Author: Vikas Kumar
30 July 2024
Key Highlights of the Report:
According to a new report by Univdatos Market Insights, AI in Drug Discovery Market, is expected to reach USD 28.4 Bn in 2030 by growing at a CAGR of 42.4%. Discovery and development of a new therapeutic candidate is one of the most laborious and time-consuming processes in the world. The biggest issue with D&D is the high rate of attrition. This is largely due to the trial-and-error approach used for drug discovery. Fewer than 1% of pharmacological drug leads are converted into drug candidates for clinical trials. Experts estimate that almost 90% of drug candidates considered in these trials fail to move forward in the development cycle. This leads to high costs. A prescription drug typically takes 10-15 years and costs an average of $1-2 billion to go from bench to market. About a third of the above costs are incurred during the drug discovery phase. To address these challenges, such as increasing capital requirements and late-stage program failure, pharmaceutical companies are exploring the use of AI-based tools to improve their drug discovery and development processes using chemical and biological information. It is expected that AI drug discovery will be able to process and analyze large amounts of clinical/medical data and leverage it to improve modern drug discovery endeavors.
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The report suggests that the costly and lengthy process of drug delivery is one of the major factors driving the AI in Drug Discovery Market during the forthcoming years. Developing a new drug typically takes 10-15 years with an average cost of up to $2.8 billion. 80-90% of drug failures occur in the clinic with Phase II PoC trials accounting for the majority of clinical failures. While the number of NMEs approved by regulatory agencies such as the US FDA has increased over the last decade ( 2010-2019) compared to the previous decade, the cost to bring a new drug to the market has increased significantly. Key factors contributing to the increase in pharmaceutical innovation costs include lost investment from late-stage clinical attrition, a more stringent regulatory regime that sets a high approval bar, and increased clinical trial costs, particularly for pivotal trials. These factors drive innovation and adoption of new technologies by pharmaceutical and biotech companies to improve productivity, reduce costs, and ensure long-term sustainability.
Only one in every 5,000 to 10,000 compounds is approved as a drug candidate for a specific condition in the drug discovery process. Al in Drug Discovery has the potential to dramatically reduce the time and cost of bringing new drugs to the market. It also has the potential to discover new treatments for conditions that were previously hard to target.
Fig1: Top countries for AI in drug discovery startups, 2021
Several players in this market are building platforms that can help with drug discovery. For example,
Oncology Segment Gaining Maximum Traction in Market
Oncology drug discovery with AI accelerates anti-cancer drug discovery. The oncology drug discovery segment is expected to grow in the near future as the incidence of cancer is on the rise. The American Cancer Society 2022 estimates that cancer is the second leading cause of mortality in the United States with more than 609,360 new cancer cases expected by 2022. AI accelerates drug discovery for anti-cancer drugs through machine learning and the use of deep learning algorithms. With the help of deep learning, drug candidates can be designed in a de novo molecular structure and their reactions can be predicted. According to a 2022 study published in Nature, AI is useful in the identification of novel drug and anti-cancer target from biological networks. Biological networks help in preserving and evaluating the interactions between the components of cancer cells. Cellular network modeling helps in quantifying the framework that connects network properties and cancer by using AI biology analysis. AI accelerates anti-cancer drug discovery in oncology. In addition, several players in the market are using Artificial Intelligence (AI) in the field of cancer drug discovery. For example, model medicines, an oncology drug discovery and drug development company, announced in October 2022 that it will develop oncology drugs that target the AXL and the BRD4 receptors. In June 2022, another oncology drug developer, schrödinger s.r.o., received approval from the United States Food and Drug Administration (USFDA) for its Investigational New Drug Application (INDA) for a drug called SGR- 1505, an inhibitor of the MALT1 receptor. The company is developing oncology drugs using a physics based software platform. The oncology market is expected to grow significantly in the coming years because of the ongoing research and clinical drug discovery using AI and key developments by market players and pharma companies.
Conclusion
As we delve into the future of drug discovery, the integration of Artificial Intelligence (AI) within this sector presents a beacon of hope in addressing the longstanding challenges of high costs, lengthy development cycles, and the daunting attrition rates that have historically plagued the pharmaceutical industry. The synthesis of AI technologies with the complex processes of drug discovery is paving the way for a new era where the daunting figures of $2.6 billion in costs and over a decade in development time are no longer the norms. Through strategic alliances and the digitalization of biomedical research, AI is enabling a significant leap in how we approach the discovery of new therapeutics. The use of AI-driven solutions in navigating the vast data generated during drug discovery processes exemplifies the shift towards more innovative and effective methodologies. Moreover, the oncology segment, in particular, stands at the cusp of revolutionary advancements with AI. The integration of AI in oncology drug discovery is not only expediting the discovery of anti-cancer drugs but also opening new avenues for treatments that were previously out of reach. With cancer remaining a leading cause of mortality worldwide, the role of AI in this field is a beacon of hope for millions. As the company stand at this pivotal moment, the trajectory of AI in drug discovery heralds a future where the development of life-saving drugs is not hindered by inefficiencies and exorbitant costs. The collaborations between tech giants and pharmaceutical companies, alongside the innovative platforms and solutions being developed, are indicative of a sector that is ripe for transformation. In conclusion, the AI in Drug Discovery Market is on the brink of a revolution, driven by the necessity to overcome the barriers of traditional drug discovery processes.
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