BioTechX 2023, one of Europe’s largest conventions, that blends diagnostics, precision medicine and digital transformation in the field of healthcare, took place in the biotech hub of Basel, Switzerland.
The event, which was held from October 4 to 6, hosted expert panelists who shared their knowledge on the digital transformation in pharma, leveraging artificial intelligence, and also touched upon what the future holds for augmented intelligence in healthcare.
In collaboration with our parent company, life science technology solutions provider Inpart, here are the highlights from BioTechX Europe 2023.
Table of contents
The next generation of customer engagement
Making the right choices in the biopharma industry is essential, and not only from the business perspective. The wrong choices at any stage of the discovery, development, and commercialization process will have major impacts on patients and their health outcomes. A delay of a handful of days might truly be the difference between life and death, thus the value of correct and confident decisions is real and significant. For Ingeborg Borgheim, senior vice president and head of Data, Digital and Technology Europe and Canada for Takeda, the best way to make those decisions is to let data lead the way.
To meet the expectations of customers and patients alike, Borgheim argued that you must adopt a data- strategy. Along with attention to charge management within the pharma organization and a commitment to getting the basics of the business right, this data-driven decision-making can help transform a pharma company from Pharma 1.0 to Pharma 2.0 and beyond. Borgheim sees this transformation as essential, explaining that the pharma industry needs to transform to meet the high expectations it has developed, to create new experiences for its patients, to better serve health care professionals, and to deliver for its own people too.
Borgheim offered her own company’s transformation journey as an example of what could be achieved. She said that Takeda is in the midst of a “digitally enabled organizational transformation” and that, in spite of what some might believe, it is not the technology that is a hurdle for a company like Takeda to overcome. “Tech is not a problem,” she said, noting that the technological tools that are required to transform the enterprise already exist. Instead, the problems that are encountered in a digital transformation are more likely to be related either to people, structures, or legacy processes and expectations.
These being the common roadblocks, Borgheim explained the steps Takeda has taken to mitigate each. First, there has been an emphasis on strategic clarity from the top of the organization down. Senior leaders need to understand the reasons for and the means to effect change, and to drive this change in their divisions. Second, the organization needs to commit to educating its employees and stakeholders. “People need to understand what is in it for them,” said Borgheim, because when they do, the changes are easier to implement. Third, the digital transformation team needs to showcase early wins in the transformation process. Borgheim offered Takeda’s myARwin project as an example of a digital win, with the augmented reality guidance for butterfly needle self-infusion rolled out in four countries and eight languages helping patients manage their own healthcare in the home. Celebrating this ‘win’ helps stakeholders understand the benefits that will flow from the transformation.
Fourthly and finally, Borgheim posited the importance of aligning expectations in the organization around the expected return on investment (ROI). According to her, digital transformation is about changing the entire pharma business model and the ROI must be understood in this long-term context.
Unlock business value through data
Big numbers have a way of pulling focus at a biopharma event, and so, when Loic Giraud, 14-year veteran from Novartis, and today, the chief operating officer at Calibo, explained that enterprise had invested $240 billion in data and analytics platforms in 2022, he had the audience’s attention. When he went on to note that this is expected to expand to a $655 billion spend in 2029, but that the CEOs spending the money expected little to no actual outcome from the spend, many were keen to understand why so much would be invested on what seemed like a fool’s errand.
Giraud responded to the question in a couple of different ways. First, simply transforming a business into something more digital and more technological is not enough to guarantee a positive impact. “Everyone expects experiences to be technological today,” he said. So, when you transform your pharma company to adopt digital tools, it is by no means a guarantee of success. Second, Giraud questions the very utility of a ‘digital transformation’ concept at all. He explained that a transformation suggests that there is an endpoint – a point in time where your company has fully ‘transformed’. Yet, for Giraud, there is no end point and so he urges companies not to ‘digitally transform’ but to just go ‘digital.’ “Digital is here, and you need to adopt it,” he said, and do so across all areas of the business.
For the life science industry, Giraud sees four areas of the enterprise as prime for advancing digitally: marketing, manufacturing and supply chain, talent recruitment and organization, and finance and enterprise. No matter the area, though, the approach to going digital should begin with a three-pronged strategy that is equal parts customer-centric (focused on the customer, not the company), agile (capable of reacting to disruptions and new information), and productized (the digital tools are not one-offs but are instead developed like any other product).
He also urged the adoption of digital platforms and technological capabilities as enablers and accelerators of change. These included augmented and virtual reality, blockchain, mobile, cloud, and SSOT, all of which offer little in terms of short-term return on investment, but create value in the long term, and make the process of digitalization easier once in place.
Giraud also joined other speakers at the 2023 BiotechX Europe event by urging the audience to eschew pilot programs and limited trials. He argued that if a sponsor could not commit to anything more than a pilot program or a limited deployment of a technology, then the problem is unlikely to be truly addressed and the pilot will rarely be expanded beyond its original minimal scope. In Giraud’s words, when faced with a problem that has a digital solution, “Find a sponsor that can identify the problem to be solved, the product to solve it, and then plan to deliver value instead of ‘just’ a pilot program.”
In a world which is increasingly complex, fragmented, and frustrating for leaders, having the confidence to choose a broad and deeply integrated solution and deploy it at scale, will help move a company forward in a way that dozens of pilot programs never will.
Examples of digital transformation in pharma
What sort of cost reductions can be expected when a mid-size pharma company undertakes a digital transformation? For Swiss pharmaceutical company Debiopharm, digitalization has helped them to improve efficiency to the point where they are tracking 20% cost savings in some departments. What’s more, digital technology has helped to lower patient burdens by demanding fewer patients for clinical trials while helping to accelerate clinical development timelines, too. With ‘digital leaders’ in pharma expected to see earnings growth 1.8 times that of ‘digital laggards’ in the industry, Debiopharm principal scientist Anna Pokorska-Bocci sees plenty of bright sides in embracing the digital revolution in the life sciences.
Pokorska-Bocci presented a number of ways in which digital technologies have helped Debiopharm to transform their business. For example, in clinical trials, the adoption and expansion of wearable devices, the expansion of the use of digital biomarkers, and tools like eConsent and the eDiary by patients have all improved efficacy and accelerated timelines in trials. In preclinical research, too, the adoption of digital models has reduced the need to rely on animal testing in laboratories. And, in the CMC (Chemistry, Manufacturing, and Controls) division, there have been significant advances in using digital tools to optimize supply chains and make them more resilient to disruptions. More broadly, supply and operation planning at Debiopharm has embraced digital tools, and benefits have flowed to the organization as logistics, manufacturing, and operations all run more efficiently and effectively.
Pokorska-Bocci explained that a major part of Debiopharm’s digital strategy is working with external partners with digital expertise, often investing in or outright acquiring the smaller, innovative digital firms that add value across the drug development process. She pointed, among others, to partners such as Iktos, a company offering productivity gains via AI-driven new drug design, the biomarker discovery platform Genialis, and Nova, a company that builds mathematical models of disease and treatment for optimized trial design.
Significant in the Debiopharm digitalization strategy is the use of strategic investments in these external innovators. Pokorska-Bocci explained that Debiopharm remains a mid-sized pharma company of only a couple of hundred employees. The company does not do any new drug discovery; instead, they focus on finding promising compounds for partnering or licensing, driving development and clinical trials, and then partnering with top pharma to take the drug to market. In a similar way, they don’t seek to develop internal expertise in digital technologies, or to develop digital innovations when such innovations are more likely to be found outside of the company. Debiopharm leverages their investment arm to acquire such firms to help them develop their technologies further, and draw transformative benefits from a symbiotic business relationship without ever deploying funds on internal R&D. This approach to digital transformation works for Debiopharm, and, based on the active Q&A session following her presentation, has captured the interest of others in the industry, too.
How data centric approaches are transforming the life sciences industry
A core theme at BiotechX Europe in 2023, was the digital transformation of the life science industry, its implications for the industry, and the best means by which to drive that transformation forward. In a panel chaired by Bérénice Wulbrecht of Ontoforce, and including representatives from some of the sector’s largest companies, the centricity of data to this digital transformation was considered both as a concept and in practical terms.
Wulbrecht opened the panel session by asking simply why data centricity is important in the life sciences. Ashwini Sondur, head of Enterprise Data and Global Informatics at Roche, responded by suggesting that data centricity is the approach for better outcomes. The amount of data that the industry produces is enormous, she said, explaining that 30% of all data generated today is from the healthcare sector, with this share expected to rise to 36% in the next two years. In this context, there can be no question that data must be central to decision-making in the life sciences. Philippe Marc, global head of Integrated Data Sciences at Novartis, concurred, adding that the pharmaceutical industry has been “data-driven since forever” anyway. Sam Khalil, head of Data Insights at Novo Nordisk, noted that data centricity is not really up for debate, but how this data is harnessed is. “We need to be constantly adding to the patient value,” he said, aiming for a model that is not only data-centric but patient-centric.
Areas of focus and key actions to take in order to become a data centric organization were shared by the panelists. Khalil suggested that there should be two main areas of focus, namely data governance and the internal organization of the company so as to collect, store, and access that data correctly. Gernot Weber, head of Data Strategy and Digital Innovation at Merck KGaA, agreed that the organization and its foundations needed to be strong, even if such work is not “sexy.” He noted that there were two significant threats to a data-centric approach – internal culture, and internal politics – and both needed to be addressed to ensure that data centricity is achieved and maintained.
In closing the panel, Wulbrecht asked the participants to consider the impact of Generative Artificial Intelligence (GenAI) on the life sciences, and she quizzed the panelists on where its impact might be felt. Khalil noted that anything around document production, whether medical writing or eventually even new drug applications, is likely to be impacted by GenAI tools. Weber agreed but suggested that the real value is going to emerge in the non-obvious use cases. “Writing is obvious,” he said, “but coming up with the concept for a protocol is less obvious.” Marc added that the value of GenAI for the industry will lie in the acceleration of writing and increased productivity. “This pays off for the patient in the end,” he said, “as it gets the therapies to market faster.”
End to end supply chain design and optimization using digital twins
In discussions of the life sciences, there is a consistent focus on the discovery and development of new drugs, therapies, and medical devices. What is less central to discussions – but no less important – are the logistics of manufacturing and delivering those drugs, therapies, and devices to patients. The life science supply chain is highly complex, stringently regulated, and subject to the same disruptions as any other global manufacturing and logistics chain. Optimizing life science supply chains, then, is an important task for any company in the industry, and it is no surprise that top pharma companies have invested heavily in tools to help them achieve just that.
At BiotechX Europe in October 2023, Saurabh Verma, director and capability leader for Digital Value Chain at Johnson & Johnson, explained how his company had leveraged digital twin technology to optimize their supply chain both for existing products and future products. He explained that, unlike supply chains in other industries, there are some constraints on the life science supply chain optimization process that his peers in other industries don’t face. For example, some countries will not accept drugs manufactured in certain other countries even if those drugs meet all quality standards. There are also regional regulations around packaging that mean certain plants cannot serve certain markets without significant retooling of their production lines.
Verma compared the life science supply chain to finding directions using a Google Map. Like the online mapping software, his team was able to construct a representative model of the company supply chain using software they called the Janssen Supply Chain Map. To this static representation of the supply chain, they added another ‘live’ layer called Options Modeling, the equivalent of using a real-time mapping software like Waze to find directions where real-time information is included in the mix. With the Janssen Supply Chain Map and Options Modeling working together, Verma’s team was able to run simulations and determine the optimal supply chain strategy, align different stakeholders on that strategy, and regularly revisit that strategy to ensure it remains optimal in the wake of new information in the face of new disruptions.
Verma confirmed that, for the moment, the digital twin that has been deployed at his company is capable of predictive analytics but has no prescriptive capacity. This means that while his team can simulate unlimited supply chain scenarios and forecast the likely future under a set of dynamic conditions, the software is not yet capable of prescribing the optimal course of action under those constraints. The supply chain team can select the best strategy from the simulated strategies, but it is not clear whether this is truly the most optimal approach. Still, this technological constraint aside, Verma is convinced that digital twin technology is having and will continue to have a real and quantifiable impact on supply chain planning and operations in the life science industry. Little wonder, then, that his company has eschewed outsourcing this capacity, and instead, invested in developing this digital twin capacity in-house.
Opportunities and challenges of augmented intelligence
Many recent discussions about applications of digital technologies in the life sciences have revolved around the best ways to deploy artificial intelligence (AI) and machine learning in a field that, for centuries and until relatively recently, relied on human intelligence and intuition to advance. AI, including Generative AI (GenAI) tools such as ChatGPT, has been widely adopted by the industry across the entire enterprise, whether in early discovery efforts, in the development process, in manufacturing and logistics optimization, or even in commercialization of new and existing therapies. It is now barely newsworthy to learn that a top pharma company applied some level of AI technology at some point in the development of a new therapy.
Augmented intelligence, however, is less discussed but, according to Nils Weskamp, associate director of Computational Chemistry and Data Science at Boehringer Ingelheim, it is worthy of greater attention. He defines augmented intelligence as an AI approach that does not seek to displace human intelligence, but to add to it. There is a need to integrate expert knowledge into AI approaches, he argues, and avoid seeking to replace the human altogether.
In his presentation at BiotechX Europe 2023, Weskamp showed how the augmented intelligence approach could prove useful for screening compounds in the chemical space. He identified 151 clinical compounds as well as the original molecules from which they were developed. He then set a basic algorithm to work to determine if his augmented intelligence approach could identify the final clinical compound from only the details of the original molecule. His goal was to determine whether this approach could replicate what had been done in the lab, but more rapidly and cost-efficiently.
The results, overall, were positive. Of the 151 clinical compounds, Weskamp’s approach correctly identified 101. Expanded slightly to include close analogs and then all analogs, the augmented intelligence approach returned 105 and 135 of the compounds, respectively. Interestingly, while the augmented intelligence algorithm was able to identify some of the most complex of the clinical compounds, it missed at least one that Weskamp described as “an incredibly trivial evolution” between the candidate molecule and final molecule. Weskamp added that this was not a ‘bug’ in the algorithm and that the augmented intelligence approach had worked exactly as designed. However, for reasons unknown, and perhaps related to the trivial difference between the candidate molecule and its clinical candidate, “the optimization algorithm missed it altogether.”
Weskamp sees both opportunities and challenges ahead for augmented intelligence in the life sciences. Clearly it seems possible to develop an algorithm encompassing both artificial intelligence and human expertise to aid in the discovery process, and to do so at a success rate that begins to mirror in minutes what takes months or even years of lab work. However, that same algorithm can be blind to even trivial changes in molecular design, perhaps a sign that augmented intelligence, like broader AI, still has some development of its own to endure.
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