The future of pharmacovigilance and the impact of automation

Pharmacovigilance (PV), the process of identifying, tracking, evaluating and preventing negative outcomes from drug therapies, is a sector that has seen huge growth in recent years. David J. Balderson, Vice President, Global Safety Operations, Sciformix, a Covance company, reports

Although PV starts during clinical development, it is not limited to clinical trials alone as post-market surveillance is crucial to monitoring a drug’s safety after it has been approved.

There are a range of challenges that the pharmaceutical industry faces when establishing and maintaining increasingly complex PV systems. The evolving regulatory environment in a progressively global industry places ever-more stringent demands on pharmaceutical companies to manage PV activities more efficiently than before.

Technological advances are playing a major role in pharmaceutical PV strategy updates. More companies are looking towards automation in the form of cloud-based solutions, mobile applications, robotic automation, artificial intelligence (AI) and big data analytics as a vital part of clinical, safety and regulatory operations in the pharmaceutical industry.

Optimising efficiency

As one of the fastest growing life science disciplines, PV strategies must be optimised for peak efficiency. A well-established principal information technology (IT) framework provides organisations with high performance.

AI has the potential to fill the gaps that traditional PV services currently leave, such as the ability to assimilate large volumes of cloud-based data and map patterns, to effectively predict adverse drug reactions (ADRs). Integrated IT solutions that combine scientific and technological expertise are capable of delivering high operational efficiency, quality and regulatory compliance.1

Robotic automation, cognitive computing and AI technologies will allow organisations to reduce the manual effort and cost of safety case processing, meaning resources can be redirected to the proactive identification, evaluation and minimisation of risks. Cloud-based solutions and big data analytics technology are helping companies to achieve end-to-end automation across the storage resource management (SRM) continuum while adhering to regulatory guidelines.

Regulatory responses to industry change

Ever-increasing volumes of drug data place an urgent need on the development and implementation of technology that’s capable of providing a secure, integrated big data repository. For example, all adverse events, regardless of their degree of severity and source, should be stored in a single drug safety database.

Cloud-based capture and reporting is a key trend in the PV space and is now being used to bring a fully integrated database to all stakeholders. Cloud-based systems can also reduce latency between reporting and analysis, leading to more timely and qualitatively improved regulatory decision making surrounding crucial public health issues.2

David Balderson

The European Medicines Agency (EMA) is in the process of implementing the standards developed by the International Organization for Standardization (ISO) for the identification of medicinal products (IDMP), in a phased programme.3 The overall aim is to simplify the exchange of information between stakeholders and improve international system interoperability … and is expected to benefit PV processes.

Automation strategy implementation

Higher levels of automation, such as robot process automation (RPA), cognitive automation or AI, enable organisations to identify patterns in unstructured data — and can automate the whole process, from case receipt to reporting. Implementing an automation strategy can not only reduce costs, but also eliminate the chance of human error, thereby improving the quality and accuracy of safety data processing.

Three main areas within the realm of safety that can be transformed through appropriate and effective use of technology are as follows:

Standardisation and automation of PV processes and safety data management: The integration of safety data by applying appropriate data and system interoperability standards, implementing best practices and technological models ensures the transparency and accessibility of safety information.

Some technology solutions are already available, including a cloud-based call centre and medical information solutions that, when coupled with a range of automation tools for downstream PV processing, can result in efficiencies along the SRM continuum.

Proactive PV and risk minimisation: Implementing data mining techniques is an important way to identify and predict emerging safety signals. Detecting the majority of safety signals is challenging and can require analysis across a number of data sets, which is time-consuming and labour intensive.

Open and transparent data sharing: Companies are required to share their data with regulators, prescribers and patients, to protect patients and to build public trust and confidence. Work flow management technology can be applied to PV to identify and distribute information to stakeholders in line with a predefined set of rules.

Adverse event (AE) data can be automatically extracted and processed with advanced cognitive solutions, with real-time views of safety issues enabled by AI. EudraVigilance governs the degree of access stakeholder groups have to ADR reports, and one of the deliverables from the WEB-RADR initiative is a mobile application that allows patients to directly report potential side-effects.4

The changing PV landscape is seeing regulatory authorities using more sophisticated tools to collect, characterise and evaluate data on AEs, so pharmaceutical organisations can implement successful PV programmes and more efficiently manage the safety of drugs.

A growing population, more unique and highly specialised therapies for unmet medical needs and a rising number of pharmaceutical organisations are drivers of the technology revolution that the industry is currently experiencing.

Automation is vital if the costs and complexities of clinical trials are to be minimised … and the collaboration between stakeholders for real-time decision making is to be improved.

References

  1. G. Coward and G. Birchnall, “The Changing Face of Post-Marketing Research,” PharmaTimes (2018): http://tiny.cc/f4957y.
  2. Informa Business Intelligence Inc., “Addressing the Data Challenges of Pharmacovigilance,” www.oracle.com/us/industries/health-sciences/address-data-challenges-pharma-wp-5018953.pdf (2018).
  3. European Medicines Agency (EMA), “Data on Medicines (ISO IDMP Standards): Overview,” www.ema.europa.eu/en/human-regulatory/overview/data-medicines-iso-idmp-standards-overview.
  4. PwC’s Healthcare Research Institute, “Unlocking the Power of Pharmacovigilance: An Adaptive Approach to an Evolving Drug Safety Environment,” www.pwc.com/th/en/publications/assets/pharmacovigilance_final.pdf (2006).