Is AI The Next Wave Of Innovation For The Life Sciences Industry?
Lifesciences is no strangers to rapid disruption. Although there is a high level of innovation in the industry, biopharma companies face a challenging environment due to increased competition and R&D cycle times. Technologies, especially Artificial Intelligence (AI), could provide a lifeline to biopharma research and development and help companies reverse this trend. Here is how AI-enabled transformation is helping biopharma companies.
Life sciences companies are discovering how AI can be leveraged to identify new indications for existing products or research new candidates. Using sophisticated AI algorithms to mine real-world structured and unstructured data to unleash insights can lead to identifying new mechanisms of disease, potential line extension, and design for preclinical experiments. Ai can also help fill knowledge gaps in how candidates act on proteins to aid the design of new drugs.
Data can be extracted in real-time from the commercial, scientific, and regulatory environment, enabling researchers to find competitive white space, avoid blind spots in research, and discover disease similarities.
Across the life sciences industry, product development timelines is several years from discovery to launch. Advancements in AI can significantly reduce the time it takes to develop, manufacture, and launch new patient therapies to support the goal of mitigating overall product development timelines.
Researchers are integrating research data, lab data, and clinical data, combined with new information sources across the drug development spectrum, developing a holistic picture of the drug development candidate. Enhancing ways to acquire and mine data in real-time allows researchers to use AI to make improved decisions faster, accelerating the product development and scale-up overall process.
AI represents a big opportunity for life sciences companies to radically transform business at all levels, including organization structures, processes, and people. However, an overall vision is needed to shape what firms want to accomplish. For this, companies need to think big, start small, and scale fast.
AI Can Improve Patient Outcomes, But Will Pharma Get There Quickly Enough?:
No matter what industry you’re in, Artificial Intelligence (AI) is all the rage. It’s the shiniest of the shiny and new, and it’s everywhere.
In pop culture alone it’s the central theme of HBO’s Westworld, where humanoid AI robots pretend to be people, or even the most recent season of Silicon Valley where a major character was an AI-powered robot named Fiona.
AI is also the central, recurring theme at every conference. Even at giant tradeshows like the Consumer Electronics Show (CES) where, this year, we saw autonomous vehicles, voice-enabled bot driving assistants within cars, L’Oreal’s thumbnail-sized UV sensor patch, and hundreds of other AI-enabled “smart” products. At South by Southwest (SXSW), it seemed every other session was about AI. Elon Musk himself, CEO of SpaceX and Tesla, even made a surprise appearance where he fielded questions from the audience and warned against the irresponsible development of AI and the requirement to “work safely’ when exploring it.
AI is omnipresent and it’s no shock that it’s even crept its way into the highly regulated pharmaceutical industry, one that is usually risk averse and slow to adapt new technologies. And with increasing R&D costs and healthcare costs in general, paired with larger and more precise data sets, AI may alleviate multiple pain-points across the industry. But will we adapt quickly enough?
Pharma’s Recent History of Emerging Tech Adoption
Look at the adoption of social media, for example. A few years ago, social media had become the norm and a crucial tactic for brand engagement in every other industry. Customers were taking brand complaints, praise, and discussions to social, but pharma had no presence. The conversation was happening with or without us, and we had a choice to make — we could either meet our customers where they wanted to engage, or miss out. Clearly we needed to be there. Yet pharma was bound by requirements to provide fair balance, privacy and safety information among all branded promotional materials.
In 2014 the Food and Drug Administration (FDA) came out with draft guidelines entitled Guidance for Industry Internet/Social Media Platforms with Character Space Limitations — Presenting Risk and Benefit Information for Prescription Drugs and Medical Devices, and everything changed. Today there are hundreds of branded pharmaceutical product pages across social channels like Facebook, Instagram, Twitter, and more, many with open comments. Pharma’s even ventured into Snapchat! These channels allow for precise targeting, but more importantly for opportunities to compliantly engage with customers for customer service purposes, gain invaluable brand and behavioral insights, and provide condition support communities.
So how is regulation affecting the pharmaceutical industry’s adoption of AI? What are the use cases within pharma for AI? With social media, it became clear that pharma had to either adapt or miss crucial patient and customer engagements. AI is no different.
AI is Already Happening in Pharma
Despite being slow to adopt other technologies, we’re already seeing AI come to life across the pharmaceutical industry. Of course, there are dozens of components within the industry where AI is applicable, including therapy discovery, product approval, commercialization, clinical trials, FDA submission strategies, product launch execution, pricing, supply chain management, market penetration and building, awareness, product adherence, clinical development and trials for new indications, and submission in other markets, to name a few.
It would take years to understand use cases of AI for every single aspect of this complex industry, but the common theme is simple: Driven by precise data sets, AI will shorten the amount of time it takes to solve business problems and meet objectives. Let’s explore some of the potential use cases.
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