Identifying and accelerating drug development is big business. The costs in this industry are significant and finding pathways to optimize using AI methods is top of mind in this fast and evolving industry.
Deloitte found that the average cost of developing a new drug among the top 20 global biopharmas it studied rose 15% ($298 million) last year, to approximately $2.3 billion. That figure includes the average cost of developing a candidate from discovery through clinical trials to the market.
Many biopharmaceutical companies are using AI to speed up drug development. For example, machine-learning models are trained using information about the protein or amino-acid sequence or 3D structure of previous drug candidates, and about properties of interest.
Did you know that fewer than 10% of such drug candidates succeed in clinical trials, and the development costs are between US$30 million and $310 million per clinical trial, potentially costing billions of dollars per drug, and wastes years of research while patients wait for a treatment.
Where can AI and machine learning add value?
Well it comes in a few areas. First AI can be used in drug development to speed up parts of the research process, helping reduce costs and improve efficiency. Research finds that AI can minimize the time taken to screen new drugs by as much as 40 to 50%, reducing the costs significantly.
Finding optimal ways to streamline end to end the entire drug development life cycle is a priority for every major player in the pharma R&D ecosystem and if they are not applying AI, they simply won’t be able to compete.
In my quest to find impactful researchers with credibility to inform my readers, I had a opportunity to interview Dr. Dave Latshaw, founder and CEO of BioPhy, who had early AI impactful roots from The Janssen Pharmaceutical Companies of Johnson and Johnson, where he was responsible for using machine learning to oversee the manufacturing of large molecule antibodies, and was later, the youngest person ever to lead an AI research group at J&J.
Dave is most passionate about using AI methods relevant to the biotechnology and pharmaceutical industries and solve use cases aimed to streamline processes, optimize decision-making, and enhance collaboration within the life sciences sector. Ultimately driving innovation and acceleration in the development of life-saving therapies and treatments for patients worldwide.
There are two key areas in AI use cases in this area that Bio-Phy and other leading AI innovative bio-tech companies that are determined to crack this big problem, including: AtomWise, BioVia, Cradle, DNANexus, exScientia, Iktos to name a few.
Of course, every large pharmaceutical company is embracing AI and have been for some time. How efficient their integrated value chain processes – well that is a story likely for another day.
Let’s look at the drug process logic more closely.
First, in the drug identification stage, what is key is quantifying biological and clinical feasibility data to act as a transparent, data-driven intermediary between biotech startups and pharmaceutical companies. By developing AI models that analyze vast amounts of information – such as scientific literature, patents, clinical trials data, and market trends, early-stage biotech startups are able to demonstrate their competitive advantages and differentiation to potential investors. As a result, these startups can increase their chances of securing funding by showcasing their unique strengths and capabilities, backed by quantitative assessments generated by AI-driven analytical tools. As a result, pharmaceutical companies are able to make better decisions in their portfolios, deploy capital more efficiently and be in a stronger position to receive approval and drive a stronger ROI.
The second area where AI can bring immense value to the drug development process is in using large language systems, to speed up critical drug development functions such as operations, quality, and regulatory. For example, in the area of regulatory intelligence, AI systems can rapidly analyze extensive documentation, guidelines, and regulations to ensure that pharmaceutical companies remain compliant and up-to-date on the latest requirements from regulatory authorities. This not only increases efficiency, but also helps to reduce the risk of non-compliance, which could lead to delays in drug development and approval processes. Customers benefiting from these AI use cases experience tangible improvements in decision-making, risk mitigation, and overall efficiency. In addition, early-stage biotech startups have found it easier to secure funding with the backing of AI-driven, quantitative assessments of their innovations, while large pharmaceutical companies have been able to expedite
Being able to optimize the drug discovery complex value chain is being improved significantly by AI driven approaches, but what is important in building a strong AI go to market offering is to ensure there are insights that others can genuinely learn from.
AI Leadership Journey
Dave also has some very interesting perspectives to share about his own leadership journey in learning about AI that are important insights for any leader undertaking designing and building an AI company, and even more critical in navigating the complexities of doing so in the pharmaceutical industry.Here are some of his key lessons learned.
First, the importance of interdisciplinary collaboration is key as developing AI requires expertise in various disciplines such as computer science, mathematics, cognitive science, and linguistics, among others. Tapping into the knowledge of experts from these diverse fields enables the creation of more robust and effective AI systems. This effect is even more pronounced when individuals have multidisciplinary expertise and understand more than one domain themselves.
Second and well understood but can never repeat this enough is that data quality matters. The success of AI algorithms is heavily influenced by the quality and diversity of the data used for training. Ensuring that the data is accurate, representative, and free from biases helps create AI tools that perform well across different applications and user groups. Algorithms are great but data makes the model.
Third, embracing the value and opportunity of AI given its potential is immense, as it can significantly transform various aspects of our lives and lead to groundbreaking advances in numerous industries. To fully realize these benefits, it is important to stay open to new possibilities, invest in cutting-edge research, and support the development of AI technologies that can empower individuals and organizations to create a better tomorrow. Encouraging responsible AI development and steering AI innovation towards enhancing human capabilities can lead to a more inclusive and prosperous future for all.
Fourth is ensuring as a leader that addressing ethical concerns is a daily ritual. As AI becomes more prevalent in various aspects of life, it is crucial to consider the ethical implications of these technologies. Developing guidelines and frameworks that help prevent unwanted consequences such as data misuse, job displacement, and bias in decision-making can allow AI tools to align more closely with human values and societal norms.
Fifth, the AI learning journey is never over and requires constant learning and evolution. The field of AI is rapidly evolving, and to stay at the forefront of innovation, continuous learning is essential. Embracing new developments allows for the creation of better AI systems that can serve the needs of an ever-changing world. Staying on top of the latest research and forging collaborations between academia and industry can greatly accelerate progress in the field of AI.
I also try in these discovery sessions to identify leading AI researchers that other AI experts admire which is like finding precious gems to help expand our AI mining knowledge.
AI Researchers to Applaud
It turns out that Jürgen Schmidhuber is one of Dave’s and also one of my favorite AI researchers in the field, due to his pioneering work and long-standing influence on the development of artificial intelligence. His early contributions to machine learning and deep learning, especially recurrent neural networks, have laid the foundation for many current AI technologies, and his vision for artificial general intelligence continues to inspire researchers worldwide. Throughout his career, Schmidhuber has pursued the goal of developing AGI, creating AI systems that can perform tasks across a wide range of domains rather than being restricted to narrow, specialized functions and has very recently resurfaced as significant public interest. His research on artificial curiosity and creative machines embodies this vision, striving to create AI systems that can continually learn and improve themselves in a generalizable, unsupervised manner.
Planning a Career In AI – What Pathway do you Choose?
I closed off our discussion to appreciate what advice would Dave like to offer to young leaders and here are his thoughtful perspectives, as I also believe that there is nothing more important that guiding our next generational talent forward and giving them the leadership confidence to shape AI responsibly.
In the world of AI, you can choose between becoming a fundamental innovator who drives cutting-edge research and breakthroughs or a practitioner who effectively applies and implements AI solutions across various industries. Both roles are crucial to the progress and adoption of AI technology, but the paths and focus areas differ between the two.
As a fundamental innovator, you will need to dive deeper into the theoretical and technical aspects of AI, often pursuing higher education like a master’s or Ph.D. in AI-related fields. This specialized education helps in understanding and researching specific domains such as natural language processing, computer vision, reinforcement learning, or robotics. In this role, you contribute by developing new algorithms, advancing the current state of AI and machine learning techniques, or exploring entirely novel applications of AI. A career as a fundamental innovator often involves working in academic research, AI research labs, or R&D departments of tech companies.
On the other hand, as an AI practitioner, your focus will be on applying AI technologies and tools to solve real-world problems across various industries such as healthcare, finance, or transportation. A strong practical understanding of AI, combined with knowledge of the target industry, is key to effectively implementing AI systems, optimizing their performance, and delivering tangible benefits. In this role, you might work on AI system integration, project management, or business analysis, collaborating closely with domain experts and stakeholders.
When deciding between these two paths, reflect on your interests and career aspirations. If you are passionate about pushing the boundaries of AI knowledge and creating groundbreaking advancements, becoming a fundamental innovator might be the right choice for you. However, if you find greater satisfaction in applying AI to derive immediate value and drive impactful change across organizations and industries, pursuing a career as a practitioner could be more fulfilling.
Regardless of the chosen path, maintain strong foundational AI knowledge, engage with the AI community, and stay informed about the latest trends and advancements.
Conclusion
In conclusion, the AI drug development lifecycle is big business. BioPhy, like many other AI bio-tech centric startups are making an invaluable impact. What I enjoyed about Dave’s story was not just in their solutioning AI methods, but in his CEO emotional intelligence and understanding how important talent is and ensuring collaboration and trust are front and center in the journey to solve some of the toughest health and medical problems facing mankind.
