There is a lot of discussion across biopharma about the increasing complexity in the design and management of clinical trials. Recent estimates suggest the cost of bringing a drug to market is as high as $3 billion, and only 1 in 10 drugs successfully navigate the process and make it to market. These trends are not sustainable, and the use of targeted clinical strategies are being seen as a critical step towards improving success rates.
Historically, drug approvals sought to maximize the potential market opportunity both in the initial indication and later line extensions. The rising average duration of trials suggests that a change in strategic approach is valuable, particularly after the recent successes with drugs like Imatinib and Keytruda. Keytruda developed by Merck is indicated for patients with metastatic or advanced non-small cell lung cancer who test positive for TPS>1% PD-L1 expression and negative for EGFR and ALK mutations. This new drug has a mechanism of action that is so tightly understood and critically linked to a clear biomarker strategy that development professionals had the confidence to carefully select patients from specific sub-groups from the earliest trials and demonstrate efficacy from the earliest opportunity, leading to a uniquely accelerated approval and ultimately benefiting both patients and Merck with the granting of a Marketing Authorization.
To determine whether these celebrated successes reflect the beginning of a new stage of therapeutic evolution, we wish to understand how deep the shift has occurred towards personalized therapies and how widespread such an approach is across the therapeutic areas within clinical development. By comparing the two periods, 2007-2011 vs. 2012-2016, we can identify a number of trends in the application of specific biomarker roles (therapeutic effect marker, toxic effect marker, disease marker) within active clinical trials. We demonstrate an increase in both the application of specific biomarker types and also highlighting some areas where there are opportunities to increase our knowledge of the underlying biological mechanisms.
Dr. Richard K. Harrison, Chief Scientific Officer, Clarivate Analytics
Dr. Richard K. Harrison is the Chief Scientific Officer at Clarivate Analytics. He has over 30 years of experience in the life sciences industry. His career has focused on all aspects of pre-clinical drug discovery. During this time he has held positions of increasing responsibility at Aventis, Merck, DuPont and Wyeth Pharmaceuticals where he delivered numerous clinical candidates and worked on several marketed drugs. In addition, he served as a founding scientist in two successful venture funded computational and structure based drug design companies. Dr. Harrison has authored over 40 peer reviewed publications and book chapters in all areas of drug discovery and has presented over 50 invited lectures on these subjects. He earned his Ph.D. in Biophysical Chemistry and Enzymology and currently serves as an adjunct professor at several universities
Gavin Coney, Head of Clinical Products, Clarivate Analytics
Gavin Coney is the Head of Clinical Products for Clarivate Analytics. In this role he manages a portfolio of products dedicated to supporting decision making by professionals within Life Science organizations who are interested in gaining intelligence relating to Clinical Development, Clinical Operations and Competitive Intelligence. Gavin has worked within informatics for 17 years and within the Life Sciences for the last 8 years.
Dr. Lee Lancashire, Principal Research Scientist, Clarivate Analytics
Dr. Lee Lancashire is Principal Research Scientist, Clarivate Analytics where he is responsible for the development of machine learning based predictive modelling strategies. He holds a Ph.D in machine learning where he was involved in developing many of the first applications of artificial neural networks to high dimensional molecular data in bioinformatics. His work leads the discovery of discriminatory biomarker signatures that will facilitate the design of molecularly targeted clinical trials, and ultimately assist in the molecular profiling of disease for improved patient stratification, pre-clinical drug discovery, and development of novel diagnostic tests aimed to select which drugs will have the greatest impact on disease.
Dr. Lancashire completed his Post-doctoral research in Biostatistics and Bioinformatics at the Paterson Institute for Cancer Research, a Cancer Research UK core facility. He has also worked as a bioinformatics project lead in personalized medicine and diagnostics companies CompanDX and Almac Diagnostics. He has published in over 30 key industry journals and holds several patents around the use of neural networks in the biomedical domain.
Who Should Attend?
Senior professionals from biopharmaceutical companies working within:
- Clinical Development
- Clinical Operations
- Clinical R&D
- Drug Discovery / R&D
- CEOs, Managing Directors, Heads of Business Development
- Chief Scientific Officers
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