This is the second article in a 3-part series examining the future of drug discovery.
Traditional drug discovery is a costly and inefficient process. The average cost of bringing a drug to market successfully is $2.8 billion and takes about 10 years . But that's just for the successful drugs. Between 2006 and 2015, only 9.6% of all new compounds being investigated actually made it to market.
However, in recent years, AI has been revolutionizing the way drug discovery happens. For example, earlier this year, the FDA approved the first clinical trial using efficacy data collected on an organ-on-chip platform, without animal trials.
Startups have been the driving force of this new era, pioneering new equipment, tools, and software to take compounds from discovery to market faster, cheaper, and more efficiently than ever. In tis article we take a look at the advantages of using AI in drug discovery and profile some of the startups revolutionizing the industry.
Applying AI to drug discovery has the potential to revolutionize the way we develop new medications. It has been predicted that AI will save the drug research and development industry $70 billion by 2028 . Along with this, there are several benefits to using AI in drug discovery:
In recent years, a host of startups have emerged focusing on applying AI to various parts of the drug discovery process. Exploring various technologies such as machine learning, predictive analytics, and data mining, these startups are able to address various challenges in the pharma industry.
Launched by Google's parent company Alphabet in November 2021, Isomorphic Laboratories is on a mission to "use AI to accelerate drug discovery and find cures for some of humanity’s most devastating diseases."
Their CEO is none other than Demis Hassabis, also the CEO of DeepMind. DeepMind's AlphaFold project was awarded "Method of the Year" in 2021 by Nature and "Breakthrough of the Year" by Science. If that's anything to go by, we can expect big things from Isomorphic Laboratories in the months to come.
Standigm prides itself on using AI-driven drug discovery methods to deliver a first-in-class lead compound in 7 months.
Founded in 2015, Standigm has raised $71.2M in funding and has developed an early-stage drug discovery workflow AI that hinges on securing novel targets with AI-based deep prediction and analysis.
Founded in 2012, Exscientia is one of the older players in the pharmatech market. They were the first company to automate drug design, and the first to have an AI-designed molecule enter clinical trials.
They are focused on both design and experiment, which accelerates the preclinical drug discovery stage. This means they work on both implementing AI design and precision data generation. Together, this creates a powerful and high-fidelity integrated AI and machine learning system.
Genome Biologics is a data-driven drug development leader in the area of cardiovascular and cardiometabolic health. They use technology platforms to accelerate the development of therapies for illnesses that still don't have an effective treatment - like cardiotoxicity, heart failure and hypertrophic cardiomyopathy.
Their technology centers on translating AI-driven big data analysis into a precision preclinical organoid (TrueCardium™). Candidate molecules identified in drug discovery are then tested and validated on this organoid, instead of in preclinical animal trials. This enables Genome Biologics to accelerate drug development while making it more accurate, efficient, and affordable.
Insitro lies at the intersection of human biology and machine learning. Their aim is to improve predictions about which paths are more likely to lead to successful medicines.
They're making waves in the pharmatech industry. Recently, Insitro raised their series C funding of $400 million, and agreed a five year drug discovery partnership with Bristol Myers-Squibb.
Born in late 2020, Zephyr AI proclaims itself "the world's most cutting-edge algorithms for advancing human health". A unified platform, Zephyr AI turns disparate data into actionable insights.
Zephyr AI does this by harnessing a range of data points from different outputs, such as protein-protein interactions, DNA methylation, animal models, clinical trial data, organoids like the AKITA Plate, and many more. This data is then translated into applications and recommendations to improve different areas such as clinical trial and design, precision treatment recommendations, and of course, drug development.
Thanks to the advent of a range of breakthrough technologies, the future of AI in drug discovery looks optimistic. According to a 2022 report  by The Business Research Company, AI in drug discovery is growing at a rate of 30% per year. The largest branch of technology driving this growth is the adoption of cloud-based applications and services.
End-users of cloud-based drug discovery platforms tend to be large pharmaceutical companies, as they hold most of the high-value market. For example, Amazon Web Services announced in December 2021 that they were partnering with Pfizer to develop cloud-based solutions that could accelerate and improve drug discovery.
Startups will continue to play a large role in the new era of drug discovery powered by AI. Their ability to be more agile and work under resource-constrained than their more established counterparts, giving them the creative and iterative advantage to problem-solve faster.
High throughput organ-on-chips like Finnadvance's AKITA Plate can greatly aid this part of the process, by more accurately and reliably replicating in vivo conditions and eliminating the need for animal testing in preclinical stages. Startups can tap into this technology to help identify candidate compounds with higher potential for success, faster.
AKITA is the product line by Finnadvance Ltd., a Finnish biotechnology company that develops highly scalable organ-on-chip platforms. Our lab and team operate in Oulu, Northern Finland. By enabling physiological model developments, drug development and research can be significantly accelerated and made highly cost and resource-efficient, while producing more reliable results. We provide the highest throughput platforms with accurate biological modeling.
This is the second article in a 3-part series examining the future of drug discovery.
The first article looked at advances in AI drug discovery
The second article demonstrated the role of startups in revolutionizing the healthcare industry.
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