Series: What Early Human Data Tells Us
Early immunology programmes often enter the clinic with a plausible drug target but an incomplete picture of how that target behaves in human tissue. A change in one cytokine or physiological measure may show that an immune response has changed, yet leave unresolved which pathways and cell populations contributed and whether the pattern fits the compound’s proposed mechanism. That uncertainty can make challenge selection and early pharmacology decisions harder.
Controlled immune challenges create a defined, timed response in people. Combining transcriptomic and proteomic profiling with cytokine measurements, immune-cell phenotyping and tissue readouts can show which pathways change, how they change, and how those changes evolve over time. The aim is to choose a model that makes the drug’s biology measurable and to build pharmacodynamic evidence that can support an early development decision.
This is the seventh article in the series What Early Human Data Tells Us. It builds on earlier articles about benchmarking inflammatory models, interpreting early inflammation signals, what controlled challenges reveal before patient studies, defining meaningful anti-inflammatory response, combining challenge models and the role of healthy participants in early immuno-oncology. Here the focus is how systems-level data can improve the choice and interpretation of a challenge model.
Why One Biomarker Rarely Settles the Question
A conventional biomarker can be useful and still leave the mechanism unclear. A circulating cytokine reflects one part of a response; skin perfusion reflects another. Neither alone shows whether the relevant target was present in the challenged compartment, which cells changed, or whether the response followed the expected pathway over time.
RNA sequencing can map broad gene-expression changes and pathway activity. Protein measurements can test whether those changes are reflected in the molecules present in blood or tissue. Cell phenotyping, histology and functional assays add information about the populations and processes contributing to the signal. Used together, these layers can reveal response patterns that a small biomarker panel would miss. They also help teams select sampling times and distinguish an informative pharmacodynamic endpoint from a convenient but insensitive one.
What Integrated Profiling Has Shown
Intravenous LPS shows that blood and bone marrow tell different stories
In a CHDR study, 13 healthy men were enrolled: three in a baseline validation group and ten who received a single intravenous dose of lipopolysaccharide (LPS) at 1 or 2 ng/kg. Blood and bone marrow were sampled before and four hours after challenge. The investigators combined bulk RNA sequencing with multiplex cytokine measurements, immune-cell phenotyping and bone-marrow immunohistochemistry.
The systems-level view showed a rapid inflammatory response in blood, while bone marrow had a distinct profile that included haematopoietic and cell-cycle programmes. The analysis identified 91 LPS-induced oncology-relevant targets in blood and 24 in bone marrow. MYC expression increased in marrow, and c-Myc protein rose with dose. The difference between compartments matters: a blood signal cannot be assumed to describe what is happening in marrow, a tissue relevant to immune-cell production.
This was a small model-characterisation study, not a trial of an oncology drug. Acute LPS stimulation does not reproduce a tumour or its microenvironment. It does show how an appropriately selected challenge can create a measurable human context for testing a drug’s effect on a pathway that the challenge engages.
Read the study: Intravenous LPS challenge in healthy participants
Extended imiquimod exposure reveals tissue changes beyond visible skin responses
In a separate study, ten healthy participants received topical imiquimod (IMQ), a TLR7 agonist, at skin sites for up to seven days. Investigators followed erythema and perfusion and collected biopsies at several time points for histology and RNA sequencing. The transcriptomic data showed TLR7 and interferon-regulatory-factor signalling, alongside TNF signalling through NF-κB. With longer exposure, the tissue response included greater infiltration of dendritic cells, macrophages and T cells, supported by both histology and gene-expression profiles.
Erythema and perfusion did not differ between 72 and 168 hours, even though prolonged exposure revealed additional cellular and pathway changes. This pattern helps explain why a visible endpoint may not capture the full biology of a challenge. It also gives study teams a basis for deciding whether a shorter exposure is sufficient for a specific target or whether a longer window is needed to study innate and adaptive components together. The model is a controlled TLR7-driven skin response, not a reproduction of chronic psoriasis.
Read the study: Deep characterization of the human TLR7 response
KLH profiling maps the timing and cellular context of adaptive immunity
A transcriptomic analysis of the dermal keyhole limpet hemocyanin (KLH) response examined skin biopsies 24 and 48 hours after an intradermal recall challenge in previously immunised healthy participants. Bulk RNA sequencing was combined with pathway analysis, computational cell deconvolution and comparison with gene sets reported in atopic dermatitis, inflammatory bowel disease, rheumatoid arthritis and asthma.
The response was predominantly T-cell driven, with CD4-associated signatures and evidence of dendritic-cell, monocyte and other innate-cell involvement. Pathway activity was strongest at 24 hours and remained detectable at 48 hours as the response began to wane. The comparison with disease-associated genes identified overlaps that could help identify potential pharmacodynamic markers and therapeutic targets for further study.
These findings need careful interpretation. Bulk sequencing cannot assign every change to a specific cell, and estimated changes in cell composition may contribute to the gene-expression signal. The study was small and sampled only two post-challenge time points. Gene overlap with a chronic disease does not mean that KLH reproduces that disease or predicts whether a drug will work in patients. Protein-level and functional measurements remain important when choosing endpoints for an intervention study.
From Molecular Profiles to Model Choice
The examples show why model selection should start with the development question. Intravenous or intradermal LPS can probe acute innate inflammation in systemic or local compartments. IMQ can test TLR7-driven skin biology. KLH can measure an antigen-specific adaptive response. Transcriptomic profiles help establish which targets and pathways are engaged in each setting, while protein, cellular and tissue readouts help determine whether that molecular activity is reflected in functional biology.
A CHDR cross-model analysis maps the expression of current immunology drug targets across LPS, KLH and IMQ challenge datasets. It highlights innate cytokine and JAK-related signals in LPS, T- and B-cell pathways in KLH, and TLR7, IL-23/IL-17 and related signalling in IMQ. The analysis also presents Olink protein-level data from LPS challenge models alongside transcriptomic results. This kind of map can help development teams identify which model is most likely to make a compound’s mechanism measurable and which endpoints deserve prospective evaluation.
See the analysis: Expression of key immunology drug targets in CHDR challenge models
What This Data Tells Us
Systems-level profiling adds value when it changes a decision. It can show whether a challenge activates the pathway of interest, identify the compartment and time window where the signal is strongest, and reveal whether a conventional endpoint captures the biology. In the LPS study, blood and bone marrow differed. In the IMQ study, tissue-level changes continued even when erythema and perfusion had stopped increasing. In KLH, the molecular profile clarified the timing and cellular composition of an adaptive response.
For sponsors, these data can guide the choice of challenge, sampling schedule and pharmacodynamic panel before a compound enters a larger or more heterogeneous patient study. Matching a drug mechanism to a human challenge can make early results easier to interpret and support better-grounded dose and go/no-go decisions. The evidence is strongest when molecular findings are paired with protein-level, cellular or functional readouts and then tested prospectively with a compound with a known mechanism of action.
Omics does not turn a challenge model into a disease model or establish clinical efficacy. Its contribution is more practical: it makes the human biology under study more visible. When teams use that information to select a model and define decision-relevant endpoints, early pharmacology can be interpreted with greater mechanistic depth and less reliance on a single biomarker.
