Biopharma Companies Integrating AI are Paving the Way for the Life Sciences Industry

It wasn’t long ago when the term “artificial intelligence” (AI) conjured up images of hyper realistic human robots or flying cars. Perhaps it still does for many. But in the biopharma industry AI is rapidly starting to replace outdated methods of research and data collection, drug development, clinical operations, and manufacturing. And although AI is some of the most highly sophisticated technology to date, like David Shaywitz implies in a recent article for Timmerman Report, AI is less flashy robot, more power tool that when used wisely drastically improves processes for delivering personalized care and developing novel medicines (2022).

AI is rapidly becoming an integral part of the way forward for next generation, innovative, and disruptive approaches in the pharma industry. In 2018, the emerging market for AI in drug development was $700M and is predicted to increase beyond $5B in 2024 (JonesDay, 2019). Investment has also been soaring despite the current markets, with the number of AI deals increasing by more than 25% in between Q1 2020 and Q1 2021, and investment doubled to $4B in 2021 for startups using AI-guided drug discovery (AlphaSense, 2022).

In this article, we will focus on the pivotal role AI plays in the life sciences industry. BayBridge’s team of talent managers specialize in varying phases of development in the biopharma industry lifecycle and are working with fascinating companies incorporating AI into their business strategy within these phases.

Research & Development

The preclinical phase is one area AI has been the most pivotal. Before its use, developing a new drug that gained market approval could take 10-20 years and cost up to $2.6B in R&D (DiMasi, et al. 2016 as cited in JonesDay, 2019). Utilizing AI in the R&D process has proven to decrease both time and cost of discovering and developing new drugs.

When it comes to research capabilities, AI is an excellent tool for studying the patterns of various diseases. It can also identify drug compounds that can best treat disease, as well as the specific biological pathways most receptive to these compounds (Goyal, 2021).

AI’s power in drug discovery is a welcome enhancement in the lab. AI doesn’t rely on predetermined drug targets, which ensures the algorithms it uses are unbiased. It also allows for drugs to be screened virtually, which can significantly streamline time and resources for a company. These capabilities mean a biopharma company is better able to invest time and money into drugs that have the highest chance of success from early on in the drug discovery process (Goyal, 2021).

Clinical Development

The current clinical trial success rate, leading to a successful new drug application (NDA) from the FDA is between 10-13%. AI can help resolve clinical development obstacles significantly in several areas. It can both identify appropriate candidates for clinical trials, enhancing and shortening the patient recruitment process; and analyze and organize the data generated from clinical trials more accurately and faster than humans can alone, resulting in better clinical trial design.

Ongoing patient monitoring is also made easier through the application of AI, whether from wearable devices that track real-time patient data or using apps on a smartphone. Ultimately, AI allows for the integration of patient data into the development of personalized medicine, which has not been possible until the last few years.

Manufacturing

AI’s role in enhanced drug manufacturing essentially comes down to refined quality control, speed, and efficiency. Whether companies are manufacturing in-house or outsourcing to CDMOs, integrating AI can assure the highest standard of uniformity in the product; and on the other hand, detect defects and anomalies in product and packaging with greater precision and consistency than humans (Javaid, 2022).

ROI is significantly increased for companies when AI is involved in the manufacturing process because of its potential to eliminate human error. In addition, the COVID-19 pandemic highlighted the need for more efficient and scalable manufacturing processes, which AI is capable of through design optimization and process automation, allowing companies to move new product to market faster and more cheaply than conventional drug manufacturing practices have allowed (Goyal, 2021).

Conclusion

 AI streamlines and enhances many aspects of the biotech lifecycle, from early to late-stage drug development and past commercialization to ongoing monitoring of patients benefiting from a new therapy. As the life sciences industry forges ahead, AI technology is becoming integral to getting innovative and disruptive therapies into the hands of patients quickly and affordably.

BayBridge is proud to work with several successful clients driven by an AI approach to drug discovery. These individuals are proving how the impact of AI cannot be understated in today’s biopharma market, steadily receiving notoriety and funding for their companies.

BayBridge is well-networked with pioneers in AI technology, individuals who embody the top 1% of talent that our consultants strive to work alongside. If your company is seeking leadership with experience in this talent-short field, please reach out today.

Sources

AlphaSense. (25 March 2022). 3 powerful ways AI enhances pharma research. AlphaSense. https://www.alpha-sense.com/blog/3-powerful-ways-ai-enhances-pharma-research/

DiMasi, A., Grabowski, H.G., & Hansen, R.W. (2016). Innovation in the pharmaceutical industry: New estimates of R&D costs. Journal of Health Economics 47, 20-33.

Goyal, K. (7 January 2021). Artificial intelligence in pharmaceutical industry: 8 exciting applications in 2022. UpGrad. https://www.upgrad.com/blog/artificial-intelligence-in-pharmaceutical-industry/

Javaid, S. (24 April 2022). Top 4 AI uses in the pharmaceutical sector. AI Multiple. https://research.aimultiple.com/ai-pharma/

JonesDay. (December 2019). Artifical intelligence and the biopharmaceutical industry: What’s next? Jones Day. https://www.jonesday.com/en/insights/2019/12/ai-and-the-biopharmaceutical-industry-whats-next

Shaywitz, D. (1 August 2022). A glimpse into the adjacent possible: Incorporating AI into medical science. Timmerman Report. https://timmermanreport.com/2022/08/a-glimpse-into-the-adjacent-possible-incorporating-ai-into-medical-science/