|Year : 2022 | Volume
| Issue : 1 | Page : 57-64
Personalized medicine in India: Mirage or a viable goal?
Somashree Chakraborty1, Anisha Wagh2, Pranay Goel1, Sanat Phatak3
1 Department of Biology, Indian Institute of Science Education and Research, Pune, Maharashtra, India
2 Department of Medicine, Byramjee Jeejeebhoy Government Medical College, Pune, Maharashtra, India
3 Department of Medicine, Byramjee Jeejeebhoy Government Medical College; KEM Hospital Research Centre, Pune, Maharashtra, India
|Date of Submission||13-Jul-2021|
|Date of Acceptance||21-Sep-2021|
|Date of Web Publication||04-Mar-2022|
Dr. Sanat Phatak
KEM Hospital and Research Centre, Sardar Moodliar Road, Rasta Peth, Pune - 411 011, Maharashtra
Source of Support: None, Conflict of Interest: None
Personalized medicine refers to using individual patient characteristics including phenotype and genotype, to tailor the therapeutic strategy. This approach seeks to challenge the “one-size-fits-all” method to patient management. In the bargain, it reduces adverse drug reactions, improves compliance, and reduces the economic burden of disease management. Traditionally, as extrapolated from usage in oncology, the term applied to using genomic, metabolomic, and epigenomic data in selecting medications; however, even simpler data such as clinical phenotype can aid therapeutic decision making. Autoimmune diseases provide many such data points, owing to the multi-organ nature of clinical manifestations as well as the availability of a wide variety of tests for antibodies as well as cytokines. Rheumatologists already use personalization intuitively, based on various factors such as organ involvement, comorbidities, fertility concerns, and costs. However, in a literature search, few studies look at tailoring treatment regimens to individual characteristics. Building coherent databases can help in better analysis of data and answering locally relevant questions in the future.
Keywords: India, multi-omics, personalized medicine, precision medicine, therapeutic decision making
|How to cite this article:|
Chakraborty S, Wagh A, Goel P, Phatak S. Personalized medicine in India: Mirage or a viable goal?. Indian J Rheumatol 2022;17:57-64
| Introduction: What is Personalized Medicine?|| |
The term “personalized medicine” or “precision medicine” is widely used across the scientific and popular media, but its definition remains nebulous. A helpful definition comes from the European Union Health Ministers, adopted by the Horizon 2020 Advisory Group: “Personalized medicine is a medical model using the characterization of individuals' phenotypes and genotypes (e.g., molecular profiling, medical imaging, and lifestyle data) for tailoring the right therapeutic strategy for the right person at the right time, and to determine the predisposition to disease and to deliver timely and targeted prevention.” The concept seeks to challenge the prevailing “one-fits-all” approach to disease management, including monitoring, drug dosages, treatment regimens, and physical measures.
Despite similarities in appearances of clinical syndromes, pathophysiology can differ at the molecular level. Autoimmune inflammatory diseases culminate from a complex interaction of many factors, including genetic susceptibility, transcription of gene risk alleles, epigenetic changes, metabolic changes in immune cells, autoantibody generation, and inflammatory cytokine milieu., An individual patient will represent many of these at varying levels. Thus, using a uniform approach for every patient is likely to leave some aspects of the process unaddressed. Personalized medicine often involves a “pan-omics” analysis (genome, transcriptome, metabolome, epigenome, and proteome) to decide and monitor targeted therapy [Figure 1]. This emphasis on the genome, proteome, and metabolome comes at a considerable monetary cost and also requires robust systems analysis capabilities to interpret the data in a clinically meaningful way. Personalization encourages medicine to be more precise, safer, and more exact to control disease while efficiently curbing the risk of medication-related morbidity. Saving on the economic burden of unnecessary medications and treatment of adverse effects, with fewer Disability Adjusted Life Years lost due to quicker disease control, might also offset costs.
|Figure 1: Inputs and possible outcomes on using individual patient characteristics in tailoring treatment regimens|
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| Levels of Precision: From Simple to Complex|| |
The narrow definition of using molecular or “omics” technologies to inform treatment choices begs the question: What is “precise” and what is not? Precision need not be a black-and-white, binary term. Any data that adds information to the decision-making process in an individual patient's disease management could, in theory, be deemed “precision medicine.” It could include well-maintained clinical data such as age, gender, number of swollen joints. For example, one may like to use less potent immunosuppressives in the geriatric population for fear of secondary infection-and here, the patient's age has brought in “personalization” to their choice of therapy. Broadening the concept of precision can give us “layers” of precision data – from the simple and readily available to the molecular and omics data used in oncology. Including such more straightforward data in personalizing treatments also makes it more feasible and practically relevant.
| Personalized Medicine in Rheumatology: Compatibility|| |
Most autoimmune diseases are syndromic diagnoses made on a collection of clinical features, buttressed by lab tests to demonstrate inflammation and an ongoing autoimmune process – in the form of autoantibodies, self-reactive cells, and tissue biopsies. They are multisystem diseases and vary widely in severity according to the organ involved. Multiple genetic pathways and environmental triggers have been identified in contributing to the pathogenesis of these diseases. The prototype autoimmune disease, systemic lupus erythematosus (SLE), can range in manifestations from only mild fatigue and skin photosensitivity to life-threatening nephritis, diffuse alveolar hemorrhage, and can result in death. Diseases like rheumatoid arthritis (RA), psoriatic arthritis (PsA), and SLE are also highly varied in their clinical response due to their heterogeneous genetic and epigenetic predisposition.
The focus of treatment is on reducing inflammation, halting the progression of the disease, and decelerating irreversible damage. Current treatment paradigms are based on algorithms that, in most diseases, start from monotherapy with a particular DMARD (methotrexate in RA, hydroxychloroquine in Sjogren's), and escalate in those who do not get an optimal response or develop adverse effects. In practice, however, treatment decisions are already often personalized, taking in other indicators:
- The organ involved and severity of disease: Patients with SLE nephritis would receive a different 1st line immunosuppressant (mycophenolate/cyclophosphamide) as compared to those with only skin inflammation (hydroxychloroquine)
- The amount of inflammation and damage: The level of immunosuppression will be tempered by the amount of damage; patients with SLE and chronic kidney disease are less likely to need aggressive immunosuppression as compared to those with active proliferative nephritis
- The presence of comorbidities: The presence of diabetes would argue for using steroid-sparing therapies early; coexistent tuberculosis would inform the biological DMARD choice in spondyloarthritis, choosing those deemed safer, such as Etanercept
- Patient age, fertility concerns: Patients in the reproductive age groups would prefer to use fertility-preserving therapies in place of those that could affect fertility such as cyclophosphamide or sulfasalazine in males
- Economic and practical considerations: Especially in India, where health care is self-funded by a vast majority, a weighing of the perceived benefits and increased costs prohibits widespread use of some medications, especially with biological DMARDs. In addition, factors such as the route of administration (oral vs. intravenous) play a part in the decision-making.
Despite the novelty of the term, as rheumatologists, we do “personalize” management to certain (physical, biochemical, and socioeconomic) data variables in selecting treatment decisions. However, as treatment remains algorithm based, we subject all patients to a specific medication at the onset of therapy. Nearly all patients with RA are given methotrexate at diagnosis. It is well known that all patients do not tolerate methotrexate well, and some develop transaminitis and cytopenia, acute lung injury, or other adverse effects such as hair loss and CNS dysfunction. In addition, more than half do not achieve remission on methotrexate alone and need additional csDMARDs or bDMARDs. Can we use any indicators to prevent this unfortunate subset from experiencing adverse reactions or choose an agent, which is more likely to be effective in the first place? With the widespread availability of lab investigations and genetic techniques, we could bring in more precision to this personalization focussing on the individual pathophysiology, genetic milieu, inflammatory cytokine and immune cell milieu, antibody presence, and other putative “biomarkers.” This may help by shortening time to remission, preventing adverse effects, improving compliance, and reducing financial burden.
| Personalized Medicine in Rheumatology: Where are we Now?|| |
The use of genetic and immunological data to drive treatment decisions in rheumatology is in its nascency compared to fields like oncology. In the latter, the tumor environment itself can be tested for genetic mutations. On the contrary, rheumatologists deal with far more diffuse diseases. In addition, there is no “gold standard” in rheumatology; multi-biomarker disease activity tests can be useful. There is already fragmented data about various genetic markers, prognostic indicators, and predictors of immune responses to multiple drugs in many autoimmune diseases. The impediment does not lie in the scarcity of predictors with outcomes but the lack of a practical analytical approach to translating multiple patient disease attributes into treatment decisions.
We performed a literature search on Pubmed with the keywords rheumatology, RA, spondyloarthritis, SLE, and personalized medicine or precision medicine. We screened original articles and review articles. We present a short, nonexhaustive list of articles in [Table 1], only as examples of various precision medicine “levels” that have been used previously. We divide the studies depending on the type of question that the study seeks to answer using personalized approaches.
|Table 1: Instances of questions asked by papers seeking to provide personalization in rheumatology|
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Personalized medicine can be used to characterize a disease course better. Simple data types such as clinical features and acute phase reactants can be used to make de-novo clusters. The presence or absence of antibodies, even commercially available such as ACPA, inform the clinician about the disease phenotype and help diagnose. Genetic risk scores have been explored in the diagnosis of various diseases such as SLE. Although these help in diagnosis, they are expensive to test for and will probably be used for cases in which the diagnosis is unclear. Similarly, researchers have tried to use various levels of data to understand the prognosis of a condition. It can range from clinical data to laboratory data, each type offering different insights. As mentioned previously, personalizing drug therapy rooted in relevant pathophysiological signals in an individual patient would help prevent unnecessary adverse effects, drug switching, and earlier remission. In an interesting clinical trial, Miyagawa et al. randomized patients with PsA to a regimen with biologic choice based on the predominant T cell immunophenotype and resultant cytokine, and the other half to the standard of care therapy as per the treating physician. Personalizing the treatment to the predominant cytokine led to better remission rates, thus providing rare evidence to use such approaches in the field. This approach was used recently, basing treatment decisions on the cell type in synovium instead of the predominant cytokine. Humby et al. found that those patients with RA who had synovial tissue enriched in B cells responded better to rituximab than tocilizumab; there was no difference in the two treatments in those whose synovia were not B-cell rich. Although currently not practical in routine clinical practice, this study is a novel biopsy-driven trial. It lays the foundation for the use of “local” characteristics of pathology, like tumor characteristics in oncology.
In SLE patients, anti-B-cell therapy (e.g., rituximab and belimumab) is indicative of a better response due to significant interferon gene group association, auto-Ab specificity with a B-cell lineage expression panel. A 'biological calculator' developed on a panel of effective time-dependent marker expression and demographic information can aid management strategies. A “collaboration” of individual and environmental data is likely to make more valuable suggestions.
Over the years, most studies focused on stratifying subgroups of patients based on gene expression data, cell-surface markers, and clinical parameters. However most of these studies have several limitations. For example, in Payet et al., ACPA quantification and non-RA diseases diagnosis was established on clinical and biological data but lacked prospective follow-up. In Bécède et al., sensitivity analysis in risk profiling of RA involved patients from the same inception cohort, ascertaining similar results. There is a requirement for well-monitored longitudinal studies and stringent validation analysis measures to reduce bias.
Complementary and companion diagnostics (CDxs) are two areas in association with personalized medicine. Assays that determine the efficacy and potential of therapeutic drugs come under complementary medicine, while CDxs identify genetic or cellular markers that can be performed before using certain medications. This has been done in the field of oncology: PD-L1 IHC assays are used prior to using cancer immunotherapy like atezolizumab for nonsmall-cell lung cancer and nivolumab for melanoma. In the case of rheumatic diseases, studies have been focused on finding significant genetic associations with drug response. In pharmacogenetics, Genome-wide association studies (GWAS) have been widely used to establish candidate gene-polymorphism associations. Integrative studies with GWAS can collate genomics and proteomics data, leading to the discovery of several biomarkers either as early indicators, prognostic and predictive. Distinct cell types associations identified 6000 genes with eQTLs, and integration with immune cell epigenome maps clearly indicated overlapping RA risk loci. Another study in SLE identified causal variants in established gene loci through spatial maps to identify newer treatment targets. The utilization of SNPs in formulating target medicine also needs to consider the interaction of environmental factors. Earlier, pharmacogenetic approaches pivoted around studying candidate gene sequence variations that are responsive to specific drugs. Pharmacogenomics encompasses identifying variance in individual antidote-responsive gene sequences and identifying new associations and drug targets in the process.
| Personalized Medicine in India: Hurdles and Opportunities|| |
Personalised medicine is in its infancy in India. The ability to offer personalized medicine solutions to Indian patients in the “traditional” sense of the term requires widespread infrastructure for multi-omic testing in addition to the ability to analyze the data, all at considerable cost. However, as mentioned earlier, precision need not be limited to using multi-omics techniques and can start from simple, clinical data. The considerable diversity and large number of patients in India offer opportunities to test personalization on such low-cost, simple data.
One can envisage the following initial steps that can be taken in this regard:
- Improve data collection and curative capabilities at various centers of the country, including standard protocols to ensure uniformity
- Investing in standardized electronic health and records makes the gathering of phenotypic data easier. We can capitalize on widespread usage of mobile phone devices in this regard
- Leverage databases and maintained cohorts in India to answer questions: e.g., the multi-center INSPIRE cohort of lupus patient data and serology can provide multiple readymade opportunities for researchers to ask relevant questions
- Multi-center collation of databases across countries: These may include Routine clinical investigations: Erythrocyte sedimentation rate, C-reactive protein, blood count, renal functions-which do not need expensive investigations
- Bio-banking of samples to enable answering questions in the future.
Such data can then be used for questions related to prognostics and diagnostics by making databases public. The Radiological Society of North America 2017 Pediatric Bone age Challenge is an example. It provides an anonymized database for competitors to use artificial intelligence to create a bone age assessment tool. Such challenges can spur interesting questions and creative answers to personalize medicine.
Personalized medicine research in India also provides the right set of circumstances to answer questions relevant to disease management in low and middle-income countries. Biologic DMARDS are prohibitively expensive for widespread use. Previous research has therefore focused on unravelling patient responsiveness to cheaper medication like methotrexate. Indian studies have elegantly demonstrated, via a hypothesis-driven approach, the association of polymorphisms in genes of the purine biosynthetic pathway (ADORA, ATIC), the ubiquitin pathway (CUL1), and receptor/transporter pathways (MDR1) with methotrexate response. On the contrary, SNPs in the folate metabolism pathway did not show significant association. In the future, it could be possible to collate these data and obtain a gene risk score that could inform practicing clinicians whether a methotrexate response is likely.
Funding opportunities for research related to personalized medicine approaches in rheumatology seem few at present but will hopefully gather momentum once actual research begins. The United States' “All of Us” Research Program (previously Precision Medicine Initiative Cohort Program) seeks to fund healthcare breakthroughs that will focus on bringing health to the individual. The program gathers data about lifestyle, health, wearable device data, environment, and laboratory investigations intending to design future studies and clinical trials, providing a starter for an excellent blueprint. The recent inception of a Genome India Project aiming to map the diversity of the population's genetic pool can lay the bedrock for medicine personalization and translation at a large and influential scale. Endemic and high-propensity diseases like tuberculosis, COPD, CVD, lung cancer, RA may become the frontrunners of large-scale research in delivering precision.
Given the widened region-ethnicities in India, driving a study to understand the epidemiology of rheumatic diseases and planning therapeutic measures according to its genetic heritability and integrity can foster the development of a blueprint in effective personalization of treatment strategies.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
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