Precision medicine in its purest form.
A hash-locked, closed-form digital twin. Glycaemic state, velocity, and time-in-range tracked against the patient's own trajectory — not against a cohort average. Hypoglycaemia signal rising 90 minutes before clinical onset.
Same patient. Same engine. Full diurnal cycle with meal boluses, postprandial excursions, and nocturnal hypoglycaemia anticipation. The σ-state runs continuously — visible alerts only when warranted.
At 04:30 the σ-state crossed threshold — 90 minutes before glucose would have crossed 70 mg/dL. Predictive 15 g rescue carbohydrate at 05:30 averted the event; glucose nadir at 06:00 stayed above the hypoglycaemia threshold. The same engine that runs the 4-hour pre-event exemplar above runs continuously across the 24-hour cycle without retuning.
The same controller, same parameter file, no per-domain retuning. The diabetes twin above is one instance of five. Each curve below shows the σ-trajectory or characteristic pattern for the corresponding domain, with the peak performance metric on publicly accessible data.
The same N=1 architecture applied to a medical device's own operational signals rather than to a patient's physiological signals yields per-device drift detection — denoted σ_machine.
Slow longitudinal degradation below conventional alarm thresholds becomes visible at the per-device scale. The same engine that governs the patient governs the device delivering the therapy.
The same closed-form engine, hash-locked, applied identically across every domain. Hill-equation kernel calibrated from the patient's own pre-perturbation trajectory.
The architecture originates in the nifedipine rate-of-rise pharmacokinetic studies at the Leiden school in the mid-1980s — specifically, the observation that the velocity of physiological change governs tolerability independently of the magnitude of the dose itself.
Generalised, that observation says: the rate at which a system moves matters as much as where it is. Per-patient analytics means giving each patient their own reference trajectory and watching the rate at which they depart from it — rather than scoring them against a population average that does not exist in their physiology.
Four decades of clinical pharmacology and direct regulatory experience across FDA, EMA, MHRA and PMDA stand behind the architecture.
Two short technical notes set out the conceptual foundation and the statistical framework underlying the Zyvolance architecture. Written for clinicians, regulatory readers, and methodologists. Institutional voice; no marketing.
The structural argument for per-patient self-reference and the abandonment of cohort means as the inferential baseline for individual care. Variance decomposition, the velocity principle, and the clinical examples — blood pressure, HbA1c — that ground the framework.
The mathematical foundation of repeated-measures inference, trajectory pharmacology, and the five-component validation framework. Convergent empirical evidence across six substrates and the science of N-of-1 digital twins.
Zyvolance engages with regulated medical-device manufacturers, clinical-research institutions, and regulatory bodies on a confidential basis. Serious enquiries are welcomed at the addresses below.