Improving the human element in drug development

From global economic concerns and escalating trade tensions to the ‘closed border mentality’ being adopted by some nation states, as we enter the new decade, there’s a real sense that the era of fervent globalisation and the pursuit of ever-more frictionless trade may now be at an end.

Whatever happens next, however, it’s clear that digitisation has forever changed the face of business. Industries have been overhauled, disrupted and in some cases entirely dismantled by scores of agile, digitally-driven companies intent on doing things with greater speed, convenience and cost-effectiveness.

That said, not everything has become easier as a result of the changes we’ve witnessed in the past ten years. Drug development is a good example of this. Entire industries still come and go in the time it takes to develop a single new drug, and the average cost now stands at an astonishing $2.6 billion, a figure that has risen 145% during this decade of digital transformation.

As our societies have evolved, our regulatory and safety standards have become more stringent (though not necessarily more uniform from one market to the next). And, despite the growth of cloud connectivity, big data and online trading platforms, pharmaceutical companies are still largely reliant on human-led processes and human decision-making to build efficient, multi-market drug development supply chains.

A deeper understanding

Drug development is a vastly complex, scientific process. It can take generations to get a product to market. Being able to access the deepest possible scientific, clinical, and technical understanding at every stage of the process is essential. This is perhaps best evidenced in the world of clinical trials.  It’s incredibly difficult for organisations to access the high precision niche insights needed to inform the strategic direction of these trials, which explains why a reduction in the time it takes to bring a drug through trials has been accompanied by a reduction in the success rate, down to just 12%.

On a day-to-day basis too many of these organisations are being hindered by the lack of scientific expertise they encounter throughout the supply chains they rely upon, leading to entire programmes being abandoned, patients being turned down for trial treatments, regulatory breaches and fines and, ultimately, an erosion of market value.

In short, pharmaceutical companies must find new ways to boost their success rate and bring down costs. Whilst machine learning may provide some of the answers, by finding new insights in large health data sets (and huge sums of money have been poured into the healthtech sector to this end), for the foreseeable future drug development is going to remain highly reliant on accessing the very best human expertise.

The limitations of traditional market research

The problem with accessing specialist expertise is two-fold: firstly, what sort of expertise is required, and secondly, how can a pharmaceutical company go about finding the real experts on the subject?

Human-led market research in this area is deeply problematic – there is far too much information on the web for a human to process everything they need to in a timely manner, and that’s assuming they’re able to find the information in the first place. Hence, historically companies have relied upon tapping into existing medical research panel vendor networks whenever they’ve needed an external source of expertise. They submit a brief to the network and the network responds by putting forward the most relevant expert on its books.

The limitations of this approach are clear. The system is closed-loop, reliant on putting forward the experts already in the network rather than the very best experts for the task at hand – a case of who you know rather than who you need. Furthermore, the world of life sciences is constantly changing. Some local experts have access to new insights or groundbreaking research that others elsewhere in the world are yet to hear about. Some experts are simply more knowledgeable than others, or at least, their scientific knowledge is more up-to-the-minute than others.  

Using technology to empower human decision-making

If they want to improve their drug development success rates, pharmaceutical companies need to break from the hegemony of this outdated network model to unlock, unearth and uncover the human expertise that supports and empowers better business decision-making.

To support them in this quest, new technologies – from ourselves and other providers – are emerging to improve the exchange of knowledge between the leading global sources of expertise and the pharmaceutical industry. By using AI-powered deep search technology it’s possible to analyse billions of global online data points to pinpoint the most qualified and relevant expert on a given subject or domain, no matter how niche – and to achieve this within days rather than weeks. Most importantly, this can be done in real time, giving businesses confidence that they’re being connected to the leading experts of today, not yesterday.

To demonstrate how this works in practice, a pharmaceutical company we worked with recently needed to connect with US-based key opinion leaders with clinical experience evaluating FGFR3 (fibroblast growth factor receptor 3) inhibitors and with a background in treating head and neck cancer. The organisation was interested in understanding how FGFR3 inhibitors can be used as targeted therapy for cancer treatment. It also had an interest in engaging with decision-maker radio-oncologists involved in therapeutic radiopharma trials. By looking beyond the traditional expert networks and taking a tech-led approach to expert sourcing, the company was able to gain access to five of the world’s most relevant experts in the field within just days, without any need for laborious manual research.

Learning from the best to achieve better development outcomes

Right now the biggest and brightest pharmaceutical companies are facing increasing pressure and scrutiny, not to mention growing criticism both for their prices and their perceived reliance on publicly funded research. The onus is on them to find more effective ways of bringing new drugs to market, despite a changing macroeconomic environment that is likely to make managing their global supply chains increasingly complex. While there’s no doubt that AI-powered tech solutions can play an increasingly influential role in shaping the future direction of drug development, right now, rather than betting patients’ lives on technologies that are still in their infancy, the best use of AI is to empower the humans within the pharmaceutical industry to make the smartest decisions.

Graham Mills is co-founder and managing director of techspert.io, the first AI-driven technology for mapping the world's expertise and connecting businesses directly to the source of technical and market insights.