies. It is estimated that only 50% of the compounds that cause liver toxicity in humans are detected by animal studies. Furthermore, some adverse reactions or idiosyncratic toxic effects are typically not detected until the drug in question has gained large exposure in a broad patient population.
Interestingly, a study evaluating the attrition of drug candidates from AstraZeneca, Eli Lilly and Company, GlaxoSmithKline and Pfizer came to the conclusion that there is a strong link between physicochemical properties of compounds and clinical failure due to safety issues. The results also suggest that further control of physicochemical properties is unlikely to have a significant effect on attrition rates and that additional work is required to address safety-related failures.
These failures are not only costly (according to the FDA, drug development takes over 10–15 years with an average cost of over $1–2 billion for each new drug to be approved), but are also putting the health and the life of patients in danger.
Consequently, Cyprotex and its parent company Evotec are very focused on assessing toxicology issues from the very beginning of its drug R&D process and have invested a significant amount of time and resources to expand its technologies for the toxicological evaluation of drug candidates.
“The idea is to make better informed decisions earlier in your discovery campaign when you can select potentially safer compounds, rather than finding a safety liability later on,” says Paul Walker PhD, Vice President, Head of Toxicology at Cyprotex, in Cheshire, UK.
This improved discovery and selection is implemented by Cyprotex by using the unbiased view of transcriptomics and its potential to predict drug-induced toxicity. Transcriptomics involves sequencing thousands of mRNA molecules to identify which processes are active in the cell and allows for a better understanding of the cell’s reaction to known and novel drugs.
This is by no means a purely academic endeavour. As an example, the Cyprotex team demonstrated via transcriptomics it was able to identify problems in liver cells treated with fasiglifam, a promising diabetes drug candidate, which was withdrawn from late-stage clinical trials by its developer, following signs of liver damage in trial participants. This example proves that transcriptomics could have raised a red flag during preclinical development and might have saved hundreds of millions of dollars.
“Our studies have found potential effects on mitochondrial function, which were previously missed in preclinical studies” says Walker.
Therefore, transcriptomics has the potential to supplement or reduce in vivo toxicology studies by effectively identifying safety issues early in drug development, saving time and money — and animal testing.
Sophisticated Human Cell-Based Models
A key advantage of transcriptomics is its use of human cells and Evotec as well as Cyprotex are not just looking at 2D cell cultures, but investigating 3D organoids. These structures formed of thousands of cells that mimic organ-specific tissues are much closer to the real organ and have valuable features: For example, 3D-organoids of the heart exhibit regular contractions, beating like a living heart, and liver organoids secrete typical liver enzymes for days.
“On top of that, a 3D system allows repeat dosing, mimicking dosing regimens in vivo and potentially helps to detect effects due to toxic metabolites,” says Walker.
As they are small, the organoids can be placed in 384-well plates and individually molecular barcoded for simultaneous sequencing. This combination of miniaturization and high-throughput screening is implemented in Evotec’s EVOpanOmics platform and allows a wider adoption of transcriptomics in preclinical toxicology studies allowing for the repeat testing of dozens or even hundreds of compounds at several doses and in multiple organs.
“People have thought about using transcriptomics for toxicology before, but it was always a numbers game,” explains Rüdiger Fritsch PhD, Principal Scientist and Project Lead for EVOpanOmics. “For any compound that’s a real troublemaker, the evidence will show up in the transcriptomics data if you profile it in a relevant model. You just need to test appropriate dosing scenarios with the breadth of genome-wide off-target effects so that you have a chance to find it.”
Complex Analysis of Transcriptomics Data
Evotec, in conjunction with Cyprotex, offers transcriptomics services to drug developers and carries out the entire process in-house, from growing the organoids to sequencing and analysis. This streamlined process allows its researchers to screen hundreds of compounds a day, each delivering tens of thousands of data points on RNA levels. To analyze all of these vast amounts of data, Evotec has developed a software platform called EVOpanHunter that allows among others the analysis of these transcriptomics in an interactive manner.
“We want to democratize data analysis for the biologists who know the biological pathways and processes, without them needing to rely on additional experts from the bioinformatics department for routine tasks,” says Carla Tameling PhD, Head of Sales and Application for EVOpanHunter at Evotec.
On top of the interactive multi-omics analysis platform machine learning is used to trawl through this immense amount of data in order to find specific patterns hinting for toxicological effects and alert the researchers to dig deeper. “The more data we get, the harder it is for a human to dig through it all,” adds Tameling. “Transcriptomics is an unbiased view. You don’t need to define what to look at prior to your studies — you get all the data, and you might see things that you didn’t think would be relevant initially.”
From publically available sources, Cyprotex has compiled a broad and highly valuable transcriptomics reference database for drug-induced liver injuries.. Machine learning is being applied to predict whether a compound is likely to have issues by comparing the observed pattern of gene activity to the activity patterns of known toxic molecules. Furthermore, this is not restricted to hepotoxicity. Cyprotex is already building databases of other organs, such as heart, kidney and brain, using publicly available drug development trial results to select a broad space of reference comounds. “We’re running reference compounds from all kinds of sources where we know there are either late-stage clinical findings or withdrawals from the market,” states Walker.
Given the rapid advancements of the technology, it may be only a matter of time before transcriptomics and other omics technologies become a regulatory standard approach for preclinical toxicity testing.
Tags: Blog, In vitro Biology, Toxicology & Safety, Modelling and Simulation
How to knock down proteins driving disease processes in a cell
Many diseases are caused by the overproduction of certain proteins. The traditional approach to interfere with these proteins is based on small molecules or antibodies blocking these proteins or their corresponding target, e.g., receptors. Thanks to the recent progress of nucleic acid research, there are several new approaches today which intervene at different stages, from gene regulation to transcription to translation: CRISPR-Cas9 methods to target the DNA, zinc finger repressors targeting gene transcription, or RNA-molecules (antisense oligonucleotides, RNA interference, micro RNAs, etc.) to inactivate the mRNA or to suppress the translation. All approaches come with advantages and disadvantages. The main problem with DNA- and RNA-based medicine is delivery, followed by off-target-effects.
There are, however other new knockdown strategies as well, e.g., enhancing the protein clearance pathways to speed up the degradation of unwanted proteins, such as the autophagy-lysosome pathway and the ubiquitin-proteasome system (UPS).
Transcriptomics, data analysis, and AI/ML platforms as basis for partnership with BMS in targeted protein degradation
Since 2018, the latter technology of targeted protein degradation is also being used in a cooperation with Bristol Myers Squibb (BMS) to identify first-in-class drug candidates in oncology to treat solid tumors. For this collaboration, Evotec uses its PanOmics platform, EVOpanOmics, which combines enhanced throughput proteomics, high-throughput transcriptomics, and cell imaging with the integrated data analysis platform EVOpanHunter and Evotec’s AI/ML-based drug discovery and development platforms.
This research has led to the discovery of novel first-in-class molecular glue degraders. These small, drug-like compounds induce interactions between an E3 ubiquitin ligase and a molecular target, leading to ubiquitination and subsequent degradation of the recruited protein. The resulting therapeutic effect is long-lasting as the molecular glue degraders themselves are not degraded in the process and can initiate the degradation process through several iterations. BMS is a leader in this field based in particular on its unique library of cereblon E3 ligase modulators (CELMoD®) with specific protein-binding properties. Based on the needs of this project, Evotec focused on the development of dedicated and innovative software solutions that greatly helped to accelerate not only the project’s progress but also contributed to the overall progression of Evotec’s PanHunter platform.
The approach has generated a pipeline of novel first-in-class programs, two of which have transitioned successfully into lead optimization after completing respective validation processes on Evotec’s platforms. In this context, Evotec´s integrated data analysis platform panHunter and the Company’s AI and machine learning tools are used to quickly screen, share, and validate results – not only by Evotec, but also by BMS scientists. In May 2022, the partnership was expanded even further for an additional 8 years with the goal to once again broaden and deepen the strategic alliance.
Targeted protein degradation is not only useful in oncology - a number of other diseases, e.g. Alzheimer’s, bacterial and viral infections lead to the presence of unwanted proteins inside cells that may be marked for destruction by this powerful technology. Evotec therefore is welcoming partners interested in exploring this approach in collaborations.
To learn more about the BMS collaboration and the use of targeted protein degradation technology read the official press release.
Tags: Oncology, Blog, Modelling and Simulation
This exciting collaboration between Evotec and Professor Dirk Grimm from the University of Heidelberg will review the current challenges facing the field of gene therapy.
The webinar includes:
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Werner Höllriegl VP, Head of In Vivo Gene Therapy | Evotec Werner Höllriegl is heading In Vivo Gene Therapy at Evotec GT in Orth an der Donau, Austria. He received his degree in Veterinary Medicine from the University of Veterinary Medicine Vienna, Austria. After spending a couple of years as Assistant Professor at the Department of Anesthesiology, he moved into industrial research spending more than 15 years in different Pharma Companies (Baxter, AstraZeneca, Novartis Institutes for BioMedical Research, Baxalta, Shire, Takeda) in Research and Nonclinical Development, focussing on biologics and in vivo gene therapy. |
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Hanspeter Rottensteiner VP, Head of In Vitro Gene Therapy | Evotec Hanspeter Rottensteiner is heading In Vitro Gene Therapy at Evotec GT in Austria. Biochemist by training, Hanspeter has more than 10 years in academia, and 15 years of experience in Pharma (Baxter, Takeda) in senior positions. He brings significant expertise in drug development of biologics and gene therapies, with a strong focus on rare diseases. He received his degree in Biochemistry from the University of Vienna, Austria.
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Rüdiger Fritsch Principle Scientist, Metabolic Disease | Evotec Rüdiger Fritsch is heading a transcriptomics team at Evotec. He has recieved his PhD from the Max Planck Institute for Biophysical Chemistry. With over 15 years of experience as a biologist, Rüdiger has a strong expertise in omics.
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Dirk Grimm Professor of Viral Vector Technologies Medical Faculty and the Department of Infectious Diseases/Virology | Heidelberg University Dirk Grimm is heading the "Virus-host interactions" research group within the Department of Infectious Diseases/Virology. He has over 25 years of experience in AAV capsid and genome engineering as well as in human gene therapy. Dirk has trained under AAV experts Jürgen Kleinschmidt (German Cancer Research Center Heidelberg) and Mark Kay (Stanford University School of Medicine). |
Watch the webinar to learn more!
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Tags: Videos & Webinars, ADME/DMPK, Modelling and Simulation