Pharma Development

Better development decisions begin with better biological evidence

Discuss your program

See functional protein biology that conventional protein measurement strategies can miss, so high-stakes development decisions are grounded in more complete biological evidence.

Despite unprecedented amounts of genomic, transcriptomic, and targeted assay data, many critical development decisions are still being made with incomplete biological visibility.

That incomplete biological view creates risk across critical development decisions, such as:

  • Is the intended mechanism occurring?
  • Which target or hypothesis should advance?
  • What explains response and resistance?
  • Which biomarkers should move forward?
  • Which patients should be selected or enriched?

Common blind spots that can leave teams with partial biological evidence

Molecular-layer blind spot

Relying on genes or transcripts misses the complexity of proteins and proteoforms that actually function.

Decision impact:
Biology is mischaracterized

False-negative biology

Assay-scope blind spot

Narrow assays capture only a
fraction of the proteome and overlook critical biology.

Decision impact:
Key biology is undetected

False confidence in a signal

Biological-context blind spot

Technical gaps and characterization depth limit what we can learn from experimental systems and implications to human biology.

Decision impact:
Mechanisms are misinterpreted

Missed therapeutic insight

Deliver more complete biology at every development decision with Proteograph® ONE

Breadth to discover

unexpected biology expands target, mechanism, and biomarker hypotheses

Resolution to reveal

hidden protein forms improves confidence in target specificity and biological interpretation

Scale to connect

biology to phenotype across discovery, translational, and clinical research settings.

Blood-based discovery

provides a minimally invasive window into disease-host biology with clinically relevant biosamples

Download more information about how Proteograph® technology measures biology more broadly

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User studies show how deeper biological visibility with the Proteograph® Product Suite strengthens development decisions

Target Discovery

PROTEOFORM ANALYSIS

Are hidden proteoforms changing the story?

Informs: target hypothesis prioritization

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GENETIC SIGNALS

Are protein-gene signals biologically real?

Informs: target validation confidence

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Preclinical

RESPONSE BIOLOGY

Is therapy driving intended response biology?

Informs: go / no-go and PD readouts

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EARLY BIOLOGY

Are you understanding critical disease early enough?

Informs: early drivers and intervention opportunities.

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Translational

TARGET ROBUSTNESS

Will your biology hold up across diverse populations?

Informs: population-specific biomarker strategies

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HETEROGENEITY & PATHWAYS

What separates responders from non-
responders?

Informs: response and resistance strategy

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Clinical Development

RISK & STRATIFICATION

Can proteins refine risk and stratification?

Informs: enrichment and patient selection

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BIOMARKER MODELING

Which signals strengthen diagnostic models?

Informs: biomarker feature selection

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Where in your development program would better biological evidence reduce uncertainty?

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Oncology Development

Critical functional biology can remain hidden across tumor, immune, stromal, vascular, and host systems, even within data-rich oncology programs.
When that biology remains out of view, decision uncertainty increases.

Deep, unbiased proteomics connects signals across those systems to strengthen target, mechanism, response, biomarker, and patient-selection decisions.

What kinds of oncology questions can be answered through deep, unbiased proteomics?

Discuss your program

Selected Proteograph User Studies

What deeper oncology biology has already revealed.

Heatmap of peptide patterns in the disease-associated isoforms study.
PROTEOFORM ANALYSIS
Disease-associated isoforms

Distinct BMP1 isoforms are differentially expressed and associated with NSCLC.

PLoS ONE, 2023

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Scatter plot comparing protein position and SNP position in the pQTL study.
GENETIC SIGNAL VALIDATION
pQTL biology at scale

Large-scale pQTL analysis identifies protein-coding effects and refines prioritization of target biology.

Nature Genetics, 2025

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Luminescence imaging across five experimental groups over four study days.
TUMOR-HOST BIOLOGY
Tumor-host response

Plasma proteomics reveals systemic responses and pathways associated with cancer therapies.

Cell Biomaterials, 2025

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JAK3 protein differences in nonresponders and responders.
IMMUNO-ONCOLOGY
IO response & resistance

Baseline protein signatures correlate with response to immune checkpoint blockade combination therapy.

Proteograph Case Study

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Progression-free survival curves for three coagulation groups.
PROGNOSTIC STRATIFICATION
Risk stratification

Proteomic risk scores improve patient survival classification beyond clinical and genetic features.

Clinical Proteomics, 2025

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Receiver operating characteristic curves for proteomics and multi-omics models.
EARLY DETECTION
Biomarker modeling

Unbiased plasma proteomics enables improved detection models for early stage lung cancer.

medRxiv, 2024

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Starting your proteomics research is effortless

Connect with an expert to discuss which path to the full proteome is right for you.

  1. Bring the Proteograph Product Suite in-house
  2. Run a project with
    Seer Technology
    Access Center
  3. Conduct a project with
    a Seer service provider
    or Center of Excellence