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Rare Insights

In the US states of New Jersey and Missouri, there are unusually high concentrations of people tweeting about a niche oncology indication. Internet traffic to a rare disease-focused website suggests the UK prevalence of the condition may be quadruple traditional estimates. And in the Romanian city of Cluj-Napoca, there is 15 times more interest in a rare metabolic disease than in Bucharest, the capital – despite the latter having a population five times larger.

Pharmaceutical sponsors and contract research organisations (CROs) may not realise it, but these are the types of regional and granular city-level insights that can be gleaned from emerging, advanced online tools. These insights are not just of interest to nerdy types who like to spot patterns and anomalies in data. Rather, they point to trends in the behaviour of e-patients – people looking for health information online – that can be incredibly valuable to drug development.

These trends can be identified in all therapeutic indications. But the greatest potential lies in rare diseases, where even the most basic patient information is often either absent or hard to find.

No Easy Answers

Uncertainty in two key areas makes life particularly challenging for clinical teams. The first is around geographic locations of patients. Are any particular countries rich pools of trial participants? Are there regions where historical, environmental and other factors have led to concentrated pockets of rare disease patients? The inability to answer these questions raises fundamental doubts over where to focus recruitment efforts.

The second uncertainty relates to patient numbers. An initial literature review for a rare disease may suggest a prevalence figure somewhere between 1 per 50,000 and 1 per 250,000 of the population – a broad range that hardly helps informed decision-making. Further complicating matters, traditional epidemiology approaches, usually based on surveys of primary-care physicians, have major limitations in rare diseases. They overlook the specialist doctors who know rare conditions the best and would be best-placed to inform accurate estimates. Faced with this uncertainty, all that sponsors and CROs can do is make a best guess about target enrolment numbers.

Online Analytics

The internet can now help to locate and quantify people interested in niche indications with astonishing granularity. Online analytics provide a new layer of intelligence that is entirely driven by e-patients. These insights are particularly powerful in rare diseases, where a lack of relevant health information from traditional sources has turned patients into hyper-users of the internet. Think of them as Formula 1 drivers on the e-patient circuit whose key destinations include disease awareness websites, online communities and patient forums.

Online tools can provide a wealth of information about how e-patients interact with these communities: they can reveal the countries, regions and even cities from which visits to a website originate; they can filter out multiple visits made from the same device, providing an approximation of the number of individual visitors in each of those locations; and they can show the percentage of e-patients who return to sites repeatedly.

Furthermore, analyses can be run over different periods, revealing changes in visitor behaviour over time. For example, analytics might show that over a six-month period, 10,000 unique visitors to a website came from Dallas, Texas; 8,000 came from Chicago, Illinois; and 2,000 came from Miami, Florida.

Clearly, not all visitors to disease websites are actual patients. But because they are reading health-related information online, they are, by definition, e-patients. And when the focus is rare diseases, the names of the conditions, the symptoms and other terms used on websites are not generally part of people’s day-to-day conversations. It can be argued, then, that a large proportion of visitors to a rare disease website will have a close personal connection to the condition. They may include carers, friends and family members: all of them important routes to actual individuals with that disease.

Data, Data, Everywhere

A promising complementary approach involves aggregating and visualising data from the millions of online interactions that are happening across the internet all the time. Big data – as this exciting new field is known – is starting to gain traction in the pharma industry. It promises to be nothing short of transformational in the planning and implementation of clinical trials.

For instance, the potential utility of big data in clinical research has been shown in several therapeutic areas and geographical markets with a technique that involves identifying keywords that are highly specific to a condition, and using a detailed algorithm to visualise where people are searching for and speaking about it online.

These analyses result in condition-specific heat maps, which often reveal concentrations of e-patient interest in locations where sponsors would not logically expect them. In the case of rare diseases, clinical teams that traditionally had little or no reliable information to go on now have valuable e-patient intelligence to guide decisions, including where to locate trial centres.

Geo-Targeted Advertising


Taken together, online analytics and big data mapping can go a long way towards addressing the uncertainties inherent in rare disease research. But these insights are not just useful in informing the location of trial centres and patient numbers; they can also maximise the effectiveness of online advertisements used to drive traffic to clinical trial recruitment websites.

One robust approach is to geo-target online advertising in countries that are strategically important to a clinical programme. To give a recent example, the AKU Society – a rare disease patient group – has used Google advertising in 10 priority European countries to drive traffic to www.developakure.eu, a website dedicated to a Phase 3 clinical trial in alkaptonuria (AKU).

An alternative, more granular approach involves geo-targeted advertising in locations where e-patients are known to be particularly active. To give a hypothetical example, online tools might indicate that 40 per cent of e-patient interest in a rare disease in the French region of Ile-de-France originates from Paris, 15 per cent from Nanterre and 5 per cent from Versailles. Those numbers provide a valuable guide to the allocation of a Google advertising budget.

Digital Disruption

Three factors point to a future in which innovative online tools and insights become ingrained in the clinical development process. Firstly, cost-conscious sponsors increasingly expect to see metrics before committing resources to digital recruitment campaigns. What better way to maximise the return on investment of Twitter advertising than to place adverts in locations where most e-patients are tweeting about the condition?

Secondly, e-patients are interacting more and more with online communities, which is increasing the strength of analytics data derived from them. Before long, this will create a virtuous circle in which online communities serve both as information channels for clinical trial planners to reach patients and as platforms for better understanding people who use them.

Thirdly, our understanding of diseases at the genetic level is growing in leaps and bounds. We will soon know diseases so well – and be able to stratify genetic sub-groups of patients with such precision – that most conditions will, by definition, be niche. Online tools will play a pivotal role in reaching genetically distinct groups.

The need for new ideas in clinical trial recruitment cannot be disputed. Pharma sponsors are already appointing individuals and teams charged with disrupting the recruitment approaches of the past. Such is the pace of digital innovation that the activities outlined here will very soon shift from being isolated case studies to the stuff of the clinical development mainstream.

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Pete Chan is Chief Innovation Advocate at Tudor Reilly Health, a specialist in patient-centred experience and knowledge shared online. He is part of a team that uses advanced online tools, such as ePatient InsightsTM, to accelerate clinical trial recruitment. Pete was previously Analysis Editor at Scrip Intelligence.
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