Dr. Manukyan's background is a mixture of academic, CRO, and Biotech / Pharma industry with over a decade of experience in Pharma Industry in various leadership roles. His last industry position was that of a Senior Director, Early Clinical development, at Pfizer.

Dr. Manukyan's research interests include optimal adaptive and Bayesian designs. He has a Ph.D. in Statistical Sciences from George Mason University (USA), an MS in Applied Mathematics from the University of Maryland, an MS in Industrial Mathematics from the Technical University of Kaiserslautern (Germany) and a Ph.D. in Biology from the National Academy of Sciences of Armenia.

Development of the medical and pharmaceutical field is highly dependent on clinical trials. Evidence related to the efficacy and safety of all new treatment methods, medications and medical devices are being generated through the trials. Usually this involves treatment of patients in medical institutions under supervision of medical personnel, collection of data during the treatment, and analysis of the collected data. Current digital devices allow gathering and analysis of health data remotely. Gadgets can monitor vital signs, blood pressure, glucose levels, and many other specific parameters. The World Health Organization (at 71st World Health Assembly in 2018) recognized the potential of digital technologies to support health, and to improve the accessibility, quality and affordability of health services.

This creates a huge ecosystem where a large amount of health-related data is being generated and stored in different sources. At this age of “big data analytics,” use and repurposing of already available data is possible within the framework of clinical trials.

In this context secure and solid IT solutions are essential for managing and analyzing the accumulating data. Integration and encryption mechanisms should be properly implemented to ensure reconsolidation of the data from various sources. Continuous risk-based monitoring should be in place to ensure data integrity. Complex statistical methodologies, machine learning and AI need to be adapted to detect corrupted, fraudulent, or erroneous data.

Finally, in trials where experimental treatment methodologies are being investigated, ethical norms and dilemmas are critical aspects. Use of historic data along with the reduction of the exposure to inferior treatments can address some of those issues. Innovative trial and analysis designs (e.g. Bayesian response adaptive) must be backed up with appropriate IT platforms to enable their implementation.