Pharmacometric Event Dataset
Subject-level event data for pharmacometric modeling, structured in a long format that mixes dosing and observation records distinguished byEVID. Multiple endpoints (e.g., plasma concentration vs. biomarker) are supported via DVID. The dataset is designed for:
- Population PK and PK/PD model fitting (compartmental, mixed-effects)
- Exposure–response analyses and simulations (e.g., VPCs)
- Covariate exploration (e.g., body weight effects, concomitant meds)
- Benchmarking classical vs. quantum/quantum-inspired algorithms for dose-finding and PK/PD tasks
Contents & schema
Shape: 2,820 rows × 11 columns (one row per subject–time–event).| Column | Type (suggested) | Units / Encoding | Description |
|---|---|---|---|
ID | int | — | Subject identifier. |
BW | float | kg | Body weight (baseline covariate). |
COMED | int | 0/1 | Concomitant medication indicator (0 = No, 1 = Yes). |
DOSE | float | mg | Nominal dose level associated with the record (arm/level). Prefer AMT for modeling. |
TIME | float | hours | Time since the first drug administration. |
DV | float | mg/L if DVID=1; ng/mL if DVID=2 | Observed value of the dependent variable; may be missing if MDV=1. |
EVID | int | 0/1 | NONMEM event type: 0 = observation, 1 = dosing event. |
MDV | int | 0/1 | Missing-DV flag: 1 = DV missing/ignored, 0 = DV present. |
AMT | float | mg (or μg per study design) | Actual dose amount for dosing events; 0 on observation rows. |
CMT | int | model-dependent | Compartment index (e.g., 1 = central), to be mapped in your model. |
DVID | int | 1/2 | Endpoint selector: 1 = concentration, 2 = biomarker. |
Event semantics (important)
- Dose rows:
EVID=1,AMT>0, typicallyMDV=1;DV(if present) must be ignored. - Observation rows:
EVID=0,AMT=0, typicallyMDV=0;DVcarries the measurement. - Multi-endpoint: Split by
DVIDprior to modeling or evaluation.
Modeling tips & good practices
- Time origin:
TIMEis since first administration. If your workflow needs per-period re-zeroing (e.g., multiple doses), realign within-subject accordingly. - Units: Keep unit discipline:
DVID=1→ mg/L,DVID=2→ ng/mL. Avoid mixing during joins/aggregations. - Compartments:
CMTindices are model-dependent; map them to your structural model (e.g., central/peripheral). - Missingness: Respect
MDV=1—do not impute DV on dosing rows. - Covariates: Explore
BW(e.g., allometric scaling on CL/V) andCOMEDas categorical covariates. - Diagnostics: For quick checks, stratify concentration–time plots by
DOSElevel andCOMED, and inspect per-subject residuals.
Data quality & caveats
- The table intentionally mixes dose and observation events—filter carefully for summaries and plotting.
DOSEreflects nominal dose level; preferAMTfor quantitative model inputs.- No BLQ/LLOQ flags are provided—apply your own censoring rules (e.g., M3 method) if required.
CMTsemantics may vary by your structural model; document your mapping for reproducibility.
Provenance
- Upstream repository (MIT License): Quantum-Innovation-Challenge /LSQI-Challenge-2025 on GitHub.
- Challenge site: Quantum Innovation Challenge 2025 (Life Sciences & Quantum Innovation, LSQI).
- This Aqora dataset mirrors the relevant challenge data and reshapes it for convenient PK/PD workflows while preserving the event semantics.