Tissue Quality and Clinical Data: The Bottleneck for Drug and Biomarker Development
Dr. Hartmut Juhl
Chief Executive Officer, Indivumed GmbH, Hamburg, Germany
Identification of drug targets as stratification and predictive biomarkers in patient populations by analyzing surgically resected tissues has become an important field in drug development. However, considerable research data demonstrate that RNA and protein expression level, including activation or inhibition of signaling pathways and their receptors can change significantly within minutes following surgical resection (“cold ischemia time”). Many of those genes and proteins are possibly involved in growth regulation and might serve as targets or stratification markers for new drugs. In addition, other pre-collection factors can impact tissue-derived data such as drug treatment and anesthesia of patients before surgical tissue removal, intrasurgical ischemia by ligation of main arteries, the location of a biopsy within a given tumor, processing and fixation of the tissue. And, last-but-not least, availability and integration of comprehensive clinical data of the individual patient (medical history, outcome of treatment etc.) significantly support understanding of tissue data in drug and biomarker development programs. Therefore, controlled and rapid tissue processing and collection of clinical data in a standardized format is a prerequisite for understanding biological differences of or within patient tumors, and when developing targeted molecular therapies. Nowadays, most hospitals and pathology departments use their own, mostly incomparable and often poor standard for biospecimen and clinical data collection – a main reason for the high rate of irreproducible research data. SOP-guided, ISO-certified processing of tissue in surgical suites and clinical data collection in hospitals and oncology practices have been established by Indivumed in a growing global network of hospitals in Germany and the US. Indivumed´s system generates biospecimens that represent the molecular patterns as they were in the human body. The clinical data base indivuNET allows highly meaningful, reproducible comparative studies. Overall, such labor intensive infrastructures are essential to accelerate drug and biomarker development in personalizing cancer therapy programs.