Skip to main content

Policy Paper published by ICMRA on Big Data Analytics; Experts from EMA, Health Canada & MHRA to examine the opportunities and limitations of Big Data and Analytics in Pharmacovigilance.


The International Coalition of Medicines Regulatory Authorities’ (ICMRA) has released a policy paper which examines strengths and limitations of big data and analytics in pharmacovigilance.
The working group of the ICMRA comprising of subject matter experts from European Medical Agency (EMA), Health Canada and Medicines and Healthcare Product Regulatory Agency (MHRA) had developed the policy paper with a list of initiatives pertaining to the implementation of big data analytics in pharmacovigilance.

‘Big Data’ is a sub group of ICMRA working group.
One of key areas in exploring the opportunities with big data and analytics would be in the spontaneous reporting system (SRS) which currently contains limited structured / unstructured data.
Data collected through voluntary reporting, often has its own challenges with limited information available about patient’s demographics, medical history, past drug history, concomitant therapies, onset date and time of adverse event and it becomes difficult to determine the incident rate of ADR, total number of ADR occurring in the population and the patient exposure.
The members from the expert committee of ICMRA working groups have agreed to share their knowledge in identifying gaps and thus contributing regulatory harmonization.
Of the many recommendations made from Big-data sub group members to the ICMRA. Key recommendations included that all ICMRA members should be invited to share the results of research and validation studies on big data sources, along with real-world data, EHR, EMR and AHD with traditional SRS data when developed.  

Reference: 
Big Data and Pharmacovigilance: ICMRA Working Group Looks at Opportunities and Challenges

By: 
Joseph Mathew
Senior Manager- Think i
We at Think I are committed to provide cost effective & robust pharmacovigilance solutions.
Think I partners with AB Cube, France to provide SafetyEasy PV™ pharmacovigilance database which is fully evolutive and pre-validated, hosted through cloud based technology and delivered through software as a solution (SaaS) in a web based access.

Comments

Popular posts from this blog

Chat Bot- the first artificial intelligence bot in Pharmacovigilance

Merck Sharp & Dohme (MSD) Corp., a subsidiary of Merck & Co., Inc has developed an Artificial Intelligence (AI) chat bot. The MSD Salute chat bot is designed to aid physicians in providing product information and pathology.  The Physicians who are registered with MSD would use the Chat bot using facebook messenger.  Chat bot is derived from the social media and uses machine learning and implements feedback to develop interactions.  Currently MSD is monitoring and not filtering the communication between Chat bot and users, as they want to analyse the open ended exchanges between the AI and the users. Chat bot currently in the testing phase, is said augment patient safety through proactive pharmacovigilance .  We at Think I are also driven to produce Pharmacovigilance safety database solutions using cloud based technology which are not only cost effective and user friendly but also fully validated for quick, easy import, entry and electronic submissions of adve

Pharmacovigilance system- Industry perspective, Market size and Operations

Monitoring adverse drug reaction is an essential component in patient safety. In the last decade we have seen how technology has influenced the entire healthcare system, leading to patient awareness, thus making the pharmaceutical companies and regulatory authorities to invest more time and resource in monitoring adverse events. Pharmacovigilance as per WHO is defined as the science and activities relating to the detection, assessment, understanding and prevention of adverse effects or any other drug-related problem Pharmacovigilance is the study of adverse events taking into account all the plausible relationship or untoward medical occurrence which the patent experienced after taking the drug and then using standard set of coordinated activities which involves first collecting adverse event reports either via telephone, email, social network, lay press , media. After adverse event reports are filed we then process and assess these reports in database to determine if indee

Artificial Intelligence Seeing through the lens of Pharmacovigilance

Pharmacovigilance as we know is a science with a set of pre defined functions to collect, analyse, monitor adverse event reports in understanding the safety profile of drug. The set pre defined functions would include case processing through data entry of adverse event forms into safety database, medical review, aggregate reporting, signal detection, risk evaluation and mitigation strategies. With patients awareness and regulatory compliance we may have seen a surge of adverse event data over last few years , resulting in the urgent need for the application of automation. Pharmacovigilance is the only discipline where in which timelines and quality data are evaluated on a benchmark of 100 % and a compromise in these two parameters are considered to be a zero tolerance. Automation of above pre defined function is possible through machine learning, which is an integral components of Artificial Intelligence. What is Artificial Intelligence ?        Artificial intellig