What Experts Say About PrecisePK's Bayesian Approach

The program that provided
the least biased estimation of AUC

was PrecisePK.

In a recent paper published by Turner, R.B., et al. in the journal Pharmacotherapy, PrecisePK was shown to be the most accurate in predicting the Vancomycin AUC in critically ill patients. The graph on the left shows the ratio between the empirically measured AUC (using the trapezoid rule) and the AUC predicted by the software.

PrecisePK was able to predict the Vancomycin AUC most accurately due to the comprehensiveness of the pharmacokinetics models and the two-compartment kinetics models used by the program in its computations.

Read This Paper
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How Bayesian-Guided Dosing Works
Curious about the science behind PrecisePK's precise and individualized dosing? PrecisePK utilizes machine learning and Bayesian principles to provide you with the best recommendations to reach your therapeutic targets. Learn how it works below.
Initial Population Model
1. SME Input & Research Paper
2. Patient-Specific Characteristics
3. Population PK Model
4. Patient Covariates
5. Initial Recommended Dose
Refined Individualized Model
6. Corresponding Serum Level Concentration
7. Individualized PK Model
8. Optimized Dose to Achieve PK Target
1. SME Input & Research Papers
By reviewing published literature and research, a population pharmacokinetic (PPK) model can be drawn for specific populations.
2. Patient-Specific Characteristics
Relevant patient-specific info such as age, sex and weight are taken into account to generate a patient’s profile.
3. Population PK Model
Based on the entered patients’ info, patients are assigned into appropriate clusters with corresponding PK parameters. This forms the population PK model.
4. Patient Covariates
Extra drug factors (i.e. obesity, ICU, burn, amputation, etc.) will be taken into account and used to adjust PK parameters accordingly.
5. Initial Recommended Dose
Population-based PK parameters are combined with new patient-specific data to generate the first dose.
6. Corresponding Serum Level Concentration
After the first dose, serum level of the drug will be measured to quantify the total drug exposure.
7. Individualized PK Model
This resulting plasma exposure concentration can be then used to produce a more refined, individualized PK model that is specific to patient’s physical adaption to the drug. This is known as Bayesian posterior.
8. Optimized Dose to Achieve PK Target
With the refined PK model, optimized & individualized dose can be calculated. PK model gets continuously refined as new lab results and information come in.
Why Bayesian-Guided Dosing?
Need help deciding? Consider these benefits below as to why Bayesian is the way to go.
Safer and more accurate vancomycin serum level predictions compared to formula-based methods, such as trapezoidal method.
When administered in excess, vancomycin can lead to acute kidney injuries (AKIs) and nephrotoxicity. Evidence shows that predicted vancomycin concentrations using Bayesian method were more accurate and closer to actual concentration levels as compared to using formula-based methods.
Improves logistical workflow by saving time and reducing human computational effort.
The Bayesian approach to AUC estimation requires as little as one serum level to calculate AUC while manual calculation of AUC requires two serum levels. Manual calculation can also be time-consuming and computationally complex. Traditional trough-based methods of monitoring require time-sensitive blood draws to get an accurate trough level. Therefore, Bayesian method allows for more flexibility and ease in the clinical workflow.
Highly adaptive and responsive to unique individual characteristics.
Both manual AUC calculation and trough-based monitoring only provide snapshots in time. In contrast, the Bayesian approach to dosing accounts for a patient’s physiological change throughout the entire course of their treatment. Each new serum level collected helps improve the modeling of a patient’s unique PK parameters, further improving dosing recommendations and predictions.
Save on immediate costs such as blood draws and long-run costs such as cost of nephrotoxicity incidence, extended hospital stay and more.
Collecting multiple serum levels not only adds inconvenience to clinical pharmacists’ workflow but also potentially increases operational cost. In the long-run, there could be added costs related to inaccurate dosing regimens using the traditional methods such as nephrotoxicity and AKI treatment. These unnecessary costs could be avoided using Bayesian-guided dosing.

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