Digital Transformation

Implementing an End-to-End Strategy for the Digitalization of the Pharmaceutical Value Chain

  • 3D visualization is key to a deeper understanding of binding events between drug molecules and their protein targets. © Bayer3D visualization is key to a deeper understanding of binding events between drug molecules and their protein targets. © Bayer
  • 3D visualization is key to a deeper understanding of binding events between drug molecules and their protein targets. © Bayer
  • Cell line development workstation in the Cell and Protein Sciences unit at the Bayer Pharma Research Center in Wuppertal, Germany. © Bayer
  • Philip Boehme, Bayer
  • Hubert Truebel, Bayer
  • Ted Castellon, Bayer
  • Ronenn Roubenoff, Novartis

The pharmaceutical industry has gone through two decades of rapid technological innovations. These include combinatorial chemistry, recombinant DNA technology, development of knockout animal models & high throughput screening, and lately the rise of the genome area. But none of these innovations will have come close to what lies ahead with the rise of the digital era.

In contrast to the innovations from the last decade, which mostly only partially affected the value chain, the digital transformation could enable an end-to-end change and potentially apply to the pharmaceutical value chain as a whole. As a result, companies that adapt their product development and market access models more quickly will have the chance to gain a competitive advantage while others failing to adapt will face challenges.

Driving Forces

Several forces can be identified driving this process. The strongest is the rise of value-based medicines, enforced by the pushback from payers regarding mass market and blockbuster drugs. Most healthcare systems with large pharma markets have been trying to cap increasing heath care spending in recent years. At the same time, industry costs for R&D have been increasing with a declining ROI across the industry. One factor is the increasing number of so called late-stage (phase III) failures, where already large investments have been made. This is largely driven by improved Standard-of-Care, harder-to-treat indications that require larger and longer studies, lack of translatability of phase II endpoints to phase III, and challenges of finding promising new targets.

Additionally, the industry is seeing an increasingly competitive landscape. In many of the large chronic diseases such as cardiovascular disease, inflammatory diseases, and diabetes, the standard of care is already advanced with generic drugs broadly available. Healthcare systems are not willing to pay high prices for only modest benefit. Moreover, new competitors are entering the healthcare market from various ends: e.g. the GAFA economy giants (Google, Amazon, Facebook, Apple) have lately been active along the pharmaceutical value chain.

An agile digital end-to-end strategy for the transformation of the entire value chain will help pharmaceutical companies overcome those challenges.

Here we provide some examples, how such a digital strategy can be put in place.

Drug Discovery

It may seem counterintuitive that the breeding ground for pharmaceutical innovation, which is based on cutting-edge science and technology, lags behind the digital curve. But today most pharma companies still focus most of their R&D budget on internal resources. Due to the rise of digital technology and the availability of data this should be refocused to a more open innovation strategy. The coming of age of open biomolecular platforms, including genome and patient data along with the availability of technology to easily perform genome editing, produce antibodies or even CAR-T cells, will lead to a democratization of preclinical research. There is an increasing landscape of academic groups and start-ups that perform research based on these technologies to develop unique insights into the disease biology for either a single disease or disease areas. It is not physically and financially feasible to capture all this knowledge within a single company. Therefore, drug discovery organizations should be partnering in exploring targets coming out of these networks providing technologies such as artificial intelligence for the analysis of data as well as for the design and optimization of new molecular entities.

Bayer’s G4Targets and the CoLaborator initiative are examples how such a strategy can be put in place successfully. A core in-house competency should be how to accelerate the asset from concept to candidate. In that spirit, Novartis and GSK, among others, are investing heavily in digitizing their internal data to make it searchable under FAIR (Findable, Accessible, Interoperable, and Reusable) principles. The new candidates should then be tested in disease animal models using digital telemetry systems to mimic human diseases. The data gained across those early experiments should flow into an asset-independent data base to guide and evaluate future experiments and collaborations.

It is important to note, that this strategy should not be limited to molecular candidates. Digital health applications as well as devices should be included and evaluated alongside of these new therapeutic entities.  Such an approach has been lauded by the new FDA commissioner Scott Gottlieb but these technologies will help to enhance the efficacy of the drug in a value based healthcare market.

Drug Development/Translational Medicine

The phase between late pre-clinical development and confirmatory clinical development (phase II+III studies) is commonly called as translational medicine and has a tremendous chance to benefit from a thought-out digital strategy. The main goal of translational medicine is to show in human proof of concept studies the true potential of a new therapeutic and if it translates from bench to bedside. Early prioritization of the evidence valued by the payer is critical. Here, digital biomarkers such as real world walking speed can not only help to reflect patients’ needs more accurately than traditional clinical endpoints, they also offer the chance to be used as surrogate markers in trials for diseases with low event rates. Additionally, these markers (including smartphone data) can be later applied in a value-based care setting for reimbursement purposes.

It needs to be demonstrated that these digital biomarkers can be developed and validated in a cross-industrial approach as endpoints for clinical, regulatory, and payer use. A joint strategy with multiple pharma sponsors has several advantages, as biomarker validation is expensive, slow, and requires acceptance from across the clinical spectrum (patients, doctors, pharmaceutical companies, regulators, payers). An excellent example is the IMI initiative Mobilise-D, where Bayer and Novartis among other academic and industrial partners aim to establish real world walking speed as a new endpoint for multiple indications. In general, the selection of these digital markers should be based on, and take advantage of, the growing acceptance of Real World Evidence (RWE) data by health authorities and payers.

Furthermore, in late-stage development data science enabled decision making for clinical trials can help to find the right patient that will benefit from the new drug. RWE could be even used whenever possible to replace randomized clinical trials (RCTs). With a currently widening regulatory window, RWE has been already used to expand the labels of drugs. RCTs, with their costs, complexity, and duration, could then be prioritized for high-payer-value subpopulations. It can be expected that with the ongoing digital transformation of medicine even more data will become available.

Marketing

Our customers’ world has changed. Millennial physicians – true digital natives – now are a majority of practicing doctors. Along with their patients, they expect companies to engage with them in a personalized way in our digital world. Pharma marketing is no longer just about pure promotion, but also about personal digital services which engage physicians and patients to help solve problems in the moment and at their fingertips. Every other industry has gone through a digital transformation, including highly regulated industries like financial services. Companies that have successfully navigated this transformation and emerged as the clear leaders have not only transformed their customer engagement, but also their core products, processes and company cultures.

Successfully engaging with customers across channels, especially digital channels, requires building new skills, capabilities and platforms. Over the past 5+ years, pharma marketing has responded to these changing demands by investing in and deploying these capabilities. Given that other industries have already gone through digital transformation, there is much pharma can and should copy from proven best practices, while adapting them to the unique realities of the pharma sector. 

At Bayer, we are using these core digital capabilities in a fully integrated way – integrated customer experience, platform and data. Expanding on these foundational capabilities, for example, we use artificial intelligence in Japan to suggest the next personalized action to be taken for an individual customer, regardless if the engagement is to be digital or traditional. We see these current investments as the foundation for future digital innovation

Conclusion

Beside the examples provided above, the most important issue in order to generate a digital end-to-end strategy is to have the right teams in place along the pharmaceutical value chain. Those teams need to have an armamentarium of digital capabilities, such as expertise in data science, data processing and data storage as well as digital marketing and digital health in general. Equally important are team members with deep understanding of the biological and medical issues; otherwise there is a risk that a digital solution will be developed that is precise and efficient, but not useful.

It is important to note that it is not the technology that will enable the successful digital transformation of the value chain – it is the mindset and capabilities of the team. The technology itself will become a commodity sooner or later. Like anyone living through a time of rapid change, it is hard to see where the digital revolution will take us. But it is already clear we will not be working the same way in 10 years as we are today. 

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