Recursion collaborated with Takeda Pharmaceutical for recognizing novel preclinical Rare diseases

Publisher: The Insight Partners

Artificial intelligence (AI) have successfully used in various areas, which include computer vision, speech recognition, and natural language processing. However, AI is now increasingly getting into the areas that need substantial domain expertise. The fields are biology, chemistry, and it also helps to lower the cost of drug discovery and drug development. Artificial intelligence is used in the drug discovery and development by growing the level of understanding of complex biology, managing drug design, and by assisting other more ordinary elements of pharmaceutical R&D and regulatory affairs. The application of AI decreases researches and development gap in the drug manufacturing process, and it helps in the targeted manufacturing of the drugs.

AI offers numerous benefits in the process of drug discovery and other developments. Some of the advantages of AI techniques include, it enhances the value of the product, makes the drug discovery faster, inexpensive, and more productive.

Various factors are owing to which the adoption rate of AI in the drug discovery has been rising. Some of them include increasing pressure on the drug manufacturer to decrease drug price, a growing number of partnerships and collaborations in the various industries.

Rising Pressure on the Drug Manufacturer to Decrease Drug Price

The global drug manufacturers are under pressure to reduce drug prices by the government of the countries. In 2019, approximately sixty pharmaceutical companies got removed due to increasing the list prices of around 300 drugs. And more drug manufacturers are anticipated to follow suit in recent years quietly.

In Europe, France, Germany, Sweden, and the United Kingdom have developed a set of government controls to limit the rise of prescription drug prices and expenditures.

The rise in drug expenditure has also been growing due to the use of newer and more expensive drugs. The new medicines have higher average prices than the medications used previously, the pressure on its budgets are growing even when consumption levels remain constant. So, these new products that range from advanced treatments to modest improvements over the existing products, which can strain drug budgets when they replace less expensive medications. 

Growing Number of Partnerships and Collaborations

Major players have come in the market by evolving innovative devices. The shift in technology provides an aim for the market players to improve their product. The innovative product assists in enhancing the better results for health and live a better life. The presence of significant players in the market makes it consolidated that are conducting various developmental activities for the AI in the drug discovery. The multiple companies of AI and pharmaceuticals are coming together for partnerships and other activities to enhance the products and systems in the market.

For instance, in January 2019, Recursion collaborated with Takeda Pharmaceutical for recognizing novel preclinical candidates for rare diseases. The aim of this partnership enabled Takeda pharmaceutical to evaluate the preclinical and clinical compounds in above 60 different indications, with new therapeutic candidates identified in more than half-a-dozen diseases. So, Takeda utilized the option for drug candidates in two rare diseases, and the two companies have extended the partnership. Moreover, in January 2019, Recursion signed a licensing deal with the Ohio State Innovation Foundation (OSIF). The foundation has acquired the rights of OSU-HDAC42, a clinical stage compound that which is developed by Recursion for neurofibromatosis type 2 (NF2), which is a rare tumor syndrome.

Furthermore, in January 2019, Atomwise signed a strategic alliance with a contract research organization (CRO) Charles River Laboratories International. Atomwise will support CRL’s discovery and lead optimization.

And in July 2017, GlaxoSmithKline signed a US$ 43 million deal with Excientia for stimulating drug development. GSK indicated it would use AI to discover novel and select molecules for up to 10 disease-related targets across various therapeutic areas. Additionally, in December 2016, IBM and Pfizer signed the latest collaboration for helping to identify next-generation cancer treatments. Pfizer utilizes the computer developed by IBM’s Watson to support drug discovery for cancer immunotherapies. Thus, innovative steps and collaboration undertook by new entrants as well as established market players to improve the quality and efficacy of AI in drug discovery will help in the growth of the market soon.

Challenges in Machine Learning for Drug Discovery

Along with the various benefits offered by AI solutions in the drug discovery industry, there are also particular challenges. The safety of the drug is among the significant challenges faced in the drug discovery process. The information about the effects of drugs and calculating their side effects is a difficult job. Scientists and engineers from Roche and Pfizer are obtaining information from clinical data with the help of AI. Interpretation of this data in the context of drug safety is an active area of research.

The clinical trials are the most expensive stage of drug development. For reducing the costs, it is vital to use the previously gained experience of clinical trials in the early stages of drug development. The safety of the drug discovery is done in by the biomedical data from research experiments should be examined and interpreted with the help of machine learning for predicting a drug’s effects and side effects. And secondly, the data from clinical trials investigated with machine learning supports the explanation of biological data. The mentioned approaches have made it possible to design better preclinical experiments for the most effective therapies with minimal side effects. To decrease the risks associated with data storage, organizations have developed advanced AI systems that are capable of enhancing drug discovery.

Nevertheless, rapidly growing technological advancements in drug discovery are helping industry players to introduce more products. Products that made with the incorporation with technology have shown more valuable benefits, and therefore, the demand is increasing among the patients that will assist them in managing their diseases. Additionally, the support from the regulatory bodies has fostered the innovation in the drug discovery, and the significant growth in product manufacturing. For instance, in July 2016, Innoplexus launched blockchain platform, iPlexus, to increase the amount of data available for drug research and discovery. The latest iPlexus is the first that will allow researchers to upload and license unpublished data on the blockchain to facilitate collaboration, decrease duplicative work, and enhance the speed of innovation. Thus, product innovation of drug discovery has offered more effective and personalized management of the various rare diseases. Hence, owing to the several driving factors, the AI for the drug discovery market is expected to experience profitable growth in the future.