Vesyntas technology integrates drug monitoring hardware with precision dosing software to create a universal bedside platform that tailors medication regimens to individual patient needs. This approach addresses the variability in drug exposure and dosing inefficiencies, enhancing therapeutic outcomes while minimizing adverse effects.
Funding
$310K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Current methods for determining drug dosage often rely on patient body weight or surface area, failing to account for individual differences in drug processing. This can lead to unpredictable drug levels, limiting therapeutic benefits or increasing the risk of toxicity and adverse effects. The resulting sub-optimal drug exposures can cause inequitable outcomes and impact patient quality of life.
Solution
Vesynta offers DosoLogic, a precision pharmacology software platform that integrates drug monitoring data with proprietary algorithms to provide personalized dosing recommendations. By incorporating patient-specific information and pharmacology modeling, DosoLogic enables clinicians to optimize drug regimens, maximizing therapeutic potential while minimizing adverse events. The platform integrates with electronic health record (EHR) systems to ensure seamless accessibility and minimal disruption to clinical workflows. DosoLogic empowers decision-makers with interpretable dosing insights, offering actionable guidance in areas such as oncology, anti-infectives, rheumatology, and immunology.
Target Audience
The primary target audience includes healthcare providers, specifically clinicians in oncology, anti-infectives, rheumatology, and immunology, seeking to optimize drug dosing for individual patients.
Features
- Integration with EHR systems for data collection of patient information, sample data, regimen details, and drug & marker levels
- Drug-specific pharmacology modeling for patient populations
- Simulation of treatment scenarios considering historic patient response
- Generation of multiple dosing options alongside predicted outcomes
- Continuous model improvement using real-world data to enhance predictive performance
- User-friendly clinical interface for interpretable dosing insights