BenchSci has introduced ASCEND, an AI software that accelerates preclinical drug development by extracting biological insights. ASCEND is an end-to-end SaaS platform that reduces unnecessary experiments, discovers biological connections, and identifies early-stage risks. Using machine learning, it extracts experimental evidence from secure internal and open external sources, compares outcomes, and creates an evidence-based "map" of disease mechanisms. ASCEND streamlines drug development by providing insights and enabling informed decision-making.

ASCEND, the AI software developed by BenchSci, aids preclinical research by providing guidance in various areas such as target selection, due diligence, hypothesis generation, investigative approaches, experimental design, and identification of safety and efficacy risks. The platform utilizes a vast amount of scientific data, including over 15 million published experiments and proprietary data from client companies. By leveraging this data, ASCEND enables R&D scientists to assess the biological feasibility of different research directions and determine the most effective approaches for testing hypotheses.

Figure .1 Launch of an AI Tool By Benchsci to Map Disease Biology for Preclinical Drug Development

Figure 1 shows ASCEND, BenchSci's AI software, assists in preclinical research by guiding target selection, hypothesis generation, experimental design, and risk identification. It utilizes extensive scientific data, including millions of published experiments and proprietary client data. ASCEND helps R&D scientists assess the feasibility of research directions and optimize their approaches for hypothesis testing.

Early adopters of ASCEND, BenchSci's AI software, have reported significant improvements in identifying new indications or targets (40%) and mitigating safety or efficacy risks to enhance R&D productivity (33%). Retrospective analyses have shown that unnecessary experiments could have been reduced by at least 40% during preclinical programs if key insights had not been overlooked.The pharmaceutical industry has historically faced challenges in the efficiency of drug discovery and development, leading to wasted time, resources, and expenses. The growing complexity of biological systems further complicates the process. Currently available tools to navigate the vast amount of scientific data and evidence are limited.

Experts emphasize the urgent need to incorporate deep technology, such as machine learning, into preclinical research to improve efficiency. The curation of diverse data sources through machine learning is seen as a crucial step forward. Additionally, there is a call for tools that can predict the complex behaviour of drugs in humans, reducing risks and avoiding dead-end drug development. BenchSci aims to enhance research efficiency by developing an AI platform that facilitates the extraction and interconnection of biological insights during the preclinical stages of drug development.

BenchSci's CEO and co-founder, Liran Belenzon, emphasizes their commitment to supporting their partners' goals of expediting patient care. They view their role as developing and training technology that can revolutionize scientific research. The ASCEND platform combines state-of-the-art AI technology, extensive expertise in disease biology, and collaboration with leading pharmaceutical companies to accelerate the development of improved medicines for patients.

According to Alex Zhavoronkov, CEO of Insilico Medicine, BenchSci's move into providing a platform for target research is a logical step for a company focused on reagent selection support. He highlights the importance of experimental validation of targets in multiple systems, including humans, for effective AI-powered target selection, which is a key requirement for customers in the field.

"With PandaOmics, we have seen cases of novel targets discovered by the systems moving into human clinical trials," Zhavoronkov continued. The case studies from the Ascend platform that have been published will be anxiously awaited by me. The pharmaceutical industry's most crucial area is targeting selection and validation, and we need new technologies that can show experimental data in this regard.[1]

References:

1. https://www.genengnews.com/news/benchsci-launches-ai-tool-to-map-disease-biology-for-preclinical-drug-discovery/

Cite this article:

Janani R (2023),Launch of an AI Tool By Benchsci to Map Disease Biology for Preclinical Drug Development, Anatechmaz, pp.290