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Comprehensive data analysis as the basis for individualised drugs

Individual patients metabolise certain drugs in very different ways, some patients metabolise drugs well and others do not respond at all. The difference in individual patients’ ability to metabolise drugs depends not only on environmental influences but also on genetic factors, which means that the sequencing of the human genome has become a major prerequisite for the application of personalised medicine. In the following interview, Dr. Jens Hoefkens, head of Genedata’s “Expressionist Business Unit”, explains why “next generation sequencing” enables patients to be treated more efficiently and how information technology can contribute to identifying potential adverse drug effects at a very early stage.

Dr. Jens Hoefkens is the head of Genedata's "Expressionist Business Unit" © Genedata

How do next generation sequencing data contribute to personalised medicine? 

Next generation sequencing data enable us to more effectively understand interindividual differences. We are looking for genetic variants, epigenetic differences and gene expression signatures that correlate with a specific therapeutic effect. Once we have found them, we need to explain the correlation mechanistically, which means that we have to come up with a causal relationship between treatment outcome and genetic state. This approach stands out from other approaches because the causal relationships focus on the entire genome, and not just on certain regions or genes.

What advantages does sequencing offer patients?

I believe that if we have the sequence information we are able to treat a patient more effectively. On the one hand, the information enables doctors to decide much more effectively whether it is useful to treat a particular patient with a certain drug. Doctors might decide not to prescribe a certain drug if they know that the patient's genome sequence suggests that he or she is at a higher risk of certain adverse drug reactions. On the other hand, doctors might decide to prescribe a certain drug to a patient who has a specific sequence profile, knowing that the drug in question will be effective in this case. To put it simply, it is anticipated that personal sequence information will provide doctors with an effective and better basis for selecting suitable drugs for individual patients.

How many data are needed to create the basis for treatment decisions?

The sequencing of a human genome generates around 100 gigabyte of data. A clinical study normally requires several hundred patients. In addition, numerous other data are required for making a treatment decision. Clinical studies therefore generate huge and complex amounts of data. Huge amounts of data are also generated during the preclinical phase. Genedata is specialised in the integration and analysis of such data and supplies effective software for this very purpose.

How does the software work?

One of our products, Genedata Expressionist®, includes a large number of analysis and visualisation tools that enable the investigation of chromosomal alterations (CNVs), methylation events, polymorphisms, gene expression and gene regulation, for example. An important aspect is the ability to analyse all these data simultaneously in order to enable the application of statistical methods, an essential prerequisite for making scientifically founded statements. And of equal importance is the need for the analyses to be processed both quickly and efficiently whilst taking huge amounts of data into account.

"Genedata Expressionist" can be used to analyse and visualise chromosomal alterations, for example. © Genedata

What biochemical mechanisms are of particular interest for personalised medicine?

Particular features of the genome can of course be identified directly from DNA sequences. These features comprise single nucleotide variations, which are referred to as polymorphisms, as well as alterations of larger regions such as deletions, inversions and amplifications. The lack or amplification of entire chromosome fragments is referred to as "copy number variations" (CNVs). In addition, the DNA sequences provide information on the regulation of genes. This can be determined from the methylation state of the DNA or from the binding of transcription factors. And last but not least, gene activity can also be measured directly from the quantification of transcripts such as messenger ribonucleic acids (mRNA).

Why do some patients respond to a certain drug and others do not respond at all?

Our basic hypothesis is that a certain phenomenon is the result of interacting genetic and environmental influences rather than one single factor. So we have to include all possible factors into the analysis right from the word go. In the past, this was very difficult to achieve with information technology, but the situation has since changed considerably. We are now trying to apply the knowledge acquired in the quantitative sciences to the biological sciences using state-of-the-art software technologies. We are also trying to bring into the pharmaceutical sector the long-standing experience that we have from numerous projects in the biological sciences. The much closer combination of clinical and genetic data is the key to success.

Are you already working with pharmaceutical companies and hospitals?

Our software is already being used in many projects, one example being the attempt to identify potential adverse drug effects at a very early stage. We are currently working with more than 30 pharmaceutical companies, and the number of clinical project partners is increasing.

When do you think next generation data will become an integral part of medicine?

I believe we are still in a very early phase. However, sequencing data are increasingly used as the basis for elucidating scientific issues.


Further information:

Genedata AG
Konstanz Office
Byk-Gulden-Strasse 2
D-78467 Konstanz, Germany
Tel: +49 7531 209900
Fax: +49 7531 209890
E-mail: germany(at)genedata.com

Website address: https://www.gesundheitsindustrie-bw.de/en/article/news/comprehensive-data-analysis-as-the-basis-for-individualised-drugs