Artificial Intelligence solution for ADMETox prediction
Discover our next generation AI solution for discovery of absorption, distribution, metabolism, excretion, and toxicity properties
ADME (Absorption, Distribution, Metabolism, Excretion) and Tox (Toxicity) properties are crucial factors in the development of safe and effective drugs.
Absorption: measures the capacity of a compound to penetrate the bloodstream - sometimes via mucous surfaces like the digestive tract.
** Distribution:** measures the capacity of a compound to be carried to its effector site.
** Metabolism:** measures how a drug will perform biotransformation through the action of given organs or tissues so it can be properly excreted.
** Excretion:** measures how well a metabolized drug compound will be eliminated from the body.
** Toxicity:** measures if a drug will have a toxic effect on body mechanisms or organs.
Establishing ADMETox properties typically involves expensive and time-consuming experimental methods, such as cellular or animal models and clinical trials, which also raise ethical concerns about animal suffering.
In vitro methods are often expensive and can only test a single sub-property (e.g. such as testing whether compounds cause a particular type of liver toxicity).
In vivo methods, using animals, bring a host of ethical challenges, can cause unneeded suffering, generate significant costs, and are not always predictive of how drugs will behave in humans. Despite their importance, there are currently few robust computational methods available to exhaustively predict ADME/Tox properties, in particular for compounds where limited data is available. This leads to high costs and late-stage failures in the drug discovery process, which can negatively impact patients.
Zepto.Ward, a next-generation ADMETox solution
Zepto.Ward is one of the applications of Kantify's Zeptomics platform. It is is a machine learning solution (AI) which predicts the ADMET properties of compounds.
It can accurately predict over 80 properties related to absorption, distribution, metabolism, excretion, and toxicity properties, how a specific compound or combination of compounds will perform.
Zepto.Ward brings together a number of AI innovations, and has been trained on millions of datapoints.
It is novel and unique in the following ways :
It can predict ADMET properties across 80 endpoints and enable drug researchers to proactively predict and mitigate unknown effects.
It works on compounds that have never been tested yet and it does not need anterior data to generate results.
It can generate results in a matter of minutes.
It has a very high performance.
For these reasons, Zepto.Ward accelerates the work of drug researchers.
Zepto.Ward enables to filter out compounds with non-promising profiles and search for compounds with better profiles purely computationally, significantly decreasing the need for in-vivo and in-vitro testing. Zepto.Ward achieves state of the art results and runs extremely quickly, enabling to search vast chemical spaces for the best possible compounds.
Using Zepto.Ward significantly increases the odds of success in clinical trials and reduces the time and cost associated with experimental ADME/Tox screening. With Zepto.Ward, we revolutionize the drug discovery process and bring new and safer drugs to patients more efficiently and effectively.
Last, Zepto.Ward can be used as a tool to reduce animal experimentation and testing, because it can filter out compounds with bad ADMETox properties, and identify how to improve a compound to make it less toxic. That way, only promising compounds are tested in vivo.
Kantify collaborated with I-Stem, the largest French laboratory for research and development dedicated to human pluripotent stem cells. In this project, Kantify and I-Stem used Zepto.Ward to perform an artificial intelligence-based predictive ADMET characterization of promising hits, which, combined to a screening performed by I-Stem, led to identification of the HDAC inhibitor givinostat as potential therapeutical candidate.
The publication Disrupting proteasomal and autophagic degradation systems of misfolded alpha-sarcoglycan protein by bortezomib and givinostat combination is available on Authorea.
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