Methodology
How Doseline Calculates Your Levels
The pharmacokinetic math behind the medication level charts — explained without the textbook.
The Short Version
Every time you log a dose, Doseline calculates an estimated concentration curve for that medication in your body. It uses a well-established pharmacokinetic model called the Bateman equation to simulate how your body absorbs and then eliminates each dose over time.
For repeated dosing — which is most of you — we stack (superpose) multiple dose curves together. After 4–5 half-lives of consistent dosing, this converges to a steady-state pattern where your peaks and troughs become predictable.
That’s the whole idea. The rest of this page is the detail.
The Bateman Equation
The Bateman equation models a two-compartment system: one for absorption (the medication entering your bloodstream) and one for elimination (your body clearing it). Two rate constants drive the math:
- ka — the absorption rate constant. How quickly the medication moves from the injection site (or gut, for oral medications) into your bloodstream.
- ke — the elimination rate constant. How quickly your body clears the medication once it’s circulating.
The formula looks like this:
concentration = dose × F × ka / (ka − ke) × (e−ke×t − e−ka×t)
Where F is bioavailability (the fraction of the dose that actually reaches your bloodstream — 100% for IV, lower for subcutaneous or intramuscular routes) and t is time since the dose.
What this produces is a curve that rises to a peak (Cmax) as the medication absorbs, then decays exponentially as your body eliminates it. The shape of that curve — how fast it rises, how high it peaks, how slowly it falls — is entirely determined by ka, ke, and F for each specific medication and route.
Superposition
Most medications aren’t single-dose affairs. You take them on a schedule — weekly semaglutide, twice-weekly testosterone, daily estradiol. When you take a new dose before the previous one has fully cleared, the concentrations overlap.
The principle of superposition says we can handle this with simple addition. Each dose generates its own independent Bateman curve. At any point in time, your total estimated concentration is just the sum of every active dose’s contribution.
This is computationally straightforward — Doseline calculates each dose’s curve separately and adds them together. No fancy interaction modelling. Just honest stacking.
Steady State
Here’s where it gets satisfying. If you dose consistently (same amount, same interval), after about 4–5 half-lives the amount entering your system with each dose equals the amount leaving between doses. Your peaks and troughs stabilise into a repeating pattern.
This is steady state. For semaglutide (half-life ~7 days), that’s roughly 4–5 weeks. For testosterone cypionate (half-life ~8 days), similar. For short-acting peptides with half-lives measured in minutes, you reach steady state within hours.
Doseline displays this on the chart. You’ll see the early doses building up, then the pattern flattening into a predictable wave. That wave is your steady-state profile — and it’s what your bloodwork results should roughly align with.
Where the Parameters Come From
The accuracy of any PK model depends entirely on the quality of its input parameters. Doseline’s ka and ke values come from published pharmacokinetic studies, stored in our medication catalog database. Each medication entry includes its source citations so you can verify the data yourself.
The catalog covers 177 compounds across GLP-1, testosterone, HRT, and peptide categories, each with route-specific PK parameters. Some medications have excellent data from large clinical trials. Others — particularly research peptides — rely on smaller studies or pharmacological estimates.
We’re transparent about this. Every medication shows its data quality tier so you know exactly how confident the estimate is.
What We Don’t Model
Honesty time. Here’s what the Bateman equation with population-average parameters cannot account for:
- Individual metabolism variation — we use population averages. Your actual ka and ke will differ based on genetics, liver function, body composition, and other factors. Two people on the same dose will have different curves.
- Protein binding — many hormones bind to carrier proteins (like SHBG for testosterone). We model total concentration, not free/bioavailable fractions.
- Drug-drug interactions — some medications affect how others are metabolised. We don’t model these interactions.
- Body composition effects — fat-soluble medications distribute differently depending on body fat percentage. We don’t adjust for this.
- Injection site absorption differences — a deltoid injection may absorb at a different rate than a glute injection. We use a single ka per route.
- Non-linear pharmacokinetics — some medications at high doses saturate their elimination pathways, changing the math. We assume linear (first-order) kinetics.
These charts show a population-average estimate. Your actual levels will vary. They’re useful for understanding patterns and timing — when your peak is, when your trough hits, whether your dosing interval makes sense — not for replacing bloodwork.
Bloodwork tells you what’s actually happening. The chart helps you understand why.
See It in Action
The medication level plotter lets you visualise these curves for any medication in the database. Pick a compound, set your dose and frequency, and watch the model build.