Ahead of the 5th Annual Data Analytics for Pharma Development Forum 2019, taking place on 12-14 November in Munich, we conducted an industry wide survey.
In this infographic you will learn about the biggest challenges that are facing those using data analytics in their drug development, the strengths of using AI & Machine Learning, how the use of Real World Data can be improved and where the future lies in data analytics in pharma development with many more insights.
Download below to view the full infographic
Ahead of the 5th Annual Data Analytics for Pharma Development Forum (Munich, 12-14thNovember 2019), we asked Nigel Hughes, Scientific Director at Janssen, to have a chat with us about the role of data analytics in the pharmaceutical industry and the impact AI & Machine Learning, RWD/RWE, GDPR and clinical trials are having on drug development.
Artificial Intelligence, Machine Learning and other technologies are expected to make the discovery and development of new pharmaceuticals quicker, cheaper and more effective. Download this article to hear experts insights from Leonardo Rodrigues, Senior Director, AI & Machine Learning at BERG Heath about how these tools will revolutionise drug development over the next decade and what the future of a drug discovery market looks like when driven by AI.
While most industries are already benefiting from better use of dataanalytics, Pharma has been slow to innovate their business mode and adopt new technologies. Pharma R&D is stalling, there is a cap onproductivity and companies need to leverage data insights to move forwards. With insights from Larry Pickett, CEO, RxDataScience, this infographic provides a step-by-step guide to overcoming the key challenges faced in establishing and implementing a coherent data analytics strategy.
In this exclusive interview conducted ahead of this year’s forum for Data Analytics for Pharma Development, Paul Agapow, Health Informatics Director at AstraZeneca shares his views on the world of data, with a particular emphasise on how artificial intelligence (AI) can help analyse and interpret huge quantities of data at all stages of drug discovery and development. Paul also discusses the challenges of using Real World Data (RWD) and Real World Evidence (RWE) when data is not always easily accessible or specific.
Download our latest interview to get the answers to the following questions:
- How is RWD becoming increasingly attractive for research?
- What are the current challenges when using RWD/RWE?
- How do you foresee evolutions in different segments and types of RWD for different therapeutic areas?
- How and in what areas are ML and AI currently impacting drug discovery and development? What challenges need to be overcome to encourage wider adoption?
- How do you see Machine Learning and AI developing over the next 5 years and the impact it will have on drug discovery and development?