AI/ML Advancements in Drug Formulation – The Potential of Self-Driving Labs
Introduction
The journey from drug discovery to market is fraught with challenges, particularly during the drug formulation stage. Formulation approaches have typically relied heavily on trial and error, a method that has led to significant bottlenecks in developing new and more effective drugs. One of the key challenges in this area is solubility; approximately 80% of new chemical entities (NCEs) in the pharmaceutical pipeline are reported to have poor solubility. This is an issue because the solubility of a drug formulation directly affects its bioavailability and therapeutic efficacy.
Biopharma companies’ expenditures on research and development have increased 51-fold over the last four decades. However, the clinical success rate for new drugs is still at 10% due to a lack of efficacy and safety concerns. Therefore, there is an urgent need to re-evaluate drug formulation strategies.
Additionally, it is critical to ensure that the data used to train formulation models is accurate. If the data is biased or unrepresentative these models will not be adequately prepared to navigate the complexities inherent in drug formulation. This could contribute to a skewed understanding of formulation processes.
Intrepid Labs
To address these critical challenges, Intrepid Labs, a biotech startup based in Toronto, is developing a novel methodology for traditional formulation approaches. The company leverages machine learning (ML) models to propose new drug formulation designs based on extensive experimental data.
A self-driving lab is an intelligent experimental platform that combines AI/ML and robotics to automate and optimise experiments. This innovative setup facilitates rapid experimentation and data collection, enabling a more efficient and effective drug formulation process. In this self-driving lab, robotic systems autonomously conduct experiments, thereby accelerating the drug development process.
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Intrepid Labs’ ML-driven platform has the capacity to explore vast formulation design spaces, sometimes encompassing as many as 1 billion potential formulations. This expansive capability allows for identifying novel and optimal formulations, ultimately enhancing therapeutic development while optimising drug properties. Christine Allen, CEO of Intrepid Labs highlighted the unique nature of Intrepid Lab’s platform: “Unlike the approach of collecting a lot of data or curating large datasets, we can start with no data. That’s really one of our greatest strengths.”
Intrepid Labs employs a business model that involves partnering with companies facing challenges with drugs in clinical trials, whether due to underperformance or the need for new formulations to extend the life of a drug coming off patent. The biggest hurdle for Intrepid Labs lies in educating the market about the potential of their technology and fostering confidence in the integration of AI into the drug development process.
Future Directions
Looking ahead, Intrepid Labs envisions a landscape of precision medicine where patients' needs can be met with reformulated drugs. The ability to quickly reformulate failing drugs could reshape clinical trials, ensuring that they remain active while simultaneously reducing costs and enhancing success rates.
Intrepid Labs is committed to keeping the public informed and educated about the benefits of their technology. They offer lab tours and share insights through case studies, fostering a better understanding of their processes. In the long term, the company anticipates a future where formulation development becomes increasingly automated and seamlessly integrated into routine clinical and pharmaceutical practices. This could potentially allow for custom formulations prepared on demand for individual patients.
Conclusion
Overall, Intrepid Labs has developed a proprietary technology that capitalises on the power of machine learning and robotics to accelerate drug development. By addressing the challenges of traditional formulation methods, Intrepid Labs aims to transform the drug development landscape and pave the way for more effective therapies and improved patient outcomes. Self-driving labs represent a promising future in which innovation and efficiency drive the next generation of pharmaceuticals.
