Transcriptomics Services

The Impact of Drug Failure

Safety is a key reason for failure of a drug. Often, drug failure occurs too late with approximately 90% failing in the clinic. Identifying these liabilities earlier will improve efficiency in the drug discovery and development process by decreasing the potential of late stage failure and reducing the escalating costs associated with developing new drugs. Despite this, translation of certain types of toxicity remain difficult to predict. For example, in the case of drug-induced liver injury (DILI), only 50% of human hepatic toxicities are picked up by preclinical animal studies. Therefore, more reliable in vitro human relevant models are required to identify this safety liability.

Next Generation Sequencing & Transcriptomics

The first step in our understanding of drug-induced toxicity is linking the molecular initiating events to potential mechanisms by evaluating specific adverse outcome perturbations and relating these to organ toxicity. These molecular initiating fingerprints can be used for future hazard identification and risk assessment.

One area of toxicological research which is growing in interest is the field of transcriptomics. Transcriptomics is the study of mRNA molecules in the cell and it provides important information on how genes are expressed and interconnected. Recent technological breakthroughs have revolutionized transcriptomics. The introduction of RNA-seq (a method based on Next Generation Sequencing (NGS)) is one such advance. This powerful technique is able to provide a quantitative measurement of the entire transcriptome of the cell. Despite this, the real potential of the transcriptomics dataset can only be realized through sophisticated data processing techniques.

Cyprotex and parent company Evotec, have launched a new service which combines cell biology with next generation high throughput whole transcriptome sequencing using RNA-seq. By scaling up and industrializing this process, a more data-rich examination of the molecular phenotype of the cell can be achieved. Through our considerable expertise in bioinformatics, we have built a sophisticated data analysis platform known as PanHunter to manage the vast amounts of data produced and to map the sequencing data to the gene of interest. Machine learning and artificial intelligence are further used to interrogate the data and allow complicated questions to be easily resolved. By monitoring early cellular transcriptional response following exposure to a chemical, a sensitive in-depth view into early molecular initiating events with respect to efficacy or toxicity can be investigated.

Through our transcriptomics service clients can work with us on custom models, combined with transcriptomics and sophisticated data analysis tools to address specific needs.

Transcriptomics fig 1

Figure 1

Fully integrated in vitro transcriptomics prediction platform.

Using Transcriptomics to Predict Drug-induced Liver Injury (DILI)

The use of transcriptomics in DILI prediction has been recognized since the early 2000’s when surrogate gene expression markers were identified in human blood. Now the challenge is to detect DILI from human in vitro cell-based models. A combination of transcriptomics and AI have been shown to deliver a superior level of DILI prediction. Using the industry current gold standard method; 2D primary human hepatocytes or 3D human liver microtissues with a seven parameter high content imaging read-out, DILI prediction accuracy is reported at 69% and 77% respectively. However, using the new DILI prediction platform, which utilizes RNA-seq transcriptomics data generation combined with PanHunter data analysis, accuracies of 80% are achieved using primary human hepatocytes, and 86% using 3D human liver microtissues.

Transcriptomics fig 2

Figure 2

Using transcriptomics and artificial intelligence to improve DILI prediction.

1Predictions are based on matched 2D Primary Human Hepatocyte assay or Human Liver Microtissues (hLiMTs) with 128 reference compounds tested (largest reference compound data base reported).

Mechanistic Analysis

The PanHunter software provides a very thorough analysis of differential gene expression along with pathways and specific signatures for different types of toxic mechanisms. This knowledge is built using large databases of reference compounds where their mechanisms are well understood. Artificial intelligence can then be used to cross-compare with the profiles of these reference compounds and predict safety profiles for new chemical entities.

Transcriptomics fig 3

Figure 3

Transcriptomics data analysis using PanHunterTM software.

Customized Transcriptomics Service

The role of transcriptomics is not just limited to DILI – it can be applied to other areas of toxicology such as cardiotoxicity and nephrotoxicity. Our transcriptomics service is flexible and can be customized for different organ-specific models and cell types. We can support all aspects of the transcriptomics workflow for an end to end in vitro toxicology transcriptomics service. The workflow incorporates:

  • Advanced cellular models.
  • High throughput transcriptomics platform.
  • Superior bioinformatics platforms (PanHunter).
  • Team of dedicated bioinformatics experts and data scientists.
  • Sophisticated machine learning capabilities.

Through our unique highly automated, cost-effective processes, we can detect up to a million mRNA molecules per well and our generic protocol has been successfully applied to more than 25 cell types as well as 3D microtissue models.

High throughput transcriptomics produces vast quantities of complex data and specialized tools and software are required to manage, analyze and interpret the data. To address this, Evotec have developed the sophisticated PanHunter platform. It streamlines the entire process by storing and managing the sequencing data, performing quality control and differential expressions analysis, and evaluating the implications for downstream processes such as pathway regulation or gene network analysis. The PanHunter platform is able to combine data from various sources including, not only transcriptomics, but also genomics, proteomics, metabolomics and other specialized screens. Having access to this truly unique and powerful system transforms the workflow process - improving efficiency, reducing cost and ultimately enhancing the quality of the data through robust analysis and interpretation.

Transforming Toxicology Prediction via PanOmics

Download your free copy of Drug Discovery Update #10 to learn more about:

  • New developments in PanOmics research.
  • How transcriptomics is being applied to DILI prediction.
  • State-of-the-art technologies such as RNA-Seq combined with Artificial Intelligence and Machine Learning.
  • Expert opinions on the future of this new technology.

Read the Drug Discovery Update

Publications black 174

Transcriptomics Brings New Era of Toxicology Prediction

Together with our colleagues at Evotec, Cyprotex sat down with Springer Nature to talk about the potential of transcriptomics to herald a new era of toxicology prediction.

Within the article we discuss the high-throughput methods of measuring gene activity that can play a major role in delivering safer drugs to the market.

Click on the link below to read the full interview with Paul Walker, Rüdiger Fritsch and Carla Tameling:


Cyprotex eStore

Order our services online.

Visit the Cyprotex eStore
Paul Walker

Paul Walker, PhD

VP Head of Toxicology & Innovation Efficiency

Logo Cyprotex white
Cyprotex enables and enhances the prediction of human exposure, clinical efficacy and toxicological outcome of a drug or chemical. By combining quality data from robust in vitro methods with contemporary in silico technology, we add value, context and relevance to the ADME-Tox data supplied to our partners in the pharmaceutical or chemical industries.