Declining pharmaceutical research and development (R&D) efficiency has been an ongoing problem in pharmaceutical sector. With emerging technologies reshaping various business, pharma is looking to capitalize on artificial intelligence (AI) to streamline operating models and address R&D’s productivity challenges, says GlobalData, a leading data and analytics company.
According to a survey as part of GlobalData’s latest report, Digital Transformation and Emerging Technology in the Healthcare Industry – 2020, AI (58%) followed by big data (46%) are expected to be the most disruptive technologies in pharma within next two years. According to the survey results, these technologies will also be the main investment targets for pharma sector during the same timeframe.
Urte Jakimaviciute MSc, Senior Director of Market Research, comments: “The rising average cost to bring a drug to market, high attrition rate for pipeline products, and increase in average clinical cycle are among the main factors behind pharma’s declining returns. No wonder digital transformation is seen as a positive step towards addressing R&D productivity challenges and developing more sustainable drug discovery processes.”
Bringing a drug to market is a risky and time-consuming process. While drug development costs and timelines continue to rise, the technological advancements are hoped to improve and streamline the drug development process.
Jakimaviciute continues: “Efficient and effective R&D is critical to the long-term success of any pharmaceutical business. The industry generates increasing amounts of data throughout all stages of value chain, and AI is seen as a valuable tool facilitating extraction of insights from those large datasets. By effectively utilizing information pharma companies could reduce drug discovery timeliness, accelerate clinical trials, cut down drug failure rates, and shorten drug approval – thus making substantial costs savings.
“Pharma’s faith in AI can be also associated with technology bringing some solid results. This year started with Sumitomo Dainippon Pharma and Exscientia’s announcement that an AI-created compound will be used in human clinical trials for the first time. According to Exscientia, the exploratory research phase for DSP-1181 took less than 12 months to complete. This is a fraction of the 4.5 years needed on average by conventional research techniques. The same year, BenevolentAI identified Eli Lilly’s drug baricitinib as a potential COVID-19 treatment, which is now in Phase III clinical trials.
“AI has a potential to revamp a spectrum of processes within pharma ranging from target identification to marketing and commercialization activities. Even though technologies like AI and big data are expected to disrupt the pharmaceutical sector, investments in these technologies will only pay off if the businesses can act on the results and insights they derive.”
