According to McKinsey and Company, the average time to bring a new drug to market is around 12 years. For life sciences organizations, the pressure to speed up the time to market of new drugs and therapies continues at pace, alongside the need to reduce the cost of doing so. What is needed is a new technology paradigm that will reinvent the life sciences business model by connecting data, knowledge, people, and insights. 

When it comes to pharmaceutical Research and Development there are plentiful fields of research that offer potential use cases for quantum computing. These include quantum simulations for molecular design, molecular similarity, protein folding and protein-ligand interactions. Other applications include the modeling mechanisms of drug action, biomarker discovery, quantitative structure activity relationships, and modeling the behavior of larger biological systems. 

 

As the industry looks at how to implement quantum computing, we must learn from what is currently happening in the industry. Huge improvements in computing power have already made the practical deployment of AI for solving complex problems a realistic possibility. From image data analysis to evaluate the molecular structure of a compound, to making diagnoses by analyzing radiology data, AI use has seen rampant growth and improvement in the last 24 months. This progress has prompted substantial investment and significant partnering and acquisition activity, including between major pharmaceutical companies and leading AI providers and startups. 

 

All this activity is opening the door to new configurations between life sciences firms and hyper-scalers, leveraging massive computing power and mastery with data. Quantum computing could follow a similar trajectory, with systems capable of practical deployment in R&D being a realistic possibility by 2035.

 

On the road to the quantum revolution and reducing time to market, businesses should focus their efforts on the following:

Research and development (R&D) is often the most time-consuming part of the drug development process, but AI can accelerate this process by up to 50% as the technology has a multiplier effect wherever it is applied. 

 

Life sciences can implement generative AI at the very beginning of the R&D cycle, to aid in searching and synthesizing available literature on a specific potential drug. Instead of beginning with a manual keyword search and sifting through hundreds of articles across various sources, teams could prompt a generative AI-enabled tool to rapidly search, gather and distil relevant articles – or even suggest unanticipated information pathways to explore. 

 

Generative AI also has the potential to change how researchers find existing literature. Usually, researchers simply type keywords into the search box. But with a generative AI tool, they could state their goal into the prompt, providing context and intent, for the technology to find reference materials to support that specific ask, saving significant time while broadening the research horizon. 

Compiling a clinical trial protocol document is a lengthy process that can take anywhere from a few months to over a year. Generative AI technology’s capabilities can automate a substantial proportion of the protocol writing process, bringing it down to days or even mere hours. 

 

Generative AI can be trained on thousands of existing protocols in industry databases and each company’s own research data in order to identify the patterns relevant to investigational products, certain conditions, specific patient populations, or other factors. As the generative AI tool identifies relevant patterns, it can combine all the insights to design a baseline study, with a defined narrative that determines eligibility, drafts exclusionary criteria, and provides other necessary details. It can generate a number of draft options that would later be evaluated and refined by a human.

Once a new therapy has been approved to launch in one market, many companies will be looking to expand the launch into others. This process takes a tremendous amount of time and resources, from strategy development and market research to agency engagement, content creation and material development. Much like in the research and protocol writing processes, a lot of the steps in this part of the process could be automated with generative AI.

 

For instance, when the drug is close to gaining approval, generative AI could support commercial teams’ research and compile strategy documents for secondary markets, taking into account specific regulations the therapy will need to adhere to in the new country. Similarly, generative AI can be used to adapt existing content – including website copy, brochures and other promotional materials – to the language and culture of the secondary market. This could shave up to a year off the go-to-market timeline in new countries and massively reduce marketing and design costs. 

Introducing generative AI into a business should be done one step at a time. It starts with fostering a culture of AI literacy, where every employee understands how the technology can be used to reshape and empower their role. It is also important to build a solid ecosystem of partners, which includes relationships with academic institutions, data providers, and specialty generative AI vendors that will support the business’ knowledge growth and internal capabilities.  

 

Once generative AI is introduced, it is a good idea to establish a body within the business to supervise how the organization uses the technology and manages the upskilling and development of employees engaging with the tech. This body should also establish best practices and develop frameworks that guide the deployment of generative AI across the business. 

Introducing generative AI into a pharmaceutical business is no mean feat and is understandably very daunting. It is, however, essential for companies that want to stay ahead of their competitors and the market to invest in generative AI. Likewise, it is crucial to ensure employees are provided training on how to best optimize the technology and create a body that supervises how the technology is being deployed across the business to avoid any misuse. As companies continue to experiment with generative AI across its various use cases, they will begin to lay the foundation needed to harness the full potential of this transformative technology, discovering, testing and bringing their drugs to market sooner. This improves patient outcomes due to safer, more effective and affordable drug development and increases revenue opportunities in a highly competitive market, driving value and improving patient outcomes at the same time.

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Bryan Hill is Chief Technology Officer and Vice President, Digital Health & Innovation within Cognizant’s Life Sciences Practice. He is responsible for shaping Cognizant’s full suite of digital capabilities and technology innovation into offerings that enable client organizations to better connect humans to their health and deliver business value. His team collaborates closely with clients and industry partners to enable transformation across the value chain in areas such as clinical development, patient engagement strategies, and digital therapeutics.