RESEARCH

Nanoform Releases Next-Generation AI-based Drug selection Tool

Nanoform | March 23, 2021

Nanoform innovative nanoparticle medicine enabling company next generation of its STARMAP® AI artificial intelligence platform nanoparticle bioavailability excipient microns nm CESS technology silico pharma drugs lifecycle managementNanoform, an innovative nanoparticle medicine enabling company, has today dispatched the next generation of its STARMAP® AI (artificial intelligence) platform, v2.0. The technology uses scanty information AI to expand trial results from its CESS® nanoparticle designing process with detailed master information, permitting dependable expectations to be made in regards to accomplices' likely success of nanoforming their medication atoms.

CESS® is a nanoparticle platform technology which produces unadulterated homogeneous medication particles from arrangement in a without excipient process. By diminishing the molecule size e.g., from 10 microns to 50 nm, the particular surface territory can be expanded by as much as 1000-crease, subsequently improving disintegration rate, solvency, and bioavailability. Subsequently, Nanoform can help pharma accomplices progress atoms into advancement that in any case might not have been conceivable. It additionally opens up energizing opportunities for a wide scope of novel medication conveyance applications.

STARMAP® is a computerized form of the CESS® technology that empowers in silico tries in enormous amounts, making quick forecasts of which atoms ought to be nanoformed. This is significant since there are more potential medication atoms than particles in the known universe. STARMAP® can be a useful asset for pharma accomplices to pick appropriate medication contender for additional improvement from their huge libraries. The benefits may incorporate quicker way to advertise and additional opportunities for widening and extending drug pipelines while at the same time expanding the likelihood of medication improvement success.

The STARMAP® platform can have wide materialness in drug disclosure and advancement just as in lifecycle management for existing promoted drugs and 505b2-like item improvement methodologies.

"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

Spotlight

Why do the largest biopharma companies struggle to deliver above-average shareholder returns? The short answer: complex, wide-ranging portfolios that undermine focus. According to Bain research, the biggest value creators in biopharma are category leaders. But even category leaders can spread their resources too thin. Companies that combine category leadership with portfolio focus create a powerful multiplier effect and significantly outperform their competitors.

Spotlight

Why do the largest biopharma companies struggle to deliver above-average shareholder returns? The short answer: complex, wide-ranging portfolios that undermine focus. According to Bain research, the biggest value creators in biopharma are category leaders. But even category leaders can spread their resources too thin. Companies that combine category leadership with portfolio focus create a powerful multiplier effect and significantly outperform their competitors.

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