What Is the Translational Gap and How Do You Close It?
10.20.2022
Drug discovery has a translatability problem.
The National Institutes of Health (NIH) once famously reported that for every drug that receives FDA approval, another 1,000 fail. In drug development, translatability refers to the basic principle of taking scientific findings from a laboratory setting and successfully translating them as therapeutic patient treatments in a clinical setting. This is sometimes also referred to as the bench-to-bedside process.
But the drug discovery landscape is notoriously difficult to navigate and translatability can be elusive; pre-clinical failure rates for novel therapies are at around ninety percent, with an average time-to-market of 10-15 years and costs ticking upwards of US $2.5 billion.
As a result, the entire biopharmaceutical industry is under increasing pressure to transform drug discovery, particularly its slow and costly R&D processes, in order to find novel ways to close the translational gap – what’s been termed the “valley of death” in drug development.
The translational gap in drug development refers to the routine failure of the bench-to-bedside process – literally the lack of successful drug candidates that make it all the way through discovery and research to clinical implementation in patients.
Translational medicine, sometimes also called translational science, is a multidisciplinary field of investigation that seeks to improve outcomes where they matter most: for patients. The goal of translational medicine is to find novel and effective ways to move basic scientific discoveries into real-world applications and practices with greater speed and efficiency – ultimately improving prevention, diagnosis and therapies.
Translational medicine is helping to bring more efficiency and predictability to diagnostics, therapeutics and medical procedures. The last decade has seen immense progress across a range of translational areas, particularly inside drug development where
AI-powered applications like deep learning, machine learning and natural language processing are being used to help close the translational gap, advancing to trial the drug candidates that have the best chance of success. These AI technologies can detect patterns and find meaningful relationships faster than conventional methods, giving them the ability to sort millions of potential molecular compounds, looking for candidates with the right properties to move forward in early lab experiments.
AI-enabled translational medicine can also help streamline trial design, improve drug efficacy and safety, accelerate speed-to-market, and significantly cut costs and reduce waste. This translational approach is helping biopharmaceutical companies mitigate their risk and scale their operations, using AI techniques that go far beyond the powers of standard modeling to prioritize the experiments that show the most promise. In this way, translational medicine is helping produce better, more targeted therapies that can reach more patients in need.
There are 3 primary types of translational research, which fall into the following broad categories: benchside, bedside, and community. These translational research types are further classified according to their phase of translation.
According to the National Center for Advancing Translational Sciences (NCATS), translational medicine relies on these 7 scientific and operational principles, which can be used as tips to help guide researchers and drug developers who are working to improve translatability.
Closing the translational gap in drug R&D will mean developing solutions that directly address its most long standing roadblocks. While very real progress has been made toward closing the translational gap using AI-based tools, barriers remain, particularly when it comes to clinical implementation. However, as innovations in AI tech continues to transform translational medicine into a more predictive science, we can expect to see more novel therapies making it from bench to bedside.
VeriSIM Life’s BIOiSIM platform and unique Translational Index™️ technology are helping close the translational gap in drug R&D. Contact us to find out how, and check out our publications for peer-reviewed research, white papers and a range of resources on topics related to AI-powered drug development.
Learn more about VeriSIM Life’s BIOiSIM platform and unique Translational Index™️ technology.
AtlasGEN Novel Drug Designer efficiently identifies new molecules based on favorable chemical properties and simultaneously evaluates them for clinical safety and effectiveness based on biology using VeriSIM Life’s groundbreaking Translational Index™ technology.
Iterative ranking of target engaging hits by their Translational Index values reduces the time and cost associated with other computational hit discovery approaches, while compressing the hit-to-lead refinement and optimization process by pre-validating hits for translatability. This unique integration dramatically reduces the number of compounds to evaluate in subsequent experimental research.
The ability to generate target engaging compound hits depends largely on the size of the molecular search library. The AtlasGEN search space is supported by more than 1 trillion compounds. Generative AI and deep learning techniques deliver unprecedented discovery capability.
AtlasGEN accurately predicts protein-lygand binding affinity by combining geometric conformer analysis with machine learning based on experimental data 72% more efficiently than molecular docking-based approaches.
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