Precision Medicine for Managing Chronic Diseases

TL;DR 

Precision Medicine is the idea that molecular information will improve the treatment of patients. There have been many cases in addition to many possibilities where precision medicine was or could be useful. 

Introduction 

The modern era is an exciting period for medicine with new findings that will improve the understanding of a disease mechanism and help prevent it with early detection. Precision Medicine is the idea of personalizing treatments using molecular analysis and patient engagement. The aim of this article is to differentiate precision medicine from personalized medicine, show its benefits, diagnosis and treatments of clinical diseases, and the use of it in clinical practices. 

What are chronic diseases? 

Chronic diseases are long ongoing health problems that require medical assistance. 

What is molecular analysis? 

Molecular analysis is the study of molecules such as DNA, RNA, or proteins to understand the human body, infectious diseases, and different conditions. 

What is patient engagement? 

The active involvement of patients in their health related decisions. 

Keywords 

● cardiovascular disease 

● chronic airway diseases 

● chronic diseases      

● diabetes 

● precision medicine 

● molecular analysts 

Precision medicine vs personalized medicine 

Personalized medicine and precision medicine (PM) are often used interchangeably because of their notable overlap—both mention adjusting the treatment to fit the individual characteristics of the patient. 

The current definition of personalized medicine was first recognized in a written study by Jain KK published in 1988. It was recognized by scientists who began to see the potential of the Human Genome Project. The current definition set by “The Personalized Medicine Coalition”, an organization that promotes the understanding and use of personalized medicine, is that personalized medicine is an evolving field where physicians use diagnostic tests and a patient’s medical records to develop a targeted treatment plan, focused on molecular factors alongside external ones (lifestyle, preferences, family dynamics, etc.).  

Meanwhile, precision medicine (PM) refers to the logical idea that molecular information will improve the accuracy of treatments and classifications in patient populations that have certain genetic characteristics. An important idea of PM is to better understand the diseases and create a classification for them that include symptoms, signs, molecular causes and environmental causes. 

The main difference between personalized medicine and PM is that personalized medicine focuses on an individual’s entire picture (both genetics and outside factors) while PM is focused solely on genetics and molecular data to then classify patients into. 

Precision Medicine in the management of chronic disease 

PM has shown to be useful for identifying cancer. Scientists used HER2, a protein found on the surface of a cell, as a biological characteristic, or biomarker, of breast cancer to develop a new drug called trastuzumab. It targets HER2 as a monoclonal antibody and is used for treatment on HER2-positive breast cancer. An antibody is a protein that counteracts a toxin, and a monoclonal antibody is an antibody produced by a line of them. Tyrosine kinase inhibitors, a type of cancer therapy, also improves the survival of patients with chronic myeloid leukemia and non-small-cell lung cancer because of the sequence genes that have been observed. One of the goals of PM is to accelerate the ability to recognize diseases and have a stronger biological understanding using a comprehensive database of molecular information. 

Chronic Airway disease 

The PM strategy for chronic airway diseases is based on the presence of airway smooth muscle contraction, loss of elastic recoil, and airway mucosal oedema which are considered “treatable traits”. Reliable biomarkers could be used in the risk assessment and prediction. The focus on biomarkers can help with the development of a more accurate drug for airway diseases. 

Diabetes 

Diabetes manifests differently in each patient. This chronic disease could be treated more successfully if patients were grouped into different subtypes of diabetes (beyond just the current 2) with more precise disease predictions and outcomes. 

Tests and Findings 

Researchers are applying PM to type 2 diabetes (T2D) to improve diagnosis and treatment. Individuals with T2D from Mount Sinai Medical Center in New York were sorted into three subtypes of T2D, using over 11,000 high-dimension electronic medical records (digital versions of patients’ medical information). These 3 subtypes each have distinct clinical profiles and genetic characteristics, suggesting that custom treatment approaches may be able to better treat individuals, as opposed to  a one-size-fits-all method that we have previously been doing with T2D patients. This study highlights the power of using large-scale patient data to uncover important subgroups beyond what we currently have. A related analysis demonstrated that accurate risk prediction tools can improve lifestyle interventions of those who need it. 

Cardiovascular Diseases 

PM (in cardiovascular diseases) now combines traditional measures of patient-specific data (also known as risk factors) with “genetic risk scores” (measures that incorporate a patient’s individualized genetic profile) to improve treatments. Our current genetic risk scores for coronary heart disease and high coronary artery calcium help both with treatment and to predict future cardiovascular events. Other scoring systems have also been helpful to providing therapy and classification of cardiovascular diseases. 

Tests and Findings 

Cardiology is applying many PM methods like phenomapping (the use of powerful computers and machines to find hidden patterns in large amounts of patient data) and machine learning to categorize patients with cardiovascular diseases. Statistical learning algorithms were applied to a dense data set that allowed patients with heart failure with preserved ejection (HFpEF) to be separated into 3 groups with distinct clinical characteristics. Notably, each group had different risk profiles and future clinical trajectories. Future uses of PM methods can refine therapy and allow for more targeted treatment. 

Integration of PM in clinical practice 

PM strategies can improve patient care greatly, but there are several obstacles: 

1. Healthcare infrastructure needs to be adjusted to securely process and collect the vast amount of data across thousands of patients to properly develop PM methods

2. Developing a network that connects various types of relevant health data and information to determine the links between diseases and allowing scientists to better understand them. This is a significant obstacle because it will be difficult to create a system to efficiently collect and store large amounts of health data.

   
   

Challenges of big data analysis 

1. Data storage: many healthcare related research fields have vast and dense data requiring extremely large amounts of storage. 

2. Security of handling such a large amount of data 

Conclusion 

The way healthcare approaches chronic disease is constantly changing with new data and research. PM medicine is not just reactive, with management of diseases, but might call for proactive care and early prevention. The complexity of PM medicine will require many resources, data, and action but the benefits are numerous. Research in PM could help doctors give more specialized care, prevent and treat disease, empower further research, and help the public.

Original article DOI: 10.20452/pamw.3503  

This paper was simplified by: Kelly Chen and Sophia Hu