Novartis partners with Microsoft to build AI solutions—showing pharma firms why they must look to partnerships for success

Pharma’s executives and artificial intelligence (AI) project leaders will undoubtedly have noticed the partnership between Microsoft and Novartis to leverage AI throughout the industry ecosystem and value chain. The competition to realize AI’s potential in pharma is already fierce—modeling disease, drug discovery, and diagnosis—before considering the challenges that the industry’s grappling with: siloed functions and data, cultural barriers, and ongoing operational transformations. If you’re late to the AI game in pharma, don’t panic, but the time to act is now. If you’re swift, you will find plenty of potential partners with expertise out there and plenty of value propositions to realize by tackling the industry’s most pressing challenges.  

The pharmaceutical industry has always been a giants’ game 

R&D can eat up as much as 20% of pharma revenue, with the total cost of new drug development exploding 250% over the past 30 years. The necessary investment to bring a drug to market through the chain of R&D, testing, regulation, and IP protection means that “big pharma” has no choice but to ramp up their margins before a drug’s IP has ended (25 years in the UK) and the “generic” drug manufacturers pick up the recipe and mass–produce at a fraction of the cost. Pioneering drug-makers then go back to the drawing board while generic manufacturers milk their cash cows.  

The battle between big pharma and generic manufacturers has brought AI to the fore, aiming to revolutionize the drug discovery process and accelerate the time–to–market from decades to months. Microsoft and Novartis are the latest powerhouse example. Their collaboration will range from personalized therapies and drug design to establishing an innovation lab to empower AI adoption throughout the ecosystem. 

The weight of this partnership suggests powerful and lucrative results, but the competition cannot be understated… 

As evidence of the vastness of the field, these comprehensive lists showcase 33 giants and almost 160 startups engaging with AI in the pharmaceutical space. These three initiatives might hope to match the power of Microsoft and Novartis’ efforts: 

• GSK boasts an in-house AI unit, and partnerships with startups are pushing the discovery of new molecules. Its involvement in the ATOM consortium hopes to achieve “target drug identification to patient-ready” in a year (yes, that’s ambitious…). Google and various universities are also involved in GSK’s efforts. 

• Gilead’s partnership with Insitro will leverage the latter’s platform to model disease progression and regression. A recent HFS webinar discusses Gilead’s intelligent automation (IA) efforts with Agilify. 
• AstraZeneca boasts Alibaba as a partner to utilize AI for patient diagnosis and treatment, as well as a long-term partnership with BenevolentAI and recent efforts with Schrodinger to model protein behavior on a machine learning (ML) platform. 

But it’s not all rosy for AI in pharma, as IBM might attest. Struggling sales have led to a cutback in its Watson AI for drug discovery offering; its flagship system will still operate where it’s already in motion, but IBM will instead focus Watson’s energy on clinical development. Cases of less-than-ideal output and senior resignations didn’t help matters.  

Siloed functions hinder business model and supply chain transformation: siloed data will be a critical challenge for AI projects 

Pharma CEOs and shareholders remain cautious of the disruption industry-wide transformation will entail. Big pharma, with or without adjoining M&As, has historically (like many sectors) organized functions into silos. Supply chain revolution, for instance, must naturally involve every “siloed” faction of the business—presenting huge data and logistical challenges. 

AI projects will have to integrate and leverage the vast amount of current and historical data and be crystal clear about what insight they’re seeking. Data ownership and access, open–source and block box initiatives, and cybersecurity are but a few additional questions to address. 

AI projects must navigate and integrate with pharma’s ongoing operational challenges 

Digital factories are facilitating the move toward continuous processing in an industry traditionally operating in batches. Digital twin simulations and advances in process analytical technology (PAT) are improving the understanding of current and future processes.  

Monitoring and connectivity improvements are leading to better–informed patients who might better–adhere to prescriptions. Regulation compliance means pharma is striving for transparency and traceability in their supply chains; big data management, systems integration, and the promise of blockchain, separately discussed by HFS, aid in this regard. 

The Bottom Line: The race for prowess in the new age of pharmaceuticals will center on partnerships like Microsoft and Novartis—but the competition will be fierce and fraught with challenges. 

Whether an executive or leading an AI project within pharma—announcements like Microsoft and Novartis will resonate through the ecosystem. But the competition is also out there, and there are still many challenges the industry as a whole must address. The good news is that the groundwork has already been done. The experts and partners are out there to leverage AI for whichever of the numerous applications will bring value to your business.  

Summarizing the state of play: Dr. Joerg Moeller, Executive Committee of Bayer AG’s Pharmaceuticals Division, Head of R&D, commented 

“We have already started to apply AI in drug discovery; however, there is still untapped potential. We will need to expand the necessary knowledge and capabilities within our company and the pharmaceutical industry in general. For me, strategic collaborations with AI-driven companies and academic partners hold the promise to establish a robust, AI-based pipeline as part of our portfolio and address new therapeutic areas. Only then we will be able to unlock the full potential of AI in pursuit of new treatments and ultimately be able to provide new solutions to patients.”