This article/interview analysis is sponsored by BenevolentAI and was written, edited, and published in alignment with our Emerj sponsored content guidelines. Learn more about our thought leadership and content creation services on our Emerj Media Services page.
Drug development is a costly, time-consuming, and risky process. The National Centre for Biotechnology Information notes the average cost to bring a new drug to market remains as as $2.8 billion.
As is well-observed throughout life sciences as an industry, the astronomical expense is primarily due to the high failure rate. According to sources as mainstream as The American Society for Biochemistry and Molecular Biology, failure rates in drug development are as high as 90%. Additionally, a study from the UK Department of Health Policy in 2020 found that roughly half of the investigational drugs entering phase 3 of clinical development fail during or after pivotal clinical trials, primarily because of concerns about safety, efficacy, or both, resulting in massive financial losses.
AI has the potential to significantly improve drug development efficiency. Even more recent analyses from the World Health Organization verifies, on a global basis, how machine learning and AI can dramatically enhance drug development and discovery by accelerating the identification of potential drug candidates and optimizing clinical trial designs, thereby reducing time and costs.
In the following analysis of conversations with leaders on Emerj’s ‘AI in Business’ podcast, we provide a closer look at how AI can improve efficiencies in pharmaceutical drug discovery and development. The conversations highlighted below focus on how AI can reduce costs, enhance precision, and increase the success rate of drug discovery and development. Executives interviewed in the series include Anne Phelan, Chief Scientific Officer and Cambridge Site Head of BenevolentAI, and Nistha Jain, Head of Innovation and Digital Technology of Takeda.
Each guest brings their extensive experience in drug development to highlight AI’s transformative potential in the pharmaceutical industry. Where Anne focuses on AI’s ability to navigate the complexities of drug discovery, significantly reducing failure rates and costs; Nishtha emphasizes AI’s role in accelerating drug development, enhancing patient outcomes, and automating processes.
Both guests stress the importance of robust data management and technological infrastructure to maximize AI’s impact. Their shared vision underscores AI’s critical role in improving efficiencies and success rates in drug discovery and development.
The following article synthesizes these conversations into two critical insights for our readers:
- Driving efficiencies in target identification: Leveraging AI to analyze vast datasets and complex relationships to help in target identification by making precise predictions about targets with therapeutic potential.
- Driving value chain efficiencies from targeting with effective strategy: Establishing a clear AI strategy aligned with business goals and encouraging each function to experiment with AI use cases in drug targeting and beyond.
Guest: Anne Phelan, Chief Scientific Officer and Cambridge Site Head, BenevolentAI
Expertise: Clinical project leadership, Drug discovery, Molecular Pharmacology
Brief Recognition: Anne has over 20 years of experience in Pharmaceutical Drug Discovery. She has previously worked with Pfizer Neusentis, where she last served as the COO and Head of Pharmacology. She obtained her PhD in Genetics from the University of Liverpool.
Guest: Nishtha Jain, Head of Innovation and Digital Technology at Takeda Pharmaceuticals.
Expertise: Digital innovation, Digital Transformation, Artificial Intelligence
Brief Recognition: Nishtha Jain heads innovation and digital technology at Takeda Pharmaceuticals. She has over 14 years of global technology experience in biopharma and healthcare and earned her Master’s in Biotechnology from Georgetown University.
Episode 1: Driving Drug Discovery Efficiencies in Life Sciences with AI – with Anne Phelan of BenevolentAI
Anne begins by discussing the challenges and costs of drug discovery and development. She highlights that the process is lengthy, complex, and risky, leading to a high failure rate and significant expenses. She especially emphasizes the importance of selecting the right target for drug development from the very start of the process. If the initial target is correct, it significantly increases the chances of success, regardless of how smartly the drug is designed and refined.
She mentions that currently, there is a 90% failure rate in drug discovery, or that only a small percentage of drugs entering clinical trials successfully make it to launch phase. This high failure rate contributes to the exorbitant cost of drug development, with each successful drug costing around the often-cited figure of $2 billion as the expenses of failed attempts are carried forward.
Anne also discusses the immense complexity involved in drug discovery due to the vast number of genes in the human genome (around 25,000) that could potentially be targeted to treat various diseases. It creates a daunting task for scientists, who must navigate this extensive pool of potential targets to find those with therapeutic relevance for specific diseases. Yet Anne emphasizes that this challenge is precisely where AI can be particularly beneficial.
She makes the case the Emerj executive podcast audience that AI can analyze vast amounts of data and complex relationships among various factors, enabling it to identify critical patterns and insights that would be difficult for humans to discern. In drug discovery, AI can aid in target identification by efficiently sifting through large datasets and making precise predictions about which targets are most likely to have therapeutic potential.
Once scientists have pinpointed a target, such as a specific gene or protein associated with a disease, they need to consider various factors before designing a drug to modulate that target. First, they must decide whether to activate or inhibit the target and understand its expression in the human body. They also need to determine if the drug needs to penetrate the brain or be kept out of the brain for optimal effectiveness:
“So this is where there’s a whole additional suite of AI models and algorithms that can really help us to design the right molecule that we think is going to very selectively, very specifically target your gene of interest. So it is very much sequential and the models, even though the AI has the capacity to reason over huge amounts of data, it can also be very exquisitely specific in the kind of questions you can pose. It’s almost a conversation you can have with the technology.”
-Anne Phelan, Chief Scientific Officer and Cambridge Site Head at BenevolentAI
She further explains two key areas where AI plays a significant role in drug discovery:
- Drug Design and optimization: In the initial phase, scientists utilize a range of tools and models to predict the properties of the drug being developed. These predictions include stability within the human body, pharmacological properties, and other relevant factors. It’s a multifaceted optimization process where scientists aim to tailor the drug to achieve desired characteristics.
- Patient Stratification: While patients may have the same diagnosis, their characteristics and traits vary significantly. AI helps identify and strategize patients based on these unique traits, allowing for a more tailored approach to treatment. By matching the right drug to the right target and patient subgroup, researchers can enhance the likelihood of clinical efficacy, leading to better treatment outcomes. Doing so creates a virtuous cycle where the precision of treatment increases, potentially improving patient outcomes.
Episode 2: Driving Efficiencies Across the Life Sciences Value Chain with AI in Drug Development – with Nishtha Jain of Takeda
Takeda Head of Innovation and Digital Technology Nishtha Jane echoes many of Anne’s sentiments and optimism about these technologies, and how they can drive value from the drug discovery process to throughout the value chain.
Putting a finer point on the precise value of drug discovery efficiencies, Nishtha highlights the improved success rate of AI-discovered drugs in citing December 2023 data where an AI-discovered molecule completed phase one trials with an 80-90% success rate, which is significantly higher than the hisftorical industry average of 40-65%.
She further discusses the impact of traditional analytic AI models and the revolutionary potential of Gen AI in the life sciences industry. While conventional AI models have been valuable in areas like disease diagnosis and stakeholder engagement, Gen AI will significantly enhance these capabilities. She cites data from McKinsey, indicating that Gen AI could generate a value of $60-210 billion for the life sciences industry.
In order to drive the value gains from drug discovery across the supply chain, Nishtha insists that making AI part of deep reservoirs in company culture is essential. Nishtha underscores that, beyond data, several components are crucial for successful AI integrations in life sciences value chains, directly driving efficiencies across the industry. These components include:
- Leadership and change management
- Technology
- Talent
- Regulation and guardrails
Leadership Strategy and Driving Change Management:
Nishtha challenges the executive audience, especially those in the c-suite, to establish a clear AI strategy aligned with business goals and promote AI adoption throughout the organization. Clarity in such a strategy includes encouraging every function to experiment with AI use cases and embedding AI in organizational goals. Leaders should, above all, communicate a compelling narrative around organization-wide digital transformation to build excitement and guide the workforce.
A recent study from Harvard Business School also discusses the importance of psychological safety in diverse teams, highlighting how leadership and effective change management can enhance team performance and drive efficiencies in pharmaceutical drug development.
“So let’s say that a goal could be that every function needs to experiment with an AI use case or needs to use GenAI in one format or the other. If it’s trickled down by the C-suite leaders, who are championing the adoption of AI for the future of business, that’s when the drive or the shift in the mindset will happen, and people will start to change their behavior.
Leaders need to do a great job of communicating a transformation story to build excitement, awareness, guidance, and motivation. Every organization will have those early adopters who are going to champion the message as well.”
— Nishtha Jain, Head of Innovation and Digital Technology at Takeda Pharmaceuticals
Technology:
Additionally, a robust and evolving technology infrastructure is essential. Nishtha advocates for a combination of centralized and decentralized approaches to AI implementation, allowing flexibility in rolling out high-value use cases.
1. Centralized Approach: A single decision-making body rolls out AI use cases across the organization, ensuring uniformity and centralized control.
2. Decentralized Approach: High-value use cases can operate outside the centralized framework, allowing flexibility and innovation across different parts of the organization.
Nishtha supports a dual strategy, ensuring the organization is not restricted by a single body, thereby extending efficiencies across the life sciences value chain.
Talent:
She also highlights the importance of attracting and upskilling talent, noting a significant increase in AI-related jobs in the biopharma industry since the release of ChatGPT.
Earlier this year, McKinsey reported AI-related jobs already quadrupled since ChatGPT’s release in late 2022, with a 43% annual increase among the top 10 pharmaceutical companies. This surge underscores the importance of attracting and recruiting the right talent, upskilling current employees, and effectively allocating talent to appropriate projects to harness the full potential of AI in the industry.
Regulation and Guardrails:
Regulatory aspects, such as data privacy, IP infringement, compliance, and cybersecurity, must be addressed with appropriate policies and guardrails. Nishtha stresses the need to consider the complexity and potential impact of AI use cases, as errors in areas like medical affairs can have serious consequences. Meanwhile, she points out that mistakes in R&D have far less severe or immediate consequences:
“Think of a use case in GenAI that is messing up a medical affairs use case. It can actually have a very negative impact, because in medical affairs, the patients might be directly impacted. Now consider that versus GenAI messing up [projects] within R&D can only destroy some experiments and maybe we’ll have some failed experiments and inaccuracy etc. So that’s the perspective. We need to think of the complexity and impact of the use case and then figure out the best approach to determine the risk appetite and where we are seeing more accuracy versus not.”
— Nishtha Jain, Head of Innovation and Digital Technology at Takeda Pharmaceuticals
Lastly, robust regulatory frameworks and compliance measures ensure the safe and effective use of AI, mitigating risks and streamlining processes.
