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Table of Contents
REVIEW ARTICLE
Year : 2022  |  Volume : 8  |  Issue : 1  |  Page : 13-15

Precision medicine in COVID-19 patients


Department of Biochemistry, Santosh Medical College, Santosh Deemed to be University, Ghaziabad, Uttar Pradesh, India

Date of Submission09-Apr-2022
Date of Decision15-Apr-2022
Date of Acceptance25-Apr-2022
Date of Web Publication21-Jul-2022

Correspondence Address:
Juhi Aggarwal
Department of Biochemistry, Santosh Medical College, Santosh Deemed to be University, Ghaziabad, Uttar Pradesh
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/sujhs.sujhs_8_22

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  Abstract 


The coronavirus disease (COVID-19) pandemic has emerged as one of the deadliest pandemics that humanity has ever seen, affecting almost all countries in the world. The pandemic has taken a severe toll on the almost all realms of the society. The management of the pandemic is further complicated by the emergence of various strains of virus and differing phenotypes of severity in a population. The complex interplay between underlying host factors and evolution of viral strains makes it a daunting task to standardize the treatment protocols across a population. In this context, it is imperative to look into the solutions that the precision medicine may provide us for categorizing patient population, in tailoring treatment and in identifying newer treatment targets. In this review, we discuss the possibilities that precision medicine put forward in tackling COVID-19 pandemic.

Keywords: Artificial intelligence, COVID-19, precision medicine


How to cite this article:
Tomo S, Batra J, Aggarwal J. Precision medicine in COVID-19 patients. Santosh Univ J Health Sci 2022;8:13-5

How to cite this URL:
Tomo S, Batra J, Aggarwal J. Precision medicine in COVID-19 patients. Santosh Univ J Health Sci [serial online] 2022 [cited 2022 Dec 8];8:13-5. Available from: http://www.sujhs.org/text.asp?2022/8/1/13/351575




  Introduction Top


The coronavirus disease (COVID-19) had evolved as a global pandemic in a very short time infecting and killing millions of patients. Even now, many questions regarding COVID-19 are still unanswered. However, knowledge of certain aspects of COVID-19 such as pathophysiology of COVID-19, the type and severity of organ involvement, the consequences of coagulopathy and endotheliopathy, the role of disproportionate cytokine response, and the importance of the evolution of the virus has improved significantly. There is growing evidence indicating that the patients with COVID-19 are have a varying spectrum of disease going well beyond the differences observed in lung changes and ventilation-to-perfusion mismatch.[1],[2] Understanding the varying phenotypes and the underlying pathophysiology can be a leading step to assess the implications of personalized therapy in COVID-19.[3]

Based on the patient's characteristics, precision medicine aims to optimize the time of administration, dose, and selection of the most appropriate medicine with the minimum risk of toxicity.[4] This multidimensional approach is essential due to the varying disease course and therapeutic responses in individual patients. Factors, such as genetics, epigenetics, environment, ethnicity, lifestyle, and diet, are known to determine the disease course and outcome of treatment. Clinical trials have demonstrated the varying effect of certain drugs in different ethnic groups or patients with different comorbidities. To accommodate this, heterogeneity of the etiologies, presentations, and treatment responses of patients in clinical practice, a revised personalized clinical approach to treatment is recommended. The first step toward this is the development of machine learning-assisted risk assessment models followed by the identification of robust multimodal data-driven prognostic indicators. The aforementioned efforts require new strategies for integrating heterogeneous information from different structured and unstructured data sources (electronic health records, administrative databases, bioimaging archives, self-quantified measurements, etc.).


  COVID-19 Pandemic and Challenges Top


Subclassification of the SARS-CoV-2 virus is done based on the variants which are identified through genetic sequencing.[5] Different COVID variants may lead to varying levels of viral RNA in blood and different severity grades of host reaction.[6] This leads to variation in tissue-cell reactions in different patients.[7] Some variants have an increased risk of mortality than others.[8] As more and more variants are being discovered, the understanding of the relationships between vaccine-induced immunity, host response, and immunopathology becomes less clear.[9] A revised COVID-19 taxonomy including the characteristics such as biological plausibility, promptly identifiable, nonsynonyms, reproducible, and treatment responsive will go a long way in tailoring treatment strategies. While there is debate over the clinical significance of these variants, the association of severity with host vulnerability in different variants may provide initial insights into why patients who appear similar in terms of demographics and comorbidities have vastly different responses.

Just like the different variants of the virus, the patients (hosts) also present a complex array of underlying risks and disease tolerance.[10] Factors such as age and the presence of respiratory comorbidity can modify the risk of infection and outcome. In addition, the host response to COVID-19 is also influenced by varying immunity due to factors such as prior infection or vaccination. Precision medicine is useful in understanding and contemplating individuals' susceptibility and responses to the COVID-19 virus.


  Precision Medicine and Artificial Intelligence Top


Precision medicine approaches are already used for identifying risks of adverse treatment effects for patients using drugs such as abacavir and carbamazepine, which can help health-care providers make decisions regarding alteration of dosages or avoiding certain drugs altogether. The precision approach emerges from the confluence of various data sources that are being leveraged to better understand the individual response to diseases. Precision medicine is also relevant to the field of public health efforts by identifying individual risks, supporting public health surveillance, and improving vaccine efficacy. In addition, artificial intelligence (AI) technologies play a central role in tracing and modeling pandemics. During the current COVID-19 pandemic, many researchers have found out that the proper use of AI technologies helped in predicting the COVID-19 waves in different regions.

Although precision medicine and AI have manifold benefits, there are numerous methodological challenges and associated risks that still need to be suitably addressed. The deficiency of lucidity and interoperability in AI models obscures the fact that the effectiveness of these technologies cannot be equated to different populations. The incidence and progression of COVID-19 differ according to various personal characteristics such as age, gender, and health status. Sensitizing AI technologies to these varying individual characteristics and ensuring assessments based on accurate data is crucial to avoid biased decisions. As a result, a critical prerequisite in employing AI is to have a true representation of the patient population with all the varying characteristics adequately represented for assessing their precise involvement in the prediction models. Another ethical concern in the application of AI is its use to optimize resource allocation in times of scarcity by deciding medical actions through risk prediction models. While risk prediction models have their uses, the application of nonoptimal models may lead to biased medical decisions in vulnerable patients whose comorbidities and varying treatment priorities are not included in the sub-optimal models. This leads to sub-optimal patient care despite a potential reduction in the allocation of resources. Thus, it is imperative to have a thorough assessment of the models for their scientific rigor before their application in clinical services.


  Precision Medicine in COVID-19 Top


The COVID-19 pandemic is accelerating a shift toward the adoption of AI in medicine. The calamity has made it imperative for the medical community to explore the relevance of AI in medicine as evidenced by the exponential growth of AI-based health apps. The major upcoming areas of exploration of AI in the current scenario of the COVID-19 pandemic are:[11],[12],[13],[14],[15]

Triage analysis and hazard calculation

  • Identification of treatment priorities for optimal resource allocation
  • Optimizing patient management
  • Prediction of risk for mortality and disease severity
  • Validation and benchmarking standards.


Drug repurposing and development

  • Insights into virus–host interactions.
  • Accelerated drug discovery pipelines.
  • Drug engineering and identification of novel compounds.


Pharmacogenomics and vaccines

  • Personalized clinical decision support
  • Identification of actionable genetic markers
  • Assessment of infection susceptibility and severity
  • Identification of potential epitopes.


Mining of the medical literature

  • Unstructured information processing
  • Knowledge representation and learning
  • Extraction of bio-entity relationships
  • Open accessibility and automated updates.


The immunological responses to SARS-CoV-2 infection have shown to be distinctly different between females and males. Evidence from literature shows that antiviral immunity differs between the sexes. The attributable factors for the aforementioned differences include sex steroid hormone (i.e., testosterone, estrogens, and progesterone), genetic variations (e.g., immune function genes that escape X inactivation), and sex-specific composition of the microbiome. Gender differences in immunosenescence and immune function, apart from impacting the immunity to viruses, affect the response to vaccines and immunotherapies also.[16],[17],[18] In the context of SARS-CoV-2, these differences could impact susceptibility as well as the initial response to the virus and the choice of acute and long-term therapy against COVID-19.

Precision medicine offers the opportunity to identify receptive populations for the use of COVID-19 therapeutics.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
  References Top

1.
Severe Covid-19 GWAS Group, Ellinghaus D, Degenhardt F, Bujanda L, Buti M, Albillos A, et al. Genomewide association study of severe covid-19 with respiratory failure. N Engl J Med 2020;383:1522-34.  Back to cited text no. 1
    
2.
Kuo CL, Pilling LC, Atkins JL, Masoli JA, Delgado J, Kuchel GA, et al. APOE e4 genotype predicts severe COVID-19 in the UK Biobank Community Cohort. J Gerontol A Biol Sci Med Sci 2020;75:2231-2.  Back to cited text no. 2
    
3.
van der Made CI, Simons A, Schuurs-Hoeijmakers J, van den Heuvel G, Mantere T, Kersten S, et al. Presence of genetic variants among young men with severe COVID-19. JAMA 2020;324:663-73.  Back to cited text no. 3
    
4.
Ginsburg GS, Phillips KA. Precision medicine: From science to value. Health Aff (Millwood) 2018;37:694-701.  Back to cited text no. 4
    
5.
Deng X, Gu W, Federman S, du Plessis L, Pybus OG, Faria NR, et al. Genomic surveillance reveals multiple introductions of SARS-CoV-2 into Northern California. Science 2020;369:582-7.  Back to cited text no. 5
    
6.
Lauring AS, Hodcroft EB. Genetic variants of SARS-CoV-2-what do they mean? JAMA 2021;325:529-31.  Back to cited text no. 6
    
7.
Dorward DA, Russell CD, Um IH, Elshani M, Armstrong SD, Penrice-Randal R, et al. Tissue-specific immunopathology in fatal COVID-19. Am J Respir Crit Care Med 2021;203:192-201.  Back to cited text no. 7
    
8.
Challen R, Brooks-Pollock E, Read JM, Dyson L, Tsaneva-Atanasova K, Danon L. Risk of mortality in patients infected with SARS-CoV-2 variant of concern 202012/1: Matched cohort study. BMJ 2021;372:n579.  Back to cited text no. 8
    
9.
Pairo-Castineira E, Clohisey S, Klaric L, Bretherick AD, Rawlik K, Pasko D, et al. Genetic mechanisms of critical illness in COVID-19. Nature 2021;591:92-8.  Back to cited text no. 9
    
10.
Medzhitov R, Schneider DS, Soares MP. Disease tolerance as a defense strategy. Science 2012;335:936-41.  Back to cited text no. 10
    
11.
Kox M, Waalders NJ, Kooistra EJ, Gerretsen J, Pickkers P. Cytokine levels in critically ill patients with COVID-19 and other conditions. JAMA 2020;324:1565-7.  Back to cited text no. 11
    
12.
Mathew D, Giles JR, Baxter AE, Oldridge DA, Greenplate AR, Wu JE, et al. Deep immune profiling of COVID-19 patients reveals distinct immunotypes with therapeutic implications. Science 2020;369:eabc8511.  Back to cited text no. 12
    
13.
Hu B, Guo H, Zhou P, Shi ZL. Characteristics of SARS-CoV-2 and COVID-19. Nat Rev Microbiol 2021;19:141-54.  Back to cited text no. 13
    
14.
Bhimraj A, Morgan RL, Hirsch Shumaker A, Lavergne V, Baden L, Cheng VC, et al. COVID-19 Guideline, Part 1: Treatment and Management [Infectious Diseases Society of America Website]; 2020. Available from: https://www.idsociety.org/practice-guideline/covid-19-guideline-treatment-and-management. [Last accessed on 2022 Mar 03].  Back to cited text no. 14
    
15.
Bhaskar S, Sinha A, Banach M, Mittoo S, Weissert R, Kass JS, et al. Cytokine storm in COVID-19-immunopathological mechanisms, clinical considerations, and therapeutic approaches: The REPROGRAM Consortium Position Paper. Front Immunol 2020;11:1648.  Back to cited text no. 15
    
16.
Prescott HC, Rice TW. Corticosteroids in COVID-19 ARDS: Evidence and hope during the pandemic. JAMA 2020;324:1292-5.  Back to cited text no. 16
    
17.
RECOVERY Collaborative Group, Horby P, Lim WS, Emberson JR, Mafham M, Bell JL, et al. Dexamethasone in hospitalized patients with covid-19. N Engl J Med 2021;384:693-704.  Back to cited text no. 17
    
18.
Therapeutic Management [NIH COVID-19 Treatment Guidelines Website]. Available from: https://www.covid19treatmentguidelines.nih.gov/therapeutic-management. [Last accessed on 2022 Mar 03].  Back to cited text no. 18
    




 

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