Skip to main content

Pharmaceutical Companies Explore New AI Applications

How can artificial intelligence (AI) technology improve traditional medicine? With 97 percent of all drug discovery programs reportedly failing, the development of a single new therapeutic involves an average cost of $2.6 billion. Helping to improve that process is a huge upside opportunity.

Today, the complex research framework that's involved in the discovery and development of new therapeutic products makes drug innovation an extremely laborious process, according to the latest worldwide market study by Frost & Sullivan.

Pharmaceutical AI Market Development

More than 60 percent of known diseases remain untreatable. Meanwhile, life sciences companies are making progress in the fields of gene and cell therapies, omics technologies, and smart molecules approaches, creating the need for advanced, cost-effective technologies that can parse large quantities of data.

"Pharmaceutical companies are increasingly recognizing the value of deploying 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, senior analyst at Frost & Sullivan.

This technology will enable scientists to derive structured and unstructured data from multiple sources as never before. Moreover, strategic collaborations with IT vendors can help 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.

The leading 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.

Outlook for Medical AI Application Growth

"Overall, there is a profound and growing scientific understanding of many metabolic and signaling pathways, especially at molecular and genomic levels, which encourages the use of sophisticated technologies to develop groundbreaking therapies," noted Van Cauwenberghe.

As the underlying causes of many diseases remain vague and imprecise, artificial intelligence-oriented approaches have emerged as the ideal mechanisms for finding novel treatments.

Popular posts from this blog

Trillion-Dollar Smart Power Grid Transformation

The global energy landscape is undergoing a significant transformation. Renewable energy sources like solar and wind are rapidly gaining ground as we transition towards net-zero emissions. However, this transition hinges on a crucial but often overlooked factor: the modernization and expansion of our aging power grids across the globe. For decades the backbone of our electricity delivery system has been largely static. The influx of variable renewable energy sources like solar and wind presents a new challenge. Smart Power Grid Market Development Integrating these resources effectively requires a Smarter, more Responsive grid that can handle fluctuating power generation and efficiently distribute it across vast distances. This is where a new trillion-dollar energy market growth opportunity emerges. According to a recent worldwide market study by ABI Research, global investments in public grid digitalization and transmission network expansion must exceed $4 trillion by 2030 to meet our