xpertsystems/hc-gen-006-sample

HC-GEN-006 — Orphan Drug Trial Dataset: Registry, Patients, Longitudinal Outcomes, Adverse Events, Regulatory Pathway & Health Economics (Sample)

Synthetic clinical-trial-level dataset for rare-disease drug development, spanning a 25-disorder orphan portfolio across metabolic, neurological, hematological, rare-oncology, immunological, and pulmonary therapeutic areas. The dataset models trial design and regulatory designations, enrolled-patient demographics and genetics, longitudinal treatment outcomes (endpoints, biomarkers, PK, QoL), MedDRA/CTCAE adverse events, multi-jurisdictional regulatory pathways, and health-economic assessments.

This is a 50-trial sample of the full HC-GEN-006 product (150 trials). It is synthetic — generated by a calibrated simulation engine. It contains no real trial, patient, or product data. Drug names (XS-*) are fictional.

Not for clinical use. This dataset is for ML development, benchmarking, schema prototyping, and education only. It must not be used to inform real patient care, trial design decisions, or regulatory submissions.

Unit of observation

Unlike the patient-level HC-GEN SKUs, the unit here is the trial. The 50 trials in this sample expand into thousands of derived patients and tens of thousands of longitudinal outcome records across six relational tables keyed on trial_id (and patient_id).

Calibration anchors

Sample-level observed values (seed 42, 50 trials):

Metric	Observed	Target	Anchor
Phase III fraction	0.340	0.18–0.40	orphan trial phase mix
Orphan designation rate	1.000	0.98–1.00	all trials carry designation
Fast-track rate	0.360	0.16–0.50	FDA expedited-program rates (engine p=0.35)
Approval rate	0.320	0.18–0.45	orphan-drug regulatory yield
Mean actual enrollment	98.0	60–130	small-N pivotal rare-disease trials
Serious-AE fraction	0.433	0.38–0.48	CTCAE grade≥3 / serious-flag
Genetic characterization rate	~0.65	0.58–0.72	genetically defined enrollment
Median ICER per QALY	$337,072	$220k–420k	orphan-therapy cost-effectiveness
Active−control responder separation	0.26	≥0.15 (floor)	efficacy signal
X-linked male skew	0.45	≥0.35 (floor)	Hemophilia/Duchenne
Pediatric age separation	30 yr	≥20 (floor)	Duchenne/Neuroblastoma/Dravet
High-grade AE → drug action	0.58	≥0.35 (floor)	grade≥4 withdrawal/hospitalization
Patient→trial referential violations	0	=0 (floor)	integrity
Trial count	50	=50 (floor)	portfolio-size contract

Validation: Grade A+ (10.00/10) across all six canonical seeds (42, 7, 123, 2024, 99, 1), deterministic.

Tables

Six relational CSVs keyed on trial_id / patient_id:

• hc_gen_006_trial_registry.csv — 50 trials × 22 cols: phase, design, MoA, disease (Orpha/ICD-10/prevalence), jurisdiction, sponsor, designations (fast-track/breakthrough/accelerated), enrollment, sites, primary endpoint.
• hc_gen_006_patients.csv — ~4,900 patients × 14 cols: demographics, genetic mutation, severity, treatment arm, dose, consent, country, site.
• hc_gen_006_treatment_outcomes.csv — ~55,000 longitudinal visit records × 13 cols: primary endpoint trajectory, responder flag, two biomarkers, PK concentration (one-compartment model), QoL, current severity, compliance, status.
• hc_gen_006_adverse_events.csv — ~4,000 AEs × 12 cols: MedDRA term/SOC, CTCAE grade, seriousness, onset/duration, causality, outcome, action taken.
• hc_gen_006_regulatory_pathway.csv — ~97 submissions × 12 cols: jurisdiction, submission type, review pathway/status, PDUFA date, advisory-committee vote, post-market requirements, exclusivity, pediatric study plan.
• hc_gen_006_health_economics.csv — 50 econ records × 9 cols: annual cost, QALY gain, ICER, eligible population, reimbursement status, payer, outcomes-based contract.

Loading

import pandas as pd
trials   = pd.read_csv("hc_gen_006_trial_registry.csv")
patients = pd.read_csv("hc_gen_006_patients.csv")
outcomes = pd.read_csv("hc_gen_006_treatment_outcomes.csv")

# Active vs control responder rate
pt = patients[["patient_id","treatment_arm"]]
oc = outcomes.merge(pt, on="patient_id")
oc["active"] = oc.treatment_arm.isin(["Active_Drug","Active_Drug_High_Dose","Combination"])
print(oc.groupby("active")["responder_flag"].mean())

from datasets import load_dataset
trials = load_dataset("xpertsystems/hc-gen-006-sample", "trial_registry")

Use cases

• Orphan-drug trial-design and enrollment-forecasting models.
• Longitudinal endpoint / biomarker / PK trajectory modeling and responder classification.
• Pharmacovigilance and adverse-event signal detection (MedDRA SOC, CTCAE grade).
• Regulatory-pathway and approval-likelihood modeling across FDA/EMA/PMDA.
• Health-economic (ICER/QALY) and market-access analysis.
• Relational/longitudinal ML pipeline prototyping over a six-table star schema.

Limitations (honestly disclosed)

• Trial-level synthetic data; fictional drugs. All trials, patients, products (XS-*), and outcomes are simulated. Disease portfolio weights and trial mix are illustrative, not market-representative.
• Legacy global-RNG engine (determinism handled by the wrapper). The engine uses NumPy's legacy global np.random API seeded once. The sample wrapper drives reproducibility by re-seeding np.random.seed(seed) before each run, verified to produce byte-identical tables across repeated same-seed runs. Recommended full-product fix: migrate to np.random.default_rng.
• Control-arm responder flag is near-zero by construction. The engine only sets the per-visit responder_flag for active-arm responders; control arms therefore show ~0 flagged responders. This produces a clean active-vs-control efficacy separation but means responder_flag should not be read as an unbiased control-arm response estimate.
• High serious-AE fraction. The CTCAE grade distribution places 35% of AEs at grade≥3, so the serious-AE fraction (0.43) runs higher than some real rare-disease trials; it reflects the engine's safety model, not a specific program.
• Marginal calibration, not full joint fidelity. Univariate rates and the engineered structural separations (active/control efficacy, X-linked sex skew, pediatric age, AE-grade action gradient) are anchored; higher-order correlations beyond those engineered are not independently validated.
• No engine defects identified. All sampling is reproducible under fixed seeding; the simulation runs cleanly and writes six internally-consistent tables with full referential integrity. The only design note is the legacy-RNG approach above.
• Small-portfolio variance. At 50 trials some trial-level rates (fast-track, phase mix, approval) carry binomial variance; scorecard ranges accommodate this without masking real misses, and structural floors are weighted to dominate.

Commercial / full version

	Sample (this)	Full (commercial)
Trials	50	150 (configurable)
Derived patients	~4,900	~15,000
Tables	6 (full schema)	6 (full schema)
Formats	CSV	CSV / Parquet
Determinism	seed-controlled via wrapper	migrated to default_rng on request
Seeds / reproducibility	6 canonical	Unlimited
License	CC-BY-NC-4.0	Commercial
Support	—	SLA, custom portfolios, vertical extensions

Contact pradeep@xpertsystems.ai · https://xpertsystems.ai

Citation

@dataset{xpertsystems_hcgen006_2026,
  title  = {HC-GEN-006: Synthetic Orphan Drug Trial Dataset --- Registry,
            Patients, Longitudinal Outcomes, Adverse Events, Regulatory Pathway
            & Health Economics (Sample)},
  author = {XpertSystems.ai},
  year   = {2026},
  publisher = {Hugging Face},
  note   = {Synthetic data. Not for clinical use. Fictional drugs. Calibration
            anchors: FDA/EMA/PMDA orphan-drug regulatory frameworks; CTCAE v5
            adverse-event grading; MedDRA system-organ-class taxonomy;
            Orphanet/ICD-10 disease coding; orphan-therapy ICER/QALY literature.},
  url    = {https://huggingface.co/datasets/xpertsystems/hc-gen-006-sample}
}