Stephan Kadauke

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Stephan Kadauke, MD, PhD

Associate Director, Cell and Gene Therapy Laboratory

Dr. Kadauke is the associate director of the Cell and Gene Therapy Laboratory at CHOP and the medical director of the Cell and Gene Therapy Informatics team. His research focuses on developing novel ways to manufacture cell therapies with the goal of accelerating and broadening patient access to these life-saving therapies.

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Bio

Dr. Stephan Kadauke is a licensed physician with expertise in Clinical Pathology, Transfusion Medicine, Cell Therapy, and Clinical Informatics.

Dr. Kadauke is the associate director of the Cell and Gene Therapy Laboratory (CGTL) at the Children's Hospital of Philadelphia (CHOP). The CGTL is a cGMP facility embedded into the hospital that performs various procedures to process cell therapies and is the sole provider of cellular therapies at CHOP. Dr. Kadauke is involved in 50+ active investigator- and industry-sponsored cell therapy clinical trials exploring hemoglobin gene therapies, chimeric antigen receptor (CAR) T cells, hematopoietic stem cell (HSC) transplants, and virus-specific cytotoxic T cells. He also serves as the Associate Director of the Penn/CHOP Cell Therapy Fellowship program.

Dr. Kadauke also serves as attending physician on the Apheresis service, where he treats patients with sickle cell disease and various autoimmune and neurological diseases. He also supervises cell collections for patients who require stem cell transplantation or other cellular immunotherapies

In addition to his clinical work, Dr. Kadauke is the medical director of Cell and Gene Therapy Informatics (CGTI). CGTI is an agile team that builds systems to deliver reliable, high-quality, and timely data to Cell and Gene Therapy clinicians and researchers to help clinical decision making, improve and automate quality operations, and accelerate Cell and Gene Therapy clinical research. CGTI currently maintains 25+ separate systems, including REDCap-based clinical trial electronic data capture (EDC) systems, custom-built R packages and R/Shiny web applications, as well as machine learning-based predictive models.