Create a strategic plan for growth and profitability in the Life Sciences industries using AI
Industrialized AI refers to taking artificial intelligence (AI) technologies beyond experimental stages and integrating them at scale within an organization’s core operations. The goal is to make AI a repeatable and optimized part of business processes, enabling AI systems to drive continuous value across the enterprise.
Here are the key components involved in industrializing AI:
By industrializing AI, companies can achieve better productivity, cost efficiency, and innovation, making AI a dependable asset rather than an experimental technology.
A study developed by PwC(1) demonstrates that pharma companies that industrialize AI use cases across their organization have the potential to double their operating profit in 2030.
Despite most pharmaceutical companies started using some AI technologies prioritizing specific use cases, the use of AI could enhance the entire pharmaceutical chain through research and development, manufacturing and operations, go-to-market, and commercial.
This study evaluated more than 200 AI use cases with 25 experts and leaders from healthcare, pharma, and technology, stipulating the percentage of profit increase for each process.
Certain steps within the pharmaceutical value chain offered a greater number of potential use cases compared to others, and the estimation considered the extent to which these use cases could potentially disrupt current business and operational models, as well as the feasibility of implementing these cases.
AI applications in operations generate 39% of the overall impact by addressing the primary cost drivers like production, materials, and supply chain, with solutions like production scheduling optimizers, predictive quality management, and production output enhancers. Research and Development contributes 26% of the impact, focusing on areas like target discovery and drug repurposing. Commercial operations follow closely, accounting for 24% of the impact through initiatives like patient segmentation, patient advisory systems, and commercial analytics.
Enabling functions, such as automated report generation, report validation, and code generation, add another 11% to the overall impact by enhancing the speed and efficiency of essential support areas, including IT, finance, HR, legal, and compliance processes.
This means that driving the growth counting with AI and suppliers that understand the market needs and requirements is the key to a successful implementation of use cases at scale.
Build strong relationships with key stakeholders, including investors,
customers, suppliers, and employees
Much of the success of a large-scale implementation depends on the involvement of top management. It is strategic that top management supports these initiatives and engages stakeholders, customers, suppliers, and investors.
One of the strategies that can help promote and implement these initiatives is the establishment of AI committees that address these key concerns.
The committee can discuss business cases and impacts on best practices and regulatory requirements, align expectations, and provide a solid planning foundation.
This committee can function both as an advisory body and a business unit, helping to maintain compliance within the highly regulated biotech, pharmaceutical, or medical device industry.
Framework for the AI Committee
These steps could help standardize and define the multidisciplinary team that would evaluate the applicability and compliance with regulations and guidelines set by FDA, EMA and OMS.
Step 1: Elaboration of the committee, including its responsibilities.
Step 2: Evaluate the status of AI adoption already established in the organization, including available solutions and services, identifying gaps such as training, governance, and processes.
Step 3: Establishing the committee framework.
Step 4: Engaging other stakeholders: involve and engage all levels of the organization, organize workshops and training sessions.
Step 5: Implementation of best practices: integrity of AI data, governance, cybersecurity, system validation, and infrastructure qualification.
Time to Market Impact
We’ve published a blog post exploring how validation influences a product's time to market—check it out HERE. In it, we discuss how early adopters often gain a competitive edge.
An example of patent expiration in the pharmaceutical sector involves a treatment for managing blood sugar levels in type 2 diabetes, using a hormone analog that influences insulin secretion. This treatment has also gained popularity for weight management due to its effects on gastric emptying, which helps promote a sense of fullness.
In the industry, medications generally have a 20-year patent protection window. When this period ends, products often experience a decline in market value, as seen in cases where a product's value dropped by 50% post-patent expiration. With its expiration date approaching, many industries are rushing to make their projects available.
According to the Biotechnology Innovation Organization (BIO)², on average to develop one new medicine from initial discovery through regulatory approval takes 10-15 years. Looking at individual stages of the process, the averages were 2.3 years for Phase I, 3.6 years for Phase II, 3.3 years for Phase III, and 1.3 years between Phase III and regulatory approval.
So, let’s imagine two scenarios:
Scenario 1
- 20 years of patent
- 10 years to finish clinical studies
- 10 years take advantage of being a pioneer.
Scenario 2
- 20 years of patent
- 5 years of study but fail in phase 2
- 10 years to resume and finish clinical studies
- 5 years take advantage of being a pioneer.
Now, imagine a scenario where you could take advantage of AI and optimize all these phases.
AI may not solve every challenge, but its strategic implementation with knowledgeable suppliers can enhance agility and mitigate business risks, while also contributing to revenue growth.
Do not forget!
Without validation, the biopharmaceutical industry
cannot register or produce their products
Automation and digitalization of manual activities have been presented as a useful solution for productivity gains.
It is possible to use AI across the company including GxP applications (impact product quality, patient health, or data integrity), however, some key points need attention.
AI validation levels define the control measures needed for regulatory compliance. When validating AI computerized system validation in a GxP context, it is essential to follow industry-specific guidelines, such as Good Automated Manufacturing Practice (GAMP5®) and principles laid out by the FDA, EMA, or equivalent regulatory bodies.
The core principles lie in the risk-based approach, AI data integrity, and documented validation processes. To effectively navigate this advancement and address the necessity for periodic review and maintenance, depending solely on traditional paper-based or manually operated electronic methods is not feasible.
The solution to this challenge lies in GO!FIVE®, a digital validation software with templates for AI and traditional technologies for Life Science industries.
Through the experience of more than 16 years and several projects carried out, FIVE has created a knowledge database that is constantly updated.
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References:
(1) Re-inventing Pharma with artificial intelligence. Accessed at: https://www.strategyand.pwc.com/de/en/industries/pharma-life-sciences/re-inventing-pharma-with-artificial-intelligence.html
(2) Biotechnology Innovation Organization (BIO). Clinical Development Success Rates and Contributing Factors 2011–2020. Accessed at: chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://go.bio.org/rs/490-EHZ-999/images/ClinicalDevelopmentSuccessRates2011_2020.pdf
