Artificial Intelligence Is Propelling the Pharmaceutical Industry by Enabling Smart Drug Discovery Solutions

AI creates growth opportunities in novel therapeutics development by leveraging multi-sourced data, finds Frost & Sullivan
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SANTA CLARA, California, Dec. 17, 2018 /PRNewswire

With 97 percent of all drug discovery programs failing, the development of a single new therapeutic involves an average cost of $2.6 billion. The complex industry/academia/government research framework involved in the discovery and development of new therapeutic products makes drug discovery an extremely laborious process. Consequently, more than 60 percent of known diseases remain untreatable. Life sciences companies, meanwhile, are making rapid strides in the fields of gene and cell therapies, omics technologies, and smart molecules approaches, creating an urgent need for advanced, cost- and time-effective technologies that can parse large databases of information to help develop novel therapies.

"Pharmaceutical companies are increasingly recognizing the value of deploying Artificial Intelligence (AI)-based platforms that can leverage data regarding gene mutations, protein targets, signaling pathways, disease events, and clinical trials to find hidden drug-disease correlations," said Cecilia Van Cauwenberghe, Associate Fellow and TechVision Senior Industry Analyst at Frost & Sullivan. "This technology will enable scientists to derive structured and unstructured data from multiple sources as never before. Strategic collaborations with AI-driven companies can help large pharmaceutical companies establish a robust, AI-based pipeline as part of their portfolios and address new therapeutic areas."

AI-driven tools are encouraging companies to develop therapies for severely underserved areas and are also paving the way for precision medicine through a stratified therapeutics discovery and development approach. Collaborations among database holders, AI developers, and drug manufacturers will facilitate the early development of multiple therapeutics, even those focused on treating rare and chronic diseases.

AI-based technology companies are also empowered to make the most of scientific results and learning systems synergy to ensure a successful clinical translation of therapeutic, diagnostic, and theranostic developments. Some of the key applications of AI technologies in pharmaceuticals include:

  • Drug development: Aids in disease modeling, drug design and development, lead identification, and drug repurposing.
  • Candidates' validation: Helps design and run pre-clinical trials, in silico/in vitro/in vivo studies, and investigational new drug (IND) process.
  • Clinical trials: Supports all processes, from designing the trial to patient identification through data collection, analysis, and report generation.
  • Regulatory approval: Facilitates the approval of application and process, labeling, and safety updates.
  • Precision medicine: Accelerates the development of preventive and personalized care, treatment surveillance, and omics adaptive models.

Source:  Frost & Sullivan

Illustration Photo: One of the first steps in drug development and toxicity testing is creating test systems (assays) to evaluate the effects of chemical compounds on cellular, molecular or biochemical processes of interest. Investigators from the biomedical research community submit ideas for assays to National Center for Advancing Translational Sciences researchers, who then assist with high-throughput small molecule screening using a robotic system. (credits: U.S. National Center for Advancing Translational Sciences, National Institutes of Health / Flickr Creative Commons Attribution-NonCommercial 2.0 Generic (CC BY-NC 2.0))

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