Save millions of dollars in R&D while drastically reducing the need for excessive and unreliable animal clinical trials.
The attrition rate is a result of the current paradigm in preclinical drug development that is too slow, expensive, and inefficient.
Differences between animal and human physiology - such as tissue composition, gene expression, and disease pathogenesis - mean that animal models may generate misleading conclusions.
In silico practices are still new, and underutilize advanced technical and engineering infrastructure to scale models to large datasets or different disease conditions.
The developed quantitative relationships overfit the data to some outcome, generating seemingly high accuracy but not translating well to new compound prediction.
Researchers developing their own models need a complete input dataset to obtain useful predictions, and manual optimization of unknown or variable drug parameters can take days.
Poorly labeled and unsuccessful datasets impact model performance.
VeriSIM has developed a platform that integrates machine learning with robust models of in vivo pharmacokinetics and pharmacodynamics, enabling faster model development, more accurate prediction, and higher scalability.
We leverage existing datasets of physiological outputs from different species to train a model that is physiologically-relevant and translatable across different experimental conditions.
With our framework, we can quickly develop custom, specific models.
We simulate various routes of administrations such as IV, oral, and transdermal and others.
Validated with both FDA and proprietary compounds.
Our platform is cloud-based and generates predictions in seconds.
We are always adding new functionality and expanding the domain of applicability.
Our model extracts conclusions from complex and convoluted data.
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