The following blog is part 2 of a 5-part series discussing results of the Q1 2020 Biopharma Confidence Index. Read part 1 of the blog series here.
An initiative of Worldwide Clinical Trials and Kineticos Life Sciences, the Biopharma Confidence Index (BCI) gauges the sentiments of C-suite and executive leadership in the industry with respect to scientific, clinical, regulatory, commercial, and business management considerations of their companies. The Q1 2020 BCI collected insights from a total of 112 respondents and divided their responses into three cohorts: private biopharma companies, start-up biopharma companies, and midsized/large pharma companies. The BCI aims to become a valuable source for insights on all aspects of the biopharmaceutical landscape.
Our five-part blog series presents highlights of the Q1 2020 Biopharma Confidence Index (BCI). This week, we draw out key observations from those BCI components related to the impacts of artificial intelligence, machine learning, and new technologies.
Artificial Intelligence and Machine Learning: A 12-Month Outlook
The BCI asked respondents to gauge their perception of the importance of artificial intelligence (AI) and machine learning to their company operations. They were also asked to indicate whether they anticipate positive return on investment in AI and machine learning for their top therapeutic areas within the next 12 months. Therapeutic areas represented in the responses included oncology, immuno-oncology, cardiovascular disease, infectious disease, neurology, metabolic conditions, and rare diseases. The data showed that AI and machine learning is valued most highly by those companies working in cardiovascular disease (CVD) research. More so than companies engaged in research for other indications, respondents from CVD-engaged companies report higher expectations for positive return on AI and machine learning investments in the coming year. There is room for discussion as to what factors of cardiovascular disease research make AI and machine learning a desirable fit particularly to this indication.
Artificial Intelligence and Machine Learning: A Five-Year Outlook
Respondents were asked to express their expectations for breakthroughs in AI and machine learning for their top therapeutic areas in the next three to five years. Further, they were asked to assess the importance of AI and machine learning capabilities to the future success of their companies. Their responses indicated that AI and machine learning are expected be instrumental to the success of breakthrough therapies, most notably in cardiovascular disease and infectious disease research. For CROs and consultancy firms, the challenge is to be prepared to support AI and machine learning in the service of drug development. Exactly what types of support sponsors may need vis-à-vis AI and machine learning is an area for CROs to explore.
Applications of Artificial Intelligence and Machine Learning
Respondents were asked whether they believe AI and machine learning will become useful tools in their pre-clinical and clinical development, both in the next 12 months and in the next five years. They were also asked to report their expectations for AI and machine learning applications in commercial planning and execution for the 12-month and five-year timelines. Commercial activities seem better positioned to take on AI and machine learning. However, in both clinical and commercial aspects, the data revealed expectations for an increase in AI and machine learning applications within the next five years. Whether these technologies present opportunities for increased engagement and cooperation between the clinical and commercial groups is an area for further exploration.
With respect to R&D innovation, respondents were asked to express their views on its importance, their confidence in their company’s efforts, and their perception of potential risk. Consistent with decades of pharmaceutical practice, R&D remains foundational to current and future efforts among all biopharma companies. When segmented into the three separate cohorts of start-ups, private companies, and large or midsized biopharma companies, BCI data revealed that the perception of risk is more pronounced for large and midsized companies. What remains unclear is whether the low level of perceived risk among start-ups and private companies is reflective of accurate assessments of limited awareness. Strategies around de-risking may be an area where CROs and consultancy firms become a valuable resource to start-ups and private biopharma companies.
Confidence in Gene Editing and CAR-T
Respondents were asked to express their views on the potential for gene editing to become a new approach in multiple therapeutic areas, both over the next 12 months and within the next five years. They were also asked whether they anticipate CAR-T to allow targeting of solid tumors within the same two time frames. For both gene editing and CAR-T, responses indicated reasonable expectations for their application within five years. It was notable that respondents from companies specializing in oncology reported slightly lower expectations for early adoption of these new technologies, as compared with respondents working in other indications.
Want to Explore These Observations Further?
Join us for “Measuring Biopharma Confidence”, a five-part on-demand webinar series. Each webinar features a panel of experts, who discuss the results of the Q1 2020 BCI in detail. Drawing on their experience and expertise, they glean valuable insights and nuances from the data.
The second webinar in the series, “The Impact of AI, Machine Learning, and New Technologies,” is available now. Watch it here.