A Revolution in Pharmacovigilance
Signal detection in post-market drug monitoring has changed little in 70 years, but now that shake-up is coming in the form of advanced AI technology combined with democratised access to standardised real-world data. The result will be a rapid and widespread adoption of proactive signal detection. And not just in adverse event monitoring, but also in identifying previously untapped opportunities for drug repurposing, says ArisGlobal’s Elizabeth Smalley.
There has been little advancement in the discipline of safety signal detection and analysis since the early 1960s, when the practice was first introduced in response to the high-profile thalidomide tragedy. Systematic monitoring of adverse drug reactions was one of the formal regulatory responses to the international event which affected more than 10,000 babies.
Since then, signal detection hasn’t progressed significantly, other than to reduce the reliance on paper-based reporting and replace manual case capture with electronic formats. Beyond the clinical environment, with the inherent limitations of its strict controls along and finite patient numbers, adverse event monitoring still relies heavily on patient and clinician reporting, for instance. And, yet, even today too many adverse events go unreported (up to 95% in the worst cases).
Individual Case Study Reports (ICSRs) take time to process before they are used in signal detection in any case, even once they have been captured and submitted. Analyses of medical literature, another important source for pharmacovigilance (PV) tracking, also inevitably involve a time delay, while monitoring social media and online forums usually means sifting through a great deal of misleading or irrelevant signals (noise) which can detract from any important insights.
Extracting Timely Value From Real-World Data
For safety signal detection, as an essential tool of pharmacovigilance, to satisfy the ultimate goal of keeping more patients safe, it follows that the discipline needs to be both more intelligent and more proactive.
And it is here that the latest smart analytics technology, combined with measures to standardise and democratise access to real-world data (RWD), is making this a reality now. Advanced analytics technology can filter for causal and sensitivity to substantially reduce signal ‘noise’, with 40%+ more accuracy than traditional signal detection methods (as demonstrated in extensive studies conducted by Aris Global).
These developments will enable Safety/PV professionals to distil precise adverse event insights directly using robust RWD from credible, complete sources including electronic medical records and healthcare claims, with the potential to advance drug safety and drive new efficiency for drug developers.
Reducing Time Lags, Mitigating More Risk
Moving signal detection closer to the patient promises to help address gaps and lag time in adverse event reporting, reducing marketing authorisation holder (MAH) risk. Yet the benefits will be felt right across the healthcare ecosystem – by patients, regulators and clinicians, as well as drug development companies.
Proactive, hypothesis-free signal detection along with improved signal strength is shown to reduce false positives and detection signals earlier. The incorporation of RWD, meanwhile, means signals are detected even faster and with impressive precision - the equivalent of a thermometer quantifying the progression of an illness, or a financial credit score objectively assessing an individual’s economic health/risk, enabling robust new protocols and better overall outcomes.
Putting the Right Tools in Safety Professionals’ Hands
What, then, is behind this new wave of transformation in PV/signal monitoring? In an artificial intelligence (AI) context, ‘large language models’ (next-generation neural networks) are transforming the precision with which Safety teams can distil insights from vast data sets, quickly learning and progressively honing their knowledge of what to look out for and what to discount.
The technology is so intuitive to use that Safety teams have less need for the intervention of epidemiologists or data retrieval experts, now being able to perform a deeper level of causal analysis themselves. Large language models (LLMs) are priming the pharma industry to easily embrace all kinds of AI, something that was not true even three years ago.
This is likely to drive extensive adoption of proactive signal detection now, with benefits for regulators, clinicians and patients alike. As long as there is an appropriate interface, and provided that the right data preparations (standardisation, quality control, validation) have been made so that Safety teams cannot be misled by any findings, these professionals will be able to execute their own investigations in an ad-hoc and highly repeatable way, as needed. They will no longer need recourse to data scientists, or epidemiologists, to test specific hypotheses as a first level of investigation.
Lateral links: Identifying New Opportunities for Drug Repurposing
The benefits of proactive signal detection, via AI-sharpened analysis of extensive and robust real-world data, in conjunction with ICSRs, are broader than simply faster speed and greater accuracy.
As correlations are detected earlier and with improved precision, drug developers will be in a position to spend more time on higher-value activities including innovation in drug discovery, and on delivering safer drugs to patients, sooner.
Safety-based communications will become much more targeted, meanwhile. Instead of stating generically that a drug may increase the risk of heart attack, the advice can specify that this risk applies specifically to women between the ages of 30 and 60 who have a pre-existing heart condition, for instance. This opportunity goes hand in hand with the growing focus on personalised medicine.
There is also important potential commercially as proactive signal detection on the fly becomes a reliable reality. That’s because the same mathematical models used in adverse event monitoring also support signal detection in drug repurposing. This could pave the way to unforeseen opportunities for drug developers to broaden the market for their products, as previously unknown and unexpected positive correlations are discovered between the drug and other conditions.
In the context of a benefit-risk profile, this is an opportunity to focus as much on the benefit as on the risk profile, and to grow the commercial potential of a drug. In this context, Safety has an unprecedented opportunity to shine as a strategic partner to the business, rather than merely a cost centre that exists to contain risk.
As the desire and drive to innovate rises in all aspects of drug development and delivery, next-generation signal detection is emerging as an exciting field to watch. Leading pharma organisations are already exploring the associated options today, certainly in the context of adverse event signal detection. The consequence will be that patient safety improves steadily, teams are liberated to devote more time to drug discovery, and give more of their focus to drug repurposing, ultimately leading to faster patient access to safer and better medicines.