Why technology innovation will trigger business model transformation in Pharma
Pharma companies are already using technology to innovate traditional processes – including clinical trials and drug development – but there’s a new world of untapped digital health business possibilities.
In this post, we explore the exciting opportunities for pharma organizations to look beyond specific products and processes, and reshape the whole way business is done.
Insight in brief
- Digital technologies offer a wealth of opportunities to pharmaceutical companies, such as cheaper R&D, around the pill solutions, and new levels of efficiency and efficacy to wider operations.
- As technological innovations are developed to seize these opportunities, it will trigger business model transformation – from targeted treatments for smaller patient cohorts, through to fully data-driven pharma companies.
- In this blogpost, we explore the changes such a transformation necessitates and outline best practices and top tips on how to succeed.
Digital innovation in pharma is gathering pace, but we are still only at the very beginning of the journey.
There are numerous examples of Pharma companies already using digital technologies in a range of areas. One application of particular interest is virtual clinical trials, which have the potential to overcome issues with traditional trial design, methodology, recruitment, and retention. According to the British Medical Journal, over 1170 trials which began in 2017 incorporated connected digital products to facilitate remote data collection and patient’s status assessment.
However, many of these initiatives are still lighthouse projects, and have yet to be fully scaled. The area where most progress has been made is one where the pharma world has already reached full maturity: the research, testing and development of new drugs.
All these developments are good for patients, and for pharma companies too.
They help reduce development costs and therefore, time-to-market. They can also help with discovery of new molecule structures, new drug combinations, and better cohort selection, to name but a few.
But technology promises so much more. Specifically, it holds the potential to transform the whole business of pharma – not just individual products and processes.
Through technology, pharma organizations can become connected, lifelong health providers rather than drug developers exclusively.
Instead of “just” selling a product, like a therapeutic compound, they could empower patients to actively participate in their own healthcare across their whole lifetime.
As the healthcare ecosystem becomes more intricate and interconnected, there may be the potential for pharma companies to start supporting the journey to preventive healthcare. However, the ultimate goal of disease prevention lies far ahead (if it’s possible at all) and depends on developments outside the pharma industry, such as the advent of new models for reimbursement. Without such models, the pharma industry has no current financial incentive to prevent diseases from happening in the first place – unless we count astronomically expensive gene therapies that can cure patients with just one shot.
On a more modest level, pharma companies can at least aim to provide a broader spectrum of health service around the patient journey than drugs. So let’s take a look at the tangible areas of change currently on the agenda.
The near-future of technology Innovation
Technology innovation is going to trigger big changes in pharma business models in the coming years.
In this blog, we’ll cover three areas where change is on the way:
- Cheaper R&D: Faster, cheaper, more efficient drug development and approval that could broaden new opportunities for business model change
- “Around the pill” solutions: Supportive digital therapies and SaMD diagnostics
- Wider operations: New ways of managing supply chains, manufacturing, cash flow and legal practices
The spiraling cost of drug development
Researching and developing new drugs is getting more difficult and expensive all the time. As reported in Policy and Medicine, a study by Tufts Center for the Study of Drug Development found that the average cost to develop a single drug is $2.6bn – an increase of 145% over a decade.
On the upside, Policy and Medicine also reported that the average time it takes to bring a drug through clinical trials has decreased. But the success rate of doing so has fallen by nearly half, to just 12%.
That means that in order to see just one compound through to approval, pharma businesses need to put eight or nine into clinical development. They can front-load risks into Phase I and II trials to minimize expensive failures in Phase III – but the costs of failed development can still be high.
How AI can help
Pharma companies are already using new technology to reduce the time and cost of developing new drugs, and boosting the number of approvals – and artificial intelligence (AI) is one of their most powerful tools.
AI has a vast range of applications for Pharma innovation – areas such as clinical trial design; enablement of virtual clinical trials; patient enrichment and enrolment; investigator and site selection; patient monitoring, medication adherence and retention; and using operational data to drive AI-enabled clinical trial analytics.
Overall, AI empowers pharma companies to make more data-informed decisions throughout development. For instance, it currently takes around four to six years to determine whether drug candidates can impact identified disease targets in early drug discovery. Algorithms significantly reduce the human workload needed to process the large data volumes involved.
New AI-enabled drug discovery platforms have already discovered the first “synthetic” molecules. Last year, the first drug developed by an AI entered Phase I clinical trials.
Another breakthrough was the arrival of Critical Assessment of Structure Prediction (CASP) – developed by Google’s AI-enabled, DeepMind project. As reported in Nature.com, CASP has the ability to accurately predict protein structures from their amino-acid sequence which can accelerate, and enhance drug discovery.
AI and machine learning (ML) models also support more accurate patient stratification, allowing pharma businesses to predict which subsets of patients will respond best to certain treatments. That means it will become easier, cheaper, and faster to validate the efficacy and safety of drugs with fewer patients.
It will also make it easier to develop more targeted treatments for smaller patient cohorts, justifying higher prices for the drug – a win-win value proposition for all stakeholders.
There are of course many requirements that AI must satisfy, whether in unregulated (R&D), or regulated applications (diagnostic imaging). A major requirement is the quality of data and access to it.
AI is only as good as the data it uses. So, preparing and ‘cleaning’ data sources will be a key part of making AI work.
Our whitepaper on the use of AI in pharma companies, highlighted the need for AI in regulated settings to set up clearly defined and transparent processes, create comprehensive documentation, and ensure that whatever results they achieve can be accurately reproduced.
Developing “Around the pill” solutions
Some of the most exciting technology developments involve broadening out the focus from the treatment phase only to the patient’s entire journey.
However, this isn’t about giving up on traditional drug treatments altogether, but rather about supporting and enhancing them. Pharma businesses can create companion products and services that help patients and healthcare providers understand and identify diseases, act earlier, and make sure patients stick to their treatment.
By providing guidance throughout the patient’s therapy, there’s a real opportunity to help them achieve a better outcome, or improve their quality of life during treatment. For example, they may be able to complete all or part of their treatment at home, instead of having to go to hospitals.
Pharma companies can also leverage AI and ML to help diagnose patients suffering from similar symptoms that require different treatments. For example, in one recent project, we helped a pharma company develop a smartphone application that analyzed the breathing patterns of patients with respiratory issues to differentiate those suffering from asthma, from those with COPD.
An outside-in perspective enables prioritization
While “around the pill” is a hugely exciting area, there are so many opportunities that it can be hard to know where to start. So, it’s crucial to focus your efforts – in terms of both business development and technical expertise.
It’s tempting to approach this problem “inside out” – considering your market positioning and existing capabilities or technologies and fitting them to whatever patient needs you can find. Instead, it can be fruitful to work “outside in,” by starting with the outcomes you want to attain.
Patient-centricity is a much discussed term but the change it necessitates is less easy to realize. The ability to put the needs of the customer, patient, or stakeholder first and build strategic decisions on their needs is a vital mind-shift and prerequisite for success in digitization overall.
For example: Are you aiming to improve the lives of patients using a field-based solution? Improve future trials? Reach more patients? Provide more value to payers?
Whoever you’re aiming to serve, focus on their needs first – the who comes first, then the what.
For example, you could be aiming to help doctors, payers, patients, or someone else – or a combination of these groups. Carry out usability studies and workshops to identify the best value proposition for each potential target group, and weigh the results against your own internal business drivers. What can you do that will add value for your users, while generating value for your business?
Digital health business managers will have an important part to play in keeping all stakeholders aligned behind your company vision. For example, while engineers are excellent at developing exciting features that make good use of in-house skills, they may need additional input from the commercial side to realize the full value for users, or for the business itself.
Expanding to wider operations
There’s a huge potential for technology innovations to bring new levels of efficiency and effectiveness to your wider operations.
In marketing, for example, AI could help you predict where, when, and how you should offer your products, building your understanding of doctors’ needs and strengthening your relationships with them.
Turning to supply chain, pharma companies face similar issues to many other industries: ongoing supplier concentration, increasingly complex products driving up demand for raw materials, and competition among the consumers of those materials. On top of that, political instability can threaten material supply – or, at least, make prices highly volatile. Even strong and prosperous economies like Germany have had to deal with shortages of drug supplies.
Lives depend on pharma businesses delivering their products, so their supply chains are more vital than most. AI and ML can play an important part, by predicting fluctuations in both supply and demand to ensure that drugs always reach those who need them. Technology can also help with meeting internal sourcing needs. For example with consumables such as cell culture media, enzymes or chemicals for lab work. This ensures functions such as R&D are never left high and dry.
As in other industries, the manufacturing process in pharma is making the transition to “Industry 4.0,” where machine-to-machine communication (M2M) and the Internet of Things (IoT) combine to drive automation, communication, and self-monitoring. Digital technologies can help with process innovation, data-driven decision making, and predictive maintenance – plus AI can support the scaling-up of production.
AI also has the ability to revolutionize quality control. Take tablet production for example. Visual appearance is a vital quality marker for pharmaceutical tablet production. Tablets are produced in vast quantities with high requirements on quality. Using an algorithm to power your image-based monitoring, you identify any quality issues with any individual pill extremely quickly.
In the legal sphere, there are already AI tools to help pharma companies identify issues within basic transactional contracts, such as NDAs. In the future, up to 80% of these standard contract processes could be automated, with humans only getting involved to review and approve the finished documents.
Munich based Celonis is taking this even further. They lead the market for AI-driven robotic process automation (RPA) with the goal to bring down repetitive office work by adaptive automation to an absolute minimum.
You could even aspire to become a completely data-driven company. Such companies use robust data insights to make decisions and systematically generate value. The key to achieving this is to put the human in the centre of your thinking.
This level of ‘human’-centricity will allow you to develop a clear vision and strategy based on a solid business case. From here you develop pilot projects all of which connect to an overarching digitization strategy.
In order to realize this, you’ll also need strong skills in data governance and management, which should be part of any digital transformation initiative.
The innovation opportunity
With new players entering the healthcare space from different highly data-driven and AI-led backgrounds, it is incumbent upon Pharma to deepen their core-competencies in these areas to ensure they do not become mere manufacturers.
As digital technology transforms every area, from R&D and trials through to manufacturing and logistics, pharma companies have the potential to fundamentally change the way they do business.
Do you need a strategic guide to help you realize the opportunities of today’s technology? At Zühlke, we are experts in pharma innovation. Talk to us to discover how we can help your business transform itself through digital innovation.