Personalized medicine
Personalized medicine, also referred to as precision medicine or systems medicine, is a medical model that separates people into different groups—with medical decisions, practices, interventions and/or products being tailored to the individual patient based on their predicted response or risk of disease. The terms personalized medicine, precision medicine, stratified medicine and P4 medicine are used interchangeably to describe this concept, though some authors and organizations differentiate between these expressions based on particular nuances. P4 is short for "predictive, preventive, personalized and participatory".
While the tailoring of treatment to patients dates back at least to the time of Hippocrates, the usage of the term has risen in recent years thanks to the development of new diagnostic and informatics approaches that provide an understanding of the molecular basis of disease, particularly genomics. This provides a clear biomarker on which to stratify related patients.
Among the 14 Grand Challenges for Engineering, an initiative sponsored by National Academy of Engineering, personalized medicine has been identified as a key and prospective approach to "achieve optimal individual health decisions", therefore overcoming the challenge to "engineer better medicines".
Development of concept
In personalised medicine, diagnostic testing is often employed for selecting appropriate and optimal therapies based on the patient's genetics or their other molecular or cellular characteristics. The use of genetic information has played a major role in certain aspects of personalized medicine, and the term was first coined in the context of genetics, though it has since broadened to encompass all sorts of personalization measures, including the use of proteomics, imaging analysis, nanoparticle-based theranostics, among others.Difference between precision medicine and personalized medicine
Precision medicine is a medical model that proposes the customization of healthcare, with medical decisions, treatments, practices, or products being tailored to a subgroup of patients, instead of a one‐drug‐fits‐all model. In precision medicine, diagnostic testing is often employed for selecting appropriate and optimal therapies based on the context of a patient's genetic content or other molecular or cellular analysis. Tools employed in precision medicine can include molecular diagnostics, imaging, and analytics.Precision medicine and personalized medicine are analogous, applying a person's genetic profile to guide clinical decisions about the prevention, diagnosis, and treatment of a disease. Personalized medicine is established on discoveries from the Human Genome Project.
In explaining the distinction from the similar term of personalized medicine, the United States President's Council of Advisors on Science and Technology writes:
The use of the term "precision medicine" can extend beyond treatment selection to also cover creating unique medical products for particular individuals—for example, "...patient-specific tissue or organs to tailor treatments for different people." Hence, the term in practice has so much overlap with "personalized medicine" that they are often used interchangeably, even though the latter is sometimes misinterpreted as involving a unique treatment for each individual.
Background
Basics
Every person has a unique variation of the human genome. Although most of the variation between individuals has no effect on health, an individual's health stems from genetic variation with behaviors and influences from the environment.Modern advances in personalized medicine rely on technology that confirms a patient's fundamental biology, DNA, RNA, or protein, which ultimately leads to confirming disease. For example, personalised techniques such as genome sequencing can reveal mutations in DNA that influence diseases ranging from cystic fibrosis to cancer. Another method, called RNA-seq, can show which RNA molecules are involved with specific diseases. Unlike DNA, levels of RNA can change in response to the environment. Therefore, sequencing RNA can provide a broader understanding of a person's state of health. Recent studies have linked genetic differences between individuals to RNA expression, translation, and protein levels.
The concepts of personalised medicine can be applied to new and transformative approaches to health care. Personalised health care is based on the dynamics of systems biology and uses predictive tools to evaluate health risks and to design personalised health plans to help patients mitigate risks, prevent disease and to treat it with precision when it occurs. The concepts of personalised health care are receiving increasing acceptance with the Veterans Administration committing to personalised, proactive patient driven care for all veterans. In some instances personalised health care can be tailored to the markup of the disease causing agent instead of the patient's genetic markup; examples are drug resistant bacteria or viruses.
Precision medicine often involves the application of panomic analysis and systems biology to analyze the cause of an individual patient's disease at the molecular level and then to utilize targeted treatments to address that individual patient's disease process. The patient's response is then tracked as closely as possible, often using surrogate measures such as tumor load, and the treatment finely adapted to the patient's response. The branch of precision medicine that addresses cancer is referred to as "precision oncology". The field of precision medicine that is related to psychiatric disorders and mental health is called "precision psychiatry."
Inter-personal difference of molecular pathology is diverse, so as inter-personal difference in the exposome, which influence disease processes through the interactome within the tissue microenvironment, differentially from person to person. As the theoretical basis of precision medicine, the "unique disease principle" emerged to embrace the ubiquitous phenomenon of heterogeneity of disease etiology and pathogenesis. The unique disease principle was first described in neoplastic diseases as the unique tumor principle. As the exposome is a common concept of epidemiology, precision medicine is intertwined with molecular pathological epidemiology, which is capable of identifying potential biomarkers for precision medicine.
Method
In order for physicians to know if a mutation is connected to a certain disease, researchers often do a study called a "genome-wide association study". Such a study will look at one disease, and then sequence the genome of many patients with that particular disease to look for shared mutations in the genome. Mutations that are determined to be related to a disease by a GWA study can then be used to diagnose that disease in future patients, by looking at their genome sequence to find that same mutation. The first GWA study, conducted in 2005, studied patients with age-related macular degeneration. It found two different mutations, each containing only a variation in only one nucleotide, which were associated with ARMD. GWA studies like this have been very successful in identifying common genetic variations associated with diseases. As of early 2014, over 1,300 GWA studies have been completed.Disease risk assessment
Multiple genes collectively influence the likelihood of developing many common and complex diseases. Personalised medicine can also be used to predict a person's risk for a particular disease, based on one or even several genes. This approach uses the same sequencing technology to focus on the evaluation of disease risk, allowing the physician to initiate preventive treatment before the disease presents itself in their patient. For example, if it is found that a DNA mutation increases a person's risk of developing Type 2 Diabetes, this individual can begin lifestyle changes that will lessen their chances of developing Type 2 Diabetes later in life.Practice
The ability to provide precision medicine to patients in routine clinical settings depends on the availability of molecular profiling tests, e.g. individual germline DNA sequencing. While precision medicine currently individualizes treatment mainly on the basis of genomic tests, several promising technology modalities are being developed, from techniques combining spectrometry and computational power to real-time imaging of drug effects in the body. Many different aspects of precision medicine are tested in research settings, but in routine practice not all available inputs are used. The ability to practice precision medicine is also dependent on the knowledge bases available to assist clinicians in taking action based on test results. Early studies applying omics-based precision medicine to cohorts of individuals with undiagnosed disease has yielded a diagnosis rate ~35% with ~1 in 5 of newly diagnosed receiving recommendations regarding changes in therapy. It has been suggested that until pharmacogenetics becomes further developed and able to predict individual treatment responses, the N-of-1 trials are the best method of identifying patients responding to treatments.On the treatment side, PM can involve the use of customized medical products such drug cocktails produced by pharmacy compounding or customized devices. It can also prevent harmful drug interactions, increase overall efficiency when prescribing medications, and reduce costs associated with healthcare.
The question of who benefits from publicly funded genomics is an important public health consideration, and attention is needed to ensure that implementation of genomic medicine does not further entrench social‐equity concerns.
Artificial intelligence in precision medicine
is providing a paradigm shift toward precision medicine. Machine learning algorithms are used for genomic sequence and to analyze and draw inferences from the vast amounts of data patients and healthcare institutions recorded in every moment. AI techniques are used in precision cardiovascular medicine to understand genotypes and phenotypes in existing diseases, improve the quality of patient care, enable cost-effectiveness, and reduce readmission and mortality rates.A 2021 paper reported that machine learning was able to predict the outcomes of Phase III clinical trials with 76% accuracy. This suggests that clinical trial data could provide a practical source for machine learning-based tools for precision medicine.
Precision medicine may be susceptible to subtle forms of algorithmic bias. For example, the presence of multiple entry fields with values entered by multiple observers can create distortions in the ways data is understood and interpreted. A 2020 paper showed that training machine learning models in a population-specific fashion can yield significantly superior performance than population-agnostic models.