HIGH POTENCY APIS complex small molecular structure, often require lengthy synthetic pathways that average around 20 steps, often resulting in extended lead times, increased raw material management and heightened supply chain vulnerabilities. 1 Consequently, process chemists must navigate the complexity of HPAPI design while adhering to stringent standards for productivity, quality, reproducibility and supply chain sustainability.
To mitigate these challenges, pharmaceutical developers are increasingly turning to AI to streamline synthetic route design. Many are using AI to predict viable pathways that experienced process chemists can use to reduce the required number of synthetic steps, thereby accelerating the retrosynthetic process. AI ' s utility extends beyond the development stage, proving instrumental in the HPAPI manufacturing process by addressing challenges related to scale-up, flexibility and containment.
Partnering with CDMOs
The complexities of manufacturing HPAPIs require significant investments in infrastructure, specialised expertise, and time. Often, those complexities motivate small and emerging biotechs to partner with CDMOs that offer these capabilities and are implementing advanced technologies, such as AI / ML models, to create efficiencies for drug developers.
For companies developing HPAPI-based medications, it is important to seek a CDMO partner that will manage the entire drug development process, from initial conception through to commercial manufacturing. This comprehensive, end-to-end approach can drive efficiencies across all stages and offers potential advantages such as streamlining operations, reducing redundancies, and accelerating time to market.
CDMOs are particularly adept at providing integrated solutions for route scouting, process development, scale-up and manufacturing, ensuring seamless transitions between each phase. To maintain and improve upon those capabilities, CDMOs are increasingly investing in AI, particularly in the areas of drug discovery and process optimisation for HPAPIs. Lonza recently launched the Design2Optimise * platform to enhance process development and manufacturing of small molecule APIs.
This combines Lonza’ s AI- Enabled Route Scouting Service, which integrates the company’ s process R & D expertise and proprietary commercial supply chain databases with external partners’ computer-aided synthesis planning technologies, and high-throughput experimentation( HTE) to help improve efficiencies and timelines of clinical trials of small molecule APIs. By combining physicochemical and statistical models with an optimisation loop, drug developers can leverage the platform to build predictive models at speed. This can significantly reduce experimentation time and resource use and accelerate the path to manufacturing.
Future applications
Looking ahead, we anticipate a surge of investment in AI innovations, driven by new developments that further integrate AI-powered data processing with automation technologies. In particular, we expect intensified efforts to incorporate AI into HPAPI manufacturing processes. Those efforts will probably utilise AI-driven simulations to predict reaction outcomes, optimise process parameters and minimise waste, leading to more efficient and sustainable production.
Additionally, AI algorithms will probably play a larger role in realtime process monitoring and control, ensuring consistent product quality and safety during HPAPI manufacturing. Growing numbers of manufacturers will discover how AI can aid in the optimisation of facility design and containment strategies, benefits that may minimise worker
exposure to noxious materials as well as the environmental impact of such materials.
We can also expect increasing use of AI-powered tools to streamline supply chain management, improving the predictability and security of raw material sourcing for HPAPI production. Overall, it is reasonable to assume that manufacturers will increasingly embrace AI as a tool that can enhance safety, improve efficacy and mitigate hazards that may arise in HPAPI production.
While the current global environment can make it difficult to predict the future, we can expect intensifying demand for complex modalities such as HPAPIs as the pharmaceutical market continues to evolve. To meet that demand, the adoption and implementation of AI-driven solutions will be critical in overcoming developmental hurdles, accelerating manufacturing timelines, and ensuring the safe and efficient production of vital therapies. Those factors alone will continue to make HPAPIs a category to watch. ●
Reference: 1: S. Kawano, K. Ito, K. Yahata et al., Sci. Rep. 2019, 9( 1): 8656
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Aaron Johnson
MANAGER, CHEMINFORMATICS & DATA SCIENCE
LONZA aaron. johnson @ lonza. com www. lonza. com
JUL / AUG 2025 SPECCHEMONLINE. COM
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