AI pioneer Innoplexus and phytopharmaceutical company DrD are partnering toRead More
Virtual pharma – now for real?
Imagining an integrated virtual pharma company
The evolution of the pharma business model
Over the last decades, the advances in medical science, the growing competition and the demands of the payers have led to permanent changes and adaptations of the pharma business model. Even the biggest companies do not do everything in-house. Instead, the industry has developed partnering as one key competence for success.
From discovery to production and commercialization; from clinical development to regulatory and HTA review, pharma has learned to work with partners by contracting out and coordinating activities that not too long ago were considered core competences.
Instead of doing everything in-house the pharmaceutical business model now relies on vendor selection, project and alliance-management to deliver continuously more specialized solutions in an increasingly competitive and price sensitive context.
Since one generation of pharma managers, the strategy question of pharma’s core competence has continuously evolved. The current state of evolution was recently summarized in a pharma executive workshop: “Pharma’s core competence is the ability to orchestrate all aspects of the value chain in-house by partnering with best-in-class expertise outside the company to enable the delivery of a permanent stream of innovations ensuring competitive profit margins around 20%.”
The Genentech model as inspiration
Genentech is often quoted as a role model for success in pharma. Founded more than forty years ago, as the first public biotech company, Genentech is today part of the Roche group. It all started long before big data and AI technologies were available, with humble beginnings, with the key competence to use recombinant E. coli to produce human proteins: a revolution in insulin manufacturing.
Today, Genentech is the world leader in delivering targeted cancer therapies mostly from monoclonal antibody technology. Genentech contributes a very significant part of Roche’s $40 billion pharma sales and employs some 14,000 people.
The Genentech acquisition by Roche is also often quoted as the model for big pharma – biotech partnerships. Roche was wise enough to have always kept Genentech as autonomous business, allowing the California based company to keep its biotech DNA and culture and to independently develop its R&D strategy.
Genentech’s business success formula is often described as a mix of scientific understanding, smart business development by partnering or acquiring highly specialized biotechs and a company culture of curiosity and readiness to fail fast.
Key success factors are:
- A solid bio-technology platform and IP situation
- A robust development plan based on “companion diagnostics”
- Visionary leaders and motivated employees
- Confident investors
Forty years later, these key ingredients of success continue to be relevant – only the categories have evolved. Managing the next generation of “virtual pharma”, key competences could read like
- A solid big data platform that uses IP protected technology to harvest relevance from the exploding wealth of life science information
- A robust technology to model pharmaceutical targets and clinical development
- Visionary leaders and well networked employees able to motivate contractors and partners for the common objective
- Investors ready to support the next step in the evolution of pharma
The quest for a more efficient pharma model
Today, approximately two-thirds of the drugs entering phase II ever make it to phase III, and this at the end of a ten years process from discovery to phase II clinical trial results.
“Just by cutting out let’s say two years in the pre-clinical and two years in the clinical phase would catapult pharma into a new era”, Vivek Chaudhari, Innoplexus COO noted.
Just like in the Genentech case, where a genetically engineered protein provided the proof of concept – in a new “integrated virtual pharma reality”, a multi-factorial model to process all the available data into a predictive algorithm could deliver the evidence that investors may require to take such a “integrated virtual pharma” to the next level.
The issue is: the field is vast and not well defined. Even a commonly accepted definition of “virtual pharma” is missing. Many fully integrated big pharma companies will claim today that they work closely together with a multitude of vendors on all aspects of the pharma value chain, forming together a “virtual pharma” context.
There is a huge number of highly specialized big data/AI firms covering very specific aspects of the value chain: from discovery to clinical development, from regulatory to commercialization. Many of them think of themselves a “virtual pharma” companies.
Other companies like Flatiron, which has recently been acquired by Roche and integrated into the diagnostic business, focusses on oncology-specific electronic health records and the generation of real-world oncology evidence.
But the true game-changing step would be to integrate the fragmented workflows into one comprehensive process. Big data and AI technology is now for the first time available to provide the backbone of such and undertaking. The time seems right for an integrated virtual pharma company.
The vision of an integrated virtual pharma company
Imagine: a fully integrated virtual pharma company merging all aspects of the pharmaceutical business through a lean, well connected business network to deliver more targeted medical solutions faster: a game changing value proposition for patients waiting for new medicines – a potential game changer for payers as development costs are likely to decrease.
Such an integrated virtual pharma concept would break down the department silos by providing
- a centralized “big data” platform that encompasses all aspects of the development and commercialization process
- with AI-enabled ability to model development processes and clinical trial design outcomes and its health economic impact
The core competence of an integrated virtual pharma company would be the ability to extract the value of the wealth of information available into actionable insights and to manage a broad group of external partners from CROs to specialists, from rented sales forces to contract manufacturers through one comprehensive business processes.
“In simple financial terms: such a fully integrated virtual pharma company could focus all its investments in a very targeted way on drug discovery, development, delivery and commercialization only when needed and with minimal fixed costs”, a pharma CFO said.
Imagine an integrated virtual pharma company that could
- speed up the development process by enabling fact-based outcomes modelling and decision making in almost real time, having the relevant data always at the “fingertips” of specialists, empowered to make decisions fast
- rationalize the commercial effort by integrating health economic scientific communication data for targeted customer communication
- or in one sentence: use big data and AI technologies to integrate all relevant business processes to get to the next level of productivity across the entire value chain.
In comparison to the traditional pharma model, an integrated virtual pharma concept would build its business along more dimensions than therapeutic areas or geography – it could optimize the virtual network of competences according to the cluster of medical and commercial competence in a network of external commercial experts.
Imagine a future in which – let’s say 50 highly qualified employees of a fully integrated virtual pharma company manage and monitor the activities of some 500-1000 external specialists delivering similar or better results in terms of discovery, clinical development, production and commercial activity as “traditional” business concepts.
So what, now?
For the first time, technology is available to get to completely new levels of understanding and delivering solutions in life sciences. It’s time to use the quantum leap of big data and AI technology to bring pharma to levels of productivity that patients in need require. Someone just needs to do it. When looking at the more than forty years old pictures of Herbert Boyer and Robert Swanson, the founders of Genentech, it feels like looking at the evidence…that it can be done.
Machine learning as an indispensable tool for Biopharma
The cost of developing a new drug roughly doubles every nine years (inflation-adjusted) aka Eroom’s law. As the volume of data…
Find biological associations between ‘never thought before to be linked’
There was a time when science depended on manual efforts by scientists and researchers. Then, came an avalanche of data…
Find key opinion leaders and influencers to drive your therapy’s
Collaboration with key opinion leaders and influencers becomes crucial at various stages of the drug development chain. When a pharmaceutical…
Impact of AI and Digitalization on R&D in Biopharmaceutical Industry
Data are not the new gold – but the ability to put them together in a relevant and analyzable way…
Why AI Is a Practical Solution for Pharma
Artificial intelligence, or AI, is gaining more attention in the pharma space these days. At one time evoking images from…
How can AI help in Transforming the Drug Development Cycle?
Artificial intelligence (AI) is transforming the pharmaceutical industry with extraordinary innovations that are automating processes at every stage of drug…
How Will AI Disrupt the Pharma Industry?
There is a lot of buzz these days about how artificial intelligence (AI) is going to disrupt the pharmaceutical industry….
Revolutionizing Drug Discovery with AI-Powered Solutions
Drug discovery plays a key role in the pharma and biotech industries. Discovering unmet needs, pinpointing the target, identifying the…
Leveraging the Role of AI for More Successful Clinical Trials
The pharmaceutical industry spends billions on R&D each year. Clinical trials require tremendous amounts of effort, from identifying sites and…
Understanding the Language of Life Sciences
Training algorithms to identify and extract Life Sciences-specific data The English dictionary is full of words and definitions that can be…
Understanding the Computer Vision Technology
The early 1970s introduced the world to the idea of computer vision, a promising technology automating tasks that would otherwise…
AI Is All Hype If We Don’t Have Access to
Summary: AI could potentially speed drug discovery and save time in rejecting treatments that are unlikely to yield worthwhile resultsAI has…