Nanoform Launches Next-Generation STARMAP v2.0, the AI-Based Drug Candidate Selection Tool for CESS

Nanoform Finland Plc, an innovative nanoparticle medicine enabling company, has launched the next generation of its STARMAP AI (artificial intelligence) platform, v2.0. The technology utilizes sparse-data AI to augment experimental results from its CESS nanoparticle engineering process with detailed expert knowledge, allowing reliable predictions to be made regarding partners’ potential success of nanoforming their drug molecules.

CESS^® is a nanoparticle platform technology which produces pure homogeneous drug particles from solution in an excipient-free process. By reducing the particle size e.g., from 10 microns to 50 nm, the specific surface area can be increased by as much as 1000-fold, thereby improving dissolution rate, solubility, and bioavailability. Consequently, Nanoform can help pharma partners progress molecules into development that otherwise may not have been possible. It also opens up exciting possibilities for a wide range of novel drug delivery applications.

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STARMAP^® is a digital version of the CESS^® technology that enables in silico experiments in large quantities, creating fast predictions of which molecules should be nanoformed. This is important since there are more potential drug molecules than particles in the known universe. STARMAP can be a powerful tool for pharma partners to pick suitable drug candidates for further development from their large libraries. The benefits might include faster path to market and new possibilities for broadening and deepening drug pipelines while simultaneously increasing the probability of drug development success.

The STARMAP^® platform can have wide applicability in drug discovery and development as well as in lifecycle management for existing marketed drugs and 505b2-like product development strategies.

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“AI algorithms developed for big data have so far struggled to live up to expectations in pharma because the data, especially for early assets (drug discovery, drug screening), that is available to pharma is typically insufficient for generating reliable predictions. We believe sparse-data AI will work much better – in practice, this means augmenting experimental results with detailed expert knowledge, which can be used to prevent the AI from predicting outcomes that are nonsensical based on prior understanding. There is a lot of untapped potential in sparse-data AI for the pharma industry and the field continues to undergo rapid development in both academia and the industry in general,” said Prof. Jukka Corander, Head of AI at Nanoform.

“By determining which drug candidates are ideal for our CESS^® process, the next-gen STARMAP platform can potentially create new opportunities for our pharma partners. These can include both revisiting drug candidates unnecessarily discarded by AIs trained on old particle engineering techniques, and rapidly picking winners among new drug candidates. Ultimately, the benefit of more advanced AI will be felt by patients as new therapies are accelerated to market,” commented Christian Jones, Chief Commercial Officer at Nanoform

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