Age-related diseases as a testbed for anti-aging therapeutics: the case of idiopathic pulmonary fibrosis

The hallmarks of aging: a framework for understanding disease

Hallmarks of aging as a concept represent the failure of the biologists’ earlier attempts to define a singular driver of aging. Mechanistic theories explaining aging as an evolutionary program or a mitochondrial dysfunction, or any other key source of degeneration could not explain the aging phenotype in its completeness [1–3]. Hence an alternative was proposed in defining aging as a combination of these various mechanisms chipping away at an organism as the “hallmarks of aging” [4].

A slightly predating concept of the “hallmarks of cancer” suggested that the development of malignant tumors rests on a set of primary enabling processes, such as somatic mutations, which eventually give way to emergent, secondary processes that result in the malignancy’s growth [5]. Similarly, the hallmarks of aging are understood as a triad of primary, antagonistic (compensatory), and integrative processes immediately linked to the aging phenotype. The critics of the aging hallmarks concept indicate, however, that the aging-cancer analogy is not complete and some key distinctions are often overlooked [6]. In particular, genomic instability is the primary cause of all tumors while its aging analog, cellular damage, is a term that is much more open to interpretation and debate [6]. Another point of criticism is what constitutes a hallmark and how many of them there are. While the original 2013 publication defined nine processes, the most recent review of the framework features 12 of them [7–9]. The partition between primary and secondary aging hallmarks is not as clear as in cancer and the boundary between two hallmarks is often diffused, as is the case with inflammation which can hardly be isolated from other hallmarks [10].

Despite their theoretical drawbacks, the hallmarks of aging have been widely adopted both in academia and the biotechnology industry as a framework for their practical utility. They offer a language to describe the proxies of aging used in research. As pointed out by the late Leonard Hayflick, the undefined terminology is the bane of biogerontology leading to “communication failures, erroneous interpretations, and misdirected decisions” [11, 12]. The hallmarks framework allows the anti-aging efforts to be more specific in what type of aging is accelerated or prevented, and thus, provides a more reliable way to find the applications for the observed anti-aging effects.

The hallmarks also give us an opportunity to explore the bidirectional and complex connection between aging and its manifestations in diseases. While aging reduces the overall resilience of an organism, certain pathologies exhibit particularly strong associations with specific aging hallmarks, making them valuable models for investigating geroprotective strategies.

This perspective explores how aging-related diseases (ARDs) can serve as experimental platforms for discovering new geroprotective interventions. We place special emphasis on idiopathic pulmonary fibrosis (IPF) as an emblematic ARD based on extensive literature evidence, findings in preclinical and clinical studies, and a hallmark deconvolution scoring (Figure 1).

Figure 1. Heatmap representation of hallmark scores across 13 aging-related diseases (ARDs). Higher numbers mean higher abundance of hallmark-specific genes among an ARD's targets. Among the scored diseases, type-2 diabetes and idiopathic pulmonary fibrosis demonstrate the strongest representation across multiple hallmarks, particularly in deregulated nutrient sensing. This analysis reveals distinct patterns of hallmark involvement across different ARDs, with some hallmarks (mitochondrial dysfunction, chronic inflammation) broadly represented across multiple conditions, while others (e.g. disabled macroautophagy) show more selective disease associations. NS: Not Significant at q-value<0.001 after Benjamin-Hochberg correction; mean enrichment scores calculated based on 250 samples of target genes for each disease; the plot is produced in Plotly 5.23 for Python 3.11.

The scoring is based on the hallmark-associated pathway enrichment in the published targets associated with a disease (see Methods). We propose to extend the concept of the hallmarks of aging by making it more practically inclined and relevant to anti-aging therapeutics. By deconvoluting the conditions as a sum of aging-related processes, we suggest a measure of biogerontological relevance that may be applied to clinical trials and case-control studies. Furthermore, this measure can make biogerontology more “empirically progressive” by serving as a criterion for a therapy’s geroprotector potential [13]. If the effectiveness of a target, mechanism of action, or compound increases proportionally with how relevant a particular setting is to biological aging, then this entity is more likely to also have positive effects on aging itself, rather than only the indicated disease. In other words, we suggest that clinical efficacy against ARDs should be considered a necessary criterion for validating putative geroprotectors, with IPF representing a particularly informative testbed due to its strong association with multiple aging hallmarks.

Aging alignment in pathologies

We propose organizing ARDs based on how aligned they are with the wider aging process based on their clinically relevant target genes. We have chosen to annotate 13 ARDs discussed in detail in [33] to identify the disease that is the most similar to the aging process. To achieve this, we scored each disease based on the participation of disease targets in hallmark-associated pathways. The score was calculated based on the results from enrichment analysis of the targets with a hallmark-annotated version of Gene Ontology Biological Processes (GO:BP) ontology with an adjustment rewarding hallmark diversity (see Methods). This method allowed us to identify the diseases whose patients were most likely to benefit from geroprotector discovery efforts, and conversely, which indications would make a better testbed for anti-aging research.

From a wider perspective, our scoring reveals distinct hallmark co-occurrence patterns across ARDs, suggesting that these aging mechanisms are fundamentally interconnected with multiple pathological manifestations (Figure 1). For instance, deregulated nutrient sensing emerges as the most broadly represented hallmark, with high scores spanning from metabolic diseases like type 2 diabetes (49.3 out of 100) to fibrotic conditions like IPF (31.2) and inflammatory disorders like osteoarthritis (21.9). Such cross-disease hallmark sharing suggests that therapies addressing metabolic dysfunction could benefit diverse ARDs beyond their primary indication.

This multi-hallmark principle is exemplified by the highest-scoring ARDs, which include type 2 diabetes, IPF, Parkinson's disease, and rheumatoid arthritis (all >60 out of 100; Figure 2). These diseases demonstrate substantial involvement across multiple hallmarks simultaneously: primarily, deregulated nutrient sensing, altered intercellular communication, chronic inflammation, cellular senescence. Consequently, preclinical interventions addressing such prevalent hallmarks may achieve superior therapeutic outcomes across all high-scoring ARDs, making them equally valuable testbeds for geroprotector discovery. Among other indications, IPF stands out within due to its rapid progression and well-defined clinical endpoints, offering practical advantages for testing anti-aging interventions.

Figure 2. Quantitative comparison of aging alignment scores across 13 aging-related diseases. The total score represents the similarity of a pathology to the general aging process, calculated as a weighted sum of partial hallmark scores (see Methods). Type-2 diabetes, idiopathic pulmonary fibrosis, Parkinson’s diseases, and rheumatoid arthritis demonstrate substantially higher alignment with aging processes compared to other conditions. This analysis supports the hypothesis that IPF is an optimal testbed for geroprotective interventions due to its strong mechanistic overlap with fundamental aging processes. Bar height represents the mean sampling score, whiskers display the standard deviation, based on the sampling procedure described in Methods; the plot is produced in Plotly 5.23 for Python 3.11.

The scoring also reveals therapeutically underexplored areas that represent a double-edged strategic opportunity. While hallmarks like deregulated nutrient sensing and mitochondrial dysfunction show broad disease representation with numerous known targets, others such as telomere attrition and disabled macroautophagy, exhibit more sparse associations. This pattern likely reflects differential target discovery investment rather than biological irrelevance, as telomere attrition remains a well-documented contributor to multiple ARDs [34]. For sparse hallmarks, fewer known targets imply higher discovery risk, but also lower competition and potentially greater impact for successful interventions. The comparison of hallmarks' contribution across the ARD spectrum may help drug developers strategically choose between targeting crowded, well-validated pathways or exploiting underexplored therapeutic niches [35].

While some authors express skepticism about the value of hallmarks in drug discovery, highlighting issues such as conflating biomarkers and drivers of aging or individual hallmarks' fuzzy definition [6, 36, 37], the framework has generated a fruitful research program that now warrants empirical validation through intensive clinical testing [38]. To further this point, some broad anti-aging strategies, such as senolytics, have been shown to lead to improve the outcomes in Alzheimer’s disease, ischemic injury, diabetes and other indications [39–43]. Such evidence validates the translational potential of hallmark-first drug development.

We argue that aging biology should be considered early in the drug design process, rather than addressed only later through repurposing efforts. The deconvolution of disease-hallmark interactions proposes a more flexible and robust alternative to the disease-centric mode of drug development. The specific approach presented here should be treated as one of many possible options for disease-hallmark annotation, with future improvements possible through additional data sources and methodological refinements.

Commercial strategy for anti-IPF drug development

In this work, we aim to provide evidence for differential alignment between ARDs and the fundamental hallmarks of aging and highlight the importance of the hallmark framework in clinical research. Our analysis demonstrates that certain ARDs more closely reflect the cellular aging processes, making them ideal candidates for testing geroprotective interventions. IPF emerges as an emblematic aging disease due to its strong representation across multiple aging hallmarks and promising findings in clinical programs.

We argue that the etiology of IPF fundamentally mirrors that of aging itself, with multiple hallmarks interacting synergistically to drive disease progression. This claim is backed both by our computational approach and literature evidence of anti-aging interventions counteracting IPF [72, 74]. This close alignment makes IPF a perfect testbed for anti-aging therapeutics, as insights gained from treating it will undoubtedly inform our approach to aging and numerous downstream ARDs.

A key implication of this work suggests that interventions potent enough to address core aging processes should demonstrate efficacy across multiple ARDs. We propose that pharmaceutical development should incorporate hallmark analysis when selecting indications, molecular targets, and compounds to maximize translational potential (Figure 3). This approach may significantly reduce development timelines and increase success rates.

Figure 3. Conceptual framework for aging alignment in disease models and therapeutic development. This schematic illustrates the spectrum of aging-related diseases (ARDs) based on their mechanistic overlap with fundamental aging processes. Based on our assessment, idiopathic pulmonary fibrosis (IPF) demonstrates the strongest aging alignment. This framework supports a bidirectional approach to therapeutic development: insights from aging biology inform disease-specific interventions, while clinical efficacy in high-alignment conditions like IPF provides validation feedback for potential geroprotective strategies. The therapeutic strategies shown (senolytics, telomere activation, TNIK inhibitors, epigenetic modulation, and metabolic regulation) represent promising approaches that may translate across multiple ARDs based on their promising results in IPF models and human patients.

From a strategic perspective, developing an anti-aging therapy for IPF offers major opportunities for indication expansion. Compared to the currently dominant approach of single disease trials, targeting aging mechanisms may prove a strictly better strategy. The promising therapeutics explored in IPF (senolytic combinations, telomerase activators, and epigenetic modulators) represent approaches that may generalize across the spectrum of ARDs. Following a successful trial in IPF, such therapies could be extended to other fibrotic or aging-related conditions including liver cirrhosis, chronic kidney disease, atherosclerosis, and many others sharing the underlying aging mechanisms [24]. The unification of multiple ARDs under the umbrella of hallmark-driven diseases offers the convenience of more informed, and thus, more cost- and time-efficient drug development pipelines (Table 2). Recent analyses demonstrate that pharmaceutical development costs range from $172-515 million per drug when accounting for direct costs, rising to nearly $900 million when including capital costs and failures [102–104]. Notably, phase-3 trials, which constitute the largest portion of this expenditure at $100-300+ million per trial, could potentially be streamlined through targeting common aging mechanisms applicable across multiple diseases, rather than conducting separate trials for each age-related condition. But most importantly, the success in IPF could then accelerate the legislation of anti-aging trials and attract much needed investment to the field of biogerontology, thus transforming the whole landscape of aging research.

Table 2. Estimated drug development timeline and costs for IPF.

Stage	Duration (Years)	Estimated cost (USD)	Notes
Target Discovery and Validation	Traditional: 1–3 years; AI-driven: <1 year	~$1–10 million	AI can compress this phase
Hit Discovery and Lead Optimization	Traditional: 2–4 years; AI-driven: 1–2 years	~$10–50 million (small molecules); up to ~$100 million with full preclinical work	AI generative design accelerated discovery to candidate in ~18 months
Preclinical Development	1–2 years	~$5–15 million	Includes Investigational New Drug enabling studies (Safety and Toxicity; Pharmacokinetics; Chemistry, manufacturing, and controls)
Phase I Clinical (Safety)	~1 year	~$5–10 million	Typically involves 50–100 subjects; IPF studies may use healthy volunteers or patients
Phase II Clinical (Efficacy)	~1–2 years	~$20–50 million	100–200 IPF patients; shorter duration than chronic diseases due to rapid progression
Phase III Clinical (Pivotal)	2–3 years (often 1-year treatment per trial)	~$100–300+ million (per trial)	Larger global trials with 500–800 patients; orphan status may reduce numbers but not costs
Regulatory Review and Approval	0.5–1.5 years	~$2–5 million	Orphan drug fees may be waived; accelerated review is possible
Post-market Surveillance	Ongoing	Variable ($10–50+ million)	Part of life-cycle management
The table summarizes the typical duration, estimated costs, and key considerations across the drug development pipeline for IPF therapies. Cost estimates are derived from [102–104] and adjusted to reflect the orphan status of IPF. AI-driven approaches can significantly compress early development phases compared to traditional methods. The high costs of later-stage clinical trials highlight the potential efficiency gains of targeting common aging mechanisms shared by multiple ARDs rather than conducting separate development programs for each indication.

To promote the shift toward hallmark-aware drug development, companies conducting clinical trials should incorporate ancillary analytics of aging biomarkers in their studies, as they may be overlooking broader anti-aging effects of their therapies. For this purpose, aging clocks serve as a perfect tool thanks to a wide range of supported data types, interpretability, a long validation record, and permissive licenses [105–109].

In conclusion, our hallmark-based analysis and literature review provide a framework for identifying optimal disease models for geroprotector discovery. IPF offers a gateway to broader therapeutic applications across multiple ARDs, supporting the view that targeting aging itself represents an efficient drug design strategy.

Methods

Our study utilized a custom approach to analyze and compare ARDs by integrating data from multiple sources (see Figure 4). Disease-associated target genes were acquired through the Open Targets Platform [110], which aggregates evidence of target-disease associations from scientific literature. Only high-evidence (target score>0.4) genes were considered in the enrichment analysis. Out of the genes passing the threshold, we randomly selected 50 out of top-60 to calculate the partial hallmark scores and the total scores. This procedure was repeated 250 times for each disease to get 95% confidence intervals of the scores and to derive statistical significance of non-zero hallmark scores. The distributions of partial hallmark and total scores are available in Supplementary Material 1. For partial scores on Figure 1, q-values were calculated with Benjamin-Hochberg correction to test the alternative hypothesis that the true hallmark score is greater than zero. All scores presented in the figures are normalized relative to the maximal score achieved in sampling.

Figure 4. A brief overview of the scoring methodology used to measure a disease's alignment with the general aging process.

For pathway enrichment analysis, we employed the GO:BP ontology accessed via Enrichr [111]. Each pathway in the GO:BP was annotated as belonging to one or more aging hallmarks. Pathway hallmark assignment was carried out by AI agents enabled with access to GPT-4 and tools providing access to GO:BP and NCBI to increase annotation relevance.

The list of hallmarks to map the diseases to is obtained from [7], except dysbiosis due to its dependence on non-human targets. We then defined a metric of a disease’s alignment with each of the eleven selected hallmarks based on its targets’ enriched pathways.

Pathway scores P_h,d were derived from pathway enrichment analysis (as implemented in Enrichr), with all significantly enriched pathways (p-value<0.001) contributing to the corresponding hallmark partial score. For a set of enriched pathways E_d, the pathway score was calculated as:

$$\begin{array}{l}{P}_{h,d}={\displaystyle \sum _{p\in R_{h,d}}[-{\log }_{10}(p\text{-}value)]\times N_h}\\ \times \left(1+\frac{{\log }_2\left(\left|R_{h,d}\right|+1\right)}{4}\right)\end{array}$$

where R_h,d is the subset of pathways in E_d relevant to hallmark h, p-value is the enrichment p-value of pathway p, and N_h is a normalization factor accounting for the uneven distribution of pathways across different hallmarks.

In the current version the weights are selected arbitrarily, with a lower weight assigned to the gene list component due to lower annotation density in the used gene lists.

Finally, the overall aging score A_d for disease d was calculated as:

$$A_d={\displaystyle \sum _{h\in H}{P}_{h,d}\times D_f}$$

where H is the set of all hallmarks and D_f is a diversity factor that rewards diseases with multiple hallmark associations rather than a single dominant one:

$$D_f=\frac{N_{nz}}{N_t}\times (1+E_n)$$

with N_nz being the number of non-zero hallmark scores, N_t the total number of hallmarks (11), and E_n the normalized entropy of the hallmark score distribution.

The final metric A_d accounts for both the magnitude and distribution of partial hallmark scores T_h,d, thus providing a quantitative basis for identifying the similarities in the aging-related mechanisms underlying different diseases.

The code for this disease scoring method is written in Python 3.11 and is available on Github with demonstration materials: https://github.com/Insilico-org/disease_hallmarks

The proposed methodology is intended as an illustration of the proposed hallmark decomposition approach to geroprotector and drug discovery. In the current form it holds several limitations that need to be elaborated on in later iterations. The scoring depends on the available information about target proteins and thus may undervalue the importance of understudied hallmarks. Similarly, gene annotation density and the sources of these annotations may bring in certain biases.

Abbreviations

ARD: Aging-related disease; AAV: Adeno-associated virus; ECM: Extracellular matrix; GOBP: Gene Ontology Biological Processes ontology; HDAC: Histone deacetylase; IPF: Idiopathic pulmonary fibrosis; MMP: Matrix metalloproteinase; NAFLD: Non-alcoholic fatty liver disease; NCBI: National Center for Biotechnology Information; TERT: Telomerase reverse transcriptase; TGF-β: Transforming growth factor β; TNIK: TRAF2 and NCK interacting kinase; TNF-α: Tumor necrosis factor α.

Author Contributions

AZ — Conceptualization, supervision, writing (review and editing); DW — Writing (original draft, review and editing), investigation; FG — Writing (original draft, review and editing), methodology, software, formal analysis, visualization; FR — Resources, validation, writing (review and editing).

Conflicts of Interest

AZ, FG, and FR are affiliated with Insilico Medicine, a commercial company developing and using generative artificial intelligence and other next-generation AI technologies and robotics for drug discovery, drug development, and aging research. Insilico Medicine has developed a portfolio of multiple therapeutic programs targeting fibrotic diseases, cancer, immunological diseases, and age-related diseases, utilizing its generative AI platform and a range of deep aging clocks and AI life models.

Funding

This research received no grants from any funding agency in the public, commercial, or not-for-profit sectors.

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