pkpd-modeling
Pharmacokinetic and pharmacodynamic modelling and simulation - non-compartmental analysis, compartmental and population PK, PK/PD and exposure-response, TMDD, PBPK orientation, bioequivalence, allometric scaling and first-in-human dose, drug interaction prediction, and Bayesian therapeutic drug monitoring. Use when analysing concentration-time data, deriving exposure metrics, fitting PK or PD models, or evaluating dosing regimens. Triggers include "pharmacokinetics", "pharmacodynamics", "PK/PD", "NCA", "non-compartmental", "AUC", "Cmax", "lambda z", "half-life", "clearance", "volume of distrib
npx skills add K-Dense-AI/scientific-agent-skills --skill pkpd-modeling --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Pharmacokinetic and Pharmacodynamic Modelling ## When to use Any question about what the body does to a drug or what the drug does to the body: deriving exposure metrics from concentration-time data, fitting a structural model, building or checking a population analysis, choosing a dose or a regimen, relating exposure to effect, comparing formulations, or scaling to a new population. ## The three rules **1. Fix the exposure metric and the analysis population before computing anything.** AUC(0-t), AUC(0-inf), AUC(0-tau) at steady state, and Cavg are different quantities and answer different questions. So do AUCinf based on observed versus predicted Clast. Choosing after seeing the numbers is how a negative study becomes positive. **2. Structural model, variability model, and covariate model are three separate decisions.** They get conflated constantly — an extra compartment added to absorb what is really unmodelled between-occasion variability, a covariate added to fix what is really a misspecified absorption model. Diagnose which one is wrong before changing any of them. **3. Convergence is not identifiability.** A fit that converges with 200% relative standard error on a paramet
- When to use
- The three rules
- Scope
- Scripts
- Workflow
- 1. Non-compartmental analysis
- 2. Compartmental fitting and model selection
- 3. Population PK
- 4. Simulation and regimen selection
- 5. Exposure-response
- 6. Bioequivalence
- 7. Scaling, paediatrics, and first-in-human
- 8. Drug interactions
- 9. Therapeutic drug monitoring
cd skills/pkpd-modeling/scripts python3 nca.py -i profile.csv --dose 100 --route extravascular --partial-auc 0-24 python3 fit_compartmental.py -i profile.csv --dose 500 --route iv-bolus --compare 1cmt,2cmt,3cmt python3 check_popk_dataset.py -i nmdata.csv --covariates WT,CRCL --time-varying WT python3 simulate_regimen.py --cl 5 --v 40 --dose 500 --interval 12 --n-doses 10 --steady-state python3 simulate_regimen.py --cl 5 --v 40 --dose 500 --interval 12 --n-doses 10 \ python3 exposure_response.py --emax -i er.csv --sigmoid python3 exposure_response.py --cqtc -i qt.csv --cmax 250 python3 bioequivalence.py -i be.csv --design 2x2 --metric AUC python3 bioequivalence.py -i be.csv --design replicate --metric Cmax --scaling both
What does the pkpd-modeling skill do?
Pharmacokinetic and pharmacodynamic modelling and simulation - non-compartmental analysis, compartmental and population PK, PK/PD and exposure-response, TMDD, PBPK orientation, bioequivalence, allometric scaling and first-in-human dose, drug interaction prediction, and Bayesian therapeutic drug monitoring. Use when analysing concentration-time data, deriving exposure metrics, fitting PK or PD models, or evaluating dosing regimens. Triggers include "pharmacokinetics", "pharmacodynamics", "PK/PD", "NCA", "non-compartmental", "AUC", "Cmax", "lambda z", "half-life", "clearance", "volume of distrib
How do I install it?
Run `npx skills add K-Dense-AI/scientific-agent-skills --skill pkpd-modeling --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From K-Dense-AI/scientific-agent-skills, a repository with 32,619 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.