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Personalized medicine as a novel therapeutic approach for autoimmune diseases: new insights and future prospects

Mahdi Nasiri-Ghiri1orcid, Yaghoob Foolad1orcid, Shirin Mahmoodi1orcid, Abdolmajid Ghasemian2orcid

Published online: May 11, 2026

1Department of Medical Biotechnology, School of Advanced Technologies in Medicine, Fasa University of Medical Sciences, Fasa, Iran

2Noncommunicable Diseases Research Center, Fasa University of Medical Sciences, Fasa, Iran

Corresponding author: Shirin Mahmoodi Department of Medical Biotechnology, School of Advanced Technologies in Medicine, Fasa University of Medical Sciences, Fasa, Fars Province, Iran E-mail: shirinm64@gmail.com
Co-Corresponding author: Abdolmajid Ghasemian Noncommunicable Diseases Research Center, Fasa University of Medical Sciences, Fasa, Fars Province, Iran E-mail: majidghasemian86@gmail.com
• Received: October 13, 2025   • Revised: January 27, 2026   • Accepted: February 5, 2026

© 2026 Korea Disease Control and Prevention Agency.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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  • Autoimmune diseases are caused by dysfunction of the immune system, leading to inappropriate attacks on healthy tissues. Because patients have diverse genetic predispositions and heterogeneous responses to therapy, personalized medicine (PM) offers an opportunity to improve treatment effectiveness. PM uses diagnostic assessments to tailor treatment through individualized medical interventions. PM may improve therapeutic precision beyond traditional trial-and-error approaches, reduce adverse consequences, and improve outcomes by integrating genomic and transcriptomic data. PM considers genetic and molecular landscapes, immunologic factors, epigenetic influences, and environmental exposures to assess treatment response. However, challenges remain related to diagnostic access, the slow pace of biomarker identification, technological limitations, sustained patient engagement, data management, and computational requirements. Nevertheless, continued efforts to improve understanding of disease pathophysiology, gene expression, and immune regulation—together with the application of novel technologies and machine learning—may advance PM-based therapies. Additional opportunities include drug–target modeling and exploratory single-cell–based approaches to clarify patient-specific therapeutic mechanisms. This review briefly introduces the potential of PM for type 1 diabetes, rheumatoid arthritis, and multiple sclerosis.
  • Keywords: Autoimmune diseases; Multiple sclerosis; Personalized medicine; Rheumatoid arthritis; Type 1 diabetes
Autoimmune diseases (ADs) represent an important and growing global health burden. In these conditions, the immune system, rather than protecting the body, inappropriately damages healthy tissues, leading to diverse clinical phenotypes. The complexity and heterogeneity of ADs pose major challenges to effective treatment. Despite advances in medical science, many patients experience little to no therapeutic benefit. The incidence of ADs is increasing, and genetic predisposition, environmental exposures, and other unidentified factors likely contribute. Key environmental factors associated with ADs include lifestyle, nutrition, air pollution, vector-borne diseases, toxin exposures, and chronic stress [1,2]. Approximately 4% of the global population is estimated to have one of more than 80 distinct ADs, underscoring the need for safer therapies that are better targeted to individual disease mechanisms. The limitations of single-therapy approaches further support the need for personalized treatment strategies. In addition, the absence of dependable predictive markers at initiation of AD therapy contributes to uncertainty in treatment response. Given interpatient variability in signaling pathways, individualized treatment plans are warranted. The Personalized Medicine Coalition describes personalized medicine as an emerging area that uses diagnostic tests to identify the most appropriate medical therapy for each patient to maximize outcomes [3]. Proponents of personalized medicine emphasize tailoring treatment strategies to each patient’s genetic, epigenomic, and proteomic profiles [4]. This approach departs from the traditional “one-size-fits-all” model and aims to improve the effectiveness of preventive and therapeutic interventions. The core principle of personalized medicine is the delivery of individualized interventions. By incorporating a patient’s genetic makeup and personal context, personalized medicine seeks to move beyond trial-and-error clinical decision-making. This approach may reduce adverse effects and improve treatment outcomes [5]. The concept of personalized medicine gained traction in the work of Boguski et al. [6], which emphasized understanding disease etiology, identifying causative factors, and targeting key mechanisms for more effective management. This framework was further strengthened by the National Research Council of the US National Academies, which emphasized integrating genetic data with clinical information to reclassify diseases and enable more precise treatments. Their emphasis on developing data platforms underscores the importance of linking genetic data with clinical observations, which remains a cornerstone of the personalized medicine framework [7]. In recent years, the term precision medicine has sometimes been used interchangeably with personalized medicine [8]. However, because personalized medicine also reflects patient–clinician tailoring at the individual level, the term personalized medicine may be more appropriate in this context [9,10]. Access to high-quality genomic and transcriptomic data can improve the quality of personalized medicine–based therapies [11]. The potential of personalized medicine is particularly promising for ADs, which arise from complex interactions between genetic and environmental factors. Although biologics have enabled more targeted approaches, treatment response remains highly heterogeneous across patients. Personalized medicine may help address these limitations by supporting more precise and effective therapies for ADs [12,13]. In general, patients with autoimmune dysfunction may share similar symptoms despite different underlying immunogenic mechanisms [14]. Personalized medicine aims to characterize environmental and immunologic context, epigenetic influences, and genetic and molecular landscapes that shape treatment response. The aim of this review is to summarize the role of personalized medicine as a therapeutic approach in major ADs, including multiple sclerosis (MS), rheumatoid arthritis (RA), and type 1 diabetes (T1D).
Autoimmunity, defined as the presence of self-reactive antibodies or T lymphocytes, occurs when the immune system mistakenly targets normal host components. The immune system is designed to defend against infectious agents, but its complexity can also lead to pathology. These pathologies can take 2 broad forms: immunodeficiency syndromes, in which the immune system fails to respond adequately to pathogens, and ADs, in which the immune system mistakenly attacks host tissues. A hallmark of ADs is a breakdown of immune tolerance that prevents the immune system from distinguishing self from non-self [15]. The global frequency of ADs is increasing and currently affects approximately 4% of the population, spanning more than 80 distinct disorders. Although the mechanisms contributing to AD development remain incompletely understood, there is growing consensus that ADs result from interactions between genetic risk and environmental exposures [16,17].
Genetic Factors Affecting ADs
Although ADs share some broad patterns (including increasing prevalence with age), they remain highly heterogeneous. ADs vary in etiology, clinical features, age at onset, sex distribution, geographic patterns, genetic architecture, and ethnic predisposition. Compounding these challenges, the cause of many ADs remains unknown. Further investigation of disease-specific biological mechanisms is necessary to identify and develop effective, targeted therapeutic interventions [18]. Immune dysregulation also contributes to AD progression, including altered Th1/Th2 balance and cytokine production [19–21]. Genetic factors implicated in ADs—including those discussed here for T1D, RA, and MS—are summarized in Figure 1.

Role of genetic factors in T1D

T1D is a major global public health concern and accounts for approximately 10% of diabetes cases [22]. The etiopathogenesis of T1D is complex and involves genetic and environmental factors. Susceptibility to T1D is partly hereditary, supported by well-established genetic risk at human leukocyte antigen (HLA) loci—particularly DR4, DQ8, and DQ2 [23–25]. The presence of a single islet autoantibody confers a moderate risk of developing T1D, whereas the presence of additional autoantibodies increases risk substantially [24]. Autoantigens are presented by major histocompatibility complex (MHC) class I and II molecules (HLA) on antigen-presenting cells (APCs) to autoreactive T cells, contributing to T1D development [24]. T1D is characterized by loss of pancreatic β-cells responsible for insulin production. Evidence supporting its autoimmune nature includes: (1) inflammatory infiltrates (insulitis) within islets, (2) strong associations with specific MHC alleles, and (3) the presence of autoantibodies targeting islet-cell autoantigens [26]. Genetic, epigenetic, and environmental factors contribute to disease manifestation, and more than 60 genes have been implicated in T1D susceptibility [27,28]. Approximately 30%–50% of T1D genetic risk is attributed to HLA class II alleles [28]. Reported genetic risk factors include HLA-DR3/DQ2, HLA-DR4/DQ8, HLA-A*02:01, variants in protein tyrosine phosphatase non-receptor type 22 (PTPN22), insulin (INS) polymorphisms, interleukin-2 receptor subunit alpha (IL2RA) variants, and increased expression associated with a common IFIH1 variant [29,30]. Individuals heterozygous for HLA-DRB1*04 and HLA-DRB1*03 are at particularly high risk [31,32]. Although genetic heterogeneity and modest effect sizes for individual variants present challenges, these approaches have improved understanding of T1D biology and have informed risk prediction, prevention, and targeted therapeutics [30,31]. The HLA region on chromosome 6p21 is a major contributor to T1D risk [33,34] and accounts for approximately 50% of familial aggregation [35]. A recent study reported that DR3 homozygous carriers of HLA-DRB3*02:02 were at significantly higher risk of developing T1D than DR3 homozygous carriers of HLA-DRB3*01:01 [35]. Siblings with the high-risk DR3/DR4-DQ8 genotype who share both haplotypes with their probands face an estimated 85% risk of T1D by age 15 years [36]. Associations between HLA and T1D likely reflect polymorphisms that influence peptide binding and the repertoire of antigens presented to T cells [37]. PTPN22, located on chromosome 1p13 and encoding lymphoid tyrosine phosphatase, a negative regulator of T-cell activation, has also been associated with T1D [38]. Mechanisms of T1D progression are depicted in Figure 2.

Role of genetic factors in RA

RA is a polygenic disease with a substantial genetic contribution (estimated heritability of approximately 60%) and is a systemic inflammatory disorder that primarily affects small joints of the hands and feet. RA is associated with reduced life expectancy (approximately 3–10 years), and family history can substantially increase RA risk [39]. RA has an estimated prevalence of approximately 1% in European populations [40]. Notably, approximately half of patients with RA have absent or low levels of synovial CD20+ B cells, which may contribute to heterogeneous response to rituximab [41]. HLA-DRB1 alleles represent the strongest genetic association with RA and account for at least 30% of the genetic contribution to the disorder [42]. Many associated alleles share a conserved 5–amino acid sequence known as the “shared epitope” [43]. The shared-epitope hypothesis proposes that these alleles contribute to RA pathogenesis through T-cell–mediated autoimmune responses [42]. Studies of RA heritability have also identified associations outside the HLA region, including PTPN22, cytotoxic T-lymphocyte–associated protein 4 (CTLA4), and peptidyl arginine deiminase 4 (PAD4) [44]. Among non-HLA loci, PTPN22 has been linked to RA susceptibility, and combined effects may account for a substantial proportion of genetic risk [45]. Key genes associated with RA susceptibility are summarized in Table 1 [46–49]. Mechanisms of RA progression are shown in Figure 3.

Role of genetic factors in MS

MS is a chronic inflammatory disease of the central nervous system characterized by demyelination and axonal degeneration. MS causes progressive neurologic impairment and affects more than 2 million people worldwide, posing substantial challenges for health systems and society. Understanding disease mechanisms and treatments is important for reducing disability and improving outcomes [50,51]. MS is influenced by both genetic and environmental factors [52]. The HLA gene cluster on the short arm of chromosome 6 (6p21) is the strongest genetic locus associated with MS among identified candidates [53]. With advances in molecular methods, the HLA-DR2 locus was refined to 2 molecular allotypes (HLA-DR15 and HLA-DR16), and subsequent work further localized association signals to HLA-DRB1*15 and later HLA-DRB1*15:01 [53,54]. Early association studies in MS were often underpowered and did not consistently identify reproducible genetic associations beyond the HLA region [55]. It is now recognized that common complex diseases with heritable risk typically have a different genetic architecture than Mendelian disorders. The “common disease–common variant” (CDCV) hypothesis proposes that common diseases arise from multiple frequent variants, each conferring small effects on disease risk [56]. Applied to MS, this model suggests that many common variants contribute modestly to risk, alongside loci with larger effects such as HLA-DRB1*15:01 [57]. Genome-wide association studies (GWAS) have identified the primary genome-wide signal at the HLA-DRB1 locus within the MHC region on chromosome 6p21.3 [58]. Associations between HLA loci and MS risk have been described extensively in recent years [59,60].
Personalized Medicine in T1D
Personalized medicine approaches T1D through individualized care plans for treatment delivery and long-term disease management [61,62]. The personalized medicine framework is often described in 4 stages. The first stage is prediction: identifying individuals who are genetically and immunologically predisposed to developing T1D. The second stage is prevention: implementing interventions that delay or prevent onset (e.g., immunomodulation). The third stage is diagnosis: using biomarkers for early detection. The fourth stage is treatment: individualizing insulin regimens and adding adjunctive therapies based on a person’s response profile. Sequential implementation of these stages may improve outcomes, reduce T1D-related complications, and shift management from reactive care toward individualized prevention. Uniform “one-size-fits-all” approaches are often inadequate given interpatient heterogeneity [63]. Genetic factors are central to T1D risk and may also influence response to therapy [64]. Incorporating patient feedback can support informed decisions regarding exercise and diet. Advances in personalized strategies rely, in part, on improved understanding of genetic pathways and may facilitate development of treatments tailored to an individual’s genetic profile [65]. Personalized medicine may improve quality of life for populations affected by metabolic disorders, supported by advances in genetics [66]. Biomarkers are important in diabetes for identifying individuals at risk and monitoring disease progression. Glycemic biomarkers such as hemoglobin A1c are essential for assessing long-term glycemic control [67]. Beta-cell function measures are also used to predict progression in type 2 diabetes [68]. Machine learning methods may enable the extraction of actionable patterns from complex datasets to support tailored therapy [67]. Genetic factors and beta-cell function play important roles in diabetes prediction and treatment [69]. Patient age, disease duration, and comorbid cardiovascular or kidney disease also influence management decisions. Recent work has supported the feasibility of individualized glycemic targets in diabetes management [69]. Pharmacogenomics evaluates how genetic variation affects responses to medications and may help predict reactions to specific drugs [70]. Personalized lifestyle interventions are another relevant factor. Clinical plans that incorporate clinical variables, genetics, and lifestyle may optimize therapy selection. Telemedicine may also support diabetes diagnosis and management [71]. HLA typing for T1D risk prediction is a well-established approach. Certain HLA class II haplotypes (e.g., HLA-DR3-DQ2 and HLA-DR4-DQ8) are strongly associated with T1D susceptibility. When incorporated into newborn screening programs, HLA genotyping can help stratify individuals into risk categories. HLA risk profiles combined with immunologic biomarkers (e.g., islet autoantibodies) can facilitate earlier monitoring, trial enrollment, and preclinical intervention before overt symptoms. This provides a clear example of how genomic data can be used to predict disease risk prior to clinical onset.
While T1D is primarily defined by autoimmune destruction of pancreatic β-cells, there is increasing evidence that genetic variation contributes to interindividual responses to insulin therapy, complicating treatment decisions and adding complexity to personalized medicine strategies. Several loci associated with type 2 diabetes and metabolic regulation—including peroxisome proliferator-activated receptor gamma (PPARG), transcription factor 7-like 2 (TCF7L2), insulin receptor (INSR) substrate 1 (IRS1), and FTO (fat mass and obesity-associated gene)—have been reported to explain differences in insulin sensitivity among individuals with T1D. These variants may influence peripheral insulin resistance and thereby affect basal insulin requirements, glucose variability, and overall glycemic control. In addition, polymorphisms affecting INSR signaling or hepatic glucose metabolism may influence insulin utilization and clearance, thereby affecting dose adjustment and treatment effectiveness.
Polymorphisms in cytokine-related genes (e.g., IL2RA and PTPN2) and innate immune factors have been reported to influence residual inflammation and β-cell survival, thereby modulating endogenous insulin production and long-term insulin needs. Some single-nucleotide polymorphisms (SNPs) may also influence autoimmune activity and, consequently, the rate of progression from partial remission to full dependence on exogenous insulin.
Genetic variability may affect insulin therapy response in T1D through effects on insulin sensitivity, immune processes, and metabolic handling. For example, nonsynonymous variants in INSR or IRS1 may reduce INSR signaling efficiency, contributing to insulin resistance even in patients with T1D. Polymorphisms in TCF7L2 or PPARG may alter glucose uptake and tissue responsiveness, influencing overall insulin requirements. On the immune side, variants in HLA and IL2RA, as well as variation in PTPN22, may influence autoimmune activity and preservation of residual β-cell function, thereby affecting glycemic variability and dependence on exogenous insulin. Additional variants in SLC22A1 (solute carrier family 22 member 1), which influences drug transport, or IGF2BP2 (insulin-like growth factor 2 mRNA-binding protein 2), a regulator of metabolism, may affect insulin absorption, clearance, and distribution. Collectively, these differences may contribute to interindividual variation in total daily insulin dose, hypoglycemia risk, and response to insulin formulations. These principles underpin precision approaches that aim to optimize insulin selection, dosing, and potential adjunct therapies based on genetic and physiologic profiles to improve long-term outcomes in T1D.
Personalized Medicine in RA
Personalized medicine offers new opportunities for RA management [72]. As in T1D, interindividual pharmacogenetic variability can influence treatment response; understanding these factors can support more individualized protocols and potentially reduce adverse events [73,74]. In RA, a treat-to-target approach with tight disease control can help prevent joint damage and disability [75]. Targeted therapies—including inhibitors directed at pathways involving CD80 and CD20—are used in some clinical contexts to improve long-term outcomes. The presence or absence of anticitrullinated protein antibodies (ACPAs) is an important marker for defining RA subsets that differ in environmental risk factors, genetic susceptibility, disease history, and outcomes. Nonsteroidal anti-inflammatory drugs can be effective in conditions where cyclooxygenase pathways contribute to the clinical phenotype. Personalized medicine aims to identify more homogeneous patient subgroups in which specific pathogenic pathways drive disease manifestations. Diagnostic testing can support pathway identification and help tailor healthcare practices and therapeutic decisions to the individual patient. In this model, diagnostic tests are used to select the most appropriate therapies for each patient [76]. RA is characterized by swelling and joint pain that can progress to disability, and persistent synovial inflammation is a central feature. Disease-modifying antirheumatic drugs have substantially improved treatment outcomes [77]. Targeting synovitis is another strategy to achieve disease control [78]. Methotrexate in patients with undifferentiated arthritis may delay progression to established RA [79]. RA risk is higher in women than in men, likely reflecting effects of sex hormones on autoimmune processes [80]. Differences in drug response reflect a combination of environmental and genetic influences and have been explored in -omics studies (genomics, epigenetics, transcriptomics, and proteomics). Interactions between methotrexate and other medications may affect outcomes and warrant consideration when optimizing personalized medicine-based treatment [81]. SNPs that alter methotrexate metabolism may also contribute to adverse effects. Variants in SLC19A1 (reduced folate transporter), ABCB1 (P-glycoprotein efflux transporter), methylenetetrahydrofolate reductase (MTHFR), and AICAR transformylase (ATIC) have been associated with differences in therapeutic response [82]. Overall, personalized medicine may support dose optimization, earlier screening and diagnosis, and selection of appropriate biologic therapy.
Predictive markers are important for personalizing RA treatment by identifying patients most likely to benefit from specific therapies. Serologic markers such as rheumatoid factor and ACPA can indicate disease severity and may predict response to B-cell–targeted therapy such as rituximab. Elevated inflammatory markers, including C-reactive protein and erythrocyte sedimentation rate, reflect active inflammation and can support treatment-response assessment. Genetic factors, such as HLA-DRB1 shared-epitope alleles and polymorphisms in TNF or IL6R, may influence responses to TNF or IL-6 inhibitors. Molecular signatures, including cytokine profiles and synovial tissue phenotypes, may further refine predictions; for example, myeloid-rich synovium has been associated with better response to TNF inhibitors, whereas lymphoid-dominant synovium may predict benefit from IL-6 or B-cell–targeted therapies. High anti-drug antibody (ADA) levels with low drug concentrations may suggest immunogenic treatment failure and support switching biologic therapy, whereas low drug levels without ADAs may suggest underdosing and inform dose adjustment.
Personalized Medicine in the Treatment of MS
Personalized medicine in MS uses advanced approaches such as metabolomics, proteomics, genomics, and imaging to identify biomarkers and disease subtypes. Personalized approaches may include immune pathway modulators and targeted treatments relevant to MS pathogenesis. Overall, personalized medicine may help improve diagnosis, prognosis, and treatment outcomes, and targeted strategies may reduce the risk of adverse events [57]. Despite these promising developments, challenges remain, including the high cost of biomarker and subtype diagnostics, the need for sophisticated and frequently updated technologies, and time-consuming laboratory workflows that vary by disease stage and patient characteristics [58,59]. Diagnosis is a key phase of personalized medicine in MS [83]. Disease monitoring is also important for MS characterization [84]. Magnetic resonance imaging (MRI) is used to evaluate lesion extent and severity in the brain and spinal cord [85]. Biomarker panels incorporating epigenetic, genetic, proteomic, and metabolic factors can include assessment of genetic variants, DNA methylation, protein expression, and metabolic profiles [84]. Anti-CD20 antibodies such as ocrelizumab and rituximab reduce B-cell activity in MS [86]. Tailored therapy involves adapting treatment strategies based on individual factors, including environmental exposures, lifestyle, and genetics [87]. Personalized medicine may improve outcomes, reduce adverse effects, and optimize healthcare resource utilization [83]. However, legal and ethical concerns remain, including the acquisition and handling of genetic information and other personal data [57]. Despite these challenges, progress in targeted therapies may drive new strategies and innovations in clinical practice. Combination therapy may offer higher efficacy than monotherapy in some contexts. Standardization of diagnostic criteria and treatment guidelines may also be required for effective implementation of personalized medicine in MS [57]. Gene expression signatures and transcriptomic profiling in MS—often using peripheral blood mononuclear cells—have been developed to identify molecular patterns that differentiate relapsing from progressive disease. Such signatures may help predict relapse activity and, in some scenarios, treatment response (e.g., to IFN-β or natalizumab). Gene expression–based classifiers represent an early step toward individualized treatment selection and may inform disease monitoring strategies. Although many signatures remain under validation, these approaches exemplify the potential of personalized medicine to improve prognostication and minimize ineffective treatment in complex AD. The authors emphasize the need to develop individualized management plans for MS that consider disease severity, comorbidities, and patient lifestyle and preferences when selecting treatment options [88,89]. An Italian Delphi consensus highlighted the importance of shared decision-making between patients and clinicians to maximize adherence and outcomes. The panel identified a trend toward more personalized approaches in MS care, aiming to improve efficacy and quality of life by tailoring therapies to clinical features and biomarker data. The panel also emphasized moving beyond a “one-size-fits-all” strategy and supported developing expert consensus to refine personalized medicine paradigms for MS care. The early initiation of high-efficacy disease-modifying therapies has been reported to improve outcomes in patients with highly active MS, as outlined in the expert consensus. The panel recommended tailoring treatment to disease activity and patient profile, including a more aggressive approach for highly active disease. Close monitoring of disease progression and timely escalation of therapy following clinical or radiologic evidence of disease activity were emphasized. MRI activity and relapse rates are expected to remain central to treatment decisions and may support a proactive, individualized approach to MS management [88,89].
RA is characterized by destructive arthritis driven by autoimmune mechanisms and remains a complex and heterogeneous disease. Inadequate treatment can lead to long-term disability, and a substantial proportion of patients may experience persistent disease activity over many years [90]. Biologic therapies are often more effective than some conventional options in RA, yet response varies considerably across individuals [86,91]. Many factors contribute to immunity and inflammation, including diverse immunocompetent cells and mediators (e.g., multiple cytokines), and personalized medicine approaches remain insufficiently standardized in this setting. For some biologics, studies have explored genetic correlates of treatment response; however, clear, clinically actionable mutations or polymorphisms have not been consistently identified. For example, predictive biomarkers for response to TNF-α inhibitors remain limited [83]. Despite established associations between HLA-DRB1 shared-epitope alleles and RA susceptibility, relationships with disease severity and clinical phenotype remain complex [92,93]. Many studies have reported associations between HLA-DRB1 and ACPA-positive RA, supporting consideration of HLA-DRB1 as a potential biomarker [94]. GWAS meta-analyses in rheumatic disorders suggest that major RA-associated polymorphisms often involve pathways targeted by current therapies, supporting meta-analysis as a useful approach for exploring novel drugs and biomarkers [11,46,95,96].
Despite the potential of personalized medicine in MS, implementation faces challenges such as difficulty identifying robust biomarkers and disease subtypes and the need for high-cost, advanced technologies [97]. In addition, developing targeted therapies can be lengthy and expensive, and treatment effectiveness may depend on disease stage and patient characteristics [98,99]. Key challenges in applying personalized medicine to AD therapy are summarized in Table 2.
Future directions for personalized medicine in AD therapy should emphasize a deeper understanding of disease-specific pathophysiology, gene expression profiles, and immune regulation and dysregulation. Advanced technologies and machine learning may support the development of this knowledge base. Building databases, strengthening data-analytic capacity, and training physicians and nurses in key personalized medicine concepts may also support implementation. Integrating genetic profiles with proteomic and transcriptomic information may help uncover actionable targets for personalized therapy. Clinical translation of personalized approaches is essential for improving AD management. Single-cell analyses of tissues, including single-cell RNA sequencing, can contribute to disease characterization and target discovery. Machine learning may also facilitate identification of therapeutic targets through drug–disease modeling. Preventive medicine is another promising strategy for predicting disease development. The outlook for personalized medicine in ADs will likely build on integrating multi-omics data to guide individualized treatment (e.g., genomics, proteomics, and metabolomics). In addition to biomarker discovery, increasing use of artificial intelligence (AI) may support earlier diagnosis, disease stratification, and prediction of therapeutic response. Standardized guidelines that incorporate genetic, environmental, and lifestyle factors will likely be needed to optimize precision-based approaches. Ethical standards for data privacy and equitable access will also need to be addressed. Successful implementation will depend on collaboration among researchers, clinicians, and policymakers to translate personalized strategies into routine practice and improve outcomes while minimizing adverse effects in AD management.
AI is increasingly supporting precision medicine applications in ADs by enabling analysis of complex biomarker patterns. Machine learning algorithms can predict disease behavior and treatment responses using serologic markers, genetic variants, imaging, and clinical history, potentially surpassing traditional approaches in some settings. For example, in RA, AI-based methods have been used to identify biomarker signatures associated with response to TNF or IL-6 inhibitors and to support early diagnosis or molecular subtyping using synovial tissue samples. AI may also support therapeutic drug monitoring by identifying patterns in drug concentrations and ADA development to optimize biologic therapy. More broadly, AI tools may accelerate biomarker discovery, automate imaging scoring (e.g., ultrasound or MRI) for inflammatory activity, and support precision pathways in conditions such as lupus, psoriasis, and inflammatory bowel disease. Through these capabilities, AI can help transform large, heterogeneous datasets into clinically useful information to improve individualized care.
Therefore, the future of personalized medicine in AD treatment will increasingly depend on integrative data analytics that combine genomics, transcriptomics, proteomics, metabolomics, microbiome-related data, and real-world clinical data to generate multidimensional disease signatures. Continued advances in machine learning and AI may enable predictive models that better capture disease heterogeneity, forecast therapeutic response, and identify patient subgroups more likely to benefit from specific interventions. This direction supports a shift from single-biomarker strategies toward system-level precision therapeutics. As large-scale genetic and molecular datasets expand, ethical considerations surrounding genetic data use become increasingly important. Key issues include data privacy, risks of re-identification, responsible data sharing, transparency of consent processes, and potential for insurance or employment discrimination. Strong governance frameworks will be needed to sustain patient trust and support the integration of personalized medicine technologies into clinical care. Ensuring equitable access to personalized medicine worldwide is also a major challenge. Many available genomic and transcriptomic datasets disproportionately represent individuals of European ancestry, which can limit generalizability to diverse populations. In addition, unequal access to laboratory infrastructure, digital health capacity, and costly therapies may constrain implementation in resource-limited settings.
To improve the effectiveness of personalized medicine in ADs, there is a need for stratification techniques and molecular profiling technologies to improve response to treatment. Furthermore, additional cellular and molecular subgroups remain to be identified. More trials and longitudinal outcomes investigations are also needed for therapy optimization in subgroups. In ADs such as T1D, RA, and MS, personalized medicine has exhibited heterogeneous outcomes mainly due to disease types and insufficient data on individual molecular targets. Several further challenges persist, such as the inaccessibility of various diagnostic tests, gradual identification of biomarkers over time, lack of proper technologies, patient engagement, and inadequacies in data collection and processing and computational analyses. The expanding understanding of the disease pathophysiology, gene expression profiles, immune regulation and dysfunction, novel technologies, and machine learning (omics-based stratification) will improve the effectiveness of personalized medicine. Drug-target modeling and single-cell analyses will also contribute to improving therapeutic outcomes.
• Autoimmune diseases are initiated by immune system dysfunction, leading to inappropriate attacks on healthy tissue.
• Personalized medicine presents an opportunity for more effective therapies.
• Personalized medicine uses diagnostic assessments to tailor treatment.

Ethics Approval

Not applicable.

Conflicts of Interest

The authors have no conflicts of interest to declare.

Funding

None.

Availability of Data

No underlying data was collected or produced in this study.

Authors’ Contributions

Conceptualization: SM, AG; Data curation: SM, AG; Formal analysis: SM, AG, MNG, YF; Investigation: MNG, YF; Methodology: SM, AG, MNG, YF; Project administration: SM, AG; Resources: MNG, YF; Supervision: SM, AG; Validation: SM, AG; Visualization: SM, AG, MNG, YF; Writing–original draft: SM, AG, MNG, YF; Writing–review & editing: all authors. All authors read and approved the final manuscript.

Figure 1.
Autoimmune diseases discussed in this study including type 1 diabetes, multiple sclerosis and rheumatoid arthritis. Genetic factors affecting personalized medicine and personalized medicine approaches and challenges have been also included.
Figure 1. Autoimmune diseases discussed in this study including type 1 diabetes, multiple sclerosis and rheumatoid arthritis. Genetic factors affecting personalized medicine and personalized medicine approaches and challenges have been also included.  
Figure 2.
T1D pathway. Beta-cell proteins can serve as autoantigens once processed by antigen-presenting cells (APCs), such as macrophages and dendritic cells, and presented alongside major histocompatibility complex (MHC)-II molecules on the APC surface. This presentation triggers immunogenic signals activating CD4+ T cells, mainly from the Th1 subset. Activated Th1 cells release cytokines, including interleukin (IL)-2 and interferon (IFN)-γ, which facilitate the activation of macrophages and cytotoxic CD8+ T cells. These effector cells may eliminate islet beta cells through 2 mechanisms: (1) direct interaction between antigen-specific cytotoxic T cells and a beta-cell autoantigen-MHC-I complex, and (2) the involvement of non-specific inflammatory mediators like free radicals, oxidants, and cytokines (IL-1, tumor necrosis factor [TNF]-α, TNF-β, IFN-γ).
Figure 2. T1D pathway. Beta-cell proteins can serve as autoantigens once processed by antigen-presenting cells (APCs), such as macrophages and dendritic cells, and presented alongside major histocompatibility complex (MHC)-II molecules on the APC surface. This presentation triggers immunogenic signals activating CD4+ T cells, mainly from the Th1 subset. Activated Th1 cells release cytokines, including interleukin (IL)-2 and interferon (IFN)-γ, which facilitate the activation of macrophages and cytotoxic CD8+ T cells. These effector cells may eliminate islet beta cells through 2 mechanisms: (1) direct interaction between antigen-specific cytotoxic T cells and a beta-cell autoantigen-MHC-I complex, and (2) the involvement of non-specific inflammatory mediators like free radicals, oxidants, and cytokines (IL-1, tumor necrosis factor [TNF]-α, TNF-β, IFN-γ).  
Figure 3.
The process of T-cell activation through the interaction between CD80/86 on dendritic cells and the CD28 receptor on T lymphocytes, which plays a pivotal role in the development of rheumatoid arthritis. Dendritic cells, as antigen-presenting cells, present costimulatory signals via CD80 and CD86 that bind to CD28 on T cells, facilitating their activation. Once activated, T cells contribute to the inflammatory response characteristic of rheumatoid arthritis. The diagram also highlights the therapeutic role of CD80/86 inhibitors, such as abatacept, which block this costimulatory pathway, thereby mitigating T-cell activation and reducing inflammation in rheumatoid arthritis.
Figure 3. The process of T-cell activation through the interaction between CD80/86 on dendritic cells and the CD28 receptor on T lymphocytes, which plays a pivotal role in the development of rheumatoid arthritis. Dendritic cells, as antigen-presenting cells, present costimulatory signals via CD80 and CD86 that bind to CD28 on T cells, facilitating their activation. Once activated, T cells contribute to the inflammatory response characteristic of rheumatoid arthritis. The diagram also highlights the therapeutic role of CD80/86 inhibitors, such as abatacept, which block this costimulatory pathway, thereby mitigating T-cell activation and reducing inflammation in rheumatoid arthritis.  
Table 1.
Important genes associated with rheumatoid arthritis susceptibility
Table 1.
Gene name Protein product References
HLA-DRB1 HLA class II histocompatibility antigen, DRB1 beta chain [47]
HLA-DPB1 HLA class II histocompatibility antigen, DP beta 1 chain [48]
HLA-DOA HLA class II histocompatibility antigen, DO alpha chain [49]
PAD4 Protein-arginine deiminase type 4 [47]
PTPN22 Tyrosine-protein phosphatase non-receptor type 22 [46]
CTLA4 Cytotoxic T-lymphocyte protein-4 [47]

HLA, human leukocyte antigen.

Table 2.
Challenges of using personalized medicine in therapy for autoimmune diseases
Table 2.
Challenges Summary of challenges
Challenges in classification and stratification Many diagnostic tests are not easily accessible
Many biomarkers utilized in distinguished “clusters” change over time
Most methods utilized for clustering require variables to be distributed; this can have negative effects on the accuracy of the results
Challenges in prevention Lack of proper technologies
Motivation of patients can be challenging
Challenges in monitoring and management Inadequate information on drug response in underrepresented racial and ethnic groups
Difficulties with data storage and computational analysis
Variability of ADs among races and sexes
Lack of data for exact approaches
Need for effective communication strategies when conveying complex or sensitive information to patients

AD, autoimmune disease.

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