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Pharma & clinical development · Real-world evidence

Real-world data shows what happened. Biological outcomes show what changed.

Longitudinal biomarkers, multi-omic diagnostics and real-world data to measure biological change and intervention outcomes in the populations who actually use your therapy.

Discuss a Real-World-Evidence programmeContact our team
An eight-channel pipette lowering its tips over a row of small glass vials glowing in warm light.

Why biological outcomes strengthen real-world evidence

For prevention, longevity and chronic-disease interventions, hard endpoints can take a decade to accrue. Repeated multi-omic measurement provides objective intermediate evidence now.

  • 01

    Earlier read-outs

    Biological-age and multi-omic trajectories shift within months, long before morbidity or mortality data mature.
  • 02

    Objective measurement

    Lab-measured outcomes reduce reliance on coded or self-reported endpoints prone to misclassification in routine-care data.
  • 03

    Response heterogeneity

    Individual trajectories reveal who responds, who does not, and the biology behind the difference.
  • 04

    Stronger causal inference

    Measured baseline biology becomes a covariate for matching, weighting and adjustment in observational designs.

From real-world events to real biological change

Real-world data is rich in events but poor in biology. Adding a biological outcomes layer turns observational data into evidence of how a therapy changes the body under real conditions of adherence, comorbidity and co-medication.

We combine epigenomic, proteomic and metabolomic measurement with claims, EHR and registry data, so real-world studies capture objective biological response years before hard endpoints mature.

How biological outcomes enrich real-world data
  • Claims
  • EHR
  • Registries
  • PROs & devices

Linked at participant level

  • EpigenomicsDNA methylation · biological age at the organ-system level
  • ProteomicsCirculating protein signatures · organ health & inflammation
  • MetabolomicsMetabolic state, resilience & disease risk
  • + Immune ageing & glycans
  • + Microbiome
  • + NAD+
  • + Clinical biomarkers
  • + Brain health
  • Baseline: genetics & PGx

Longitudinal biological outcomesChange · Durability · Heterogeneity

Real-world data

  • Diagnoses & prescriptions
  • Hospitalisations & events
  • Treatment persistence
  • Patient-reported outcomes
  • Costs & utilisation
+

Biological outcomes

  • Whole-body biological age
  • Organ/system-specific age
  • ProteoAge & hallmarks of ageing
  • Metabolic Age & disease risk
  • Immune & inflammatory ageing
  • Longitudinal trajectories
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Evidence of what actually changed

Objective, mechanistic and longitudinal proof of effect in real-world populations.

  • Clinical development
  • Translational medicine
  • Real-world evidence
  • Market access
  • Lifecycle management

A multi-omic portfolio built for repeated measurement

Our portfolio combines dynamic layers that change with intervention and a stable genetic layer that helps explain who responds. Real-world studies need both: one to measure change, the other to understand it.

Core longitudinal outcome layers

  • Epigenomics

    Advanced Biological Age & Epigenetics

    How fast is the body ageing, and where?

    RWE roleprimary or key secondary endpoint; baseline covariate for matching

    What it measures

    DNA methylation records the cumulative biological impact of disease, treatment and environment. Repeated measurement shows whether an intervention slows, halts or reverses ageing, and in which organ systems.

    • 460+ biomarkers across 21 physiological systems
    • Whole-body and organ-system biological age
    • Ageing patterns and epigenetic health

    Sample: at-home collection kit or in-clinic blood draw

  • Proteomics

    Advanced Proteomics

    Which biological processes respond?

    RWE rolemechanism, responder signatures, highly scalable repeat sampling

    What it measures

    Proteins are the functional read-out of biology. Mapping change onto the hallmarks of ageing links what a therapy does in real-world patients to its mechanism of action.

    • ProteoAge: protein-based biological age
    • Hallmarks of Aging profile
    • Functional protein insights

    Sample: non-invasive cheek swab, at home or in clinic

  • Metabolomics

    Metabolic Resilience

    What is the metabolic state, resilience and risk?

    RWE roleearly pharmacodynamic signal; links biology to risk for HEOR

    What it measures

    Metabolites move fastest of all layers, capturing the combined effect of therapy, diet, lifestyle and co-medication, and translating biology into future disease risk.

    • Metabolic health and Metabolic Age
    • 10-year risk for five chronic diseases
    • Targeted metabolomics, cardiometabolic panel, amino acids & energy metabolism

    Sample: one finger-prick, at home or in clinic

Complementary layers

  • Immune Ageing & Glycan Biology

    IgG glycan biological age with pro- and anti-inflammatory indexes. Finger-prick at home.Inflammatory response to treatment

  • Microbiome Blueprint

    Gut composition, diversity, dysbiosis and metabolic function. Stool sample.Gut-host mediation of response

  • NAD+ Levels

    NAD+ status and cellular energy. Minimally invasive blood test.Target engagement for NAD+ interventions

  • Clinical Blood Biomarkers

    Cardiometabolic, hormonal, nutritional and organ-function markers.Bridge to EHR lab values and safety

  • Brain Health & Cognitive Resilience

    Brain health biomarkers, cognitive assessment and fatty acid profile.Cognitive outcomes over time
Discuss a Real-World-Evidence programmeContact our team

Baseline layer · measured once

Genetics & Pharmacogenetics

Polygenic risk assessment and pharmacogenetic panel from one saliva sample.

Genetics & Pharmacogenetics

In real-world data, drug response varies for reasons the record cannot see. Pharmacogenetic genotype and inherited risk make that variation measurable: as effect modifiers when analysing heterogeneity, as covariates for confounding control, and as a basis for defining subgroups.

Five collection routes, designed for decentralised studies

Most layers can be collected at home

  • SalivaGenetics & pharmacogenetics
  • Cheek swabAdvanced Proteomics
  • Finger-prickMetabolic Resilience, Immune Ageing
  • StoolMicrobiome Blueprint
  • BloodBiological Age, NAD+, Clinical Biomarkers

Six real-world study designs. One measurement layer.

  • 01

    Post-authorisation effectiveness

    Measure biological response in older, multimorbid and diverse populations under-represented in pivotal trials. Supports label expansion and post-marketing commitments.
  • 02

    External & synthetic control arms

    Match comparators on measured biological status, not only diagnosis codes, to reduce residual confounding when randomisation is impractical.
  • 03

    Pragmatic & decentralised studies

    Add objective biological endpoints without adding site burden, with standardised collection across large, dispersed cohorts.
  • 04

    Responder stratification

    Combine pharmacogenetic genotype with multi-omic baselines to find signatures that predict benefit, for enrichment of the next interventional trial and precision positioning.
  • 05

    Durability & persistence

    Follow biological change after discontinuation, dose change or switching, to show whether effects last in routine care.
  • 06

    HEOR & market access

    Link measured biological change to utilisation and cost in linked datasets, strengthening HTA submissions and outcomes-based agreements.

Real-world questions across therapeutic areas

Examples of questions a biological outcomes layer can answer in real-world programmes.

  • Obesity & metabolic health

    Does biological benefit extend beyond weight loss, and does it persist after treatment stops?

  • Cardiometabolic disease

    Which organ systems respond, in which patients, under real-world adherence?

  • MASH & liver disease

    Can liver-specific and systemic change be followed without repeated biopsy?

  • Healthy ageing & geroscience

    Does an intervention slow the pace of biological ageing where disease endpoints are too rare to power a study?

  • Regenerative medicine

    Is systemic biological recovery durable over years of follow-up?

  • Oncology survivorship

    How does treatment burden affect long-term biological ageing and resilience in survivors?

From protocol to analysis-ready data

  1. 01

    Endpoint strategy

    Fit-for-purpose omic layers, endpoints and sample size.
  2. 02

    Sampling cadence

    Timepoints aligned with each layer’s biology and the exposure window.
  3. 03

    Collection & logistics

    Home or in-clinic, with standardised pre-analytics and chain of custody.
  4. 04

    Integration & delivery

    Quality-controlled data, ready for linkage with EHR, claims or registries.

Add a biological outcomes layer to your real-world programme

Tell us about your intervention, population and data sources. Our scientific team will propose an endpoint and sampling strategy.

Discuss a Real-World-Evidence programmeContact our team

  • Scientific rigour

    Documented precision and locked assay versions for the life of a study.

  • Implementation ready

    Infrastructure for study-size observational and pragmatic programmes.

  • Privacy by design

    Pseudonymised, GDPR-compliant data flows with consent for linkage.

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