Personalized medicine
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Personalized medicine, also referred to as precision 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.[1] The terms personalized medicine, precision medicine, stratified medicine and P4 medicine are used interchangeably to describe this concept[1][2] though some authors and organisations use these expressions separately to indicate particular nuances.[2]
While the tailoring of treatment to patients dates back at least to the time of Hippocrates,[3] the term has risen in usage in recent years given the growth of new diagnostic and informatics approaches that provide an understanding of the molecular basis of disease, particularly genomics. This provides a clear evidence base on which to stratify (group) related patients.[1][4][5]
Among the 14 Grand Challenges for Engineering, an initiative sponsored by National Academy of Engineering (NAE), 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".[6][7]
Development of concept
In personalised medicine,
Relationship to personalized medicine
Precision medicine (PM) is a
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 subpopulations 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.[14]
On the other hand, 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."[16] Hence, the term in practice has so much overlap with "personalized medicine" that they are often used interchangeably.[17]
Background
Basics
Every person has a unique variation of the human genome.[18] 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.[19][20]
Modern advances in personalized medicine rely on technology that confirms a patient's fundamental biology,
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.[24] 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.[25]
Precision medicine often involves the application of
Inter-personal difference of
Method
In order for physicians to know if a mutation is connected to a certain disease, researchers often do a study called a "
Disease risk assessment
Multiple genes collectively influence the likelihood of developing many common and complex diseases.
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.[37] While precision medicine currently individualizes treatment mainly on the basis of genomic tests (e.g. Oncotype DX[38]), several promising technology modalities are being developed, from techniques combining spectrometry and computational power to real-time imaging of drug effects in the body.[39] 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.[40][41][42] 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.[43] 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.[44][45]
On the treatment side, PM can involve the use of customized medical products such drug cocktails produced by pharmacy compounding[46] or customized devices.[47] It can also prevent harmful drug interactions, increase overall efficiency when prescribing medications, and reduce costs associated with healthcare.[48]
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.[49]
Artificial intelligence in 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.[54] A 2020 paper showed that training machine learning models in a population-specific fashion (i.e. training models specifically for Black cancer patients) can yield significantly superior performance than population-agnostic models.[55]
Precision Medicine Initiative
In his 2015
Benefits of precision medicine
Precision medicine helps health care 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:[citation needed]
- 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
Applications
Advances in personalised medicine will create a more unified treatment approach specific to the individual and their genome. Personalised medicine may provide better diagnoses with earlier intervention, and more efficient drug development and more targeted therapies.[62]
Diagnosis and intervention
Having the ability to look at a patient on an individual basis will allow for a more accurate diagnosis and specific treatment plan.
An aspect of this is pharmacogenomics, which uses an individual's genome to provide a more informed and tailored drug prescription.[64] Often, drugs are prescribed with the idea that it will work relatively the same for everyone, but in the application of drugs, there are a number of factors that must be considered. The detailed account of genetic information from the individual will help prevent adverse events, allow for appropriate dosages, and create maximum efficacy with drug prescriptions.[18] For instance, warfarin is the FDA approved oral anticoagulant commonly prescribed to patients with blood clots. Due to warfarin's significant interindividual variability in pharmacokinetics and pharmacodynamics, its rate of adverse events is among the highest of all commonly prescribed drugs.[6] However, with the discovery of polymorphic variants in CYP2C9 and VKORC1 genotypes, two genes that encode the individual anticoagulant response,[65][66] physicians can use patients' gene profile to prescribe optimum doses of warfarin to prevent side effects such as major bleeding and to allow sooner and better therapeutic efficacy.[6] The pharmacogenomic process for discovery of genetic variants that predict adverse events to a specific drug has been termed toxgnostics.[67]
An aspect of a theranostic platform applied to personalized medicine can be the use of
In addition to specific treatment, personalised medicine can greatly aid the advancements of preventive care. For instance, many women are already being genotyped for certain mutations in the BRCA1 and BRCA2 gene if they are predisposed because of a family history of breast cancer or ovarian cancer.
A tool that is being used now to test efficacy and safety of a drug specific to a targeted patient group/sub-group is companion diagnostics. This technology is an assay that is developed during or after a drug is made available on the market and is helpful in enhancing the therapeutic treatment available based on the individual.[73] These companion diagnostics have incorporated the pharmacogenomic information related to the drug into their prescription label in an effort to assist in making the most optimal treatment decision possible for the patient.[73]
Drug development and usage
Having an individual's genomic information can be significant in the process of developing drugs as they await approval from the FDA for public use. Having a detailed account of an individual's genetic make-up can be a major asset in deciding if a patient can be chosen for inclusion or exclusion in the final stages of a clinical trial.[62] Being able to identify patients who will benefit most from a clinical trial will increase the safety of patients from adverse outcomes caused by the product in testing, and will allow smaller and faster trials that lead to lower overall costs.[74] In addition, drugs that are deemed ineffective for the larger population can gain approval by the FDA by using personal genomes to qualify the effectiveness and need for that specific drug or therapy even though it may only be needed by a small percentage of the population.,[62][75]
Physicians commonly use a trial and error strategy until they find the treatment therapy that is most effective for their patient.[62] With personalized medicine, these treatments can be more specifically tailored by predicting how an individual's body will respond and if the treatment will work based on their genome.[18] This has been summarized as "therapy with the right drug at the right dose in the right patient."[76] Such an approach would also be more cost-effective and accurate.[62] For instance, Tamoxifen used to be a drug commonly prescribed to women with ER+ breast cancer, but 65% of women initially taking it developed resistance. After research by people such as David Flockhart, it was discovered that women with certain mutation in their CYP2D6 gene, a gene that encodes the metabolizing enzyme, were not able to efficiently break down Tamoxifen, making it an ineffective treatment for them.[77] Women are now genotyped for these specific mutations to select the most effective treatment.
Screening for these mutations is carried out via
]Pharmacy
One active area of research is efficiently delivering personalized drugs generated from pharmacy compounding to the disease sites of the body.
Theranostics
Theranostics is a personalized approach in
Respiratory proteomics
Respiratory diseases affect humanity globally, with chronic lung diseases (e.g., asthma, chronic obstructive pulmonary disease, idiopathic pulmonary fibrosis, among others) and lung cancer causing extensive morbidity and mortality. These conditions are
highly heterogeneous and require an early diagnosis. However, initial symptoms are nonspecific, and the clinical diagnosis is made late frequently. Over the last few years, personalized medicine has emerged as a medical care approach that uses novel technology
Cancer genomics
Over recent decades cancer research has discovered a great deal about the genetic variety of types of cancer that appear the same in traditional pathology. There has also been increasing awareness of tumour heterogeneity, or genetic diversity within a single tumour. Among other prospects, these discoveries raise the possibility of finding that drugs that have not given good results applied to a general population of cases may yet be successful for a proportion of cases with particular genetic profiles.
"
- chronic myeloid leukemia (CML), in which the BCR-ABL fusion gene (the product of a reciprocal translocation between chromosome 9 and chromosome 22) is present in >95% of cases and produces hyperactivated abl-driven protein signaling. These medications specifically inhibit the Ableson tyrosine kinase (ABL) protein and are thus a prime example of "rational drug design" based on knowledge of disease pathophysiology.[90]
- The FoundationOne CDx report produced by Foundation Medicine, which looks at genes in individual patients' tumor biopsies and recommends specific drugs
- High mutation burden is indicative of response to immunotherapy, and also specific patterns of mutations have been associated with previous exposure to cytotoxic cancer drugs.[91]
Population screening
Through the use of genomics (microarray), proteomics (tissue array), and imaging (fMRI, micro-CT) technologies, molecular-scale information about patients can be easily obtained. These so-called molecular biomarkers have proven powerful in disease prognosis, such as with cancer.[92][93][94] The main three areas of cancer prediction fall under cancer recurrence, cancer susceptibility and cancer survivability.[95] Combining molecular scale information with macro-scale clinical data, such as patients' tumor type and other risk factors, significantly improves prognosis.[95] Consequently, given the use of molecular biomarkers, especially genomics, cancer prognosis or prediction has become very effective, especially when screening a large population.[96] Essentially, population genomics screening can be used to identify people at risk for disease, which can assist in preventative efforts.[96]
Genetic data can be used to construct polygenic scores, which estimate traits such as disease risk by summing the estimated effects of individual variants discovered through a GWAS. These have been used for a wide variety of conditions, such as cancer, diabetes, and coronary artery disease.[97][98] Many genetic variants are associated with ancestry, and it remains a challenge to both generate accurate estimates and to decouple biologically relevant variants from those that are coincidentally associated. Estimates generated from one population do not usually transfer well to others, requiring sophisticated methods and more diverse and global data.[99][100] Most studies have used data from those with European ancestry, leading to calls for more equitable genomics practices to reduce health disparities.[101] Additionally, while polygenic scores have some predictive accuracy, their interpretations are limited to estimating an individual's percentile and translational research is needed for clinical use.[102]
Challenges
As personalised medicine is practiced more widely, a number of challenges arise. The current approaches to intellectual property rights, reimbursement policies, patient privacy, data biases and confidentiality as well as regulatory oversight will have to be redefined and restructured to accommodate the changes personalised medicine will bring to healthcare.[103]
For instance, a survey performed in the UK concluded that 63% of UK adults are not comfortable with their personal data being used for the sake of utilizing AI in the medical field.
Regulatory oversight
The FDA has already started to take initiatives to integrate personalised medicine into their regulatory policies. An FDA report in October 2013 entitled, "Paving the Way for Personalized Medicine: FDA's role in a New Era of Medical Product Development," in which they outlined steps they would have to take to integrate genetic and biomarker information for clinical use and drug development.[74] They determined that they would have to develop specific regulatory science standards, research methods, reference material and other tools in order to incorporate personalised medicine into their current regulatory practices. For example, they are working on a "genomic reference library" for regulatory agencies to compare and test the validity of different sequencing platforms in an effort to uphold reliability.[74] A major challenge for those regulating personalized medicine is a way to demonstrate its effectiveness relative to the current standard of care. The new technology must be assessed for both clinical and cost effectiveness, and as it stands, regulatory agencies have no standardized method.[106]
Intellectual property rights
As with any innovation in medicine, investment and interest in personalised medicine is influenced by intellectual property rights.[103] There has been a lot of controversy regarding patent protection for diagnostic tools, genes, and biomarkers.[107] In June 2013, the U.S. Supreme Court ruled that natural occurring genes cannot be patented, while "synthetic DNA" that is edited or artificially- created can still be patented. The Patent Office is currently reviewing a number of issues related to patent laws for personalised medicine, such as whether "confirmatory" secondary genetic tests post initial diagnosis, can have full immunity from patent laws. Those who oppose patents argue that patents on DNA sequences are an impediment to ongoing research while proponents point to research exemption and stress that patents are necessary to entice and protect the financial investments required for commercial research and the development and advancement of services offered.[107]
Reimbursement policies
Reimbursement policies will have to be redefined to fit the changes that personalised medicine will bring to the healthcare system. Some of the factors that should be considered are the level of efficacy of various genetic tests in the general population, cost-effectiveness relative to benefits, how to deal with payment systems for extremely rare conditions, and how to redefine the insurance concept of "shared risk" to incorporate the effect of the newer concept of "individual risk factors".[103] The study, Barriers to the Use of Personalized Medicine in Breast Cancer, took two different diagnostic tests which are BRACAnalysis and Oncotype DX. These tests have over ten-day turnaround times which results in the tests failing and delays in treatments. Patients are not being reimbursed for these delays which results in tests not being ordered. Ultimately, this leads to patients having to pay out-of-pocket for treatments because insurance companies do not want to accept the risks involved.[108]
Patient privacy and confidentiality
Perhaps the most critical issue with the commercialization of personalised medicine is the protection of patients. One of the largest issues is the fear and potential consequences for patients who are predisposed after genetic testing or found to be non-responsive towards certain treatments. This includes the psychological effects on patients due to genetic testing results. The right of family members who do not directly consent is another issue, considering that genetic predispositions and risks are inheritable. The implications for certain ethnic groups and presence of a common allele would also have to be considered.[103]
Moreover, we could refer to the privacy issue at all layers of personalized medicine from discovery to treatment. One of the leading issues is the consent of the patients to have their information used in genetic testing algorithms primarily AI algorithms. The consent of the institution who is providing the data to be used is of prominent concern as well.[104] In 2008, the Genetic Information Nondiscrimination Act (GINA) was passed in an effort to minimize the fear of patients participating in genetic research by ensuring that their genetic information will not be misused by employers or insurers.[103] On February 19, 2015, FDA issued a press release titled: "FDA permits marketing of first direct-to-consumer genetic carrier test for Bloom syndrome.[8]
Data biases
Data biases also play an integral role in personalized medicine. It is important to ensure that the sample of genes being tested come from different populations. This is to ensure that the samples do not exhibit the same human biases we use in decision making.[109]
Consequently, if the designed algorithms for personalized medicine are biased, then the outcome of the algorithm will also be biased because of the lack of genetic testing in certain populations.[110] For instance, the results from the Framingham Heart Study have led to biased outcomes of predicting the risk of cardiovascular disease. This is because the sample was tested only on white people and when applied to the non-white population, the results were biased with overestimation and underestimation risks of cardiovascular disease.[111]
Implementation
Several issues must be addressed before personalized medicine can be implemented. Very little of the human genome has been analyzed, and even if healthcare providers had access to a patient's full genetic information, very little of it could be effectively leveraged into treatment.[112] Challenges also arise when processing such large amounts of genetic data. Even with error rates as low as 1 per 100 kilobases, processing a human genome could have roughly 30,000 errors.[113] This many errors, especially when trying to identify specific markers, can make discoveries and verifiability difficult. There are methods to overcome this, but they are computationally taxing and expensive. There are also issues from an effectiveness standpoint, as after the genome has been processed, function in the variations among genomes must be analyzed using genome-wide studies. While the impact of the SNPs discovered in these kinds of studies can be predicted, more work must be done to control for the vast amounts of variation that can occur because of the size of the genome being studied.[113] In order to effectively move forward in this area, steps must be taken to ensure the data being analyzed is good, and a wider view must be taken in terms of analyzing multiple SNPs for a phenotype. The most pressing issue that the implementation of personalized medicine is to apply the results of genetic mapping to improve the healthcare system. This is not only due to the infrastructure and technology required for a centralized database of genome data, but also the physicians that would have access to these tools would likely be unable to fully take advantage of them.[113] In order to truly implement a personalized medicine healthcare system, there must be an end-to-end change.
The Copenhagen Institute for Futures Studies and Roche set up FutureProofing Healthcare[114] which produces a Personalised Health Index, rating different countries performance against 27 different indicators of personalised health across four categories called 'Vital Signs'. They have run conferences in many countries to examine their findings.[115][116]
See also
- Cancer genome sequencing
- Chemogenomics
- Companion diagnostic
- Drug discovery
- Elective genetic and genomic testing
- Evidence-based medicine
- Exposome
- Foundation Medicine
- Molecular medicine
- Next-generation sequencing
- Personal genomics
- Pharmacodiagnostic testing
- Pharmacogenetics
- Pharmacogenomics
- Phenotypic screening
- Precision environmental health
- Race and health
- Swiss Personalized Health Network (initiative)
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