A group of scientists from Hamburg may have taken a big step towards more effective cancer drug development, Europe's largest cancer congress, ECCO 15 - ESMO 34 [1], heard today (Wednesday 23 September). Dr Ilona Schonn, Director of Cell Culture Research at Indivumed GmbH, told the conference that they had developed a preclinical drug test platform that would enable researchers to analyse tumour tissue for individual patient drug responses on the molecular level.
To date most tests for drug metabolism and toxicity testing have used tissue slices of normal organs like liver, kidney and lung. The new test was created specifically for oncology drug testing and uses tumour tissues from colorectal and lung cancer patients.
A major problem of drug development at present is the inability to extrapolate response in preclinical cell models to patients. "Approximately 90% of clinical trials fail because the drugs used are too ineffective or too toxic," explained Dr Schonn. "Not only does this result in unacceptably high costs for drug development, but it also exposes patients to risks from toxicity or simply wastes their time in testing a substance which proves to be ineffective."
The problem arises from the fact that patients respond individually to drugs. In addition, each tumour consists of a variety of different cancer cells that interact in different ways with the framework of individual non-tumour cells, resulting in highly variable growth behaviour and response to drugs. Dr Schonn and her team set out to try to develop a drug test that would eliminate these problems and provide an accurate model of individual patient response.
"Based on freshly cultivated intact tissue from surgically treated cancer patients we are now able to analyse numerous tumours from different patients, to identify differences in drug response between those patients, and to understand variations of response in cell subtypes within one tumour," said Dr Schonn.
The new test allows scientists to translate findings from commonly used cell lines to a preclinical model which is as close as possible to using the same drug in the clinical setting, thus enabling a better estimate of the number of patients who are likely to respond to the treatment. It also helps to identify biomarkers that can predict drug response for patients entering a clinical trial, allowing researchers to include only those patients whose participation will provide a significant answer to the question being asked.