The pharma industry is heading towards an era where advanced analytics, AI, and ML will lead to new discoveries and personalized treatments.
Today, humanity faces health challenges that are greater than ever. Skyrocketing medical costs, a resurgence of infectious diseases, and the incidence of illnesses such as cancer, diabetes, and other autoimmune disorders threaten the well-being of populations worldwide. Thus, conventional pharma R&D models can no longer meet the healthcare requirements of the day – a digital-first approach based on advanced analytics, AI, and ML is the need of the hour.
Currently, the development to approval process of a new medication takes up to 10-12 years. According to the FDA, for every 100 drugs entering phase I trials, only 20 enter phase III trials, and 12 receive approvals. This inexpedient system is propelling pharma R&D toward the adoption of a digital-first strategy for accelerating trials through to mass production. According to a report by ABI Research, the industry may spend over $4.5 billion on digital transformation by 2030. In this pursuit of a data-driven pharma R&D ecosystem, two tools can strengthen every stage in the R&D value chain: advanced analytics and artificial intelligence (AI) and machine learning (ML).
Analytics: Unlocking the potential of data
In order to ensure that both business and research objectives are aligned, it is essential to capture data that is generated across the healthcare delivery continuum. Importantly, building platforms that achieve this goal while making the gathered data accessible to internal and external research communities is critical. In recent years, the pharma and healthcare industries have begun using a specific category of data to enhance the value delivered: real-world data (RWD).
According to McKinsey, leveraging advanced analytics on RWD throughout the value chain of a care provider can potentially unlock annual savings of over $300 million for an average top-20 pharma company in the next three to five years. This underscores the importance of incorporating targeted and robust tools with RWD to derive greater value from it.
Typically, companies that apply advanced analytics on RWD must follow a three-step approach:
- Form interdisciplinary teams (comprising clinical and method experts, translators, and analysts)
- Prioritize use cases that showcase improved patient and operational value
- Establish platforms that facilitate an integrated data environment
The implementation of RWD analytics at scale enables pharma companies to not only engage in objective decision-making but also shift their focus from products to patients. Such an analytics-driven approach to R&D expedites the conducting of safe trials, reviews, and, ultimately, approval of treatments – enabling the industry to provide the right drug to the right patient at the right time.
Empowering R&D innovation with AI/ML
Imagining any process across any industry without AL/ML embedment is nearly impossible today. At the forefront of this adoption is the pharma industry. Pharmaceutical companies are increasingly breaking new ground and disrupting the industry through the use of AI.
From developing new vaccines and drugs to enabling precision medicine, integrating AI/ML in the R&D process can accelerate the development-approval cycle. Let’s take the example of vaccine development in recent years. Companies such as Moderna, Pfizer, and Johnson & Johnson leveraged AI/ML methodologies in developing COVID-19 vaccines to pathbreaking effect. The swift rollout of these vaccines – within time constraints – was unprecedented. Pharma companies find themselves at a crucial stage where they must take notes from these early adopters and explore how AI can be applied across trials in their business processes.
Through sophisticated analysis, AI systems can efficiently navigate complex pathogenic structures and identify components more likely to trigger a robust immune response. They can also pinpoint components that are less prone to mutation, thereby ensuring the long-term effectiveness of a drug or vaccine undertrial. Additionally, computational analyses and ML algorithms aid researchers in the selection of potential vaccine components and help comprehend experimental data.
AI also facilitates the integration of data from various sources, including RWD and from experiments, enabling scientists to gain valuable insights. Additionally, AI technologies aid in monitoring a pathogen’s genetic mutations, which are vital for assessing the efficacy of future vaccines and medications.
Leading the way in the digital-first R&D journey
For the digital-first approach to receive wider acceptance, it must be substantiated through proactive adoption. Fortunately, several global pharmaceutical companies are paving the way for this industry-wide overhaul. Below are a few examples:
- French pharmaceutical giant Sanofi signed a $20 million strategic multi-target research deal with US-based AI drug discovery company Atomwise in 2020. Through the collaboration, Sanofi will leverage Atomwise’s computational platform, AtomNet, to research small molecules aimed at up to five drug targets. The platform utilizes deep-learning algorithms for structure-based drug design. This enables the AI-led scanning of Atomwise’s proprietary library of over three trillion synthesizable compounds.
- In January 2023, UK-based AstraZeneca announced the expansion of its partnership with BenevolentAI, a clinical-stage AI-enabled drug discovery and development company, for another three years. The partnership focuses on drug discovery in systemic lupus erythematosus and heart failure; it allowed AstraZeneca to discover two additional novel AI-generated targets for chronic kidney disease and idiopathic pulmonary fibrosis.
- Exscientia, a British pharmatech company that uses AI to design and discover new drugs, made a revolutionary breakthrough in 2020, as they announced the first-ever AI-designed drug candidate to enter clinical trials. Following this milestone, other notable companies in the industry, such as Insilico Medicine, Evotec, and Schrödinger, have also announced their initiation of phase I trials for AI-designed drug candidates.
AI is the way
Numbers do not lie. And according to McKinsey, the transformative power of a digital-first approach to R&D in pharma is already reaping benefits. It was found that this strategy is helping reduce the time required for early-stage drug discovery by around 20 to 50% and to cut early development costs by up to 50%. This is indicative of the reality that the industry is heading towards an era where advanced analytics and AI/ML will propel us toward new discoveries and personalized treatments.