About this database

This is the work of Iris Minichmayr, Vincent Aranzana-Climent and Lena Friberg from the Pharmacometrics research group at Uppsala University.  

If you would like to see detailed information about the drugs, pathogens and/or models included in one article, you can click on the green “plus” sign on the left of each row. In the following, the contents of each detailed table are outlined:  

Minichmayr IK, Aranzana-Climent V, Friberg LE, PPharmacokinetic-pharmacodynamic models for time courses of antibiotic effects, In manuscript, 2022.  

The database presented in this page is dynamic in nature, it will be updated regularly by the authors. If you want to be kept up to date please follow the attached github repository https://github.com/atb-models-review/atb-models-review.github.io  

Search last updated : September 2021  

How to read this table

In the default view, you will see a summary table with the same format as Table S1.
It is searchable using the search field in the top right corner of the table.
The table is paginated and by default 10 articles are shown.
If you want detailed information about the drugs, pathogens and/or models included in one article you can click on the green plus sign at the beginning of each row. Here follows information about what is in each detailed table :

  1. Drug characteristics:
    • Drug class: Self-explanatory
    • Drug: Self-explanatory
  2. Pathogen characteristics:
    • Pathogen(s): Full name of the pathogen(s) investigated
    • Number of strains: How many strains were used to produce the data the model
  3. Growth model characteristics:
    • Growth model: Can comprise: 1, 2 or 3 metabolic states, growing and resting bacteria, 2-state life-cycle model, or other form
    • Growth equation: Equation characterizing the growth rate
    • Delay in growth: Does the growth rate equation describe any delay?
    • Comments: Any additional information
  4. Effect model characteristics:
    • Effect equation: Does the equation link drug concentrations to effect via a linear, linear with power, Emax or sigmoid Emax model?
    • Effect on death or growth: Is the drug effect implemented as a reduction of growth or an increase in death rate?
    • Delay in effect: Does the effect equation include any delay component?
    • Drug: Name of the drug causing the effect
    • Pathogen: Full name of the studied pathogen
    • Comments: Any additional information
  5. Regrowth model characteristics:
    • Regrowth model: Type of regrowth model included in the paper e.g. adaptive resistance, subpopulations, other
    • Comments: Any additional information
  6. Interaction model characteristics:
    • Interaction model: type of interaction model e.g. additivity, Loewe additivity, Bliss independence, Greco model, Other
    • Interaction effect: Parametrization of the interaction effect
    • Perpetrator: Which drug affect the effect of the other drug? (concept by Wicha et al., 2017)
    • Victim: Which drug effect is affected by the other drug? (concept by Wicha et al., 2017)
    • Comments: Any additional information
  7. PK data characteristics:
    • Patient population: Patient population underlying the PK parameters used in the publication?
    • Used for?: Were the PK parameters used as the basis for the kill-curve (dynamic) experiments or for simulations of expected bacterial densities using the final PKPD model?
    • Comments: Any additional information

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