|Part of a series on|
|History and topics|
Personalized medicine or 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. The terms personalized medicine, precision medicine, stratified medicine and P4 medicine are used interchangeably to describe this concept though some authors and organisations use these expressions separately to indicate particular nuances.
While the tailoring of treatment to patients dates back at least to the time of Hippocrates, the term has risen in usage in recent years given the growth of new diagnostic and informatics approaches that provide understanding of the molecular basis of disease, particularly genomics. This provides a clear evidence base on which to stratify (group) related patients.
- 1 Development of concept
- 2 Background
- 3 Applications
- 4 Challenges
- 5 See also
- 6 References
Development of concept
In personalised 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. The use of genetic information has played a major role in certain aspects of personalized medicine (e.g. pharmacogenomics), and the term was first coined in the context of genetics, though it has since broadened to encompass all sorts of personalization measures.
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 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” (GWAS). A GWAS 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 GWAS 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 GWAS, conducted in 2005, studied patients with age-related macular degeneration (ARMD). It found two different mutations, each containing only a variation in only one nucleotide (called single nucleotide polymorphisms, or SNPs), which were associated with ARMD. GWAS studies like this have been very successful in identifying common genetic variations associated with diseases. As of early 2014, over 1,300 GWAS 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 preventative 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.
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 therapies.
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. Genotyping is the process of obtaining an individual’s DNA sequence by using biological assays. By having a detailed account of an individual’s DNA sequence, their genome can then be compared to a reference genome, like that of the Human Genome Project, to assess the existing genetic variations that can account for possible diseases. A number of private companies, such as 23andMe, Navigenics, and Illumina, have created Direct-to-Consumer genome sequencing accessible to the public. Having this information from individuals can then be applied to effectively treat them. An individual’s genetic make-up also plays a large role in how well they respond to a certain treatment, and therefore, knowing their genetic content can change the type of treatment they receive.
An aspect of this is pharmacogenomics, which uses an individual’s genome to provide a more informed and tailored drug prescription. 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. The pharmacogenomic process for discovery of genetic variants that predict adverse events to a specific drug has been termed toxgnostics.
An aspect of a theranostic platform applied to personalized medicine can be the use of diagnostic tests to guide therapy. The tests may involve medical imaging such as MRI contrast agents (T1 and T2 agents), fluorescent markers (organic dyes and inorganic quantum dots), and nuclear imaging agents (PET radiotracers or SPECT agents). or in vitro lab test including DNA sequencing and often involve deep learning algorithms that weigh the result of testing for several biomarkers.
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. As more causes of diseases are mapped out according to mutations that exist within a genome, the easier they can be identified in an individual. Measures can then be taken to prevent a disease from developing. Even if mutations were found within a genome, having the details of their DNA can reduce the impact or delay the onset of certain diseases. Having the genetic content of an individual will allow better guided decisions in determining the source of the disease and thus treating it or preventing its progression. This will be extremely useful for diseases like Alzheimer’s or cancers that are thought to be linked to certain mutations in our DNA.
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. 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.
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. 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. 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.,
Today in medicine, it is common that physicians often use a trial and error strategy until they find the treatment therapy that is most effective for their patient. With personalised medicine, these treatments can be more specifically tailored to an individual and give insight into how their body will respond to the drug and if that drug will work based on their genome. The personal genotype can allow physicians to have more detailed information that will guide them in their decision in treatment prescriptions, which will be more cost-effective and accurate. As quoted from the article Pharmacogenomics: The Promise of Personalised Medicine, “therapy with the right drug at the right dose in the right patient” is a description of how personalized medicine will affect the future of treatment. 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 some 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 their cancer. Since then, women are now genotyped for those specific mutations, so that immediately these women can have the most effective treatment therapy.
Screening for these mutations is carried out via high-throughput screening or phenotypic screening. Several drug discovery and pharmaceutical companies are currently utilizing these technologies to not only advance the study of personalised medicine, but also to amplify genetic research; these companies include Alacris Theranostics, Persomics, Flatiron Health, Novartis, OncoDNA and Foundation Medicine, among others. Alternative multi-target approaches to the traditional approach of "forward" transfection library screening can entail reverse transfection or chemogenomics.
Pharmacy compounding is yet another application of personalised medicine. Though not necessarily utilizing genetic information, the customized production of a drug whose various properties (e.g. dose level, ingredient selection, route of administration, etc.) are selected and crafted for an individual patient is accepted as an area of personalised medicine (in contrast to mass-produced unit doses or fixed-dose combinations).
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  aiming to personalize treatments according to the particular patient's medical needs.
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.
Cancer Genomics, or “Oncogenomics,” is the application of genomics and personalized medicine to cancer research and treatment. High-throughput sequencing methods are used to characterize genes associated with cancer to better understand disease pathology and improve drug development. Oncogenomics is one of the most promising branches of genomics, particularly because of its implications in drug therapy. Examples of this include:
- Trastuzumab (trade names Herclon, Herceptin) is a monoclonal antibody drug that interferes with the HER2/neu receptor. Its main use is to treat certain breast cancers. This drug is only used if a patient's cancer is tested for over-expression of the HER2/neu receptor. Two tissue-typing tests are used to screen patients for possible benefit from Herceptin treatment. The tissue tests are immunohistochemistry(IHC) and Fluorescence In Situ Hybridization(FISH) Only Her2+ patients will be treated with Herceptin therapy (trastuzumab)
- Tyrosine kinase inhibitors such as imatinib (marketed as Gleevec) have been developed to treat 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.
As personalised medicine is practiced more widely, a number of challenges arise. The current approaches to intellectual property rights, reimbursement policies, patient privacy and confidentiality as well as regulatory oversight will have to be redefined and restructured to accommodate the changes personalised medicine will bring to healthcare. Furthermore, the analysis of acquired diagnostic data is a recent challenge of personalized medicine and its adoption. For example, genetic data obtained from next-generation sequencing requires computer-intensive data processing prior to its analysis. In the future, adequate tools will be required to accelerate the adoption of personalised medicine to further fields of medicine, which requires the interdisciplinary cooperation of experts from specific fields of research, such as medicine, clinical oncology, biology, and artificial intelligence.
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. 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.
Intellectual property rights
As with any innovation in medicine, investment and interest in personalised medicine is influenced by intellectual property rights. There has been a lot of controversy regarding patent protection for diagnostic tools, genes, and biomarkers. 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.
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".
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. 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. On February 19, 2015 FDA issued a press release titled: "FDA permits marketing of first direct-to-consumer genetic carrier test for Bloom syndrome.
- Stratified, personalised or P4 medicine: a new direction for placing the patient at the centre of healthcare and health education (Technical report). Academy of Medical Sciences. May 2015. Retrieved 6 Jan 2016.
- "Many names for one concept or many concepts in one name?". PHG Foundation. Retrieved 6 Jan 2015.
- Egnew, Thomas (1 Mar 2009). "Suffering, Meaning, and Healing: Challenges of Contemporary Medicine". Annals of Family Medicine. 7 (2): 170–175. doi:10.1370/afm.943. PMC . Retrieved 6 January 2016.
- "The Case for Personalized Medicine" (PDF). Personalized Medicine Coalition. 2014. Retrieved 6 Jan 2016.
- Smith, Richard (15 Oct 2012). "Stratified, personalised, or precision medicine". British Medical Journal. Retrieved 6 January 2016.
- "Personalized Medicine 101". Personalized Medicine Coalition. Retrieved 26 April 2014.
- Dudley, J; Karczewski, K. (2014). Exploring Personal Genomics. Oxford: Oxford University Press.
- "Personalized Medicine 101: The Science". Personalized Medicine Coalition. Retrieved 26 April 2014.
- Lu, YF; Goldstein, DB; Angrist, M; Cavalleri, G (24 July 2014). "Personalized medicine and human genetic diversity". Cold Spring Harbor perspectives in medicine. 4 (9): a008581. doi:10.1101/cshperspect.a008581. PMC . PMID 25059740.
- Battle A, Mostafavi S, Zhu X, Potash JB, Weissman MM, McCormick C, et al. (2014). "Characterizing the genetic basis of transcriptome diversity through RNA-sequencing of 922 individuals". Genome Research. 24 (1): 14–24. doi:10.1101/gr.155192.113. PMC . PMID 24092820.
- Cenik, Can; Cenik, Elif Sarinay; Byeon, Gun W; Candille, Sophie P.; Spacek, Damek; Araya, Carlos L; Tang, Hua; Ricci, Emiliano; Snyder, Michael P. (Nov 2015). "Integrative analysis of RNA, translation, and protein levels reveals distinct regulatory variation across humans". Genome Research. 25: 1610–21. doi:10.1101/gr.193342.115. PMC . PMID 26297486.
- Linfeng Wu; Sophie I. Candille; Yoonha Choi; Dan Xie; Lihua Jiang; Jennifer Li-Pook-Than; Hua Tang; Michael Snyder (2013). "Variation and genetic control of protein abundance in humans". Nature. 499 (7456): 79–82. Bibcode:2013Natur.499...79W. doi:10.1038/nature12223. PMC . PMID 23676674.
- Snyderman, R. Personalized Health Care from Theory to Practice, Biotechnology J. 2012, 7
- Haines, J.L. (Apr 15, 2005). "Complement Factor H Variant Increases the Risk of Age-Related Macular Degeneration". Science. 308 (5720): 419–21. Bibcode:2005Sci...308..419H. doi:10.1126/science.1110359. PMID 15761120.
- "A Catalog of Published Genome-Wide Association Studies". Retrieved 28 June 2015.
- "Personalized Medicine 101: The Promise". Personalized Medicine Coalition. Retrieved April 26, 2014.
- "Research Portfolio Online Reporting Tools: Human Genome Project". National Institutes of Health (NIH). Retrieved April 28, 2014.
- "Genetics Home Reference: What is pharmacogenomics?". National Institutes of Health (NIH). Retrieved April 28, 2014.
- Church D, Kerr R, Domingo E, Rosmarin D, Palles C, Maskell K, Tomlinson I, Kerr D (June 2014). "'Toxgnostics': an unmet need in cancer medicine". Nature Reviews. Cancer. 14 (6): 440–5. doi:10.1038/nrc3729. PMID 24827503.
- Xie, Jin; Lee, Seulki; Chen, Xiaoyuan (2010-08-30). "Nanoparticle-based theranostic agents". Advanced Drug Delivery Reviews. Development of Theranostic Agents that Co-Deliver Therapeutic and Imaging Agents. 62 (11): 1064–1079. doi:10.1016/j.addr.2010.07.009. PMC . PMID 20691229.
- Kelkar, Sneha S.; Reineke, Theresa M. (2011-10-19). "Theranostics: Combining Imaging and Therapy". Bioconjugate Chemistry. 22 (10): 1879–1903. doi:10.1021/bc200151q. ISSN 1043-1802.
- Perkovic, MN; Erjavec, GN; Strac, DS; Uzun, S; Kozumplik, O; Pivac, N (30 March 2017). "Theranostic Biomarkers for Schizophrenia". International Journal of Molecular Sciences. 18 (4). doi:10.3390/ijms18040733. PMC . PMID 28358316.
- Kamps, R; Brandão, RD; Bosch, BJ; Paulussen, AD; Xanthoulea, S; Blok, MJ; Romano, A (31 January 2017). "Next-Generation Sequencing in Oncology: Genetic Diagnosis, Risk Prediction and Cancer Classification". International Journal of Molecular Sciences. 18 (2). doi:10.3390/ijms18020308. PMC . PMID 28146134.
- Yahata, N; Kasai, K; Kawato, M (April 2017). "Computational neuroscience approach to biomarkers and treatments for mental disorders". Psychiatry and clinical neurosciences. 71 (4): 215–237. doi:10.1111/pcn.12502. PMID 28032396.
- "Fact Sheet: BRCA1 and BRCA2: Cancer and Genetic Testing". National Cancer Institute (NCI). Retrieved April 28, 2014.
- "BIOMARKER TOOLKIT: Companion Diagnostics" (PDF). Amgen. Archived from the original (PDF) on August 1, 2014. Retrieved May 2, 2014.
- "Paving the Way for Personalized Medicine: FDA's Role in a New Era of Medical Product Development" (PDF). Federal Drug Administration (FDA). Retrieved April 28, 2014.
- Hamburg MA, Collins FS (July 22, 2010). "The Path to Personalized Medicine". New England Journal of Medicine (NEJM). 363: 301–304. doi:10.1056/nejmp1006304. PMID 20551152. Retrieved April 28, 2014.
- Mancinelli L, Cronin M, Sadée W (2000). "Pharmacogenomics. The Promise of Personalized Medicine". AAPS PharmSci. 2 (1): E4. PMC . PMID 11741220.
- Ellsworth RE, Decewicz DJ, Shriver CD, Ellsworth DL (2010). "Breast Cancer in the Personal Genomics Era". Current Genomics: Bentham Science. 11: 146–61. doi:10.2174/138920210791110951. PMC . PMID 21037853.
- Priyadharshini VS, Teran LM. Personalized Medicine in Respiratory Disease: Role of Proteomics. Adv Protein Chem Struct Biol. 2016;102:115-46. doi: 10.1016/bs.apcsb.2015.11.008. Epub 2015 Dec 31. Review. PubMed PMID 26827604.
- Carney, Walter (2006). "HER2/neu Status is an Important Biomarker in Guiding Personalized HER2/neu Therapy" (PDF). Connection. 9: 25–27.
- Telli, M. L.; Hunt, S. A.; Carlson, R. W.; Guardino, A. E. (2007). "Trastuzumab-Related Cardiotoxicity: Calling Into Question the Concept of Reversibility". Journal of Clinical Oncology. 25 (23): 3525–3533. doi:10.1200/JCO.2007.11.0106. ISSN 0732-183X. PMID 17687157.
- Saglio G; Morotti A; Mattioli G; et al. (December 2004). "Rational approaches to the design of therapeutics targeting molecular markers: the case of chronic myelogenous leukemia". Ann. N. Y. Acad. Sci. 1028 (1): 423–31. Bibcode:2004NYASA1028..423S. doi:10.1196/annals.1322.050. PMID 15650267.
- "Personalized Medicine 101: The Challenges". Personalized Medicine Coalition. Retrieved April 26, 2014.
- Huser, V; Sincan, M; Cimino, J. J. (2014). "Developing genomic knowledge bases and databases to support clinical management: Current perspectives". Pharmacogenomics and Personalized Medicine. 7: 275–83. doi:10.2147/PGPM.S49904. PMC . PMID 25276091.
- "Analyze Genomes: Motivation". Schapranow, Matthieu-P. Retrieved July 20, 2014.
- "Paving the Way for Personalized Medicine: FDA's Role in a New Era of Medical Product Development" (PDF). U.S Food and Drug Administration. Retrieved April 26, 2014.
- "Intellectual Property Issues Impacting the Future of Personalized Medicine". American Intellectual Property Law Association. Retrieved April 26, 2014.[dead link]