Role of AI in drug commercialisation

The role of AI in the biopharmaceutical sector to date has focused mostly on optimising and accelerating R&D processes and on innovating clinical trials. Those activities belong to the pre-commercialisation stage of bringing a new therapy to market, and have historically absorbed the lion’s share of investment and attention in the industry. But with companies increasingly looking to AI to also support their commercialisation efforts, a new horizon is sliding into view: realising the technology’s potential to revolutionise biopharma’s marketing and sales processes.

Taking an asset to market is the culmination of what is usually a multi-year and multi-billion dollar/euro investment. The granting of a marketing authorisation, however, is not an assurance of success and often many challenges must be addressed to achieve a successful launch. One key factor that is critical to the success of a new medicine  is having robust data  and insights on which to base your launch strategy and make investment decisions. This could be information about unmet patient needs, treatment paradigms, prescription trends, influence maps in companies’ top accounts, share of scientific communication by segment, KOLs, digital opinion leaders (DOLs), and healthcare providers. In short, access to data is an opportunity for companies to gain competitive advantage  before, during, and after product launch, and if there is one characteristic that neatly sums up AI capabilities, it is “expresswaying” access to and interpretation of large volumes of data.

The implication for the industry? Thanks to AI-enabled data analytics, medical affairs, sales, and marketing teams can gain deep commercial insights that not only measure the impact of existing market access and adoption strategies, but also serve as a guide on how to improve them by collecting, interlinking, and pinpointing the most relevant data and messaging approaches for each target stakeholder group. In particular, by gaining a deeper understanding of their KOLs and relationship networks in real time, medical affairs teams can craft highly tailored scientific communications, and by harnessing the power and speed of AI in the pre-launch phase, improve their understanding of a new therapy area at scale very quickly.

Use cases

One of the most intuitive applications of AI in drug commercialisation is enabling high-precision profiling of HCPs, so that marketers can design customised outreach strategies instead of relying on a traditional one-size-fits-almost-all approach. AI technologies make this possible by micro-segmenting key stakeholder groups not only across therapy area, publishing output, or visibility – the traditional approach taken in audience segmentation. They also do so according to size of individual physicians’ professional networks and the degree centrality they occupy in them, communications preferences, availability, and location. Further, AI can search beyond companies’ existing customer data sets and unearth new cohorts of practitioners who may be open to considering novel treatments for some of their patients. The results can be applied to optimise engagement strategies, connect with would-be partners via their preferred communication channels, and ultimately increase market adoption rates.

In the same way that AI technologies can tap into public online databases and extract KOL and HCP-level data at a fraction of the time it would take a person to do it, they can garner patient-level data. For example, an AI algorithm trained to process natural language in the medical domain can harness data found in public forums or social media channels where patients share personal experiences of using a particular therapy or a drug. Those comments can then be cross-linked with physician-level observations shared in a similar social context, yielding multi-perspective insights from openly shared real-world evidence (RWE). Having such data enriches pharma companies’ understanding and monitoring of product performance across indication, region, physician specialty, type of patient (e.g. GP practice, hospital, specialty clinic), or type of payer (public vs. private). Lastly, AI-enabled social listening can also be leveraged to detect patterns in prescribing behaviour and to assess prevailing public sentiment towards new or existing medications, which are essential tools for running brand diagnostics.

On the regulatory and compliance front, AI can accelerate drug commercialisation by reducing the time and human resources that are invested into compliance review, allowing the latter to be shifted towards more demanding cognitive tasks that cannot be automated. AI-powered analyses can also help leaders decide how to deploy  sales teams  across a geographic region or therapy area for greatest return on investment, using insights from the micro-segmentation and social listening applications described above to determine what type of content and communications channel would be most impactful for each.

Last but not least, data-driven AI technologies can support drug commercialisation in the post-marketing or pharmacovigilance stage by compiling publicly shared data on undesirable side effects in real time. This can help companies conduct causality assessment on an ongoing basis and react faster to minimise damage to both patients’ health and the health of their own brand image.

Adoption challenges in Europe

Despite the competitive edge promised by embedding AI and machine learning-powered technologies into pharma´s commercial operations, the EU biopharma industry has been slow to adopt such technologies , according to a report by IQVIA published in 2018. Although the survey cited dates back to 2018 and company readiness to embrace AI has likely improved since then, it is an “open secret” in the industry that in comparison to the U.S. and some Asian countries, Europe is not moving as fast as it should and could.

How PeakData can help

PeakData’s Healthscape software can support organisations that are ready to deploy AI for drug commercialisation. It comprises a set of tools that perform real-time web wide market intelligence  based on publicly available, decision-shaping conversations about drug uses and indications, real-world outcomes, and patient and provider-generated insights across social media, peer-reviewed publications, preprint repositories, press releases, conference agendas, and other formal and informal sources. This comprehensive search is complemented by rigorous and nuanced analysis of the findings, according to client-defined variables and characteristics, to obtain a panoramic view of all the open data and social signals generated around a drug that is about to go to market or is already being commercialised. As an added value, Healthscape’s capabilities are governed by a clear strategy for sourcing the data such complex AI searches require.

If you are interested in learning more about Healthscape and how it can make AI a reality in your company´s marketing and sales strategies, we invite you to schedule a demo.

Building new capabilities into Medical Affairs

Over the past few years, physicians and other KOLs have been increasingly mindful of the time they spend with pharma sales representatives. Many of them have been reducing this time investment in favour of engaging with more scientifically knowledgeable MSL’s (Medical Science Liaisons) that can double as educators and help them stay abreast of new therapeutic products and developments. This shift in attitudes has thrust medical affairs teams into the spotlight and is transforming them into one of the pharma industry´s most prized assets.

The rise to stardom of the MA function corresponds with an ever-growing attention being paid by HCPs and payors to patient outcomes and other aspects of value-based healthcare that MA professionals are uniquely qualified to address. At the same time, they are highly attuned to the constantly evolving regulatory environment and its implications, especially as concerns transparency and compliance. This understanding of the regulatory landscape, combined with their deep product knowledge and sensitivity to the demands of value-based pricing and contracting, empower them to have meaningful exchanges with HCP’s in ways no other business function can. Yet, in many pharma companies this powerhouse of ingenuity is held back, and medical science liaison (MSL) staff are still regarded as mere product advisers.

Indeed, research conducted by Bain´s Healthcare practice as far back as 2017 highlighted this organisational blindspot when it found that one of the main reasons 50% of new drug launches fail is that companies do not adapt fast enough to new information channels used by physicians. The report foresaw three upgraded roles for MA teams that address this and other challenges: leading the communication of scientific evidence, feeding stakeholder insights into all stages of the R&D process and not just during post-launch assessment, and overseeing initiatives to produce outcomes research and real-world evidence. 

The rationale for concentrating this triple leadership role into the hands of MA teams is that they can authoritatively explain how a new product or indication fits into clinical practice at the same time as give evidence on how it impacts patient outcomes – the latter being the holy grail of any biopharmaceutical offering. With patients themselves having become important stakeholders in healthcare, conveying patient-reported outcomes or unmet medical need to physicians in the language of real-world evidence both helps advance the practice of medicine and builds trust in the industry as a good-faith actor. And with more than four-fifths of physicians both in Europe and in the U.S. citing RWE as their key criterion for prescribing drugs, according to the Bain findings, it is clear why professionals versed in outcomes research are the right messenger. 

Further, as clinicians begin using new treatments and sharing their observed impact on patients, MSLs are uniquely positioned to bring those insights back to the lab to drive improvements to next-generation drug development. Thus, they can effectively become the glue that binds together the expertise of physicians, the needs of patients, and the drive for innovation in the industry. 

But while recognizing the vast potential of MA teams is long overdue, laying all these responsibilities on their shoulders without additional support would be unrealistic. The creation of value for pharma companies, KOLs, and patients they can realise requires the untapping of deep, sometimes simultaneous, insights from all healthcare stakeholders and painting them together into a coherent knowledge map. Finding these insights in turn depends on the mining and exploration of data spread across a multitude of official and unofficial web sources and social media platforms. It is a Herculean task that, to be completed efficiently, exceeds the capacity of the human brain if it tries to “go it alone.” 

That´s why we created Healthscape, an AI-powered data mining and analytics platform designed to lend a virtual hand to your MA team and be its partner in navigating the stormy waters of data. It can uncover and transform into practical information all types of structured and unstructured data, including those related to RWE, KOL insights, social listening, and even business intelligence. So send us an email or give us a call and let us show you how Healthscape can equip your company with all it needs to keep an edge on the competition.

KOLs vs LOLs: How social network analysis can help identify bona fide opinion leaders

Tried-and-true methods of identifying and managing KOLs do not always yield optimal results. They tend to lean toward high-profile experts who are “top of mind” for the professionals entrusted by pharma and biotech companies with spotting and engaging them, and the hidden risk in this is that some of those prominent KOLs are in fact LOLs – “loud” but unimpactful opinion leaders in the clinical research ecosystem who produce more noise than signal.

The traditional approaches to KOL identification, which rely primarily on observation, surveys methods, and literature searches based on standard publishing metrics, suffer from observer and response biases and are far from unearthing the most effective potential collaborators, while at the same time overlooking social information that could turn up powerful insights. Because they tend to prize quantity – in the form of peer-reviewed journal articles, conference appearances, clinical trials management, advisory board participation, or other quantifiable scientific activity – over quality, they risk failing to distinguish between purely prolific producers of output and truly influential advocates whose work has meaningful repercussions beyond publications and the echo chamber of conference halls.

Indeed, research has shown that relying on bibliographic searches or considering only the number of publications or conferences attended as indicators of thought leadership fails to spot more than 50% of influential actors compared to social network analysis (SNA) – and this is research dating back to 2010, when SNA did not have the capabilities it has today. (The study, which questioned the dominant KOL identification methodology even then, can be consulted here.)

The “secret” to identifying KOLs using SNA is that this approach, which makes explicit the webs of relationships between thought leaders and derives insights from their centrality and connectedness to others in the network, allows for selection of criteria that are not easily mappable using traditional methods. Such criteria may include identifying KOLs with more recent publications rather than with a greater number of publications, with recency being assigned higher value than sheer volume, thus potentially reducing the impact of age or career length on KOL selection; the frequency of collaboration within a network of experts, which can be interpreted as evidence of authors´ preferred working relationships; or the social reverberation of publications, keynotes or other appearances related to a given therapeutic area, measured as the sum of substantive comments and engagements on social media.  

An example of a KOL identified through SNA who may not necessarily have been captured using a literature search would be an author of a publication that is well received on social media and commented by Twitter or LinkedIn members as having an impact on clinical practice or real-world outcomes research. By contrast, an example of a LOL identified through conventional bibliographic search would be a highly visible, prolifically published expert whose work falls short of meaningfully impacting clinical practice, physicians´ prescribing practices, or community conversations.

Of course, using SNA for KOL identification should be done discerningly because it has its own dark side: the presence of hyperactive social media commentators and self-proclaimed experts who “talk the talk” and have all the bearings of – and perhaps even the networks typically associated with – KOLs, but whose engagement with and impact on others remains at the level of metaphorical back-patting without translating into concrete actions (e.g. evangelizing new research ideas, driving market adoption of a new drug or treatment).

In the era of AI and Big Data, it is paramount for pharmaceutical and biotech companies of all sizes to get smarter about their KOL strategies and upgrade their tactics. If you have already leveraged SNA in your practice or are considering doing so after reading this blog, we invite you to share your experience in the comments or contact us directly with feedback. Here at PeakData we are always interested in learning how we can best help our clients and are constantly tweaking, fine-tuning, and adjusting our methodologies so we can be of service to you.