Pharmacokinetics · General
Two-Level Elimination
Use two declining post-distribution concentrations to estimate the elimination rate and half-life, with optional forward projection.
Step 1
Enter two levels
Private by design. Values are calculated on this device and are not saved or sent to analytics.
Step 2
Calculated decay
Evidence packet
What this tool is based on
This versioned record keeps the citations, equation or decision rule, units, test coverage, intended population, and exclusions attached to the tool.
How evidence review works →A general educational and clinical-support calculation when two concentrations are known to lie on the same post-distribution log-linear elimination phase.
k = ln(C1/C2)/(t2−t1); half-life = ln(2)/k; optional first-order forward projection from the second level.
Concentrations use mg/L and time uses hours; k returns hr⁻¹ and half-life returns hours.
Known half-life case, optional projection, time-to-target calculation, rising-level rejection, and invalid inputs.
Do not skip
Limitations and exclusions
- Not valid when the later concentration is equal to or higher than the earlier concentration.
- Does not establish that a one-compartment, first-order model is appropriate for the drug or patient.
- Distribution-phase samples, changing renal function, extracorporeal clearance, additional doses, or assay error can invalidate the slope.
- The result does not determine a dose, interval, target, or next sample time.
One clearly labeled, contextually relevant sponsor may support this reference area. Sponsorship will never interrupt inputs, obscure results, influence formulas, or imply clinical endorsement.
About this tool
Two-Level Pharmacokinetics Calculator
Use this two-level pharmacokinetics calculator to estimate an elimination rate constant, half-life, and a projected concentration from two properly timed levels.
The calculation uses a first-order, log-linear elimination model. Distribution, dose timing, sampling accuracy, changing clearance, and the drug-specific clinical context determine whether that model is appropriate.
