- Indian Rheumatology association
Rajesh Kanumuri MD, DM
Senior Consultant & Head, Department of Clinical Immunology & Rheumatology, Krishna Institute of Medical Sciences (KIMS), Ongole, Andhra Pradesh, India.
Precision medicine aims to tailor treatment strategies to the individual characteristics of each patient, moving away from a one-size-fits-all approach. In the context of connective tissue disease-associated interstitial lung disease (CTD-ILD), this involves considering the specific underlying autoimmune diseases (e.g., rheumatoid arthritis, systemic sclerosis), the pattern and severity of lung involvement, genetic predispositions, and other individual biological markers to optimize therapeutic decisions and improve patient outcomes. Instead of broadly applying the same immunosuppressants to all CTD-ILD patients, precision medicine aims to identify which patients are most likely to respond to specific therapies, thereby minimising unnecessary side effects and maximising efficacy.
Effective implementation of precision medicine in CTD-ILD needs a multifaceted framework, including:
Advanced Diagnostics: Advanced diagnostics are required to identify the presence of ILD and characterise its specific subtype and pattern. HRCT identifies lung patterns (e.g., nonspecific interstitial pneumonia [NSIP], usual interstitial pneumonia [UIP]), while autoantibody panels (e.g., anti-CCP for RA, anti-Jo-1 for myositis, etc.) confirm CTD subtypes. Pulmonary function tests (FVC, DLCO) monitor disease progression.
Identification and Validation of Biomarkers: Biomarkers are fundamental to precision medicine. CTD-ILD could include genetic markers that predict susceptibility or response to treatment, serological markers that reflect disease activity or specific pathogenic pathways, or imaging biomarkers derived from quantitative analysis of HRCT scans. Rigorous validation of these biomarkers in independent cohorts is essential before they can be used to guide clinical decision-making.
Therapeutic Access: Availability of antifibrotics (nintedanib, pirfenidone) and biologics (tocilizumab, rituximab) at affordable prices is essential for tailored treatment.
Multidisciplinary Teams (MDTs): Collaboration among rheumatologists, pulmonologists, radiologists, and pathologists integrates clinical and molecular data.
Data Integration: Electronic health records and artificial intelligence (AI) platforms combine imaging, biomarkers, and clinical data for real-time decision-making.
Patient Engagement: Educating patients fosters adherence to complex, personalized regimens.
These components ensure that precision medicine is actionable, enabling rheumatologists to deliver individualized care despite variations in resources.
The “treatable traits” concept offers a complementary approach to precision medicine in managing CTD-ILD. Instead of solely focusing on the underlying CTD diagnosis, this framework emphasizes identifying specific pathophysiological mechanisms or clinical manifestations amenable to targeted interventions, regardless of the primary autoimmune disease.
Key traits include:
Pulmonary: Inflammation (e.g., NSIP in SSc-ILD) responds to immunosuppressants,such as mycophenolate, whereas fibrosis (e.g., UIP in RA-ILD) may require antifibrotic agents. Hypoxemia necessitates oxygen therapy, and pulmonary hypertension in SSc-ILD may need vasodilators.
Extrapulmonary: Systemic inflammation in RA-ILD warrants biologics, and comorbidities like gastroesophageal reflux require management to prevent ILD exacerbations.
Behavioural: Smoking cessation and pulmonary rehabilitation improve outcomes, addressing lifestyle factors.
Implementation of the treatable traits concept involves a comprehensive assessment (including HRCT, biomarkers, and clinical evaluation) and dynamic monitoring to adjust interventions as the traits evolve. This approach enables rheumatologists to create personalised treatment plans, thereby optimising patient outcomes across various CTD-ILD presentations.
Biomarkers indicate disease susceptibility, diagnosis, prognosis, and response to treatment. They help stratify patients into subgroups with distinct disease characteristics, predict the likelihood of disease progression, and monitor the effectiveness of therapy.
Examples include:
Serological biomarkers: Autoantibodies (e.g., anti-Ro52, anti-MDA5), acute phase reactants (e.g., CRP, ESR), and specific cytokines or chemokines.
Genetic biomarkers: Identifying specific gene polymorphisms associated with an increased risk of ILD (e.g., MUC5B & TERT gene polymorphisms in idiopathic pulmonary fibrosis) or differential treatment responses.
Imaging biomarkers: Quantitative analysis of HRCT scans to assess the extent and pattern of fibrosis, along with changes over time.
Bronchoalveolar lavage (BAL) fluid analysis: Identifying cellular profiles and specific protein markers that may indicate disease activity or specific inflammatory patterns.
Various technologies are crucial for the discovery, validation, and application of biomarkers and for implementing precision medicine, includes:
Imaging: Quantitative HRCT, enhanced by AI, quantifies fibrosis and predicts progression, surpassing traditional scoring.
Omics: Genomics and transcriptomics elucidate disease pathways, identifying therapeutic targets.
AI and Machine Learning: These integrate multimodal data (imaging, biomarkers, clinical) to forecast outcomes and optimize therapy.
Wearables: The latest wearable devices for monitoring oxygen saturation, activity, and sleep hygiene may help in real-time disease management.
Several challenges remain in its widespread implementation:
Disease heterogeneity: CTD-ILD encompasses a broad spectrum of underlying autoimmune diseases and patterns of lung involvement, making it challenging to identify universally applicable biomarkers or treatment strategies.
Limited understanding of pathogenesis: The precise mechanisms driving ILD in the context of various CTDs are still not fully understood, which hinders the identification of relevant therapeutic targets.
Lack of validated biomarkers: Many biomarkers (e.g., KL-6, MMP-7) lack standardised assays and prospective validation, which limits their reliability.
Complexity of data integration and analysis: Integrating multi-omics data and clinical information requires sophisticated analytical tools and expertise.
Cost and accessibility: The technologies and analyses required for precision medicine can be expensive, potentially limiting their accessibility in routine clinical practice.
Patient Factors: Socioeconomic constraints for regular follow-ups and low health literacy can impede adherence to personalised regimens.
The Need for Prospective Clinical Trials: Demonstrating the clinical utility and cost-effectiveness of precision medicine approaches requires well-designed prospective clinical trials that incorporate biomarker stratification.
