Call for Papers – Drug Safety

Call for Papers – Drug Safety

Drug Safety invites the submission of original research articles (up to 6000 words) on the Role of Artificial Intelligence/Machine Learning in Pharmacovigilance for a themed issue of the journal to be published in 2021. The issue is guest edited by Dr. Andrew Bate (GSK) and Dr. Yuan Luo (Northwestern University). With a mix of invited review articles and original research articles, the theme issue will provide a comprehensive coverage of how emerging technologies can affect work tasks and further improve the field of pharmacovigilance.

We welcome proposals for original research articles on the following topics:

  • Methods for identifying safety signals in any or combinations of data source

Author Brief: The article will showcase novel research on how pharmacovigilance can benefit from the continuously increasing number and form of data sources, especially electronic health record data and patient self-tracked/self-collected data. In addition, the article will discuss how machine learning has helped improve identifying safety signals from these data sources for pharmacovigilance.

  • Prediction, in future numbers of adverse events or types of safety outcomes, or change in severity in RCT, PSPs, RWD or other data sources e.g. prospective surveys

Author Brief: The article will showcase novel machine learning methods for predicting outcomes such as future numbers of adverse events or types of safety events, or change in severity in randomized controlled trials (RCTs), patient support programs (PSPs), real world data (RWD) or other data sources e.g. prospective surveys. Of particular interests is how the methods will account for longitudinality and missing data from the above data sources for pharmacovigilance prediction.

  •  Visual pattern recognition in safety

Author Brief: This article will describe the novel application of visual pattern recognition to support signal detection in pharmacovigilance.

  • Developing test sets for assessing machine learning algorithmic performance

Author Brief: The article will showcase efforts for developing test datasets for assessing machine learning performance in pharmacovigilance tasks such as predicting future numbers of adverse events or types of safety events. The article will need to demonstrate rigorous annotation process e.g., inter-annotator agreement evaluation and quality control. Of particular interests is the actual release of a  dataset for pharmacovigilance prediction.

  • Developing frameworks for increasing transparency or explainability of machine learning outputs (including visualization) as well as assessing how to best implement into routine use

Author Brief: The article will showcase efforts for developing novel methods to assess and ideally increase the transparency of machine learning models in predicting pharmacovigilance outcomes either at systemic cohort level or at individual patient level. Adoption and/or implementation of such efforts in real world pharmacovigilance is a plus. Integration of multiple data sources to collate on each other and improve transparency is also a plus. Of particular interests is the open-source release of such a system.

  • Methods work in building capabilities to facilitate case processing and intake

Author Brief: Both regulators and pharmaceutical companies have very complex processes with many steps to get reports into their databases. The article will focus on robotic process automation and machine learning-based approaches to make report entry more efficient and effective.

  • Approaches for assessing quality, reliability, credibility and trust in machine learning outputs

Author Brief: The article will showcase efforts for evaluating quality and reproducibility of machine learning models in predicting pharmacovigilance outcomes either at systemic cohort level or at individual patient level. Evaluation on a large multi-institute dataset is ideal. Detailed error analysis on generalizability and reproducibility is a plus. Of particular interests is the open-source release of such a system to establish some “industrial standard”.

Please submit an abstract describing your proposed paper by 30 April 2021 to nitin.joshi@springer.com. Full papers will be invited by 30 May 2021 and manuscripts due by 31 August 2020.

Drug Safety is the official journal of the International Society of Pharmacovigilance (ISoP) and is indexed in all major databases. It is published by Springer Nature under the Adis imprint.

The future has arrived – report from the DIA PVRMS Conference 2020

The future has arrived – report from the DIA PVRMS Conference 2020

What struck me most about many of the presentations at the recent DIA Pharmacovigilance and Risk Management Strategies conference (27-29 January in Washington DC) was that the future is already here, because change is already happening. For the last 2-3 years, many sessions at PV conferences focused on the potential of game-changers such as artificial intelligence, real-world evidence, personalised medicine and social media: Can we utilise these? What role do they play? Are they effective? It seems that the time is now for such concepts to be embraced by the industry and regulators alike in the pharmacovigilance world. And all of this against the backdrop of Brexit coming to fruition. Continue reading “The future has arrived – report from the DIA PVRMS Conference 2020”

Safety strategies and efficiencies – what is the best solution?

Safety strategies and efficiencies – what is the best solution?

This was the subject of many discussions at the recent World Drug Safety Congress Americas 2019 in Philadelphia. As has been the case at many pharmacovigilance meetings in the last few years, artificial intelligence, outsourcing solutions and automation were hot topics, with debates over the future use of such strategies to achieve operational efficiency whilst strengthening patient safety. Continue reading “Safety strategies and efficiencies – what is the best solution?”

1998 Shirov: Bishop to h3! Dubbed an all-time strategic chess move… How do future pharmacovigilance strategies compare?

1998 Shirov: Bishop to h3! Dubbed an all-time strategic chess move… How do future pharmacovigilance strategies compare?

Report: Pharmacovigilance Strategy Meeting, Boston, MA, USA November 2017

In less time than it took for me to travel to the meeting from New Zealand – the day was over! Hard to believe that within a short eight hours, 25 lively roundtables were conducted, two keynote speeches were delivered, and the panel discussed the learning of the day, including challenges of monitoring diverse sources, importance of real-world evidence in the context of risk benefit, and the game-changing impact of artificial intelligence. Continue reading “1998 Shirov: Bishop to h3! Dubbed an all-time strategic chess move… How do future pharmacovigilance strategies compare?”

A brave new world: what does the future hold for pharmacovigilance?

A brave new world: what does the future hold for pharmacovigilance?

Report from the DIA Pharmacovigilance and Risk Management Strategies Conference, January 23-25, 2017, Washington, DC.

Written by Suzanne Berresford, Product Manager, Pharmacovigilance, Springer Nature

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Report author, Suzanne Berresford

The nature of medicinal products is in a rapidly shifting world, with therapeutic innovation becoming a major global trend that is here to stay. The precision medicine approach and use of targeted therapies such as biologicals, gene therapy and stem cell therapy is weaving its way into standard practice but questions still remain about monitoring the long-term safety and efficacy of such advanced therapies. The DIA Pharmacovigilance and Risk Management Strategies Conference set out to discuss some of the challenges and to provide an opportunity for pharmacovigilance professionals to share their experiences in these areas. Continue reading “A brave new world: what does the future hold for pharmacovigilance?”