There has been significant interest in artificial intelligence (AI) for drug discovery. AI can be used to model alternative applications of current drug compounds and determine how particle engineering can produce optimal drug design and formulation. Nanoform is implementing this innovative approach to drug discovery and development as part of an effort to significantly increase the likelihood of identifying successful compounds that can quickly progress to market.
 
Nanoform, a drug enabling nanotechnology company, recently appointed Jukka Corander as head of AI. Corander will be working closely with Nanoform to apply sparse data AI to enhance Nanoform’s STARMAP nanonization technology.  Corander is a leading expert in AI, employing state-of-the-art machine learning techniques to create simulation-based models from sparse data. His recent work with the Wellcome Sanger Institute Cambridge, UK includes the application of statistical machine learning and Bayesian inference algorithms on biological data.
 
The implementation of AI will help define the physical characteristics of drug candidate molecules from limited data to understand how these parameters influence solubility and bioavailability. Sparse data AI will combine with Nanoform’s technology to predict nanonization success for new drug candidates and form a more efficient particle engineering process for drug development. The software will also be used to enhance Nanoform’s manufacturing process by implementing deep learning for consistent, iterative improvement.
 
Contract Pharma spoke with Nanoform’s Niklas Sandler and Jukka Corander about AI and the company’s STARMAP nanonization technology. –KB
 
 
Contract Pharma: Please describe the STARMAP nanonization technology and how it is leveraged in drug development.
 
Nanoform: STARMAP is a navigation tool that identifies the best processing parameters to solve solubility and bioavailability problems using our CESS Technology. We can explore chemical and biological space to identify how susceptible a molecule is to being nanonized and what processing conditions may provide the best results.
 
CP: What aspects of the drug development continuum have the potential to benefit from AI?
 
Nanoform: Most aspects of drug development can benefit from AI as it can be leveraged from early drug discovery through to commercial manufacturing. The main industry focus at present is on drug discovery, however manufacturing is seeing a rise in the use of AI and machine learning to enhance QbD and continuous process improvement.
 
CP: What are some of the current obstacles with AI in the pharmaceutical industry? How can they be overcome?
 
Nanoform: Most applications of AI use Big Data, however, it is not easy to access large datasets in Pharma as a lot of companies prefer to keep their data in-house and not share with others. Therefore, there is a great need for other AI approaches. The application of Sparse Data AI is now providing a significant opportunity for the utilization of diverse data sets where information is limited.