How Personalized Medicine is Changing the Healthcare Landscape

By Deliana InfanteReviewed by Lily Ramsey, LLM

Personalized medicine, also known as precision medicine, allows for the development targeted therapies that aim to prevent diseases, reduce the side effects of typical procedures, and promote better outcomes for individual patients.

This has been achieved through advances in omics sciences, which have shown promising results in identifying the individualized genetic makeup of patients.

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Introduction

The emerging field of personalized medicine aims to use an individual's genetic profile to guide disease prevention, diagnosis, and treatment decisions. This approach generates a precise clinical picture of a patient based on their unique biological makeup, including genetic, RNA, or protein variances that could affect their disease susceptibility.¹

Advances in omics are now used alongside data science tools to create a detailed, holistic view of individual patients. This individualized approach helps predict which treatments work best for specific patients, improving overall care and surgical outcomes.¹

Personalized medicine also emphasizes prevention. Healthcare providers can develop prevention plans tailored to individual patients by understanding genetic markers and high-risk genes. This shift from reaction to prevention significantly advances modern healthcare.¹

The integration of artificial intelligence, omics, and clinical data has allowed the development of digital patient models that help identify high-risk genes and tailor treatments accordingly, ultimately improving patient outcomes and a sense of well-being.¹

Personalized Medicine: Foundations and Impact

Precision medicine represents a transformative approach in healthcare, moving away from the traditional "one-size-fits-all" model to a more individualized strategy.²

The study of specific responses to drugs depending on an individual's genetic profile (pharmacogenomics) plays a critical role in personalized medicine by guiding the development of targeted therapeutics. This applies to several medical disciplines, with key applications in chronic diseases and cancer.²

Cancer patients have different responses to the same treatment; this represents a major drawback in patient prognosis.² Different studies have focused on understanding the role of specific gene variants and polymorphisms in patients’ responses to cancer treatments.2

For example, precision medicine has been applied to study how fluoropyrimidine (an anti-cancer treatment) causes severe toxicity in some patients with gastrointestinal tumors³, as well as in the identification of prognosis markers for chronic diseases, such as inflammatory bowel disease.⁴

Notably, personalized medicine is not limited to these medical conditions. It has also been applied in infectious diseases to study hosts’ specific responses against pathogens (e.g. infection by Mycobacterium tuberculosis).⁵

Due to the nature of rare inherited diseases, precision medicine could also help to identify gene relationships shaping the outcomes of these patients.¹

It is important to note that, in addition to genetic information, personalized medicine considers the epigenetic makeup of individuals, which arises from genetic and environmental factors, influencing how individuals respond to medications and treatments.⁶

Suits You; Challenges in Personalized Medicine

Mechanisms of Personalized Medicine

Personalized medicine leverages molecular and genetic information to tailor treatments to individual patients.⁷ This approach involves various omics technologies, including genomics, transcriptomics, proteomics, metabolomics, and immunogenomics.⁷

These technologies help identify specific pathways and biomarkers linked to disease mechanisms, enabling the development of personalized treatment plans.⁷

Genomics is the study of an organism's complete set of DNA.⁷ It plays a crucial role in personalized medicine by identifying genetic variations influencing disease susceptibility and treatment response.⁷

Transcriptomics involves the study of RNA transcripts.⁷ Although it is usually applied to study gene expression by measuring mRNAs, non-coding RNAs have been shown to play regulatory roles in gene expression, making them prospective targets for treatment and biomarkers for diagnosis.⁷

Proteomics is the high-throughput technology that studies protein structures and functions since they are the main effectors of cellular functions and are directly involved in disease mechanisms.⁷ Specific patterns of protein expression can serve as biomarkers for diseases.⁷

Certain proteomic signatures are used to diagnose and monitor the progression of diseases like Alzheimer's disease⁸. Metabolomics, on the other hand, provides a snapshot of the metabolic state of a cell or organism.⁷

Ultimately, immunogenomics combines genomics and immunology to understand how genetic variations affect immune responses.⁹ This field is crucial for developing personalized immunotherapies for diseases like cancer and autoimmune disorders.^9-10

Analytical Chemistry in Personalized Medicine

Techniques for Implementing Personalized Medicine

Personalized medicine utilizes advanced methods and technologies to tailor treatments to individual patients based on their unique molecular and genetic profiles.¹¹ Key technologies include next-generation sequencing (NGS), mass spectrometry, bioinformatics algorithms, and computational biology pipelines.¹¹

NGS is a powerful technology that can sequence multiple genes simultaneously and is commonly applied in genomics, transcriptomics, and metagenomics studies.¹²

Mass spectrometry analyzes the mass-to-charge ratio of ions. It is widely used in proteomics to identify and quantify proteins, study protein interactions, and understand post-translational modifications and metabolomics studies.¹³

Omics data requires interpretation through specific algorithms.¹⁴ Bioinformatics involves using computational tools and algorithms to analyze the extensive biological data generated by these technologies.¹⁴

It helps identify genetic variants, understand gene expression patterns, and predict the impact of genetic changes on protein function and disease mechanisms, commonly called precision diagnostics.¹⁴

Despite the promise of personalized medicine, several challenges, such as data interpretation, costs, and integration into clinical practice, need to be addressed.¹⁵

Accurate data interpretation requires advanced bioinformatics tools and expertise, which may not be readily available in all clinical settings, causing important impacts in costs.¹⁵

Integrating personalized medicine into clinical practice also requires changes in clinical workflows, training of healthcare professionals, and the development of privacy guidelines and standards, posing a challenge to the widespread adoption of personalized medicine.¹⁵

Therapeutic Implications and Future Directions of Personalized Medicine

Personalized medicine tailors treatments to individual molecular and genetic profiles using targeted therapies, pharmacogenomics, and personalized drug regimens.¹⁵

Targeted therapies address specific genetic variants, improving efficacy and reducing side effects, while pharmacogenomics optimizes drug selection and dosage based on genetic variations.¹⁵

In oncology, personalized regimens integrate genomics and clinical data for individualized treatment strategies.¹⁵

Several FDA-approved precision medicines treat various conditions, and numerous clinical trials aim to identify genetic features for individualized treatments.^11,16 Most trials involve omics technologies, and some involve AI for diagnosis and prognosis.¹¹

By 2024, AI was used in approximately eight precision medicine clinical trials, highlighting the growing potential of AI in personalized medicine.¹⁷

References

1. Goetz, L. H., & Schork, N. J. (2018). Personalized medicine: motivation, challenges, and progress. Fertility and Sterility, 109(6), 952–963. https://doi.org/10.1016/j.fertnstert.2018.05.006
2. Feehley, T, et al. (2023a). Drugging the epigenome in the age of precision medicine. Clinical Epigenetics, 15(1). https://doi.org/10.1186/s13148-022-01419-z
3. Ikonnikova, A, et al.(2024). MIR27A Gene Polymorphism Modifies the Effect of Common DPYD Gene Variants on Severe Toxicity in Patients with Gastrointestinal Tumors Treated with Fluoropyrimidine-Based Anticancer Therapy. International Journal of Molecular Sciences, 25(15), 8503. https://doi.org/10.3390/ijms25158503
4. Minea, H., et al. (2024). The Contribution of Genetic and Epigenetic Factors: An Emerging Concept in the Assessment and Prognosis of Inflammatory Bowel Diseases. International Journal of Molecular Sciences, 25(15), 8420. https://doi.org/10.3390/ijms25158420
5. Cebani, L., & Mvubu, N. E. (2024). Can We Exploit Inflammasomes for Host-Directed Therapy in the Fight against Mycobacterium tuberculosis Infection? International Journal of Molecular Sciences, 25(15), 8196. https://doi.org/10.3390/ijms25158196
6. Feehley, T, et al. (2023b). Drugging the epigenome in the age of precision medicine. Clinical Epigenetics, 15(1). https://doi.org/10.1186/s13148-022-01419-z
7. Hasanzad, M, et al.(2021). Precision medicine journey through omics approach. Journal of Diabetes & Metabolic Disorders, 21(1), 881–888. https://doi.org/10.1007/s40200-021-00913-0
8. Jain, M, et al. (2023). Unveiling the Molecular Footprint: Proteome-Based Biomarkers for Alzheimer’s Disease. Proteomes, 11(4), 33. https://doi.org/10.3390/proteomes11040033
9. Kiyotani, K, et al. (2021). Immunogenomics in personalized cancer treatments. Journal of Human Genetics, 66(9), 901–907. https://doi.org/10.1038/s10038-021-00950-w
10. Immunogenomics | NGS in immune repertoire & autoimmunity studies. (n.d.). [Online] https://www.illumina.com/areas-of-interest/immunogenomics.html
11. Ho, D, et al. (2020). Enabling Technologies for Personalized and Precision Medicine. Trends in Biotechnology, 38(5), 497–518. https://doi.org/10.1016/j.tibtech.2019.12.021
12. Qin, D. (2019). Next-generation sequencing and its clinical application. Cancer Biology and Medicine, 16(1), 4–10. https://doi.org/10.20892/j.issn.2095-3941.2018.0055
13. Garg, E., & Zubair, M. (2023). Mass Spectrometer. StatPearls - NCBI Bookshelf. https://www.ncbi.nlm.nih.gov/books/NBK589702/
14. Lesk, A. M. (2009). Bioinformatics | Genomics, Proteomics & Data Analysis. Encyclopedia Britannica. [Online] https://www.britannica.com/science/bioinformatics
15. Kouba, A. (2024). Top 8 Pros And Cons Of Personalized Medicine | Babirus LLC. Babirus LLC. [Online] https://www.babirus.ae/advantages-disadvantages-personalized-medicine/
16. Virginia, Y. C. T. B. P. R. C. P. W. H. (2023). Advancing Research in Personalized Medicine. [Online] https://www.uspharmacist.com/article/advancing-research-in-personalized-medicine#:~:text=A%20study%20published%20in%20JCO%20Precision%20Oncology%20indicated%20that%20personalized%20medicine%20presents%20innovative%20opportunities%20to%20treat%20patients%20with%20cancer
17. ClinicalTrials.gov. (n.d.). [Online] https://clinicaltrials.gov/search?term=Artificial%20Intelligence&aggFilters=phase:1%202%203%204%20NA&sort=StudyFirstPostDate&intr=Precision%20Medicine