From the regulatory framework to strategies, operational methods and supporting technologies. How to exploit the wealth of up-to-date unstructured pharmacovigilance related information that can be found in the Internet (tweets, blog, vlog, social networks, forums, chat, etc.) to collect valuable knowledge and comply with present and future regulations. Give an overview of the available data in the pharmacovigilance domain and of how to turn it into useable information and valuable knowledge.
Scope of the training is to enable participants to make informed decisions and plan/implement strategies on the subject to manage the unstructured pharmacovigilance information.
Who is this course for?
This two-day training course is designed to benefit functional/technical professionals working in the pharmaceutical and health care system dealing with the Pharmacovigilance system, such as:
• Qualified Person for Pharmacovigilance (QPPV)
• Pharmacovigilance Officers
• Quality Assurance
• Pharmacovigilance Auditor
• Knowledge Manager
• IT Manager
coming from pharmaceutical and Biotech companies, Clinical Research Organizations (CROs) and public health centers.
- The scenario
– Present and possible future regulations
– Recent and future developments
– Volume vs. Quality
– Case reports vs. Intelligence
– New technologies (apps, « wearables », etc.)
– Web crawling
– Web scraping
– Automated reporting
– Machine Learning/AI
- Why do we need to do that?
– Examples from reality
- Challenges and rewards
- A possible approach: modularity
- The Signal detection: GVP Module VI
- Guidelines and measures for monitoring suspected adverse reactions via the Internet and Digital Media
- Procedures for the handling of reports of suspected adverse reactions for which no reporter information are available
- A case analysis: pharmacovigilance and Signal Management
– Entry-level solutions
– High-level solutions
- Building a simple case
Knowledge of basic pharmacovigilance. Basic knowledge of computers and productivity packages (Office, etc.)
Presentation, including hands-on exercises and debates.
At the end of the training, you will be able to:
- Have a working knowledge of relevant information on Pharmacovigilance freely available on the Internet
- Manage the signal detection in the social media
- Select the appropriate tools and processes to implement “Internet based PV knowledge gathering”
- Set up simple specific strategies
- Interact with the tools and service vendors/provides to set up more complex solutions
- Manage and report the information
Marco Anelli, Head of Medical Affairs and Pharmacovigilance Advisory Practice – PLG (Product Life Group)
Marco Anelli has been appointed in January 2016 “Head of Pharmacovigilance and Medical Affairs Advisory Services” at PLG. Previously, Marco has been R&D Director at Keypharma, an Italy-based ProductLife Group company, and was responsible for the coordination of all clinical and preclinical aspects of projects run internally and on behalf of clients. Drawing on a career in the pharmaceutical industry that spans 25 years, Marco provides expert oversight on a wide range of R&D and Medical Affairs related activities. Marco has participated in and coordinated all stages of drug development – from formulation to Phase I-IV and pharmacovigilance. In addition, Marco is a qualified QPPV and has prepared and overseen more than 200 non-clinical and clinical overviews and summaries. Before joining Keypharma and PLG, Marco was Medical Affairs Director at Eurand. In recent years, has worked extensively in the fields of pharmacoeconomics and health technology assessment. He has a medical degree from Milan University, specializations in Medical Statistics and Clinical Pharmacology from Pavia University and an international master’s degree in health economics and pharmacoeconomics from Pompeu Fabra University in Barcelona. As “Deputy Chief Scientific Officer” of PLG, Marco is coordinating all delivery and research projects (internal and on behalf of clients) linked to Big Data, Knowledge Management, Artificial Intelligence and Machine Learning.