AI Pill

AI and pharma pair up to accelerate drug discovery, development, and commercialization

The pharmaceutical industry grapples with a daunting challenge—producing and delivering more effective drugs at ever increasing costs. Over the years, it has become more difficult for drug companies to keep pace with grueling market and regulatory demands. The cost of drug research, clinical trials, manufacturing, and compliance are reaching new highs and competition is pressuring the industry to adopt new technologies that can deliver efficiency to every aspect of the development and distribution process.

Let’s examine 3 core areas within the drug product life cycle in which AI can boost performance and results:

1.     Drug discovery. Discovering even a single new drug requires tremendous effort and commitment to experimentation. For instance, “it takes about a decade of research — and an expenditure of $2.6 billion” for a single drug to go from the research phase to being available on the shelves for purchase. Scientists painstakingly assess each compound within the initial screening to verify the possibility of success or failure. All the while, the company has to spend significant amounts of time and money to keep up with the regulatory and scientific rigor required during the drug discovery process. This is where AI can help. Uses of AI and machine learning (ML) to enhance the drug discovery process include quicker initial screenings of different compounds within a particular drug as well as targeting and identifying specific components needed to formulate a certain drug using advanced data analytics. The result? Faster and more cost-effective discovery, which could ultimately create more treatment choices and more affordable healthcare for all.

2.     Drug development. Unlike drug discovery, drug development focuses on transforming the newly discovered compound to a product that is safe for market consumption and approved by the appropriate regulatory authorities. In pharma, drug development brings to light a trend that was first observed in the 1980s, “Eroom’s law” (Moore’s law but spelled backwards). Eroom’s law states that despite technological advancements, cost of drug development is increasing year over year while the number of actual drug approvals are decreasing. This is a concern for many within the pharma industry and AI is being targeted as a solution to help reverse this trend.  

Clinical trials represent important steps in the drug development process and are designed to collect safety and efficacy data related to new drugs. These clinical trials consist of multiple phases, “with Phase III trials requiring a larger pool of patients and being significantly more expensive and complex than Phase I trials.” Even with the significant amount of resources allocated to such trials, only 1 out of 10 drugs that enter Phase I are approved by the FDA. In general, clinical trials are fraught with inefficiencies including bottlenecks in recruitment, flaws in study design, and data management issues related to participants taking the right dosage or delays. Application of AI can help improve the entire process and put large volumes of data to use in unprecedented ways, including information contained in clinical notes, authorized medical records, and patient-generated data. 

3.     Commercialization. After years of research and clinical development as well as the required approvals by the FDA, a new drug can finally have the opportunity to be marketed and be made available for sale to the public. During the commercialization phase, drug companies manage a number of important operations including manufacturing, quality, and supply chain to ensure a successful delivery and market adoption of their newly approved drugs. Whether it is related to customer service, supply chain, personalization of medicine targeted to specific patients, regulatory compliance, or risk management, AI can perform a role in making commercialization more efficient and productive. For example, customer service bots can help create a more interpersonal connection for the patient in the process of finding an optimal treatment option. In terms of the supply chain, AI can implement multiple projections of analytics in the real time to “better forecast demand, and automatically identify and mitigate supply risks.” AI can help determine “a new therapy’s efficacy and side-effects profile for a specific patient or patient group.” This allows for more personalized treatment options that differ between patients and their respected medical histories. Within post-marketing surveillance, AI and ML can also help better manage risk by monitoring both web and social platforms continuously.

Patients and doctors are already benefiting from the impacts of AI in ways that felt more like science-fiction only a few years ago. In pharma, the meteoric rise in costs in face of growing market and regulatory demands, the need for efficiency is more prevalent than ever. So, what will the future hold for the pharma industry? If the early activity is any indication, advanced technologies powered by AI are slowly transforming the pharma industry, promising to disrupt the future of drug discovery, development, and commercialization. Article contributors:Entefy