According to the Tufts Center for the Study of Drug Development, the average cost of bringing a new drug to market almost doubled between 2003 and 2013 to $2.6 billion. The lab-to-market timeline increased to reach 12 years, and 90% of drugs wash out in one of the phases of human trials.
Pharma companies are spending more on drug discovery and development than ever, yet developing less successful drugs. Ten years ago, one dollar invested in R&D generated a return of 10 cents. Today, it yields less than two cents.
There is no question that drug discovery has become increasingly competitive and expensive, driving pharmaceutical companies to look into new methods for reducing R&D costs. Artificial Intelligence promises to solve key industry challenges and significantly speed up the discovery process to help companies reclaim a sizeable chunk of their costs.
Whether it’s cost savings or faster drug development, AI approaches such as machine learning create tangible value for companies. The McKinsey Global Institute estimated that AI solutions applied in the pharma industry could bring almost $100 billion annually - and that’s across the healthcare system only in the United States.
Drug discovery is a process that involves sorting and cross-referencing millions of compounds and molecular designs. The effort is time-consuming, even with machine learning support.
AI tools can help by sorting and cross-referencing data to deliver targeted results to speed up the discovery process. To unleash the full power of AI analytics, it is crucial to optimize models on vast amounts of the so-called prediction-first datasets, which entails a continuous feedback loop between the results of wet laboratory and in silico experiments. A good example of this approach is NaturalAntibody’s partnership with Icosagen Cell Factory. Computational solutions trained in this way enable precise and more effective verification of biological hypotheses, allowing the data to be translated into real therapeutic strategies.
Many companies use AI to mine historical information to predict clinical design pitfalls and target drugs for specific disease categories. For example, Microsoft and Eagle Genomics are developing an enterprise research platform that processes large amounts of data on how bacteria, fungi, and viruses play a role in disease.
Process workflows, prioritization, and pipeline management also present a data challenge. Pharma companies typically work on many potential new drugs at once, using multiple complex workflows, including sequencing, molecular engineering, validation, and mapping.
AI can help standardize and streamline data integration across disparate processes; it also improves the speed and efficiency of drug development by reducing costs.
NaturalAntibody solutions have supported many organizations in finding suitable candidates faster thanks to an extensive antibody database and AI-driven antibody analytics.
Learn how AI can help from our latest publication published in Briefings in Bioinformatics: Machine-designed biotherapeutics: opportunities, feasibility, and advantages of Deep Learning in antibody discovery
And if you work with antibodies, see how AI can help you streamline the drug discovery process for yourself - get in touch with us and book a demo to see how our platform works.