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Faster and Cheaper Clinical Trials: The Benefit of Synthetic Data

Take the innovation leap: Four things pharma companies can do now for a synthetic data-driven approach to clinical trial design.

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Faster and Cheaper Clinical Trials: The Benefit of Synthetic Data

  1. 1. Accenture Life Sciences Patient Inspired. Outcomes Driven. Fasterandcheaper clinicaltrials The benefit of synthetic data
  2. 2. With drawn-out timelines and billions in investments, traditional clinical trial methods are increasingly a barrier to cost-efficient and timely drug development.
  3. 3. A role for synthetic data What is it? Synthetic data is generated by applying a sampling technique to historic data or by creating simulation scenarios where models and processes interact to create completely new data not directly taken from the real world.* How will it improve clinical development? Minimize or replace the control arm of a trial Reduces the demand for patient recruitment which saves time and money, and mitigates ethical issues around patients receiving placebos Model target patient population and define the boundaries of a trial Optimizes design and feasibility which positively impacts operational success *Adapted from Gartner
  4. 4. Overcoming obstacles Pharma companies need to exploit the dynamics of data along these three dimensions Velocity Pharma companies are often utilizing historical and distinct data sets without incorporating new data as it is generated and have limited capacity to interpret incoming data. Variety Pharma companies typically do not have broad external data and have been reluctant to look beyond their own clinical data to other sources, such as industry data from other clinical trials and real-world data from EMRs. Volume Pharma companies have their own separate data, but this is just a fraction of the potential data available for use at an indication level and are usually not sufficient for rigorous statistical analysis.
  5. 5. Essential enablers Using data efficiently, pharma companies need to do more to identify and adopt advanced technologies Pharma companies need to invest in: Technology Skills Leadership Without the right algorithms, companies are unable to extract meaningful insights from the data and build relationships between data sets. Algorithms need to be deployed in a way that enables an integrated analysis and application of data. Without the technical and data science skills, companies are unable to effectively apply digital solutions like predictive modeling and have a limited understanding of big data processing techniques such as artificial intelligence and machine learning. Strong leadership both within the life sciences industry and in key regulatory agencies is needed to drive transformation and build the infrastructure to facilitate data and knowledge exchange. Copyright © 2021 Accenture. All rights reserved. 5 5
  6. 6. Where to start 01 02 03 04 Draft your big plan while getting started with pilots Create a bold global vision for leveraging synthetic data in clinical trials; prioritize assets with which to pilot the new approach. Take stock of your data and look outside for more Supplement internal data with external data sources to achieve a robust validated data set. Deploy smart algorithms for “what-if” analysis Integrate and automate algorithms into analytics platforms to evaluate and predict potential outcomes. Evolve your operating model New governance, skills and processes are needed so that predictive data analytics can inform clinical deployment plans.
  7. 7. For more information Dr. Boris Bogdan Managing Director, Global Lead Precision Oncology and Personalized Healthcare, Accenture Dr. Sanjay Jaiswal Managing Director, R&D Analytics Lead – NA, Accenture Jonathan Peachey Chief Operations Officer, Phesi Copyright © 2021 Accenture. All rights reserved. 7 7
  8. 8. Thank You