Precision Medicine - PM 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.
Most medical treatments are designed for the "average patient" as a one-size-fits-all-approach, which may be successful for some patients but not for others. Precision medicine, sometimes known as "personalized medicine" is an innovative approach to tailoring disease prevention and treatment that takes into account differences in people's genes, environments, and lifestyles. The goal of precision medicine is to target the right treatments to the right patients at the right time.
Advances in precision medicine have already led to powerful new discoveries and FDA-approved treatments that are tailored to specific characteristics of individuals, such as a person's genetic makeup, or the genetic profile of an individual's tumor. Patients with a variety of cancers routinely undergo molecular testing as part of patient care, enabling physicians to select treatments that improve chances of survival and reduce exposure to adverse effects.
Next Generation Sequencing (NGS) Tests
Precision care will only be as good as the tests that guide diagnosis and treatment. Next Generation Sequencing - NGS tests are capable of rapidly identifying or 'sequencing' large sections of a person's genome and are important advances in the clinical applications of Precision Medicine. Patients, physicians and researchers can use these tests to find genetic variants that help them diagnose, treat, and understand more about human disease.
The FDA's Role in Advancing Precision Medicine
The FDA is working to ensure the accuracy of NGS tests, so that patients and clinicians can receive accurate and clinically meaningful test results. The vast amount of information generated through NGS poses novel regulatory issues for the FDA. While current regulatory approaches are appropriate for conventional diagnostics that detect a single disease or condition (such as blood glucose or cholesterol levels), these new sequencing techniques contain the equivalent of millions of tests in one. Because of this, the FDA has worked with stakeholders in industry, laboratories, academia, and patient and professional societies to develop a flexible regulatory approach to accommodate this rapidly evolving technology that leverages consensus standards, crowd-sourced data, and state-of-the-art Open-Source Computing Technology to support NGS test development. This approach will enable innovation in testing and research, and will speed access to accurate, reliable genetic tests.
SOURCE: The United States Food and Drug Administration - FDA
SOURCE URL: https://www.fda.gov/medical-devices/in-vitro-diagnostics/precision-medicine
Precision Medicine's Relationship to Personalized Medicine
In explaining the distinction from a similar common term of Personalized Medicine, the National Research Council explains:
Precision Medicine refers to the tailoring of medical treatment to the individual characteristics of each patient. It does not literally mean the creation of drugs or medical devices that are unique to a patient, but rather the ability to classify individuals into sub-populations that differ in their susceptibility to a particular disease, in the biology or prognosis of those diseases they may develop, or in their response to a specific treatment. Preventive or therapeutic interventions can then be concentrated on those who will benefit, sparing expense and side effects for those who will not. Although the term 'personalized medicine' is also used to convey this meaning, that term is sometimes misinterpreted as implying that unique treatments can be designed for each individual.
On the other hand, 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.
Precision Medicine's Scientific Basis
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 (possibly in combination) 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 (versus true outcomes, such as five-year survival rate), 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, different 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.
Precision Medicine in 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 (e.g. Oncotype DX), 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 (e.g., Proteome, Microbiome), 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.
On the treatment side, Precision Medicine can involve the use of customized medical products such drug cocktails produced by pharmacy compounding or customized devices. Precision Medicine 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 - AI in Precision Medicine
Artificial Intelligence - AI is a providing paradigm shift toward precision medicine. Machine Learning - ML algorithms and models 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.
Artificial Intelligence - 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 - ML was able to predict the outcomes of Phase III clinical trials (for treatment of Prostate Cancer) 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.
Precision Medicine Initiative
In his 2015 State of the Union address, U.S. President Barack Obama stated his intention to fund an amount of $215 million to the "Precision Medicine Initiative" of the United States National Institutes of Health. A short-term goal of the Precision Medicine Initiative was to expand Cancer Genomics to develop better prevention and treatment methods. In the long term, the Precision Medicine Initiative aimed to build a comprehensive scientific knowledge base by creating a national network of scientists and embarking on a national cohort study of One Million Americans to expand our understanding of health and disease.
The Mission Statement of the Precision Medicine Initiative read:
"To enable a new era of medicine through research, technology, and policies that empower patients, researchers, and providers to work together toward development of individualized treatments". In 2016 this initiative was renamed "All of Us" and an initial pilot project had enrolled about 10,000 people by January 2018.
Benefits of Precision Medicine
Precision Medicine helps Healthcare Providers better understand the many things—including environment, lifestyle, and heredity—that play a role in a patient's health, disease, or condition. This information lets them more accurately predict which treatments will be most effective and safe, or possibly how to prevent the illness from starting in the first place.
In addition, benefits are to:
- Shift the emphasis in medicine from reaction to prevention
- Predict susceptibility to disease
- Improve disease detection
- Preempt disease progression
- Customize disease-prevention strategies
- Prescribe more effective drugs
- Avoid prescribing drugs with predictable negative side effects
- Reduce the time, cost, and failure rate of pharmaceutical clinical trials
- Eliminate trial-and-error inefficiencies that inflate health care costs and undermine patient care