Expanding on AI Reagent Selector, which helps scientists select reagents with greater efficiency, BenchSci has launched a new AI software that aims to expedite preclinical phase drug development pipelines by extracting biological insights underlying disease.

The end-to-end SaaS (software as a service) platform, ASCEND, enables the discovery of biological connections, reduces unnecessary experiments, and uncovers risks at early stages. ASCEND uses BenchSci’s machine-learning technology to extract experimental evidence from secure internal and open external sources. The platform uses curated ontology datasets and compares experimental outcomes. This enables the software to create an evidence-based “map” of the biological mechanisms underlying different diseases.

ASCEND can guide preclinical research by enhancing target selection, conducting due diligence, generating hypotheses, developing optimal investigative approaches, designing experiments, and identifying safety and efficacy risks to support IND (investigational new drug) submissions for clinical translations.

ASCEND incorporates publicly available scientific data from over 15 million published experiments and proprietary data generated by client companies that is securely available to respective customers. This approach helps R&D scientists understand the biological feasibility of new or existing lines of investigation and identify optimal approaches for testing hypotheses.

“BenchSci has developed a technology with the potential to transform the speed and success of preclinical research,” said Philip Tagari, vice president of research at Amgen. Amgen’s preclinical R&D teams have adopted ASCEND to extract and link scientific evidence generated in-house and published in various fields of therapeutic interest.

Early adopters of ASCEND have reported improvements in the identification of new indications or targets (40%), and risks to safety or efficacy that improve R&D productivity (33%). Retrospective analyses of workflows at pharmaceutical companies showed unnecessary experimentations could have been reduced by at least 40% during preclinical programs, had scientists not missed key insights.

The pharmaceutical industry has historically lacked efficiency in the process of discovering and developing new drugs, including considerable wastage of time, reagents, expertise, and expenses. Moreover, biological complexities uncovered in recent times pose a challenge for further discoveries. Tools that help scientists navigate the magnitude of scientific data and evidence currently available are limited.

“Applying deep technology throughout the preclinical research process is desperately needed. Using machine learning to curate large, disparate data sources to better inform researchers is an important step in the right direction, said Jo Varshney, PhD, founder and CEO of VeriSIM Life. “We also need tools that predict the complex behavior of drugs in humans, to reduce risk and pre-empt dead-end drug development.”

BenchSci intends to fill this gap at preclinical stages through the development of an AI platform that expedites the extraction and interconnection of insights from biological evidence to improve research efficiency.

“At BenchSci, we share our partners’ visions to help bring hope to patients faster. Our role in solving this enormous challenge is to develop and train technology that can change the world through the eyes and minds of scientists,” said Liran Belenzon, CEO and co-founder, BenchSci. “It’s not simply the proprietary AI that’s revolutionary. What’s remarkable about ASCEND is the unification of cutting-edge technology, a depth of experience in disease biology and our collaboration with leading pharmaceutical companies that has created the potential to advance the speed and success of better medicine to patients.”

Alex Zhavoronkov, PhD, founder and CEO of Insilico Medicine said, “To the best of my knowledge, BencSci is a reagent selection support company. It is very logical for a company of this type to provide a platform for target research. Qiagen, one of the largest players in the field, acquired Ingenuity to help drive the reagent business, and Ingenuity IPA is still a popular tool for pathway and target research. When it comes to real AI-powered target selection, what matters most is the experimental validation of targets in multiple experimental systems and in humans. This is what the customers desire.”

Zhavoronkov added, “With PandaOmics we have seen cases of novel targets discovered by the systems progressing into human clinical trials. I will be eagerly waiting for the published case studies from the Ascend platform. Target selection and validation is the most important area in the pharmaceutical industry and we need more tools that can demonstrate experimental evidence.”

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