Personalized Medicine, also known as Precision Medicine, Individualized Medicine, and Stratified Medicine refers to a novel area of medicine that uses an individual's genetic profile to develop a tailored approach for the prevention and treatment of diseases.
Unlike conventional medical treatments that are designed for the general population based on a "one size fits all approach", personalized medicine provides medical treatment to cater to the individual needs of the patient. It involves the use of diagnostic tests to identify specific biological markers to determine the most suitable medical treatments and procedures for each patient.
The Personalized Medicine Coalition (PMC), a non-profit organization dedicated to advancing personalized medicine and tailoring medical treatments to individual patient characteristics, personalized medicine can improve treatment outcomes of many diseases by providing targeted therapies. A 2022 report by the PMC has revealed that at least one in every four drugs approved by the FDA is based on personalized medicine. In 2022 alone, 34% of all the medicines approved by the FDA were personalized medicines.
Thus, personalized medicine can help clinicians understand an individual's genetic makeup, and then use it to provide targeted therapies.
In the article, we will take a deep dive into the topic including the historical perspective on personalized medicine, biomarkers in targeted therapies, pharmacogenomics, and drug personalization, the role of data and technology in precision medicine, and integrating personalized medicine in clinical practice. We will also walk you through some successful examples of PM implementation.
So with no further delay, let's get straight into the topic.
As much as it sounds like a "new" concept, personalized medicine has been around for a while.
In fact, the term "personalized medicine" was first used 25 years ago in The Wall Street Journal and then in The Oncologist. This very first article on personalized medicine entitled "New Era of Personalized Medicine: Targeting Drugs for Each Unique Genetic Profile" described how current medical treatments have poor efficacy due to disease heterogeneity and genetic variability.
It was also around this time that a novel treatment was approved for metastatic breast cancer with overexpressed HER2 protein - a monoclonal antibody Trastuzumab. Guided by an immunohistochemical assay for HER2 overexpression, trastuzumab became the first drug to provide targeted therapy.
Fast forward to today, personalized medicine has achieved remarkable milestones, especially in oncology and hematology. These key developments have reshaped disease management and treatment approaches, setting the stage for the personalized medicine landscape we explore today.
Biomarkers and Targeted Therapies
The WHO defines a Biomarker as,
"Any measurement reflecting an interaction between a biological system and a potential hazard, which may be chemical, physical, or biological. The measured response may be functional and physiological, biochemical at the cellular level, or a molecular interaction."
Thus, biomarkers are quantifiable biological indicators that provide information about
an individual's physiological or pathological status. They are used to support medical product development and in the advancement of personalized medicine to improve human health.
In personalized medicine, healthcare providers can adjust medical treatments through disease-specific biomarkers for individuals or groups of patients. Analytical tools like molecular imaging, genomics, proteomics, metabolomics, and next generation sequencing (NGS) are being used to develop disease-specific biomarkers for use in personalized medicine.
Successful Targeted Therapies
Some of the most successful applications of targeted therapies in personalized medicine are in the field of oncology. Unlike traditional chemotherapy that targets actively multiplying cancer cells, targeted therapies "target" the cancer-causing genes, slowing and stopping their growth at molecular level.
At present, approved targeted therapies are available for breast cancer, chronic myeloid leukemia, colorectal cancer, lung cancer, lymphoma, and melanoma.
Challenges in Targeted Therapy Research
The biggest challenge in developing targeted therapies stems from the complexity of the biomarker research field. To date, hundreds and thousands of citations on PubMed are based on biomarkers, yet only a few of them are validated for clinical use. It's because the diversity of potential biomarkers, including genetic mutations, proteomic variations, and metabolic signatures make the identification of biomarkers difficult. Moreover, the dynamic nature of biomarkers, influenced by age, environment, and comorbidities also requires continuous monitoring to develop accurate treatment strategies.
Pharmacogenomics and Drug Personalization
Pharmacogenomics is a specialized branch of pharmacology that focuses on the impact of "genetic variation" such as gene expression on "drug response" in patients.
Scientists can use pharmacogenomics to provide personalize medical treatment through drug personalization. In this process, drug therapy is optimized to achieve maximum efficacy and minimum adverse effects.
Real-world Applications and Impact on Patient Outcomes
Several real-world examples of successful implementation of personalized medicine through pharmacogenomics are found in the literature. Let's have a look at a few:
Warfarin is a narrow therapeutic index with variable dose requirements in different patients. Its dose must be adjusted to prevent the risk of bleeding or thrombosis in critically ill patients.
Pharmacogenomics Contribution in Warfarin Dosing
Pharmacogenomic testing for variants in the CYP2C9 and VKORC1 genes has been shown to significantly impact warfarin dosing. Variants in these genes can help predict an individual's warfarin dose requirements, allowing for more precise dosing and reducing the risk of adverse events.
Pharmacogenomics has also helped healthcare providers optimize the dose of an anti-platelet medication - Clopidogrel. Genetic testing for CYP2C19 variants has been shown to identify poor metabolizers of clopidogrel, who are at an increased risk of cardiovascular events. This knowledge allows healthcare providers to develop personalized antiplatelet therapy, with alternative medications or dosing strategies for individuals with identified genetic variants to improve treatment outcomes.
"Personalized" Use of Tamoxifen in Breast Cancer
Tamoxifen is used for hormone receptor-positive breast cancer. Genetic variations can influence its efficacy in the CYP2D6 gene, which is involved in the metabolism of tamoxifen into its active form.
Pharmacogenetic testing for CYP2D6 variants can identify patients with reduced enzyme activity, impacting the conversion of tamoxifen into its active metabolites. This information can guide personalized treatment decisions, such as adjusting tamoxifen dosage or considering alternative therapies to optimize treatment efficacy.
The Role of Big Data and AI in Personalized Medicine
Data science and AI technology play a pivotal role in advancing personalized medicine research. Through data science and AI, healthcare providers can extract patient information from EHRs, identify trends, assess treatment outcomes, and predict disease progression to make informed decisions and provide "tailored care" to individual patients.
Big Data in Personalized Medicine
Big data refers to extremely large and complex datasets, the processing of which requires sophisticated analytics tools and technologies. These datasets can come from various sources at high speed and need to be processed promptly. Examples include genetic or lifestyle information and medical history. Healthcare providers can use these datasets to select more effective and individualized treatment options for their patients.
Artificial Intelligence and Machine Learning Applications
AI can process large amounts of real-time data, detect patterns, and generate insights. In personalized medicine, this ingenuity of AI can help healthcare providers in interpreting vast genomic datasets. With machine learning and deep learning techniques, AI can also identify hidden links in these datasets, allowing clinicians to create custom, "genome-based" treatment plans for their patients.
Combining Big Data and AI in Personalized Medicine
Using big data to extract valuable patient information and then using AI to process it further is disrupting the personalized medicine research industry. The two technologies are being used synergistically in areas like cancer treatment, disease detection, and drug discovery.
Integrating Personalized Medicine into Clinical Practice
Integrating personalized medicine in clinical practice is easier said than done since it changes the way medicine is practiced. However, the successful implementation of personalized medicine can improve prevention, diagnosis, and therapeutic efforts by providing customized treatments suitable for each patient.
It is also important to understand that the integration of personalized medicine into current medical practice requires collaborative efforts from healthcare providers, employers, policymakers, patients, and their families.
Starting from building awareness of personalized medicine concepts and technologies among all individuals involved, the next step is to create policies in place to ensure data protection and address ethical, legal, and societal issues. For instance, obtaining consent from patients regarding the use of their genetic information to devise treatment strategies.
Once the policies and legal framework are sorted, then comes the process of creating healthcare delivery infrastructure, and data management systems to guide clinical decisions. Finally, there must be SOPs in place to promote best practices for healthcare delivery approaches, ensuring that access to personalized medicine must be established and implemented.
Challenges in Implementation and Adoption
Several challenges are hindering the integration of personalized medicine into clinical practice. The biggest challenge is the lack of awareness and education among healthcare providers, payers, policymakers, and patients. Poor consumer awareness and limited demand further hinder its adoption, fueled by the complexity of the scientific principles used in personalized treatment.
One of the biggest advantages of personalized medicine is patient empowerment. However, the lack of clearly defined consent policies for molecular information use of patients, data privacy issues, and inadequate patient involvement in healthcare decision-making pose significant challenges to realizing this potential.
Without well-established consent frameworks, patients may be hesitant to share their genetic information for personalized treatments, preventing the integration of tailored healthcare solutions. Concerns about data privacy and security further exacerbate these challenges, as patients need assurance that their sensitive genetic data will be handled with the utmost confidentiality.
Other challenges include infrastructure and information management challenges arising from the lack of clear decision-making processes, insufficient coordination across healthcare institutions, and inadequate communication within the continuum of care.
The complex nature of personalized medicine requires comprehensive strategies including awareness, education, active patient participation, recognition of value, resilient infrastructure, and policies to ensure its seamless integration and adoption in current clinical practice.
Future Trends and Challenges
The future of personalized medicine is marked by a dynamic interplay of emerging technologies and evolving research directions. As advancements continue in personalized medicine research, a critical challenge that looms large is addressing data privacy and security issues.
Emerging Technologies and Research Directions
In personalized medicine, emerging technologies are likely to expand beyond the conventional boundaries of genomics. Innovations in proteomics, metabolomics, and advanced imaging techniques will produce more precise diagnostics and treatment strategies. Integrating these technologies into personalized medicine will lead to more individualized patient care.
Overcoming Data Privacy and Security Concerns
Since personalized medicine relies on vast datasets, safeguarding sensitive patient information from unauthorized access and ensuring compliance with stringent privacy regulations are inevitable. To address these issues, it is important to build robust frameworks for data sharing in research advancements, and thus protect patient confidentiality.
The personalized medicine field has evolved significantly over the last few years. The journey from the historical roots of personalized medicine, recognizing biomarkers, to successful targeted therapies, and the integration of big data and AI, showcases the ongoing evolution of this disruptive area of medicine.
Despite remarkable progress, challenges persist in integrating personalized medicine into clinical practice. Patient empowerment, a cornerstone advantage, faces hurdles such as undefined consent policies and data privacy concerns, emphasizing the need for robust frameworks and clear communication. Infrastructure challenges and awareness gaps among healthcare stakeholders make its adoption even more difficult.
Looking ahead, the future holds promise and challenges alike. Emerging technologies like proteomics and metabolomics offer precise diagnostics while addressing data privacy and security concerns remains a critical imperative. The role of healthcare professionals and policymakers is also crucial here to build awareness, establish policies, and implement best practices.
About the Author:
Saher Binte Haider
Saher Binte Haider is a pharmacy graduate from Dow University of Health Sciences. She started her career as a Quality Management professional in the pharmaceutical industry where she developed a keen interest in good documentation practices, SOP creation, and content writing. She has 7+ years of experience in healthcare & life sciences content writing. Her key areas of expertise are healthcare, pharmaceuticals, health tech, and AI in healthcare.