Harnessing the power of AI to expedite HPAPI development
Aaron Johnson, manager of cheminformatics and data science at Lonza, discusses how AI-enabled CDMOs can help address the specific challenges of HPAPI development
High-throughput experimentation is among the techologies Lonza and others use to accelerate clinical trials of small molecule APIs
The pharmaceutical landscape is witnessing a surge in the manufacture of highly potent APIs( HPAPIs), a sector that constitutes an estimated 30 % of the drug development pipeline. Driven by the efficacy of HPAPIbased treatments, significant investment is flowing into this sector, particularly from small and emerging biopharmaceutical companies that seek to leverage the growing market demand for specialised therapies.
Despite their promise, HPAPIs pose substantial challenges for drug developers throughout the development and manufacturing lifecycle. Such challenges include stringent safety protocols, specialised handling requirements and the need for advanced containment technologies, all of which contribute to the high cost and complexity of HPAPI production.
To address these challenges and accelerate development of HPAPI-containing medications, these drugs developers are increasingly outsourcing to CDMOs with the requisite expertise and specialised equipment.
CDMOs, for their part, are making substantial investments in artificial intelligence( AI) and machine learning( ML) to enhance their drug development and manufacturing processes. Their investments are already translating into growing use of AI to optimise synthetic route design, improve process efficiency, and enhance quality control – trends that reflect the broader pharmaceutical industry’ s increasing reliance upon AI-driven solutions.
Leveraging AI for HPAPIs
The intricate molecular architecture of HPAPIs presents a significant hurdle in drug development, notably in the design of efficient synthetic routes. HPAPIs, with their
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