Trained Immunity: RoadMap for drug discovery and development
- Kupando GmbH, Germany
- Target to Treatment Consulting Ltd, Stevenage Bioscience Catalyst, United Kingdom
Figures
Figure 1
Illustration of Trained Immunity with a focus on the induction of Trained Immunity via Toll-like receptors, Dectin-1, and Nod-like receptor activation.
These receptors are triggered by pathogen-associated molecular patterns (PAMP), and innate immune cells such as monocytes and macrophages are activated to produce inflammatory cytokines. Dectin-1 is the receptor that recognizes β-glucan and thereby initiates β-glucan-induced Trained Immunity. NOD-like receptors recognize Bacillus Calmette-Guérin (BCG) and thereby mediate BCG-induced Trained Immunity. Toll-like receptors recognize various types of PAMPs and can also mediate Trained Immunity (a). The primary immune response to infection is characterized by cytokine production and antigen presentation (b). The primary innate immune response dissipates, and the innate immune cell returns to baseline activation state; however, epigenetic changes, driven by metabolic reprogramming, persist at the chromatin level. This epigenetic reprogramming underlies the induction of Trained Immunity (c). Subsequent restimulation with an unrelated pathogen or immunological trigger initiates a Trained Immunity response that is characterized by adaptive and enhanced effector functions such as increased cytokine production, enhanced antigen presentation, and increased phagocytosis (d). As a secondary effect of Trained Immunity, this may lead to enhanced adaptive immune responses.
Figure 2
Relevant domains for Trained Immunity-targeted drug discovery and development.
Five relevant domains for investigation are illustrated. Epigenetic, metabolic, and inflammatory changes, as well as differentiation and memory, are important for drug discovery and development. (Icons created by the Noun project: https://thenounproject.com/.)
Figure 3
Simplified overview of a drug discovery and development pipeline.
The main stages of drug discovery and development are included in the chevrons from target identification and validation through Phase 3 clinical trials. Each stage has a brief description in the boxes below the chevrons. The diamond shapes represent significant milestone achievements along the pathway. These include the selection of the single candidate that will be progressed to the clinic and the submission of the data package and clinical protocol to the regulators for approval to enter the clinic (FDA: open an IND application; EMA: CTA). Clinical proof of concept (PoC) is achieved when clinical effects of the new drug are demonstrated in patients (usually Phase 2a). The ‘commit to Phase 3’ meeting with regulators occurs when the therapeutic dose has been identified in Phase 2b and has been ratified by the regulators. At the end of Phase 3, the entirety of the data is submitted to the regulators for approval to make the new drug available to patients.
Figure 4
All steps from research to development up to the start of clinical development can be supported by AI.
In an iterative process, data is generated in the laboratory as shown in the upper half of the figure in gray, with AI offering valuable input at each stage as shown in the lower half of the figure in blue: From target identification to the selection of nonclinical in vitro and in vivo models, and finally, the identification of patient subsets and biomarker and outcome assessment once a drug candidate progresses to the clinical phase. Once the lead has been identified, development incorporates stringent adherence to development standards such as the GLP, GMP, and GCP guidelines and standards. Regulatory advice needs to be included to streamline drug development and entry into Phase 1 and to start building a target product profile. Drug discovery and development in Trained Immunity seeks to leverage and modify the innate immune system to achieve a durable and balanced immune response.
Figure 5
Illustration of potential types of models to assess Trained Immunity during the translational phase of drug development.
Figure 6
Types and applications of biomarkers (icons from the Noun project).
Figure 7
Discovery and development of biomarkers in parallel with the discovery and development of the drug.
Figure 8
Overview of a possible in vitro study design to investigate the activity of a test compound in a model of Trained Immunity.
A stimulus to prime innate immunity may be added to the model and endpoints measured (cytokines, metabolism, and epigenetics). Over time, the effect of the priming stimulus fades and most of the endpoints return to baseline levels (the exception being epigenetics, where the modifications may be longer-lived). The test compound may be added at various concentrations at the same time as the secondary challenge. Subsequent effects on cytokines, metabolism, and epigenetics may be measured. The basal response (measured in the absence of treatment) may be compared to the posttreatment response. The compound may decrease the trained immune response, or it may augment the response.
Figure 9
Selected clinical trials investigating the modification and regulation of Trained Immunity.
(a) Trials investigating induction of Trained Immunity for therapeutic benefit, (b) trials investigating modulation of Trained Immunity for therapeutic benefit, and (c) trials investigating inhibition of Trained Immunity for therapeutic benefit.
Figure 10
RoadMap to discovery and development of molecules that are designed to provide clinical benefit in indications relevant to Trained Immunity.
Tables
Table 1
Summary of the main classes of drug targets that target Trained Immunity and the main drug development domains they modulate.
| Target class | Main affected drug development domain |
|---|---|
| Pattern recognition receptors Toll-like receptors (TLRs) (Alexopoulou and Irla, 2025) NOD-like receptors (NLRs) (Kleinnijenhuis et al., 2012) C-type lectin receptors (CLRs) (Moorlag et al., 2020; Moerings et al., 2021) | Epigenetic Metabolic Differentiation Inflammatory Memory |
| Cytokines and cytokine receptors IL-1β and IL-1R (Moorlag et al., 2020; Teufel et al., 2022) IL-4 and IL-4R (Schrijver et al., 2023) | Metabolic Inflammation Differentiation |
| Epigenetic enzymes Histone acetyltransferases (Ziogas et al., 2025; Fanucchi et al., 2021), Histone deacetylases (Cheng et al., 2014; Mourits et al., 2021a), Histone methyltransferases (Keating et al., 2020b; Mourits et al., 2021b) Histone demethylases (Arts et al., 2016) Lactyltransferase (p300) (Ziogas et al., 2025) | Metabolic Epigenetic Memory |
| Metabolism Hexokinase (Cheng et al., 2014) Succinate dehydrogenase (Domínguez-Andrés et al., 2019) Acetyl-CoA carboxylase (Arts et al., 2016) Glutaminase (Scarpa et al., 2025) | Metabolic |
Table 2
Trained Immunity-regulating compounds.
| Description of Trained Immunity regulating compound | Type | Trained Immunity target | Inducing or inhibiting Trained Immunity* | Cellular location of Trained Immunity target | Status | Reference |
|---|---|---|---|---|---|---|
| BCG vaccine | Live-attenuated vaccine | NOD2 receptor, TLR2, TLR4 | Inducing | Intracellular | Marketed | Moorlag et al., 2024 |
| MV130 | Whole heat-inactivated bacteria (90% Gram-positive, 10% Gram-negative) | Combination of TLRs and NLRs | Inducing | Intracellular and extracellular | In development | Brandi et al., 2022 |
| MDP combined with HPV E7 peptide encapsulated by polylactic-co-glycolic acid PLGA nanoparticles | Nanoparticle | NOD2 receptor | Inducing | Intracellular | Experimental | Li et al., 2023 |
| MTP10-HDL | Nanoparticle | NOD2 receptor | Inducing | Intracellular | In development | Priem et al., 2020 |
| PEG-PDLLA polymersome containing β-glucan | Polymersome | Dectin-1 | Inducing | Extracellular/transmembrane receptor | In development | Wauters et al., 2024 |
| PLGA nanoparticles containing β-glucan | Nanoparticle | Dectin-1 | Inducing | Extracellular/transmembrane receptor | Experimental | Ajit et al., 2022 |
| Saccharomyces cerevisiae-derived whole glucan particles containing β-glucan | Sonicated yeast particles | Dectin-1 | Inducing | Extracellular/transmembrane receptor | Experimental | Horneck Johnston et al., 2024 |
| BIX‐01294 | Small molecule | G9a histone methyltransferase | Inducing | Nucleoplasm | Experimental | Mourits et al., 2021b |
| Monophosphoryl lipid A (MPLA) | Modified lipid | TLR4 | Inducing | Extracellular/transmembrane receptor | Experimental | Owen et al., 2022 |
| Oxidized low-density lipoprotein (OxLDL) | Lipoprotein | TLR4 | Inducing | Extracellular/transmembrane receptor | Experimental | Bekkering et al., 2014 |
| CpG-ODN | Oligonucleotide | TLR9 | Inducing | Intracellular | Experimental | Owen et al., 2022 |
| β-Glucan in arabinoxylan | Hemicellulose | Complement receptor 3 (CR3) | Inducing | Extracellular/transmembrane | Experimental | Moerings et al., 2022; Patin et al., 2019 |
| Fusion protein of apolipoprotein A1 and IL4 | Lipid nanoparticle | IL-4 receptor (type 1 and type 2) | Inducing | Extracellular/transmembrane | In development | Schrijver et al., 2023 |
| Anakinra | Recombinant protein | IL-1 receptor | Inhibiting | Extracellular | Marketed | Flores-Gomez et al., 2024; Mitroulis et al., 2018; Ciarlo et al., 2020 |
| Canakinumab | Monoclonal antibody | IL-1β protein inhibitor | Inhibiting | Extracellular | Marketed | Xu et al., 2025 |
| 5′-Deoxy-5′methylthio adenosine (MTA) | Synthetic organic compound | Histone methyltransferase inhibitor | Inhibiting | Nucleoplasm | Experimental | Quintin et al., 2012 |
| Resveratrol | Natural polyphenol | Sirtuin 1 (histone deacetylase) activator | Inducing | Nucleoplasm | Marketed (as supplement) | Mourits et al., 2021a; Bulut et al., 2025; Wang et al., 2021a |
-
*
The aim of inducing or inhibiting Trained Immunity is to change the inflammatory state in a certain (disease) setting. Depending on the clinical indication, modulation of Trained Immunity responses can either boost or diminish inflammatory responses. For example, boosting immune responses via Trained Immunity can change the equilibrium during immunoparalysis. On the contrary, in a chronic hyperinflammatory state, inhibiting Trained Immunity responses could potentially serve as a tool to change the equilibrium the other way around.
Table 3
Examples of biomarkers and models of Trained Immunity.
| Domains of Trained Immunity | Key markers | Assay | Context | Models | Example references |
|---|---|---|---|---|---|
| Epigenetics | Histone methylation H3K4me3 (active promoters), Histone acetylation H3K27ac (enhancers), H3K9me3 (repressive epigenetic marks) Chromatin accessibility | ChIP-seq, CUT&RUN, ATAC-seq, ChIP-PCR | Trained Immunity induces chromatin remodeling at cytokine and metabolic gene loci | In vitro monocyte training (e.g. with β-glucan or BCG), in vitro training of mouse bone marrow-derived macrophages | Young et al., 2011 Meers et al., 2019 Saeed et al., 2014 |
| Metabolic | Glycolysis: ↑ lactate, ↑ Glut1 expression TCA cycle: Itaconate, succinate, fumarate accumulation mTOR/HIF-1α activation reactive oxygen species (ROS) NAD+ metabolism | Seahorse (ECAR, OCR), metabolomics (LC-MS), western blot for pathway markers HK2, LDHA (glycolysis), HIF-1α, mTOR, AMPK, SDH (succinate dehydrogenase) ROS assay | Pathways altered | In vitro Trained Immunity models with monocytes or PBMCs, both with mouse and human cells | Stefanoni, 2023 Wang et al., 2021b Mourits et al., 2021a Wu et al., 2024 |
| Inflammatory | IL-6, TNF-α, IL-1β, IL-10, IL-18, IFN-γ | ELISA, Luminex, multiplex bead-based assays | Cytokine Production after stimulation with unrelated secondary ligands (e.g. LPS, Pam3CSK4), trained cells show enhanced cytokine production | In vitro monocyte and PBMC Trained Immunity assays, both with mouse and human cells. Primary human and mouse cells from in vivo studies can be used for ex vivo restimulation assays | Moorlag et al., 2020 Smith et al., 2017 |
| Differentiation | Monocytes/macrophages: CD11b, CD14, CD16, CD80, CD86, HLA-DR NK cells: CD69, NKG2D, CD107a (degranulation) | Flow cytometry, mass cytometry (CyTOF) | Cell Surface and Activation Markers Trained cells may show altered expression profiles reflecting activation or increased antigen presentation | Human PBMCs, mouse peritoneal, or splenic macrophages | Gill et al., 2022 Zhang et al., 2021 |
| Memory | Persistence: Epigenetic and transcriptional changes lasting weeks to months Bone marrow signatures: Myeloid progenitor reprogramming Clinical: BCG-vaccinated individuals show altered monocyte and cytokine profiles even after 3–12 months | See above examples | Longitudinal Biomarkers of Trained Immunity in in vivo studies | Ex vivo, mouse models and human models | Bomans et al., 2018 Cassone, 2018 Kuznetsova et al., 2020 |
Table 4
Selected clinical trials investigating interventions in the context of Trained Immunity.
| (a) Trials investigating induction of Trained Immunity for therapeutic benefit | ||
|---|---|---|
| 1 | NCT ID | NCT06257212 |
| Title | Live Vaccines and Innate Immunity Training in COPD | |
| Dates | 2024/02/28 to 2025/09 | |
| Phase | Phase 4 | |
| Enrolment | 60 (Estimated) | |
| Condition(s) | COPD | |
| Intervention(s) | BCG vaccine MMR vaccine | |
| Primary Outcome | Innate immune training measured by fold-changes in cytokine production capacity of innate immune cells following pro-inflammatory stimulation. Measured from inclusion in the trial to 4 months’ post-inclusion. Cytokines include: IL-1β, IL-10, TNF-α, IFN-γ | |
| 2 | NCT ID | NCT06266754 |
| Title | The Non-Specific Immunological Effects of Providing Oral Polio Vaccine to Seniors in Guinea-Bissau | |
| Dates | 2024/01/29 to 2024/12/31 | |
| Phase | Phase 4 | |
| Enrolment | 80 (Estimated) | |
| Condition(s) | Vaccine Reaction | |
| Intervention(s) | Oral Polio vaccine | |
| Primary Outcome |
| |
| 3 | NCT ID | NCT05208060 |
| Title | Study to Evaluate the Ability of Sublingual MV130 to Induce the Expression of Trained Immunity in Peripheral Blood Cells | |
| Dates | 2023/09/01 to 2025/12/31 | |
| Phase | Phases 1 and 2 | |
| Enrolment | 48 (Estimated) | |
| Condition(s) | Immune Response | |
| Intervention(s) | MV130 vaccine | |
| Primary Outcome | Increase in ex vivo PBMCs cytokine response (TNF-α, IL-6, IL-1β) to secondary restimulation compared to placebo at days 15, 45, and 70 with respect to baseline | |
| Selected Secondary Outcomes relevant to Trained Immunity |
| |
| 4 | NCT ID | NCT02403505 |
| Title | Early Phase Clinical Trial About Therapeutic Biological Product Mix for Treating CEA Positive Rectal Cancer | |
| Dates | 2021/12/28 to 2025/02/28 | |
| Phase | Phase 1 | |
| Enrolment | 20 (Estimated) | |
| Condition(s) | Rectal Cancer | |
| Intervention(s) | CEA protein antigen and BCG vaccine mix for percutaneous use | |
| Primary Outcome | Timeframe: up to 90 days
| |
| 5 | NCT ID | NCT05507671 |
| Title | The Role of BCG Vaccine in the Clinical Evolution of COVID-19 and in the Efficacy of Anti-SARS-CoV-2 Vaccines | |
| Dates | 2021/05/27 to 2023/12/31 | |
| Phase | Phase 3 | |
| Enrolment | 556 (Estimated) | |
| Condition(s) | COVID-19 | |
| Intervention(s) | BCG vaccine | |
| Primary Outcome |
| |
| Selected Secondary Outcomes relevant to Trained Immunity | Serum concentrations of cytokines TNF-α, IFN-γ, IL-1β, IL-4, IL-6, and IL-10 in 50 participants of BCG group versus 50 participants of placebo group 2 months after recruitment | |
| 6 | NCT ID | NCT06628544 |
| Title | Trained Immunity in Fungal Infection and Its Mechanism | |
| Dates | 2020/09/01 to 2023/12/01 | |
| Phase | Early Phase 1 | |
| Enrolment | 79 (Actual) | |
| Condition(s) | BCG vaccination | |
| Intervention(s) | BCG vaccine Metformin | |
| Primary Outcome | IL-6 and TNF-α cytokine production by PBMCs isolated after 5 days of continuous medication and restimulated with C. albicans or Mycobacterium tuberculosis | |
| 7 | NCT ID | NCT03296423 |
| Title | Bacillus Calmette-Guérin Vaccination to Prevent Infections of the Elderly | |
| Dates | 2017/09/21 to 2020/11/30 | |
| Phase | Phase 4 | |
| Enrollment | 200 (Actual) | |
| Condition(s) | Infection Hospitalization Mortality | |
| Intervention(s) | BCG vaccine | |
| Primary Outcome | Time to first infection. Timeframe: 12 months | |
| Selected Secondary Outcomes relevant to Trained Immunity |
| |
| 8 | NCT ID | NCT02114255 |
| Title | Effects of BCG on Influenza Induced Immune Response | |
| Dates | 2014/05 to 2014/09 | |
| Phase | Phases 2 and 3 | |
| Enrolment | 40 (Actual) | |
| Condition(s) | Influenza virus infection Trained Immunity | |
| Intervention(s) | BCG vaccine | |
| Primary Outcome |
| |
| Selected Secondary Outcomes relevant to Trained Immunity |
| |
| 9 | NCT ID | NCT01734811 |
| Title | Efficacy and Safety Evaluation in Recurrent Wheezing Attacks (MV130) | |
| Dates | 2012/10 to 2017/02 | |
| Phase | Phase 3 | |
| Enrolment | 120 (Actual) | |
| Condition(s) | Bronchospasm Bronchiolitis Bronchitis | |
| Intervention(s) | MV130 vaccine | |
| Primary Outcome | Number of Recurrent Bronchospasm (Wheezing Attacks) | |
| (b) Trials investigating modulation of Trained Immunity for therapeutic benefit | ||
| 10 | NCT ID | NCT06624436 |
| Title | Immunomodulatory Effects of Dexamethasone, Tocilizumab and Anakinra During Experimental Human Endotoxemia | |
| Dates | 2024/10/24 to 2025/12 | |
| Phase | Phase 4 | |
| Enrolment | 52 (Estimated) | |
| Condition(s) | Sepsis Neuroinflammatory Response Immunosuppression Endotoxemia | |
| Intervention(s) | Dexamethasone Tocilizumab Anakinra | |
| Primary Outcome |
| |
| Selected Secondary Outcomes relevant to Trained Immunity |
| |
| 11 | NCT ID | NCT03332225 |
| Title | A Trial of Validation and Restoration of Immune Dysfunction in Severe Infections and Sepsis | |
| Dates | 2017/12/15 to 2019/12/31 | |
| Phase | Phase 2 | |
| Enrolment | 36 (Actual) | |
| Condition(s) | Sepsis Macrophage Activation Syndrome | |
| Intervention(s) | Anakinra Recombinant human IFN-γ | |
| Primary Outcome | Mortality. Timeframe: 28 days | |
| Selected Secondary Outcomes relevant to Trained Immunity |
| |
| (c) Trials investigating inhibition of Trained Immunity for therapeutic benefit | ||
| 12 | NCT ID | NCT05790499 |
| Title | LDL-c Level Variability and Trained Immunity | |
| Dates | 2023/03/20 to 2024/01/31 | |
| Phase | N/A | |
| Enrollment | 12 (Estimated) | |
| Condition(s) | Cholesterol Variability Trained Immunity | |
| Intervention(s) | Atorvastatin | |
| Primary Outcome | Changes in LDL-C levels between baseline and atorvastatin treatment cycles. Timeframe: 16 weeks | |
| Selected Secondary Outcomes relevant to Trained Immunity | Timeframe: 16 weeks
| |
| 13 | NCT ID | NCT05210725 |
| Title | Trained Immunity by Dual-pathway Inhibition in Coronary Artery Disease | |
| Dates | 2022/03/01 to 2022/07/01 | |
| Phase | Phase 4 | |
| Enrolment | 20 (Actual) | |
| Condition(s) | Coronary Artery Disease | |
| Intervention(s) | Rivaroxaban and Acetylsalicylic acid | |
| Primary Outcome | Whole blood immune responsiveness to LPS stimulation when switching from acetylsalicylic acid monotherapy to acetylsalicylic acid and low-dose rivaroxaban dual pathway inhibition. Timeframe: 12 weeks | |
| Selected trial outcomes relevant to Trained Immunity |
| |
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Table of examples of interventional clinical trials related to Trained Immunity. Primary and secondary outcome fields have been simplified from the original data. Only secondary outcomes related to Trained Immunity are included. Source: https://clinicaltrials.gov/.
Appendix 1—table 1
Sources of cells that are potentially suitable for use in models of Trained Immunity.
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The cell lines listed in this table are examples only and form a subset of all of the available cell lines.
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Trained Immunity: RoadMap for drug discovery and development
eLife 14:e108465.
https://doi.org/10.7554/eLife.108465
