Bayesian dosing, from Research to Clinical Practice.
Bayesian dosing uses patient data and laboratory results to estimate a patient’s ability to absorb, process, and clear a drug from their system. Using a published population model, DoseMeRx algorithms adjusts the pharmacokinetic and/or pharmacodynamic parameters so that a patient-specific, individualized drug model is built. This individual model is then used to provide a patient-specific dosing recommendation to reach a therapeutic target.
Where there is no laboratory results yet available, DoseMeRx can use a population model with individual patient details (age, height, weight, sex, even genotype if available) to calculate an initial dose estimate.
Bayesian dose forecasting has been used extensively around the world, although its use in a clinical setting has been previously isolated to pockets of clinicians who were familiar with the technology and had a good understanding of both IT and mathematics.
DoseMeRx no longer requires users to be Bayesian experts, making it easy for clinicians to use, and allows for a collaborative team approach to therapy that can be implemented out of the box.
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