Trained Immunity: RoadMap for drug discovery and development

This article serves as a focused RoadMap for discovering and developing new medicines within the field of Trained Immunity. It is of particular interest to those who are looking to translate their novel scientific discoveries into new treatments targeting Trained Immunity, rather than a systematic review. The content is specifically designed for individuals who may be unfamiliar with the translational aspects of drug discovery and development (DDD), but are experts in Trained Immunity.

The objective is to support and guide non-pharma experts in converting their scientific breakthroughs into impactful medicines that address unmet needs in diseases influenced by Trained Immunity.

Trained Immunity is the long-lasting altered functional state of innate immune cells and is the immunologic memory of the innate immune system (Figure 1). Via Trained Immunity, innate immune cells show an altered immunologic response after a second, unrelated challenge (Netea and Joosten, 2024). Trained Immunity is independent of cells of the adaptive immune system and does not need priming or activation via antigens which are required by T cells and B cells (Ochando et al., 2023). Trained Immunity is characterized by altered responsiveness via increased production of cytokines upon secondary stimulation and the upregulation of cell surface receptors. Metabolic and epigenetic changes have an important underlying role that leads to this state of altered responsiveness. Examples of metabolic changes include an increase in glycolysis, changes in oxidative phosphorylation (OxPHOS) and glutaminolysis, altered fatty acid, cholesterol, sphingolipid, and oxylipin synthesis, and increased mitochondrial metabolism (Ferreira et al., 2024). Examples of epigenetic changes that underlie the induction of Trained Immunity are histone methylation and histone modifications which remodel the chromatin structure, leading to the change of transcriptionally repressive heterochromatin into transcriptionally permissive euchromatin (van der Heijden et al., 2018).

Figure 1

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Trained Immunity confers nonspecific protection of the host against reinfection. This has been most extensively studied in Bacillus Calmette-Guérin (BCG) vaccination studies. The BCG vaccine was developed to protect against Mycobacterium tuberculosis. It has now been established that the nonspecific protective effects of the BCG vaccine against other infectious diseases are mediated (at least partly) through Trained Immunity. Other studied inducers of Trained Immunity include the cell wall component β-glucan of Candida albicans and oxidized low-density lipoprotein (oxLDL).

Trained Immunity is divided into central and peripheral Trained Immunity. Central Trained Immunity is the induction of Trained Immunity at the level of the bone marrow, and specifically at the level of hematopoietic stem and progenitor cells (HSPCs). These HSPCs produce innate immune cells such as monocytes. The induction of central Trained Immunity via the reprogramming of HSPCs results in long-term and systemic changes of circulating innate immune cells, with altered responsiveness (Netea and Joosten, 2024). The BCG vaccine and β-glucan induce central Trained Immunity by introducing epigenetic and metabolic changes in HSPCs (Cirovic et al., 2020; Tran et al., 2025). Furthermore, the pro-inflammatory cytokine IL-1β also induces central Trained Immunity. In fact, IL-1β plays a central role in the induction of Trained Immunity, both by BCG and β-glucan (Moorlag et al., 2020; Arts et al., 2018). Thus, the IL-1β pathway presents one of the important targets to mediate Trained Immunity responses, as will be discussed later. Peripheral Trained Immunity occurs outside of the bone marrow and the lymphoid organs. It is a tissue-specific form of Trained Immunity where innate immune cells are ‘trained’ locally and acquire altered responsiveness after a primary stimulus (Ochando et al., 2023). Training of cells residing in these peripheral tissues can also occur in non-immune cells, such as epithelial cells and fibroblasts (Knight et al., 2025).

The immunological, metabolic, and epigenetic pathways that underlie the induction of Trained Immunity present potential therapeutic targets for drug discovery. Developing drugs that target the receptors or metabolic and epigenetic processes that mediate Trained Immunity could potentially be used in disease settings that require altered effector functions of innate immune cells, such as during immune paralysis, in cancer, or as a vaccine adjuvant. The BCG vaccine, an inducer of Trained Immunity, is already approved for the treatment of bladder cancer. Downregulating Trained Immunity responses in clinical indications with hyperinflammation, such as rheumatic diseases, may also provide novel therapeutic approaches.

For the purposes of developing a RoadMap to discovery and development of drugs targeting Trained Immunity, we use five drug development domains to facilitate discussing the different aspects of Trained Immunity (Figure 2). The drug development domains that we describe and discuss in this review are epigenetic, metabolic, differentiation, inflammatory, and memory changes that together help to describe the effects and aspects of Trained Immunity that are relevant for a RoadMap of DDD. These drug development domains can be used to:

• guide the identification and validation of molecular targets;

• design preclinical translational models (in vitro, in vivo, and in vivo);

• understand the pharmacology and dose of a candidate molecule;

• identify biomarkers of Trained Immunity.

Figure 2

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Epigenetic changes associated with Trained Immunity are the molecular basis for the memory of innate immune cells and are embedded at the chromatin level. In addition, Trained Immunity is mediated and maintained by metabolic changes in target cells that improve their ability to react to repeated insults. Consequently, trained cells show increased phagocytic capacity, increased potential for antigen presentation (González-Pérez et al., 2025), and enhanced inflammatory responses due to increased cytokine and chemokine production upon secondary stimulation. Constitutive cytokine expression or inflammation is not typical of the trained state. In fact, following the initial priming event, the inflammatory response subsides and becomes quiescent only to react when the second event occurs. To characterize Trained Immunity responses, it is therefore necessary to study various clinical and preclinical endpoints that underpin each of these domains.

In general, DDD is a complex, multistage process that typically takes 10–15 years and costs around $2.6 billion (Figure 3).

Figure 3

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DDD consists of several key stages that are described in detail in Appendix 1.

DDD in Trained Immunity using AI and machine learning

The immune system is a complex biological system which poses a challenge to identifying lead candidates for drug development, and AI/ML could play a key role in advancing discoveries in this field. Notably, the FDA acknowledges the importance of AI in the drug discovery process in a recent extensive discussion paper (FDA, 2025). Figure 4 illustrates the steps involved from research and discovery to drug development, highlighting the potential AI-supported aspects in the lower half.

Figure 4

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Drugs that enhance Trained Immunity may be developed to protect against infections, assist the immune system in combating cancer, and prevent metastasis (Ding et al., 2023; Vuscan et al., 2025). On the other hand, drugs that regulate Trained Immunity may be developed for diseases involving hyperinflammation such as autoimmune or autoinflammatory diseases (Mora et al., 2023; Badii et al., 2022).

Target identification

Machine learning models may be used to pinpoint proteins or pathways implicated in immune memory formation, accelerating the identification of therapeutic targets for Trained Immunity. Additionally, AI can inform at which level of Trained Immunity (i.e. central or peripheral Trained Immunity; Netea et al., 2020a), the target has the most potential to reach its therapeutic effect.

AI may be used to screen large databases of molecular and genetic data to identify novel inducers of Trained Immunity without triggering excessive inflammation. For example, machine learning algorithms have been used to discover the role of Toll-like receptor (TLR) agonists and other immune-modulating molecules, such as nanoparticle-based adjuvants in Trained Immunity (Abiri et al., 2021; Zhang et al., 2024; Sobhani et al., 2024; Knight et al., 2024).

AI may be used to simulate immune system dynamics to identify molecules that induce Trained Immunity through epigenetic and metabolic reprogramming. This includes screening compounds that target pathways like mTOR or NLRP3 inflammasome activation (Ferreira et al., 2022a).

Hit identification

Machine learning algorithms can be used to prioritize hit compounds based on their potential to induce or regulate Trained Immunity phenotypes in vitro and in vivo. This approach can help pinpoint noninflammatory inducers of Trained Immunity, e.g., glucocorticoids (Knight et al., 2024) could be particularly valuable for therapeutic applications. For example: A-HIOT (Automated Hit Identification and Optimization Tool) integrated chemical and protein space-driven stacked ensemble models to refine hit selection for CXC chemokine receptor 4. It provided a reliable approach for bridging the gap between ligand-based and structure-based virtual screening in finding the optimized hits for CXCR4 (Kumar and Acharya, 2022).

Multi-omics

AI algorithms may help to integrate and analyze various omics datasets (genomics, epigenomics, transcriptomics, and metabolomics) to identify the most promising indications for Trained Immunity-based interventions. This comprehensive approach can reveal complex interactions and mechanisms underlying Trained Immunity in different disease contexts. SIMON AI software accelerates the discovery of human immune memory responses to viruses by integrating multi-omics analysis. While it focuses on viral immunity, this approach could be applicable to Trained Immunity as viral stimuli can also induce Trained Immunity and antiviral immune responses such as increased production of type 1 interferons can be augmented in Trained Immunity settings (Taks et al., 2022; Tomic et al., 2022; Koeken et al., 2021).

Immune response and Trained Immunity prediction

AI models, like the AI-Cell tool, can predict how human immune cells might respond to nanomedicines, which is crucial for developing safer and more personalized gene therapies that could induce Trained Immunity. These predictive models can help anticipate potential immune reactions to treatments, allowing for optimization of therapies that harness Trained Immunity (Chandler et al., 2022; Priem et al., 2020).

Machine learning frameworks like scifAI may be used to analyze millions of images of interacting immune cells to measure changes caused by specific antibodies or compounds (Shetab Boushehri et al., 2023). This approach can predict T cell activation and cytokine production, which are key aspects of Trained Immunity responses. Universal LIPSTIC (uLIPSTIC) offers a way to address the challenge of investigating transient interactions between cells of the immune system that come into close physical proximity (Nakandakari-Higa et al., 2024).

Machine learning models specifically applied to multi-omics data can also be used to classify patients into Trained Immunity responders and nonresponders, as was shown by Moorlag et al., 2024. In this study, the researchers generated a model that could classify patients into Trained Immunity responders and nonresponders, and the model with the most predictive value included chromatin accessibility profiles at baseline (before BCG vaccination), host factors, and vaccination day.

Potential targets in Trained Immunity currently under investigation include but are not limited to:

• Epigenetic regulators: Modulating histone modifications, DNA methylation, and histone lactylation can influence Trained Immunity responses.

• Metabolic pathways: Enzymes involved in glycolysis, OxPHOS, glutaminolysis, autophagy, fatty acid metabolism, and lipid metabolism play a role in immune cell reprogramming.

• Pattern recognition receptors (PRRs): These extracellular or intracellular receptors detect pathogen-associated molecular patterns and can be targeted to regulate Trained Immunity.

• Cytokine signaling pathways: Controlling inflammatory cytokines like IL-1β and IL-4 may help fine-tune Trained Immunity, e.g., targeting IL-1β signaling via the IL-1 receptor (IL-1R).

To date, several molecular targets and mechanisms that regulate Trained Immunity have been identified. These targets, pathways, and mechanisms can be targeted by numerous Trained Immunity-regulating compounds (Table 1).

PRRs can be stimulated or inhibited to regulate Trained Immunity responses (Alexopoulou and Irla, 2025; Owen et al., 2020). These PRRs include TLRs, NOD-like receptors (NLRs), and C-type lectin receptors (CLRs) (Ochando et al., 2023; Kleinnijenhuis et al., 2012; Moerings et al., 2022). PRRs may be intracellular or extracellular. Examples of intracellular receptors that mediate Trained Immunity responses are NOD1, NOD2, TLR3, TLR7, and TLR9. Examples of extracellular receptors are Dectin-1, TLR2, TLR4, and TLR5.

In addition to PRRs, receptors that can be bound by cytokines or growth factors can also mediate Trained Immunity responses. For example, IL-1β induces Trained Immunity via the IL-1R, and IL-4 mediates Trained Immunity via the IL-4 receptor (both type 1 and type 2 IL-4 receptors). IL-1β plays an important role in mediating central Trained Immunity (Moorlag et al., 2020; Teufel et al., 2022; Flores-Gomez et al., 2024; Mitroulis et al., 2021); therefore, it represents an important target in disease indications where Trained Immunity contributes to immunopathology, such as in autoimmunity and transplantation (Ochando et al., 2023). IL-1R can be targeted with a recombinant interleukin 1 receptor antagonist protein which is already approved and on the market (Anakinra). Furthermore, the IL-1β protein itself can be inhibited using canakinumab, which is approved for several auto-inflammatory diseases by the FDA and EMA.

The enzymes that regulate epigenetic changes that modulate Trained Immunity responses are typically located in the nucleoplasm. Thus, to target epigenetic enzymes such as histone acetyltransferases, histone deacetylases, histone methyltransferases, or histone demethylases, the active pharmaceutical ingredient should be able to reach the nucleoplasm. This poses a challenge for drug development. Small molecules that are lipophilic are suitable because they can reach the nucleoplasm via passive diffusion through the nuclear envelope. Moreover, some biological drugs with a molecular weight under 40 kDa can pass through nuclear pore complexes to reach the nucleoplasm (Klughammer et al., 2024). Thus, the lipophilic characteristic and the size of the small molecule are important aspects that allow diffusion to the nucleosome. The G9a histone methyltransferase modulates Trained Immunity responses via methylation of histone 3 lysine 9 (H3K9) and can be inhibited by BIX-01294 leading to enhanced Trained Immunity responses in human monocytes (Mourits et al., 2021b). Its small size (approximately 400 Da) and lipophilicity allow it to cross both the plasma membrane and the nuclear envelope efficiently. Sirtuin 1 is another example of an epigenetic enzyme that regulates the induction of Trained Immunity (Mourits et al., 2021a). This enzyme is a histone deacetylase. Activation of Sirtuin 1 using resveratrol (a natural polyphenol) enhanced the Trained Immunity effect induced by BCG (Bulut et al., 2025).

Metabolic regulation of Trained Immunity also occurs intracellularly in the cytoplasm of target cells. Targeting metabolic processes and enzymes therefore also requires a pharmaceutical active ingredient that can reach the cytoplasm. Some potential metabolic targets that are involved in Trained Immunity include (Ferreira et al., 2024; Ferreira et al., 2022a; Ziogas et al., 2025; Arts et al., 2016; Domínguez-Andrés et al., 2019; Hao et al., 2025; Bekkering et al., 2018):

• Glycolysis: The enzyme hexokinase is a critical regulator of glucose metabolism and has been implicated in Trained Immunity responses (Cheng et al., 2014; Keating et al., 2020a).

• Tricarboxylic acid (TCA) cycle: Succinate dehydrogenase influences immune cell activation and epigenetic modifications (Domínguez-Andrés et al., 2019).

• Lipid metabolism: Acetyl-CoA carboxylase plays a role in fatty acid synthesis, which affects immune cell function (Arts et al., 2016).

• Glutaminolysis: Glutaminase regulates glutamine metabolism, which is essential for Trained Immunity (Scarpa et al., 2025).

Furthermore, at the level of gene expression, there are several transcription factors that regulate Trained Immunity responses. Notable transcription factors that regulate Trained Immunity are HIF1α, NF-κB, and RORα (Kilic et al., 2024; Mulder et al., 2019; Hu et al., 2022). Some of these transcription factors are also involved in regulating the metabolic changes that occur in Trained Immunity (Cheng et al., 2014).

Some examples of compounds that regulate Trained Immunity are shown in Table 2.

The pharmaceutical formulation facilitates the biodistribution, clearance, and pharmacological effect of the Trained Immunity-regulating drug. The formulation also plays a key role in the delivery of the drug to the target organ, target receptor, or target protein. As discussed previously, the receptors, proteins, and enzymes that regulate Trained Immunity processes can be located in the nucleoplasm, intracellular, or as an extracellular or transmembrane receptor. Moreover, depending on the clinical indication, the target cell or organ will determine the required characteristics of the pharmaceutical formulation as well. For example, a Trained Immunity inducer that is to reach the HSPCs in the bone marrow will likely need to be administered as an intravenous drug, whereas a mucosal vaccine, targeting immune cells in mucosa, does not have to be administered intravenously per se, but can be applied topically in the mucosa. Thus, depending on the disease context, target tissue, and the distinction between central versus peripheral Trained Immunity, the delivery system of the Trained Immunity-regulating compound will be different. The administration, location, and delivery vehicle should be designed to ensure that the target tissue or cell type is targeted with minimal side effects. In case a long-term effect on Trained Immunity is required, delivery to the bone marrow, as well as stability in blood and bone marrow tissue, is important. Such a stable formulation prevents the degradation of the compound during administration and preserves its biological effect. It is therefore important to assess the stability of the Trained Immunity-regulating compound in different matrices such as plasma, whole blood, and liver microsomes and to perform early absorption, distribution, metabolism, and excretion (ADME) studies.

Early nonclinical work is often done with simple formulations, e.g., compounds dissolved in DMSO. However, the formulation may have an impact on the pharmacology and kinetics of an active substance. Depending on the route of administration, nanoparticle-based systems offer versatility in payload incorporation, cellular targeting, and ability to modulate immune responses, making them particularly well suited for inducing and regulating Trained Immunity (van Leent et al., 2022).

The use of models and biomarkers in the context of DDD changes as the asset proceeds along the pipeline. Initially, the focus of these models is on target identification and target validation. However, after candidate selection, the focus of the models becomes translational science. This means that the objectives of the models change compared to the target validation phase. The objective of translational science is to understand the pharmacology of the molecule under development, rather than understanding the basic mechanisms of Trained Immunity. The focus becomes the molecule rather than the target of Trained Immunity. This change in focus is illustrated in this section of the review.

The design of translational science models and the use of translational biomarkers depend on knowledge of the basic mechanisms of Trained Immunity and the molecular target of the candidate molecule. The question to be answered therefore changes from ‘what is the role of the molecular target in Trained Immunity?’ to ‘what is the effect of the candidate molecule on Trained Immunity?’. Translational science models are therefore designed to fully display the characteristic effects of the candidate molecule on Trained Immunity.

The cellular components of Trained Immunity include monocytes, macrophages, natural killer cells, and granulocytes. In addition, the research domains that are relevant for DDD in Trained Immunity include epigenetic, metabolic, and inflammatory changes. Therefore, in designing relevant pharmacological models, the cell types involved and potential biomarker outcomes have been included. Reference to the literature can identify commercial or academic providers that can supply relevant cell types and relevant endpoints. These can be focused on developing suitable models and biomarkers to showcase the pharmacology of the candidate molecule.

These models should focus on pharmacological objectives that include a full description of the concentration response. Generally speaking, up to 10 different concentrations should ideally be investigated in order to determine an EC₅₀. The EC₅₀ is the concentration of the candidate molecule that affects the outcome by 50% (increase or decrease). This value is essential in the calculation of the early dose estimation and therapeutic window. It is possible that the EC₅₀ value calculated in different models with different endpoints may not be identical. Therefore, it is important to study a number of different models and biomarkers in order to determine a range of EC₅₀s. This range of EC₅₀s can be used to estimate the likely range of exposures/doses that may be effective in subsequent clinical trials. Pharmacokinetics (PK)/pharmacodynamics (PD) modeling, together with the safety profile, is involved in determining the clinical starting dose and the maximum permitted dose in clinical trials.

This section provides practical information that facilitates the development of suitable models and meaningful biomarkers. The types of available models are illustrated in Figure 5. The models are ranked from top to bottom, with those models that are relatively low cost and high throughput but potentially of less relevance to translational science at the top of the figure. While those with higher cost and lower throughput but of high relevance are at the bottom of the figure. During DDD, the models would be implemented over time starting with the lower cost and high-throughput type of model, and based on the outcomes of these models, the more relevant but higher cost and lower-throughput models could be defined and implemented more effectively.

Figure 5

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The cellular components of the model are chosen to demonstrate the particular function of Trained Immunity that is of interest. These can range from cell lines, primary cells, and induced pluripotent stem cells (see Appendix 1). The process of choosing which cell lines to include in your models involves a bioinformatics exercise to investigate the expression of your drug target in the various cell lines. If a cell line does not express the molecular target of your asset, then it would be excluded from your models. In addition, some experimental work would be required to understand if the target is modulated in a particular cell line when the cells are exposed to stimulators of Trained Immunity. This evidence of modulation would prioritize this cell line for inclusion in a model.

Biomarkers

General overview

The definition of a ‘biomarker’ differs between experts and publications. The FDA/NIH biomarker workshop describes them as ‘defined characteristics that can be measured as an indicator of normal biological processes, pathogenic processes, or responses to an exposure or intervention, including therapeutic interventions’ (FDA-NIH Biomarker Working Group, 2016). The different types of biomarkers and their application during DDD are shown (Figure 6).

Figure 6

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A large portion of the activities referred to as translational science involves the development of biomarkers that can be reliably applied during clinical development of a new drug. The biomarker assay needs to be robust, measurable in an accessible matrix (blood, sputum, urine, skin, etc.), have low variability, and exhibit a relationship with the biological process of interest. These biomarkers are not necessarily ‘validated’ or even ‘qualified’; they are for internal use and would not be suitable as regulatory endpoints for approval. To obtain regulatory approval for a biomarker requires a large amount of data and is beyond the scope of this review. However, the use of biomarkers in DDD is well accepted for internal decision-making.

Trained Immunity-related nonclinical models can, together with disease-specific models, provide information on the potential benefit of a candidate molecule. In addition to focusing on therapeutic benefits, it is crucial to thoroughly investigate the potential risks, as epigenetic changes may persist for an extended period and could potentially impact existing or emerging comorbidities, such as inflammation or autoimmune diseases (Divangahi et al., 2021; Bhargavi and Subbian, 2024).

The International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use (ICH) is developing guidelines about model-informed drug development (MIDD). MIDD is defined as the strategic use of computational modeling and simulation methods that integrate nonclinical and clinical data (including biomarkers), prior information, and knowledge (e.g. drug and disease characteristics) to generate evidence (International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use, 2024). This initiative is groundbreaking as it allows modeling of different types of data to inform decision-making during drug development. Previously, each dataset was analyzed independently of the others, providing limited insight into safety, tolerability, and efficacy. MIDD permits a more ‘holistic’ approach that means that even preclinical and early biomarker work can still play a role at later stages in development. It enables extrapolation of that modeled data to unstudied situations and populations to mitigate risks and increase the probability of success. This has elevated the role of biomarkers in drug development from ‘nice-to-have’ to a necessity.

The process for identifying potentially useful biomarkers may involve ‘omics data (transcriptomics, proteomics, etc.) generated from the relevant translational models that are conducted during the development of a new candidate (Figure 7). They could be identified as those markers that change in a dose-response manner to the pharmacological agent in a particular translational model.

Figure 7

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An important step is to develop and validate a suitable model that expresses all of the features of Trained Immunity that may be affected by your new candidate (based on the known molecular target and/or the phenotypic screen). A good starting point could be an in vitro model using a cell line. This would allow a preliminary characterization of the potential pharmacology of the candidate. A schematic is shown in Figure 8. In this experimental design, a suitable cell line would be chosen based on a bioinformatic investigation to measure levels of expression of the molecular target. The cell line with the appropriate level of expression could be cultured in vitro and differentiated into the cell type of interest (e.g. HL-60 cells differentiated into granulocytes). These cells could then be primed using the known initiators of Trained Immunity (β-glucan, BCG, LPS). Increases in the relevant domains of DDD in Trained Immunity could be assessed in the model. The time required for these upregulated analytes to return to baseline values could be determined. Once the analytes have returned to baseline, the secondary challenge (e.g. virus) could be applied and the memory increase in Trained Immunity could be assessed. This model should be validated by showing that the data generated is robust, reproducible, and reliable.

Figure 8

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Thinking of the timing for adding the candidate molecule to the model depends on the desired product profile of the drug under development and its ultimate planned use in the clinic. It is likely that a modulator of Trained Immunity would be administered after the initial prime has taken place but before/during the secondary challenge. In the example below (Figure 8), the candidate is added at the time of the secondary challenge; however, this could be varied based on emerging data. The effect of the candidate on the trained immune response can then be assessed. The candidate may enhance or diminish all of the aspects of Trained Immunity, or it may provide a more complex pattern of effect with some analytes enhanced and others diminished.

Comprehensive dose-response data should be provided for each of the endpoints measured in the model, such that an EC₅₀ can be calculated for each endpoint (Sebaugh, 2011).

The translational value of a model is somewhat difficult to assess, particularly in the field of Trained Immunity where you may be developing a treatment to increase Trained Immunity or a treatment to decrease Trained Immunity. In addition, the timing of the first application of your asset into the model is also crucial. Thinking about the development of a vaccine adjuvant (Figure 8), the asset would be most appropriately added prior to the priming event, as this would mimic the clinical situation. However, in a complex waxing and waning systemic disease like rheumatoid arthritis, when is it most appropriate to administer the asset to the model? One could argue that the priming event occurred long before diagnosis, and the disease is now driven by self-sustaining immune responses. In this case, the asset could be administered during the challenge or even later at peak effects. Alternatively, one could argue that the priming event is repeated continuously with generation of Trained Immunity as an ongoing cycle of priming and challenge. In this case, the administration of the asset at or before priming could still be relevant.

In conclusion, the design of the model used to test your asset and determine its pharmacology should be guided by the proposed indication and use in the clinic. This is the target product profile – a description of the use and expected effects of your molecule in a particular patient population.

Based on the mechanism of action (a memory-like response after exposure to certain stimuli, leading to enhanced responsiveness to future infections) and the cells involved (macrophages, NK cells, monocytes, etc.), Trained Immunity offers promising applications across various medical conditions, primarily in enhancing host defense against infections and modulating chronic inflammatory diseases.

It is tempting to think of separating indications into those that require Trained Immunity to be augmented and those that require a decrease in Trained Immunity. However, the reality is likely to be more nuanced, with each indication requiring modulation of Trained Immunity with some aspects being augmented and others decreased. This emphasizes the need to understand the pharmacology of a new asset and its overall effect on the various models of Trained Immunity. This information can then be used to find the patient population that is most likely to derive benefit from the new therapeutic agent.

AI drug positioning engines can screen multiple disease indications and rank them based on their relevance to specific molecular targets or pathways involved in Trained Immunity. This approach can help prioritize the most promising indications for further investigation.

Here are some of the most promising indications for Trained Immunity.

In drug development, clinical trials are the point at which a drug is tested in humans with the purpose of establishing safety and efficacy in the chosen indication(s). Arrival at this point in development brings additional ethical, financial, medical, and scientific considerations that need to be carefully considered to design studies capable of demonstrating whether or not the drug works while limiting the risk to the health of the participants (FDA, 2018). As discussed in Appendix 1, clinical trials are typically classified into phases; the most relevant of which for development of a new drug are Phase 1, Phase 2, and Phase 3. Trials may be classified as multiple phases if relevant.

Clinical trials and Trained Immunity

The concept of Trained Immunity was defined, relatively recently, in 2011, and translation of these findings into clinical use is still somewhat limited. Accordingly, while there are numerous novel therapies aiming to influence Trained Immunity under investigation, the available clinical trial data is dominated by investigation of already developed or approved therapies, particularly vaccines such as the BCG vaccine and the MV130 vaccine (Wilkie et al., 2022; Montalbán-Hernández et al., 2024).

Thus, there is not a defined clinical path to the market for novel assets based upon clear clinical trial precedents of drugs approved on the basis of their modification of Trained Immunity. Nonetheless, there are clinical trials that have examined the response of the innate immune system to interventions. Included in this section are several clinical trials that have been selected to inform on how to proceed through clinical development with drugs targeting Trained Immunity (Figure 9).

Figure 9

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These trials are categorized into three groups:

1. trials investigating induction of Trained Immunity for therapeutic benefit,

2. trials investigating modulation of Trained Immunity for therapeutic benefit, and

3. trials investigating inhibition of Trained Immunity for therapeutic benefit.

The examples given in group (a) are primarily investigating vaccines, particularly the BCG vaccine, and whether they confer protection from diseases other than the typical target of the vaccine, by inducing a Trained Immunity effect.

Group (b) includes two trials investigating whether immunomodulatory drugs can alter features of sepsis and experimental endotoxemia such as immunoparalysis. These trials provide interesting examples of how modulating immune responses, e.g., with Anakinra (recombinant IL-1R antagonist), to achieve therapeutic goals relevant to Trained Immunity can be tested in human participants.

Group (c) includes two trials where different treatment regimens, dual versus single pathway inhibition in coronary artery disease and continuous versus intermittent statin treatment for control of cholesterol, are compared and investigated for their effects upon Trained Immunity in the context of cardiovascular health. These trials thus demonstrate examples for how inhibiting maladaptive Trained Immunity responses may be investigated.

The conduct of these trials over time is illustrated by Phase (Figure 9). While the earliest trial selected began in 2012, most of the trials selected are more recent.

More details of the individual trials show the various endpoints that are included in the trials in order to showcase the effect of the investigational molecule on Trained Immunity in humans (Table 4).

Table 4

(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	Levels of proinflammatory cytokines (including IL1-β, TNF-α, IFN-γ) after stimulation of PBMCs with non-OPV antigens and mitogens 1 month after intervention Levels of plasma markers of systemic inflammation (e.g. TWEAK and SIRT2) 1 month after intervention Investigating epigenetic changes in PBMCs by single-cell ATAC-sequencing and whole-genome methylation assays 1 month after intervention Investigate transcriptional effects on immune cells by single-cell RNA-sequencing 1 month after intervention. Identifying proportions of immune cell subsets
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	Epigenetic and metabolic changes in purified monocytes from PBMCs, including specific Trained Immunity-associated miRNAs (miR155, miR146, miR21), lactate production, glucose consumption, and mitochondrial activity at day 45 with respect to baseline Change in proportions of immune cells (including T cells, B cells, NK cells, and subsets of monocytes) in peripheral blood at days 15, 45, and 70 with respect to baseline
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 Participants with positive CEA blood test Participants with positive IGRA blood test with CEA protein antigen after percutaneous use Participants with IGRA blood test with TB antigens (negative before percutaneous use, positive after percutaneous use)
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	Incidence of SARS-CoV-2 infection. Timeframe: 6 months from recruitment day Incidence of COVID-19 symptoms. Timeframe: 6 months from recruitment day Intensity of efficacy of first dose of vaccine against COVID-19. Timeframe: 6 months from recruitment day Duration of efficacy of the second vaccine dose against COVID-19. Timeframe: 1 year from recruitment day
	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	Cytokine stimulation from PBMCs. Timeframe: month 3 Epigenetic changes of circulating monocytes. Timeframe: month 3
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	Difference in influenza antibody titers at days 14, 21, 28, and 42 Difference in thrombocyte function at days 0, 14, 21, 28, and 42
	Selected Secondary Outcomes relevant to Trained Immunity	IFN-γ, IL-10, type 1 IFN, IL-17, IL-22 production by ex vivo leukocytes stimulated with inactivated/live influenza virus at days 0, 14, 28, and 42 Production of inflammatory mediators (including TNFα, IL-1β, IFN-γ, IL-10, IL-17, and IL-22) by ex vivo leukocytes stimulated with different stimuli (including M. tuberculosis, S. aureus, C. albicans, and inactivated influenza) at days 0, 21, 28, and 42 qPCR/microarray of inflammatory transcriptional pathways at days 0, 14, 21, 28, and 42. Granzyme B production by ex vivo leukocytes stimulated with inactivated/live influenza virus at days 0, 14, 21, 28, and 42
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	Plasma TNF concentrations upon second LPS challenge Cerebrospinal fluid TNF concentrations during repeated experimental human endotoxemia
	Selected Secondary Outcomes relevant to Trained Immunity	Plasma cytokine (IL1RA, IL-6, IL-8, IL-10, MIP-1α, MIP-1β, MCP-1, G-CSF, IP-10, CX3CL1, YKL-40) concentrations (plasma and cerebrospinal fluid), other inflammatory protein biomarkers (Olink Target 96 inflammation panel) (plasma and cerebrospinal fluid), and mHLA-DR during first and second LPS challenges Blood leukocyte single-cell and bulk mRNA profiles/transcriptomic pathways upon LPS challenges Cytokine production of ex vivo leukocyte cultures
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	Cytokine stimulation from PBMCs. Timeframe: 4 and 7 days Gene expression of PBMCs. Timeframe: 7 days Epigenetic changes of circulating monocytes. Timeframe: 7 days
(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 PBMCs subgroup percentage and activation status PBMCs secreting cytokines PBMCs change in gene expression Levels of hs-CRP, IL-6, IL-18, and sVCAM-1
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	White blood cell count and distribution. Timeframe: 3 months Monocyte immune responsiveness to LPS stimulation. Timeframe: 3 months Enrichment of epigenetic gene marks. Timeframe: 3 months

1. 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/.

Drug development needs to adhere to national and international rules and guidelines such as those of the International Conference of Harmonization (FDA, 2022). Further, Regulatory Agencies also develop class- or indication-specific guidelines when new areas of therapy evolve, such as the guideline for safety assessment of cancer immunotherapeutic drugs, including monoclonal antibodies, anticancer vaccines, and cytokines (Khameneh et al., 2024). These guidelines will also have to be applied to Trained Immunity-inducing drugs; however, specific guidelines for drugs targeting the innate immune system and Trained Immunity are not yet available, reflecting the novelty of this approach.

Drugs that modulate Trained Immunity fall into several therapeutic classes. The best-known approved agent that directly induces Trained Immunity is the live-attenuated bacterial vaccine BCG, authorized for tuberculosis prevention and for the treatment of non-muscle invasive bladder cancer, where its efficacy is linked to its capacity to induce Trained Immunity (Netea et al., 2020a). In Europe, another approved prescription immunomodulator is the bacterial lysate OM-85 (Broncho-Vaxom) (Röring et al., 2024), which has also been shown to act through Trained Immunity mechanisms. Furthermore, the measles, mumps, and rubella vaccine (MMR) has demonstrated similar effects (Lee et al., 2022). In addition, licensed vaccines contain adjuvants capable of stimulating Trained Immunity, such as monophosphoryl lipid A (MPLA, a TLR4 agonist), CpG oligodeoxynucleotides (TLR9 ligands), and β-glucans (dectin-1 ligands) (Lee et al., 2022). Furthermore, several small molecules and polysaccharides are in development, such as NOD2 agonists and β-glucans (Table 2).

The recognition of their impact on Trained Immunity of these prophylactic training approaches has largely come from retrospective studies. The primary intent for the development was primary vaccine-focused.

The development of modulators of Trained Immunity, whether inducers or suppressors, targeting prophylactic or therapeutic training approaches, presents distinct scientific and regulatory challenges. Regulatory Agencies are expected to require detailed elucidation of mechanisms of action and safety, extending beyond conventional concerns of off-target effects. Key open questions include whether these agents act on peripheral versus central Trained Immunity, whether they carry risks of epigenetic inheritance, and how individual immune status or microbiome profiles influence therapeutic outcomes.

Regulatory scrutiny will focus on the risks of hyperactivation or suppression of innate immunity. Critical determinants such as dose, timing, and route of administration will shape the magnitude of immune training, necessitating careful characterization of immune responses, vigilant safety monitoring, and robust pharmacovigilance. Additional uncertainties remain regarding the duration and reversibility of trained immune effects. Will the immune response to a second administration profoundly intensify the immune reaction? Will subsequent or existing infections of immune inflammatory status have an impact on the immune training?

In summary, Trained Immunity-targeting drugs must meet the highest standards of safety and efficacy. Emerging risk-based regulatory frameworks in the USA and Europe aim to address their unique immunological properties while enabling accelerated development of innovative immune-based therapies.

DDD in Trained Immunity is a relatively new endeavor and therefore does not yet have many precedents or regulatory guidelines. This raises potential risks (unprecedented development pathway) but also several benefits (relatively limited competition providing room for innovation). However, Trained Immunity is involved in many different indications, and many of these are likely to have a precedented regulatory pathway to approval which can be consulted and modified to form a RoadMap for assets targeting Trained Immunity. Early interactions with Regulatory Agencies are recommended as with any other newly evolving therapeutic approach.

We have categorized the five domains which are relevant for drug development in Trained Immunity, as this facilitates the production of a RoadMap for DDD. The drug development domains categorize the mechanisms of Trained Immunity into five ‘buckets’ – epigenetic, metabolic, differentiation, inflammatory, and memory – which form a common thread linking the stages in the DDD process. These domains are used to consider targets, models, biomarkers, pharmacology, translational science, indications, and clinical trials that could be important in the RoadMap to discover and develop new medicines in the field of Trained Immunity. They form a common thread which can be traced throughout the process, from target identification, through translational science and into clinical trials.

A RoadMap has been provided in this review to guide the development of new therapeutics that modulate the Trained Immunity response to provide clinical benefit for patients (Figure 10). Five major steps are described, including target identification and validation, candidate selection, translational science, indication prioritization, and clinical development. Within each of these steps is a large variety of options to achieve the planned objectives while leaving ample opportunity for innovation and originality.

Figure 10

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It is worth noting that some areas of DDD are outside the scope of this review. These include:

• Chemistry, manufacturing, and controls (CMC): Chemistry, Manufacturing and Controls: Regulatory Considerations Through Clinical Development

• Intellectual property (IP) strategy.

• Regulatory strategy including Clinical Trial Applications: Regulatory Strategy: An Overview

• Statisticians in the Pharmaceutical Industry (PSI): PSI Homepage

• Quality, toxicology, and clinical guidelines from the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH, https://www.ich.org/) and from Regulatory Agencies such as from the Food and Drug Administration (FDA).

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Author details

1. Jelmer H van Puffelen

   Kupando GmbH, Schönefeld, Germany

   Contribution

   Conceptualization, Investigation, Writing – original draft, Writing – review and editing

   For correspondence

   jvanpuffelen@kupando.com

   Competing interests

   Jelmer H van Puffelen is affiliated with Kupando GmbH. The author has no other competing interests to declare

   "This ORCID iD identifies the author of this article:" 0000-0003-3079-027X

2. Callum Campbell

   Target to Treatment Consulting Ltd, Stevenage Bioscience Catalyst, Stevenage, United Kingdom

   Contribution

   Conceptualization, Investigation, Writing – original draft, Writing – review and editing

   Competing interests

   Callum Campbell is affiliated with Target to Treatment Consulting Ltd. The author has no other competing interests to declare

   "This ORCID iD identifies the author of this article:" 0009-0006-5285-2178

3. Irene Gander-Meisterernst

   Kupando GmbH, Schönefeld, Germany

   Contribution

   Conceptualization, Investigation, Writing – original draft, Writing – review and editing

   Competing interests

   Irene Gander-Meisterernst is affiliated with Kupando GmbH. The author has no other competing interests to declare

   "This ORCID iD identifies the author of this article:" 0009-0005-5450-4559

4. Johanna Holldack

   Kupando GmbH, Schönefeld, Germany

   Contribution

   Conceptualization, Investigation, Writing – original draft, Writing – review and editing

   Competing interests

   Johanna Holldack is affiliated with Kupando GmbH. The author has no other competing interests to declare

5. Pauline T Lukey

   Target to Treatment Consulting Ltd, Stevenage Bioscience Catalyst, Stevenage, United Kingdom

   Contribution

   Conceptualization, Investigation, Writing – original draft, Writing – review and editing

   Competing interests

   Pauline Lukey is affiliated with Target to Treatment Consulting Ltd. The author has no other competing interests to declare

   "This ORCID iD identifies the author of this article:" 0000-0001-6160-8325

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