{{Short description|Medical model that tailors medical practices to the individual patient}} {{Use American English|date=April 2024}} {{Use dmy dates|date=April 2024}} {{cs1 config|name-list-style=vanc|display-authors=3}} {{Genetics sidebar}}
'''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,<ref>{{Cite journal |last1=Češková |first1=Eva |last2=Šilhán |first2=Petr |date=2021 |title=From Personalized Medicine to Precision Psychiatry? |journal=Neuropsychiatric Disease and Treatment |volume=17 |pages=3663–3668 |doi=10.2147/NDT.S337814 |doi-access=free |issn=1176-6328 |pmc=8684413 |pmid=34934319 |quote=However, the concept of personalisation is broader and dates back at least to the time of Hippocrates (460–377 BC), who is considered the father of modern medicine based on clinical observation and rational conclusions}}</ref> 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.<ref name="AcMedSci">{{cite tech report |url=http://www.acmedsci.ac.uk/download.php?f=file&i=32644 |title=Stratified, personalized or P4 medicine: a new direction for placing the patient at the center of healthcare and health education |date=May 2015 |access-date=6 January 2016 |archive-url=https://web.archive.org/web/20161027013007/http://www.acmedsci.ac.uk/download.php?f=file&i=32644 |url-status=dead |archive-date=27 October 2016 |publisher=Academy of Medical Sciences}}</ref><ref name="PMC">{{cite web|title=The Case for Personalized Medicine |url= http://www.personalizedmedicinecoalition.org/Userfiles/PMC-Corporate/file/pmc_case_for_personalized_medicine.pdf |publisher=Personalized Medicine Coalition |date=2014 |access-date=6 January 2016}}</ref><ref>{{cite journal | vauthors = Smith R |date=15 October 2012 |title=Stratified, personalised, or precision medicine |url=http://blogs.bmj.com/bmj/2012/10/15/richard-smith-stratified-personalised-or-precision-medicine/ |journal=British Medical Journal |access-date=6 January 2016}}</ref>
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".<ref name="Lesko_2007">{{cite journal | vauthors = Lesko LJ | title = Personalized medicine: elusive dream or imminent reality? | journal = Clinical Pharmacology and Therapeutics | volume = 81 | issue = 6 | pages = 807–16 | date = June 2007 | pmid = 17505496 | doi = 10.1038/sj.clpt.6100204 | s2cid = 17860973 }}</ref><ref name="EC_2020">{{cite web |title=Grand Challenges - Engineer Better Medicines |url=http://www.engineeringchallenges.org/challenges/medicines.aspx |access-date=2020-08-03 |website=www.engineeringchallenges.org |archive-date=25 October 2023 |archive-url=https://web.archive.org/web/20231025135827/https://engineeringchallenges.org/challenges/medicines.aspx |url-status=dead }}</ref>
== 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.<ref>{{cite journal |last1=Annadurai |first1=Kalaivani |last2=Danasekaran |first2=Raja |last3=Mani |first3=Geeta |title=Personalized medicine: A paradigm shift towards promising health care |doi=10.4103/0975-7406.171732 |journal= Journal of Pharmacy and Bioallied Sciences |date=2016 |volume=8 |issue=1 |pages=77–78 |doi-access=free |pmid=26957875 |pmc=4766785 }}</ref> 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,<ref name="PMC_101">{{cite web |title=Personalized Medicine 101 |url=http://www.personalizedmedicinecoalition.org/Resources/Personalized_Medicine_101 |archive-url=https://web.archive.org/web/20190510000328/http://www.personalizedmedicinecoalition.org/Resources/Personalized_Medicine_101 |archive-date=10 May 2019 |access-date=26 April 2014 |publisher=Personalized Medicine Coalition}}</ref> including the use of proteomics,<ref name="Priyadharshini2016">{{cite book |doi=10.1016/bs.apcsb.2015.11.008 |chapter=Personalized Medicine in Respiratory Disease |title=Chapter Five - Personalized Medicine in Respiratory Disease: Role of Proteomics |series=Advances in Protein Chemistry and Structural Biology |year=2016 | vauthors = Priyadharshini VS, Teran LM |volume=102 |pages=115–146 |pmid=26827604 |isbn=978-0-12-804795-8 }}</ref> imaging analysis, nanoparticle-based theranostics,<ref name="Xie_2010">{{cite journal | vauthors = Xie J, Lee S, Chen X | title = Nanoparticle-based theranostic agents | journal = Advanced Drug Delivery Reviews | volume = 62 | issue = 11 | pages = 1064–79 | date = August 2010 | pmid = 20691229 | pmc = 2988080 | doi = 10.1016/j.addr.2010.07.009 | veditors = Donev R |series=Personalized Medicine|publisher=Academic Press | bibcode = 2010ADDR...62.1064X }}</ref> 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.<ref name="mlp">{{cite web |title=What is the difference between precision medicine and personalized medicine? What about pharmacogenomics? |url=https://medlineplus.gov/genetics/understanding/precisionmedicine/precisionvspersonalized/ |publisher=MedlinePlus, National Library of Medicine, US National Institutes of Health |access-date=24 April 2024 |date=17 May 2022}}</ref><ref name="prec">{{cite web |title=Precision medicine |url=https://www.genome.gov/genetics-glossary/Precision-Medicine |publisher=National Human Genome Research Institute, US National Institutes of Health |access-date=24 April 2024 |date=24 April 2024}}</ref> 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.<ref name="Lu2014">{{cite journal|vauthors=Lu YF, Goldstein DB, Angrist M, Cavalleri G|date=July 2014|title=Personalized medicine and human genetic diversity|journal=Cold Spring Harbor Perspectives in Medicine |type=Essay |volume=4| issue=9| article-number=a008581 |doi=10.1101/cshperspect.a008581 |pmc=4143101|pmid=25059740|doi-access=free}}</ref> Tools employed in precision medicine can include molecular diagnostics, imaging, and analytics.<ref name=prec/><ref>{{cite journal|vauthors=Jones DT, Banito A, Grünewald TG, Haber M, Jäger N, Kool M, Milde T, Molenaar JJ, Nabbi A, Pugh TJ, Schleiermacher G, Smith MA, Westermann F, Pfister SM|date=August 2019|title=Molecular characteristics and therapeutic vulnerabilities across paediatric solid tumours|url=https://www.nature.com/articles/s41568-019-0169-x|journal=Nature Reviews Cancer|type=Review|volume=19|issue=8|pages=420–438|doi=10.1038/s41568-019-0169-x|pmid=31300807|s2cid=196350118|url-access=subscription}}</ref>
Precision medicine and ''personalized'' medicine (also ''individualized'' medicine) are analogous, applying a person's genetic profile to guide clinical decisions about the prevention, diagnosis, and treatment of a disease.<ref name="pers">{{cite web |title=Personalized medicine |url=https://www.genome.gov/genetics-glossary/Personalized-Medicine |publisher=National Human Genome Research Institute, US National Institutes of Health |access-date=24 April 2024 |date=24 April 2024}}</ref> Personalized medicine is established on discoveries from the Human Genome Project.<ref name=pers/>
In explaining the distinction from the similar term of ''personalized medicine'', the United States President's Council of Advisors on Science and Technology writes:<ref name="pcast">{{Cite web |last=President's Council of Advisors on Science and Technology |author-link=President's Council of Advisors on Science and Technology |date=September 2008 |title=Priorities for Personalized Medicine |url=https://www.whitehouse.gov/files/documents/ostp/PCAST/pcast_report_v2.pdf |archive-url=https://web.archive.org/web/20160610024658/https://www.whitehouse.gov/files/documents/ostp/PCAST/pcast_report_v2.pdf |archive-date=2016-06-10 |access-date=2024-04-24 |website=The White House |page=19}}</ref>
{{blockquote|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.<ref name="pcast" />}}
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."<ref>{{cite news|date=23 February 2015|title=Changing medicine with 3-D bioprinting, where organs can be synthesized by technology|work=Los Angeles Times|url=https://www.latimes.com/brandpublishing/localplus/ucsandiego/la-ss-ucsd2015-3dbioprinting-dto-story.html|vauthors=Clerk J}}</ref> 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.<ref>{{Citation |last=National Research Council |title=Glossary |date=2011 |work=Toward Precision Medicine: Building a Knowledge Network for Biomedical Research and a New Taxonomy of Disease |url=https://www.ncbi.nlm.nih.gov/books/NBK92148/ |access-date=2024-04-25 |publisher=National Academies Press (US) |language=en |author-link=National Research Council (United States)}}</ref>
== Background ==
=== Basics === Every person has a unique variation of the human genome.<ref name = "Dudley_2014" >{{cite book| vauthors = Dudley J, Karczewski K |title=Exploring Personal Genomics.|date=2014|publisher=Oxford University Press|location=Oxford}}</ref> 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.<ref name="PMC_S_2014">{{cite web |title=Personalized Medicine 101: The Science |url=http://personalizedmedicinecoalition.org/Resources/Personalized_Medicine_101_The_Science |publisher=Personalized Medicine Coalition |access-date=26 April 2014 |archive-date=19 February 2019 |archive-url=https://web.archive.org/web/20190219015555/http://personalizedmedicinecoalition.org/Resources/Personalized_Medicine_101_The_Science }}</ref><ref name="Lu2014" />
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,<ref>{{cite journal | vauthors = Battle A, Mostafavi S, Zhu X, Potash JB, Weissman MM, McCormick C, Haudenschild CD, Beckman KB, Shi J, Mei R, Urban AE, Montgomery SB, Levinson DF, Koller D | title = Characterizing the genetic basis of transcriptome diversity through RNA-sequencing of 922 individuals | journal = Genome Research | volume = 24 | issue = 1 | pages = 14–24 | date = January 2014 | pmid = 24092820 | pmc = 3875855 | doi = 10.1101/gr.155192.113 }}</ref> translation,<ref name="Cenik_2015">{{cite journal | vauthors = Cenik C, Cenik ES, Byeon GW, Grubert F, Candille SI, Spacek D, Alsallakh B, Tilgner H, Araya CL, Tang H, Ricci E, Snyder MP | title = Integrative analysis of RNA, translation, and protein levels reveals distinct regulatory variation across humans | journal = Genome Research | volume = 25 | issue = 11 | pages = 1610–21 | date = November 2015 | pmid = 26297486 | pmc = 4617958 | doi = 10.1101/gr.193342.115 }}</ref> and protein levels.<ref>{{cite journal | vauthors = Wu L, Candille SI, Choi Y, Xie D, Jiang L, Li-Pook-Than J, Tang H, Snyder M | title = Variation and genetic control of protein abundance in humans | journal = Nature | volume = 499 | issue = 7456 | pages = 79–82 | date = July 2013 | pmid = 23676674 | pmc = 3789121 | doi = 10.1038/nature12223 | bibcode = 2013Natur.499...79W }}</ref>
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.<ref>{{cite journal | vauthors = Snyderman R | title = Personalized health care: from theory to practice | journal = Biotechnology Journal | volume = 7 | issue = 8 | pages = 973–9 | date = August 2012 | pmid = 22180345 | doi = 10.1002/biot.201100297 | s2cid = 31447416 }}</ref> 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.<ref>{{cite journal | vauthors = Altmann A, Beerenwinkel N, Sing T, Savenkov I, Doumer M, Kaiser R, Rhee SY, Fessel WJ, Shafer RW, Lengauer T | title = Improved prediction of response to antiretroviral combination therapy using the genetic barrier to drug resistance | journal = Antiviral Therapy | volume = 12 | issue = 2 | pages = 169–78 | date = 2007 | doi = 10.1177/135965350701200202 | pmid = 17503659 | s2cid = 26286925 | id = {{INIST|18647009}} | doi-access = free }}</ref>
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.<ref>{{cite journal |vauthors=Blau CA, Liakopoulou E |date=24 October 2012 |title=Can we deconstruct cancer, one patient at a time? |journal=Trends in Genetics |department=Opinion |publisher=CellPress |volume=29 |issue=1 |pages=6–10 |doi=10.1016/j.tig.2012.09.004 |pmc=4221262 |pmid=23102584}}</ref><ref>{{cite journal | vauthors = Tavassoly I, Hu Y, Zhao S, Mariottini C, Boran A, Chen Y, Li L, Tolentino RE, Jayaraman G, Goldfarb J, Gallo J, Iyengar R | title = Genomic signatures defining responsiveness to allopurinol and combination therapy for lung cancer identified by systems therapeutics analyses | journal = Molecular Oncology | volume = 13 | issue = 8 | pages = 1725–1743 | date = August 2019 | pmid = 31116490 | pmc = 6670022 | doi = 10.1002/1878-0261.12521 }}</ref> The branch of precision medicine that addresses cancer is referred to as "precision oncology".<ref>{{cite journal | vauthors = Garraway LA, Verweij J, Ballman KV | title = Precision oncology: an overview | journal = Journal of Clinical Oncology | volume = 31 | issue = 15 | pages = 1803–5 | date = May 2013 | pmid = 23589545 | doi = 10.1200/jco.2013.49.4799 | s2cid = 42163820 | doi-access = free | hdl = 1765/54298 | hdl-access = free }}</ref><ref>{{cite journal | vauthors = Shrager J, Tenenbaum JM | title = Rapid learning for precision oncology | journal = Nature Reviews. Clinical Oncology | volume = 11 | issue = 2 | pages = 109–18 | date = February 2014 | pmid = 24445514 | doi = 10.1038/nrclinonc.2013.244 | s2cid = 11225698 | author2-link = Marty Tenenbaum }}</ref> The field of precision medicine that is related to psychiatric disorders and mental health is called "precision psychiatry."<ref>{{cite journal | vauthors = Fernandes BS, Williams LM, Steiner J, Leboyer M, Carvalho AF, Berk M | title = The new field of 'precision psychiatry' | journal = BMC Medicine | volume = 15 | issue = 1 | article-number = 80 | date = April 2017 | pmid = 28403846 | pmc = 5390384 | doi = 10.1186/s12916-017-0849-x | doi-access = free }}</ref><ref>{{cite journal | vauthors = Fernandes BS, Berk M | title = Staging in bipolar disorder: one step closer to precision psychiatry | journal = Revista Brasileira de Psiquiatria | volume = 39 | issue = 2 | pages = 88–89 | date = June 2017 | pmid = 28591270 | doi = 10.1590/1516-4446-2017-3902 | pmc = 7111440 | doi-access = free }}</ref>
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"<ref name="pmid23307060">{{cite journal | vauthors = Ogino S, Lochhead P, Chan AT, Nishihara R, Cho E, Wolpin BM, Meyerhardt JA, Meissner A, Schernhammer ES, Fuchs CS, Giovannucci E | title = Molecular pathological epidemiology of epigenetics: emerging integrative science to analyze environment, host, and disease | journal = Modern Pathology | volume = 26 | issue = 4 | pages = 465–84 | date = April 2013 | pmid = 23307060 | pmc = 3637979 | doi = 10.1038/modpathol.2012.214 }}</ref> 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.<ref>{{cite journal |last1=Ogino |first1=Shuji |last2=Fuchs |first2=Charles S |last3=Giovannucci |first3=Edward |title=How many molecular subtypes? Implications of the unique tumor principle in personalized medicine |journal=Expert Review of Molecular Diagnostics |date=July 2012 |volume=12 |issue=6 |pages=621–628 |doi=10.1586/erm.12.46 |pmid=22845482 |pmc=3492839 }}</ref> 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.<ref name="pmid23792451">{{cite journal | vauthors = Ogino S, Lochhead P, Giovannucci E, Meyerhardt JA, Fuchs CS, Chan AT | title = Discovery of colorectal cancer PIK3CA mutation as potential predictive biomarker: power and promise of molecular pathological epidemiology | journal = Oncogene | volume = 33 | issue = 23 | pages = 2949–55 | date = June 2014 | pmid = 23792451 | pmc = 3818472 | doi = 10.1038/onc.2013.244 }}</ref>
=== 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" (GWA 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 (ARMD).<ref name="Haines_2005">{{cite journal | vauthors = Haines JL, Hauser MA, Schmidt S, Scott WK, Olson LM, Gallins P, Spencer KL, Kwan SY, Noureddine M, Gilbert JR, Schnetz-Boutaud N, Agarwal A, Postel EA, Pericak-Vance MA | title = Complement factor H variant increases the risk of age-related macular degeneration | journal = Science | volume = 308 | issue = 5720 | pages = 419–21 | date = April 2005 | pmid = 15761120 | doi = 10.1126/science.1110359 | bibcode = 2005Sci...308..419H | s2cid = 32716116 | doi-access = free }}</ref> 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. 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.<ref>{{cite web|title=A Catalog of Published Genome-Wide Association Studies|url=http://www.genome.gov/26525384|access-date=28 June 2015}}</ref>
=== Disease risk assessment ===
Multiple genes collectively influence the likelihood of developing many common and complex diseases.<ref name="PMC_S_2014" /> 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.{{citation needed|date=March 2023}}
==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.<ref name=":0">{{cite journal | vauthors = Ashley EA, Butte AJ, Wheeler MT, Chen R, Klein TE, Dewey FE, Dudley JT, Ormond KE, Pavlovic A, Morgan AA, Pushkarev D, Neff NF, Hudgins L, Gong L, Hodges LM, Berlin DS, Thorn CF, Sangkuhl K, Hebert JM, Woon M, Sagreiya H, Whaley R, Knowles JW, Chou MF, Thakuria JV, Rosenbaum AM, Zaranek AW, Church GM, Greely HT, Quake SR, Altman RB | title = Clinical assessment incorporating a personal genome | journal = Lancet | volume = 375 | issue = 9725 | pages = 1525–35 | date = May 2010 | pmid = 20435227 | pmc = 2937184 | doi = 10.1016/s0140-6736(10)60452-7 }}</ref> While precision medicine currently individualizes treatment mainly on the basis of genomic tests (e.g. Oncotype DX<ref>{{cite web|url=http://www.breastcancer.org/symptoms/testing/types/oncotype_dx|title=Oncotype DX: Genomic Test to Inform Breast Cancer Treatment|date=2019-06-13}}</ref>), several promising technology modalities are being developed, from techniques combining spectrometry and computational power to real-time imaging of drug effects in the body.<ref>Precision Medicine: Harnessing the Extraordinary Growth in Medical Data for Personalized Diagnosis and Treatment http://claudiacopeland.com/uploads/3/6/1/4/3614974/hjno_novdec_2016_precision_medicine.pdf {{Webarchive|url=https://web.archive.org/web/20221113131158/http://claudiacopeland.com/uploads/3/6/1/4/3614974/hjno_novdec_2016_precision_medicine.pdf |date=2022-11-13 }}</ref> 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.<ref name="pm">{{cite journal | vauthors = Huser V, Sincan M, Cimino JJ | title = Developing genomic knowledge bases and databases to support clinical management: current perspectives | journal = Pharmacogenomics and Personalized Medicine | volume = 7 | pages = 275–83 | year = 2014 | pmid = 25276091 | pmc = 4175027 | doi = 10.2147/PGPM.S49904 | doi-access = free }}</ref><ref name="pm2">{{cite journal | vauthors = Ashley EA | title = The precision medicine initiative: a new national effort | journal = JAMA | volume = 313 | issue = 21 | pages = 2119–20 | date = June 2015 | pmid = 25928209 | doi = 10.1001/jama.2015.3595 }}</ref><ref name="pmid27528417">{{cite journal | vauthors = Ashley EA | title = Towards precision medicine | journal = Nature Reviews. Genetics | volume = 17 | issue = 9 | pages = 507–22 | date = August 2016 | pmid = 27528417 | doi = 10.1038/nrg.2016.86 | s2cid = 2609065 }}</ref> 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.<ref name="pmid30304647">{{cite journal |last1=Splinter |first1=Kimberly |last2=Adams |first2=David R. |last3=Bacino |first3=Carlos A. |last4=Bellen |first4=Hugo J. |last5=Bernstein |first5=Jonathan A. |last6=Cheatle-Jarvela |first6=Alys M. |last7=Eng |first7=Christine M. |last8=Esteves |first8=Cecilia |last9=Gahl |first9=William A. |last10=Hamid |first10=Rizwan |last11=Jacob |first11=Howard J. |last12=Kikani |first12=Bijal |last13=Koeller |first13=David M. |last14=Kohane |first14=Isaac S. |last15=Lee |first15=Brendan H. |last16=Loscalzo |first16=Joseph |last17=Luo |first17=Xi |last18=McCray |first18=Alexa T. |last19=Metz |first19=Thomas O. |last20=Mulvihill |first20=John J. |last21=Nelson |first21=Stanley F. |last22=Palmer |first22=Christina G.S. |last23=Phillips |first23=John A. |last24=Pick |first24=Leslie |last25=Postlethwait |first25=John H. |last26=Reuter |first26=Chloe |last27=Shashi |first27=Vandana |last28=Sweetser |first28=David A. |last29=Tifft |first29=Cynthia J. |last30=Walley |first30=Nicole M. |last31=Wangler |first31=Michael F. |last32=Westerfield |first32=Monte |last33=Wheeler |first33=Matthew T. |last34=Wise |first34=Anastasia L. |last35=Worthey |first35=Elizabeth A. |last36=Yamamoto |first36=Shinya |last37=Ashley |first37=Euan A. |author38=Undiagnosed Diseases Network |title=Effect of Genetic Diagnosis on Patients with Previously Undiagnosed Disease |journal=New England Journal of Medicine |date=29 November 2018 |volume=379 |issue=22 |pages=2131–2139 |doi=10.1056/NEJMoa1714458 |pmid=30304647 |pmc=6481166 }}</ref> 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.<ref>{{cite journal |last1=Serpico |first1=Davide |last2=Maziarz |first2=Mariusz |date=2023-12-14 |title=Averaged versus individualized: pragmatic N-of-1 design as a method to investigate individual treatment response |journal=European Journal for Philosophy of Science |language=en |volume=13 |issue=4 |page=59 |doi=10.1007/s13194-023-00559-0 |issn=1879-4920|doi-access=free |hdl=2434/1045468 |hdl-access=free }}</ref><ref>{{cite book |last=Nikles, J., & Mitchell, G. |editor-first1=Jane |editor-first2=Geoffrey |editor-last1=Nikles |editor-last2=Mitchell |date=2015 |title=The Essential Guide to N-of-1 Trials in Health |url=https://link.springer.com/content/pdf/10.1007/978-94-017-7200-6.pdf |language=en |doi=10.1007/978-94-017-7200-6|isbn=978-94-017-7199-3 |s2cid=33597874 }}</ref>
On the treatment side, PM can involve the use of customized medical products such drug cocktails produced by pharmacy compounding<ref>{{cite web|url=http://www.pmlive.com/pharma_news/divining_your_future_in_healthcare_511068|title=Divining your future in healthcare|work=pmlive.com|date=2013-10-18}}</ref> or customized devices.<ref>{{cite web|url=http://www.livescience.com/39339-how-fda-regulates-3d-printed-devices.html|title=3D-Printed Medical Devices Spark FDA Evaluation|work=LiveScience.com|date=30 August 2013}}</ref> It can also prevent harmful drug interactions, increase overall efficiency when prescribing medications, and reduce costs associated with healthcare.<ref>{{cite web|url=http://genetichealth.jax.org/personalized-medicine/what-is/benefits.html|title=Personalized Medicine Benefits - The Jackson Laboratory|work=jax.org|access-date=23 September 2022|archive-date=10 May 2015|archive-url=https://web.archive.org/web/20150510133930/http://genetichealth.jax.org/personalized-medicine/what-is/benefits.html}}</ref>
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.<ref>{{cite journal |last1=Belcher |first1=Andrea |last2=Mangelsdorf |first2=Marie |last3=McDonald |first3=Fiona |last4=Curtis |first4=Caitlin |last5=Waddell |first5=Nicola |last6=Hussey |first6=Karen |title=What does Australia's investment in genomics mean for public health? |journal=Australian and New Zealand Journal of Public Health |date=June 2019 |volume=43 |issue=3 |pages=204–206 |doi=10.1111/1753-6405.12887 |pmid=30830712 |doi-access=free }}</ref>
===Artificial intelligence in precision medicine=== Artificial intelligence is providing a paradigm shift toward precision medicine.<ref>{{cite journal|last1=Mesko|first1=Bertalan |title=Expert Review of Precision Medicine and Drug Development|journal=Journal Expert Review of Precision Medicine and Drug Development|date=2017|volume=2|issue=5|pages=239–241|doi=10.1080/23808993.2017.1380516|doi-access=free}}</ref> 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.<ref>{{cite journal |last1=Ray |first1=Amit |title=Artificial Intelligence and Blockchain for Precision Medicine|journal=Compassionate AI |date=20 May 2018 |volume=2 |issue=5 |pages=60–62 |url=http://amitray.com/artificial-intelligence-and-blockchain-for-precision-medicine/|publisher=Inner Light Publishers|access-date=21 May 2018}}</ref> 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.<ref>{{cite journal | vauthors = Krittanawong C, Zhang H, Wang Z, Aydar M, Kitai T | title = Artificial Intelligence in Precision Cardiovascular Medicine | journal = Journal of the American College of Cardiology | volume = 69 | issue = 21 | pages = 2657–2664 | date = May 2017 | pmid = 28545640 | doi = 10.1016/j.jacc.2017.03.571 | doi-access = free }}</ref> A 2021 paper reported that machine learning was able to predict the outcomes of Phase III clinical trials (for treatment of prostate cancer) with 76% accuracy.<ref>{{cite journal |last1=Beacher |first1=Felix D. |last2=Mujica-Parodi |first2=Lilianne R. |last3=Gupta |first3=Shreyash |last4=Ancora |first4=Leonardo A. |title=Machine Learning Predicts Outcomes of Phase III Clinical Trials for Prostate Cancer |journal=Algorithms |date=5 May 2021 |volume=14 |issue=5 |page=147 |doi=10.3390/a14050147 |doi-access=free }}</ref> This suggests that clinical trial data could provide a practical source for machine learning-based tools for precision medicine.{{citation needed|date=November 2024}}
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.<ref>{{cite web|last1=Ferryman|first1=Kadija|last2=Pitcan|first2=Mikaela|date=2018-02-26|title=Fairness in Precision Medicine|url=https://datasociety.net/library/fairness-in-precision-medicine/|access-date=2021-10-27|website=Data & Society|language=en-US}}</ref> 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.<ref>{{cite journal |last1=Bhargava |first1=Hersh K. |last2=Leo |first2=Patrick |last3=Elliott |first3=Robin |last4=Janowczyk |first4=Andrew |last5=Whitney |first5=Jon |last6=Gupta |first6=Sanjay |last7=Fu |first7=Pingfu |last8=Yamoah |first8=Kosj |last9=Khani |first9=Francesca |last10=Robinson |first10=Brian D. |last11=Rebbeck |first11=Timothy R. |date=2020-04-15 |title=Computationally Derived Image Signature of Stromal Morphology Is Prognostic of Prostate Cancer Recurrence Following Prostatectomy in African American Patients |journal=Clinical Cancer Research|volume=26 |issue=8 |pages=1915–1923 |doi=10.1158/1078-0432.CCR-19-2659 |issn=1557-3265 |pmc=7165025 |pmid=32139401}}</ref>
===Precision Medicine Initiative=== In his 2015 State of the Union address, then-U.S. President Barack Obama stated his intention to give $215 million<ref>{{cite web|url=https://weillcornell.org/news/the-impact-of-precision-medicine-on-cancer|title=The Impact of Precision Medicine on Cancer|work=weillcornell.org}}</ref> of funding to the "Precision Medicine Initiative" of the United States National Institutes of Health.<ref>{{Cite web |date=2015-01-30 |title=Obama proposes 'precision medicine 'to end one-size-fits-all |url=https://apnews.com/general-news-b6d25f42f3394bcd801dd8a0cf137898 |access-date=2024-04-20 |website=AP News |language=en}}</ref> A short-term goal of this initiative was to expand cancer genomics to develop better prevention and treatment methods.<ref>{{cite web|url=http://www.nih.gov/precisionmedicine/goals.htm|archive-url=https://web.archive.org/web/20150202155439/http://www.nih.gov/precisionmedicine/goals.htm|archive-date=2 February 2015|title=Near-term Goals|work=nih.gov}}</ref> 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.<ref>{{cite web|url=http://www.nih.gov/precisionmedicine/future.htm|archive-url=https://web.archive.org/web/20150203102913/http://www.nih.gov/precisionmedicine/future.htm|archive-date=3 February 2015|title=Longer-term Goals|work=nih.gov}}</ref> 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".<ref>{{cite web |url=https://obamawhitehouse.archives.gov/precision-medicine |via=National Archives |publisher=whitehouse.gov |title=The White House Precision Medicine Initiative}}</ref> In 2016 this initiative was renamed to "All of Us" and by January 2018, 10,000 people had enrolled in its pilot phase.<ref>{{cite news|url=https://www.washingtonpost.com/news/powerpost/paloma/the-health-202/2018/01/16/the-health-202-nih-wants-1-million-americans-to-contribute-to-new-pool-of-gene-data/5a5ba45a30fb0469e8840135/|title=The Health 202: NIH wants 1 million Americans to contribute to new pool of gene data|last=Cunningham|first=Paige Winfield |date=2018-01-16|newspaper=The Washington Post|access-date=2018-01-20|issn=0190-8286}}</ref>
=== 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|date=March 2023}} * 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.<ref name="PMC_2014">{{cite web |title=Personalized Medicine 101: The Promise |url=http://personalizedmedicinecoalition.org/Resources/Personalized_Medicine_101_The_Promise |publisher=Personalized Medicine Coalition |access-date=April 26, 2014 |archive-date=December 19, 2017 |archive-url=https://web.archive.org/web/20171219194239/http://personalizedmedicinecoalition.org/Resources/Personalized_Medicine_101_The_Promise }}</ref>
=== 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.<ref name=six>{{cite web|title=Research Portfolio Online Reporting Tools: Human Genome Project|url=http://report.nih.gov/NIHfactsheets/ViewFactSheet.aspx?csid=45|publisher=National Institutes of Health (NIH)|access-date=April 28, 2014|archive-date=September 25, 2019|archive-url=https://web.archive.org/web/20190925065615/http://report.nih.gov/NIHfactsheets/ViewFactSheet.aspx?csid=45}}</ref> 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.<ref name="Dudley_2014" /> 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.{{citation needed|date=November 2024}}
An aspect of this is pharmacogenomics, which uses an individual's genome to provide a more informed and tailored drug prescription.<ref name=seven>{{cite web|title=Genetics Home Reference: What is pharmacogenomics?|url=http://ghr.nlm.nih.gov/handbook/genomicresearch/pharmacogenomics|archive-url=https://web.archive.org/web/20060929005200/http://ghr.nlm.nih.gov/handbook/genomicresearch/pharmacogenomics|archive-date=29 September 2006|publisher=National Institutes of Health (NIH)|access-date=April 28, 2014}}</ref> 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.<ref name="Dudley_2014" /> 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.<ref name="Lesko_2007"/> However, with the discovery of polymorphic variants in CYP2C9 and VKORC1 genotypes, two genes that encode the individual anticoagulant response,<ref>{{cite journal | vauthors = Breckenridge A, Orme M, Wesseling H, Lewis RJ, Gibbons R | title = Pharmacokinetics and pharmacodynamics of the enantiomers of warfarin in man | journal = Clinical Pharmacology and Therapeutics | volume = 15 | issue = 4 | pages = 424–30 | date = April 1974 | pmid = 4821443 | doi = 10.1002/cpt1974154424 | s2cid = 11777099 }}</ref><ref>{{cite journal | vauthors = Rieder MJ, Reiner AP, Gage BF, Nickerson DA, Eby CS, McLeod HL, Blough DK, Thummel KE, Veenstra DL, Rettie AE | title = Effect of VKORC1 haplotypes on transcriptional regulation and warfarin dose | journal = The New England Journal of Medicine | volume = 352 | issue = 22 | pages = 2285–93 | date = June 2005 | pmid = 15930419 | doi = 10.1056/NEJMoa044503 | doi-access = free }}</ref> 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.<ref name="Lesko_2007" /> The pharmacogenomic process for discovery of genetic variants that predict adverse events to a specific drug has been termed toxgnostics.<ref name="Church_2014">{{cite journal | vauthors = Church D, Kerr R, Domingo E, Rosmarin D, Palles C, Maskell K, Tomlinson I, Kerr D | title = 'Toxgnostics': an unmet need in cancer medicine | journal = Nature Reviews. Cancer | volume = 14 | issue = 6 | pages = 440–5 | date = June 2014 | pmid = 24827503 | doi = 10.1038/nrc3729 | s2cid = 21601547 }}</ref>
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).<ref name="Xie_2010" /><ref name="Kelkar">{{cite journal | vauthors = Kelkar SS, Reineke TM | title = Theranostics: combining imaging and therapy | journal = Bioconjugate Chemistry | volume = 22 | issue = 10 | pages = 1879–903 | date = October 2011 | pmid = 21830812 | doi = 10.1021/bc200151q | author-link2 = Theresa M. Reineke }}</ref> or in vitro lab test<ref>{{cite journal | vauthors = Perkovic MN, Erjavec GN, Strac DS, Uzun S, Kozumplik O, Pivac N | title = Theranostic Biomarkers for Schizophrenia | journal = International Journal of Molecular Sciences | volume = 18 | issue = 4 | page = 733 | date = March 2017 | pmid = 28358316 | pmc = 5412319 | doi = 10.3390/ijms18040733 | doi-access = free }}</ref> including DNA sequencing<ref>{{cite journal | vauthors = Kamps R, Brandão RD, Bosch BJ, Paulussen AD, Xanthoulea S, Blok MJ, Romano A | title = Next-Generation Sequencing in Oncology: Genetic Diagnosis, Risk Prediction and Cancer Classification | journal = International Journal of Molecular Sciences | volume = 18 | issue = 2 | page = 308 | date = January 2017 | pmid = 28146134 | pmc = 5343844 | doi = 10.3390/ijms18020308 | doi-access = free }}</ref> and often involve deep learning algorithms that weigh the result of testing for several biomarkers.<ref>{{cite journal | vauthors = Yahata N, Kasai K, Kawato M | title = Computational neuroscience approach to biomarkers and treatments for mental disorders | journal = Psychiatry and Clinical Neurosciences | volume = 71 | issue = 4 | pages = 215–237 | date = April 2017 | pmid = 28032396 | doi = 10.1111/pcn.12502 | doi-access = free }}</ref>
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.<ref name=eight>{{cite web|title=Fact Sheet: BRCA1 and BRCA2: Cancer and Genetic Testing|url=http://www.cancer.gov/cancertopics/factsheet/Risk/BRCA|publisher=National Cancer Institute (NCI)|access-date=April 28, 2014}}</ref> 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.<ref name="PMC_2014" /> 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.<ref name="PMC_2014" />
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.<ref name=other>{{cite web |title=BIOMARKER TOOLKIT: Companion Diagnostics |url=http://www.amgen.com/pdfs/misc/vpk_biomarker_diagnostics.pdf |publisher=Amgen |access-date=May 2, 2014 |archive-url=https://web.archive.org/web/20140801145112/http://www.amgen.com/pdfs/misc/vpk_biomarker_diagnostics.pdf |archive-date=August 1, 2014 }}</ref> 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.<ref name=other /> thumb|397x397px|An overall process of personalized cancer therapy. Genome sequencing will allow for a more accurate and personalized drug prescription and a targeted therapy for different patients.
===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.<ref name="PMC_2014" /> 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.<ref name="FDA_2013">{{cite web |title=Paving the Way for Personalized Medicine: FDA's Role in a New Era of Medical Product Development | date = October 2013 |url= https://www.fda.gov/downloads/scienceresearch/specialtopics/personalizedmedicine/ucm372421.pdf | archive-url = https://web.archive.org/web/20140123201440/http://www.fda.gov/downloads/scienceresearch/specialtopics/personalizedmedicine/ucm372421.pdf | archive-date = 23 January 2014 |publisher= U.S. Food and Drug Administration (FDA) |access-date=April 28, 2014 }}</ref> 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.,<ref name="PMC_2014" /><ref name=ten>{{cite journal | vauthors = Hamburg MA, Collins FS | title = The path to personalized medicine | journal = The New England Journal of Medicine | volume = 363 | issue = 4 | pages = 301–4 | date = July 2010 | pmid = 20551152 | doi = 10.1056/nejmp1006304 | s2cid = 205106671 | doi-access = free }}</ref>
Physicians commonly use a trial and error strategy until they find the treatment therapy that is most effective for their patient.<ref name="PMC_2014" /> 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.<ref name="Dudley_2014" /> This has been summarized as "therapy with the right drug at the right dose in the right patient."<ref name = eleven>{{cite journal | vauthors = Mancinelli L, Cronin M, Sadée W | title = Pharmacogenomics: the promise of personalized medicine | journal = AAPS PharmSci | volume = 2 | issue = 1 | pages = 29–41 | year = 2000 | pmid = 11741220 | pmc = 2750999 | doi = 10.1208/ps020104 }}</ref> Such an approach would also be more cost-effective and accurate.<ref name="PMC_2014" /> 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.<ref name=twelve>{{cite journal | vauthors = Ellsworth RE, Decewicz DJ, Shriver CD, Ellsworth DL | title = Breast cancer in the personal genomics era | journal = Current Genomics | volume = 11 | issue = 3 | pages = 146–61 | date = May 2010 | pmid = 21037853 | pmc = 2878980 | doi = 10.2174/138920210791110951 }}</ref> Women are now genotyped for these specific mutations to select the most effective treatment.{{citation needed|date=November 2024}}
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. Alternative multi-target approaches to the traditional approach of "forward" transfection library screening can entail reverse transfection or chemogenomics.{{citation needed|date=March 2023}}
Pharmacy compounding is another application of personalised medicine. Though not necessarily using 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). Computational and mathematical approaches for predicting drug interactions are also being developed. For example, phenotypic response surfaces model the relationships between drugs, their interactions, and an individual's biomarkers.{{citation needed|date=March 2023}}
One active area of research is efficiently delivering personalized drugs generated from pharmacy compounding to the disease sites of the body.<ref name="EC_2020"/> For instance, researchers are trying to engineer nanocarriers that can precisely target the specific site by using real-time imaging and analyzing the pharmacodynamics of the drug delivery.<ref name="Soni_2018">{{cite journal | vauthors = Soni A, Gowthamarajan K, Radhakrishnan A | title = Personalized Medicine and Customized Drug Delivery Systems: The New Trend of Drug Delivery and Disease Management | journal = International Journal of Pharmaceutical Compounding | volume = 22 | issue = 2 | pages = 108–121 | date = March 2018 | pmid = 29877858 }}</ref> Several candidate nanocarriers are being investigated, such as iron oxide nanoparticles, quantum dots, carbon nanotubes, gold nanoparticles, and silica nanoparticles.<ref name="Xie_2010"/> Alteration of surface chemistry allows these nanoparticles to be loaded with drugs, as well as to avoid the body's immune response, making nanoparticle-based theranostics possible.<ref name="EC_2020" /><ref name="Xie_2010" /> Nanocarriers' targeting strategies are varied according to the disease. For example, if the disease is cancer, a common approach is to identify the biomarker expressed on the surface of cancer cells and to load its associated targeting vector onto nanocarrier to achieve recognition and binding; the size scale of the nanocarriers will also be engineered to reach the enhanced permeability and retention effect (EPR) in tumor targeting.<ref name="Xie_2010" /> If the disease is localized in the specific organ, such as the kidney, the surface of the nanocarriers can be coated with a certain ligand that binds to the receptors inside that organ to achieve organ-targeting drug delivery and avoid non-specific uptake.<ref>{{cite journal| vauthors = Wang J, Poon C, Chin D, Milkowski S, Lu V, Hallows KR, Chung EJ |s2cid=104241223|date=2018-10-01|title=Design and in vivo characterization of kidney-targeting multimodal micelles for renal drug delivery|journal=Nano Research |volume=11 |issue=10 |pages=5584–5595 |doi=10.1007/s12274-018-2100-2 }}</ref> Despite the great potential of this nanoparticle-based drug delivery system, the significant progress in the field is yet to be made, and the nanocarriers are still being investigated and modified to meet clinical standards.<ref name="Xie_2010" /><ref name="Soni_2018" />
===Theranostics=== {{Main|Theranostics}} Theranostics is a personalized approach in nuclear medicine, using similar molecules for both imaging (diagnosis) and therapy.<ref name="lang">{{cite journal |vauthors=Langbein T, Weber WA, Eiber M |title=Future of Theranostics: An Outlook on Precision Oncology in Nuclear Medicine |journal=Journal of Nuclear Medicine|volume=60 |issue=Suppl 2 |pages=13S–19S |date=September 2019 |pmid=31481583 |doi=10.2967/jnumed.118.220566 |s2cid=201830244 |url=https://jnm.snmjournals.org/content/60/Supplement_2/13S.long|doi-access=free }}</ref><ref>{{cite journal | vauthors = Herrmann K, Schwaiger M, Lewis JS, Solomon SB, McNeil BJ, Baumann M, Gambhir SS, Hricak H, Weissleder R | title = Radiotheranostics: a roadmap for future development | journal = The Lancet. Oncology | volume = 21 | issue = 3 | pages = e146–e156 | date = March 2020 | pmid = 32135118 | pmc = 7367151 | doi = 10.1016/S1470-2045(19)30821-6 }}</ref><ref>{{cite journal | vauthors = Jadvar H, Chen X, Cai W, Mahmood U | title = Radiotheranostics in Cancer Diagnosis and Management | journal = Radiology | volume = 286 | issue = 2 | pages = 388–400 | date = February 2018 | pmid = 29356634 | pmc = 5790308 | doi = 10.1148/radiol.2017170346 }}</ref> The term is a portmanteau of "therapeutics" and "diagnostics". Its most common applications are attaching radionuclides (either gamma or positron emitters) to molecules for SPECT or PET imaging, or electron emitters for radiotherapy.{{Citation needed|date=April 2024}} One of the earliest examples is the use of radioactive iodine for treatment of people with thyroid cancer.<ref name=lang/> Other examples include radio-labelled anti-CD20 antibodies (e.g. Bexxar) for treating lymphoma, Radium-223 for treating bone metastases, Lutetium-177 DOTATATE for treating neuroendocrine tumors and Lutetium-177 PSMA for treating prostate cancer.<ref name=lang/> A commonly used reagent is fluorodeoxyglucose, using the isotope fluorine-18.<ref>{{cite book | vauthors = Pandey S, Giovenzana GB, Szikra D, Baranyai Z |title=Metal Ions in Bio-Imaging Techniques |publisher=Springer |year=2021 |pages=315–346 |chapter=Chapter 11. Positron Emission Tomography (PET) Driven Theranostics |doi=10.1515/9783110685701-017 |s2cid=233659232 }}</ref>
===Respiratory proteomics=== [[File:Protein pattern analyzer (1).jpg|thumb|312x312px|The preparation of a proteomics sample on a sample carrier to be analyzed by mass spectrometry]] 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 <ref name="Priyadharshini2016" /> aiming to personalize treatments according to the particular patient's medical needs. In specific, proteomics is used to analyze a series of protein expressions, instead of a single biomarker.<ref name="Priyadharshini_2020">{{cite book |doi=10.1016/B978-0-12-819178-1.00024-1 |chapter=Role of respiratory proteomics in precision medicine |title=Precision Medicine for Investigators, Practitioners and Providers |year=2020 | vauthors = Priyadharshini VS, Teran LM |pages=255–261 |isbn=978-0-12-819178-1 | veditors = Faintuch J, Faintuch S |publisher=Academic Press |s2cid=213865547 }}</ref> Proteins control the body's biological activities including health and disease, so proteomics is helpful in early diagnosis. In the case of respiratory disease, proteomics analyzes several biological samples including serum, blood cells, bronchoalveolar lavage fluids (BAL), nasal lavage fluids (NLF), sputum, among others.<ref name="Priyadharshini_2020" /> The identification and quantification of complete protein expression from these biological samples are conducted by mass spectrometry and advanced analytical techniques.<ref>{{cite journal | vauthors = Fujii K, Nakamura H, Nishimura T | title = Recent mass spectrometry-based proteomics for biomarker discovery in lung cancer, COPD, and asthma | journal = Expert Review of Proteomics | volume = 14 | issue = 4 | pages = 373–386 | date = April 2017 | pmid = 28271730 | doi = 10.1080/14789450.2017.1304215 | s2cid = 3640863 }}</ref> Respiratory proteomics has made significant progress in the development of personalized medicine for supporting health care in recent years. For example, in a study conducted by Lazzari et al. in 2012, the proteomics-based approach has made substantial improvement in identifying multiple biomarkers of lung cancer that can be used in tailoring personalized treatments for individual patients.<ref>{{cite journal | vauthors = Lazzari C, Spreafico A, Bachi A, Roder H, Floriani I, Garavaglia D, Cattaneo A, Grigorieva J, Viganò MG, Sorlini C, Ghio D, Tsypin M, Bulotta A, Bergamaschi L, Gregorc V | title = Changes in plasma mass-spectral profile in course of treatment of non-small cell lung cancer patients with epidermal growth factor receptor tyrosine kinase inhibitors | journal = Journal of Thoracic Oncology | volume = 7 | issue = 1 | pages = 40–8 | date = January 2012 | pmid = 21964534 | doi = 10.1097/JTO.0b013e3182307f17 | s2cid = 205738875 | doi-access = free }}</ref> More and more studies have demonstrated the usefulness of proteomics to provide targeted therapies for respiratory disease.<ref name="Priyadharshini_2020" />
===Cancer genomics=== {{More citations needed section|date=April 2024}} 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 tumor heterogeneity, or genetic diversity within a single tumor. 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.
Personalized oncogenomics is the application of personalized medicine to cancer genomics. 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)<ref>{{cite journal|vauthors=Carney W|title=HER2/neu Status is an Important Biomarker in Guiding Personalized HER2/neu Therapy|journal=Connection|date=2006|volume=9|pages=25–27|url=http://www.dako.com/us/index/knowledgecenter/kc_publications/kc_publications_connection/kc_publications_connection9.htm/28824_2006_conn9__her2-neu_biomarker_therapy_carney.pdf|access-date=2014-01-03|archive-date=2016-03-04|archive-url=https://web.archive.org/web/20160304060703/http://www.dako.com/us/index/knowledgecenter/kc_publications/kc_publications_connection/kc_publications_connection9.htm/28824_2006_conn9__her2-neu_biomarker_therapy_carney.pdf}}</ref> Only Her2+ patients will be treated with Herceptin therapy (trastuzumab)<ref name="TelliHunt2007">{{cite journal | vauthors = Telli ML, Hunt SA, Carlson RW, Guardino AE | title = Trastuzumab-related cardiotoxicity: calling into question the concept of reversibility | journal = Journal of Clinical Oncology | volume = 25 | issue = 23 | pages = 3525–33 | date = August 2007 | pmid = 17687157 | doi = 10.1200/JCO.2007.11.0106 }}</ref> * 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.<ref>{{cite journal | vauthors = Saglio G, Morotti A, Mattioli G, Messa E, Giugliano E, Volpe G, Rege-Cambrin G, Cilloni D | title = Rational approaches to the design of therapeutics targeting molecular markers: the case of chronic myelogenous leukemia | journal = Annals of the New York Academy of Sciences | volume = 1028 | issue = 1 | pages = 423–31 | date = December 2004 | pmid = 15650267 | doi = 10.1196/annals.1322.050 | bibcode = 2004NYASA1028..423S | s2cid = 19332062 }}</ref> * 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.<ref name="Pleasance_2020">{{cite journal | vauthors = Pleasance E, Titmuss E, Williamson L, Kwan H, Culibrk L, Zhao EY, Dixon K, Fan K, Bowlby R, Jones MR, Shen Y, Grewal JK, Ashkani J, Wee K, Grisdale CJ, Thibodeau ML, Bozoky Z, Pearson H, Majounie E, Vira T, Shenwai R, Mungall KL, Chuah E, Davies A, Warren M, Reisle C, Bonakdar M, Taylor GA, Csizmok V, Chan SK, Zong Z, Bilobram S, Muhammadzadeh A, D'Souza D, Corbett RD, MacMillan D, Carreira M, Choo C, Bleile D, Sadeghi S, Zhang W, Wong T, Cheng D, Brown SD, Holt RA, Moore RA, Mungall AJ, Zhao Y, Nelson J, Fok A, Ma Y, Lee MK, Lavoie JM, Mendis S, Karasinska JM, Deol B, Fisic A, Schaeffer DF, Yip S, Schrader K, Regier DA, Weymann D, Chia S, Gelmon K, Tinker A, Sun S, Lim H, Renouf DJ, Laskin J, Jones SJ, Marra MA | title = Pan-cancer analysis of advanced patient tumours reveals interactions between therapy and genomic landscapes | journal = Nature Cancer | volume = 1 | issue = 4 | pages = 452–468 | date = 2020 | doi = 10.1038/s43018-020-0050-6 | pmid = 35121966 | doi-access = free }}</ref>
=== Population screening === {{see also|List of biobanks}} 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.<ref>{{cite journal | vauthors = Duffy MJ | title = Biochemical markers in breast cancer: which ones are clinically useful? | journal = Clinical Biochemistry | volume = 34 | issue = 5 | pages = 347–52 | date = July 2001 | pmid = 11522269 | doi = 10.1016/S0009-9120(00)00201-0 }}</ref><ref>{{cite journal | vauthors = Piccart M, Lohrisch C, Di Leo A, Larsimont D | title = The predictive value of HER2 in breast cancer | journal = Oncology | volume = 61 Suppl 2 | issue = 2 | pages = 73–82 | date = 2001 | pmid = 11694791 | doi = 10.1159/000055405 | s2cid = 23123767 }}</ref><ref>{{cite journal | vauthors = Baldus SE, Engelmann K, Hanisch FG | title = MUC1 and the MUCs: a family of human mucins with impact in cancer biology | journal = Critical Reviews in Clinical Laboratory Sciences | volume = 41 | issue = 2 | pages = 189–231 | date = 1 January 2004 | pmid = 15270554 | doi = 10.1080/10408360490452040 | s2cid = 46460945 }}</ref> The main three areas of cancer prediction fall under cancer recurrence, cancer susceptibility and cancer survivability.<ref name="Cruz_2007">{{cite journal | vauthors = Cruz JA, Wishart DS|author2-link=David S. Wishart | title = Applications of machine learning in cancer prediction and prognosis | journal = Cancer Informatics | volume = 2 | pages = 59–77 | date = February 2007 | pmid = 19458758 | pmc = 2675494 | doi = 10.1177/117693510600200030 | citeseerx = 10.1.1.795.3458 }}</ref> Combining molecular scale information with macro-scale clinical data, such as patients' tumor type and other risk factors, significantly improves prognosis.<ref name="Cruz_2007" /> Consequently, given the use of molecular biomarkers, especially genomics, cancer prognosis or prediction has become very effective, especially when screening a large population.<ref name="Early Lessons from the Implementati">{{cite journal | vauthors = Williams MS | title = Early Lessons from the Implementation of Genomic Medicine Programs | journal = Annual Review of Genomics and Human Genetics | volume = 20 | issue = 1 | pages = 389–411 | date = August 2019 | pmid = 30811224 | doi = 10.1146/annurev-genom-083118-014924 | s2cid = 73460688 | doi-access = free }}</ref> Essentially, population genomics screening can be used to identify people at risk for disease, which can assist in preventative efforts.<ref name="Early Lessons from the Implementati"/>
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 GWA study. These have been used for a wide variety of conditions, such as cancer, diabetes, and coronary artery disease.<ref name="thomas2020">{{cite journal |vauthors=Thomas M, Sakoda LC, Hoffmeister M, Peters U, Hsu L |title=Genome-wide Modeling of Polygenic Risk Score in Colorectal Cancer Risk |journal=Am J Hum Genet |volume=107 |issue=3 |pages=432–444 |date=September 2020 |pmid=32758450 |pmc=7477007 |doi=10.1016/j.ajhg.2020.07.006}}</ref><ref name="khera2018">{{cite journal |vauthors=Khera AV, Chaffin M, Aragam KG, Haas ME, Roselli C, Choi SH, Natarajan P, Lander ES, Lubitz SA, Ellinor PT, Kathiresan S |title=Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations |journal=Nat Genet |volume=50 |issue=9 |pages=1219–1224 |date=September 2018 |pmid=30104762 |pmc=6128408 |doi=10.1038/s41588-018-0183-z |url=}}</ref> 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.<ref name="marnetto2020">{{cite journal |vauthors=Marnetto D, Pärna K, Läll K, Molinaro L, Montinaro F, Haller T, Metspalu M, Mägi R, Fischer K, Pagani L |title=Ancestry deconvolution and partial polygenic score can improve susceptibility predictions in recently admixed individuals |journal=Nat Commun |volume=11 |issue=1 |article-number=1628 |date=April 2020 |pmid=32242022 |pmc=7118071 |doi=10.1038/s41467-020-15464-w|bibcode=2020NatCo..11.1628M }}</ref><ref name="wang2020">{{cite journal |vauthors=Wang Y, Guo J, Ni G, Yang J, Visscher PM, Yengo L |title=Theoretical and empirical quantification of the accuracy of polygenic scores in ancestry divergent populations |journal=Nat Commun |volume=11 |issue=1 |article-number=3865 |date=July 2020 |pmid=32737319 |pmc=7395791 |doi=10.1038/s41467-020-17719-y |bibcode=2020NatCo..11.3865W |url=}}</ref> Most studies have used data from those with European ancestry, leading to calls for more equitable genomics practices to reduce health disparities.<ref name="martin2019">{{cite journal |vauthors=Martin AR, Kanai M, Kamatani Y, Okada Y, Neale BM, Daly MJ |title=Clinical use of current polygenic risk scores may exacerbate health disparities |journal=Nat Genet |volume=51 |issue=4 |pages=584–591 |date=April 2019 |pmid=30926966 |pmc=6563838 |doi=10.1038/s41588-019-0379-x |url=}}</ref> 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.<ref name="lewis2020">{{cite journal |vauthors=Lewis CM, Vassos E |title=Polygenic risk scores: from research tools to clinical instruments |journal=Genome Med |volume=12 |issue=1 |article-number=44 |date=May 2020 |pmid=32423490 |pmc=7236300 |doi=10.1186/s13073-020-00742-5 |url= |doi-access=free }}</ref>
==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.<ref name="PMC_C_2014">{{cite web|title=Personalized Medicine 101: The Challenges|url=http://personalizedmedicinecoalition.org/Resources/Personalized_Medicine_101_The_Challenges|publisher=Personalized Medicine Coalition|access-date=April 26, 2014|archive-date=May 3, 2014|archive-url=https://web.archive.org/web/20140503010439/http://personalizedmedicinecoalition.org/Resources/Personalized_Medicine_101_The_Challenges}}</ref> 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.<ref name="Machine learning in medicine: Addre">{{cite journal | vauthors = Vayena E, Blasimme A, Cohen IG | title = Machine learning in medicine: Addressing ethical challenges | journal = PLOS Medicine | volume = 15 | issue = 11 | article-number = e1002689 | date = November 2018 | pmid = 30399149 | pmc = 6219763 | doi = 10.1371/journal.pmed.1002689 | doi-access = free }}</ref> Furthermore, the analysis of acquired diagnostic data is a recent challenge of personalized medicine and its implementation.<ref name="pm"/> For example, genetic data obtained from next-generation sequencing requires computer-intensive data processing prior to its analysis.<ref>{{cite web | vauthors = Schapranow MP | date = 27 June 2013 |title=Analyze Genomes: Motivation |access-date= 20 July 2014 |url=http://we.analyzegenomes.com/ | publisher = Hasso Plattner Institute }}</ref> 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.{{citation needed|date=November 2024}}
===Regulatory oversight=== {{Update|part=section|date=April 2024|reason=All sources are from 2013}} The U.S. Food and Drug Administration (FDA) has started taking initiatives to integrate personalised medicine into their regulatory policies. In October 2013, the agency published a report 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.<ref name="FDA_2013" /> These included developing specific regulatory standards, research methods and reference materials.<ref name="FDA_2013" /> An example of the latter category they were working on is a "genomic reference library", aimed at improving quality and reliability of different sequencing platforms.<ref name= "FDA_2013" /> A major challenge for those regulating personalized medicine is a way to demonstrate its effectiveness relative to the current standard of care.<ref name=":1" /> The new technology must be assessed for both clinical and cost effectiveness, and {{As of|2013|lc=y}}, regulatory agencies had no standardized method.<ref name=":1">{{cite journal | vauthors = Frueh FW | title = Regulation, reimbursement, and the long road of implementation of personalized medicine--a perspective from the United States | journal = Value in Health | volume = 16 | issue = 6 Suppl | pages = S27-31 | date = September 2013 | pmid = 24034309 | doi = 10.1016/j.jval.2013.06.009 | doi-access = free }}</ref>
===Intellectual property rights=== As with any innovation in medicine, investment and interest in personalised medicine is influenced by intellectual property rights.<ref name="PMC_C_2014" /> There has been a lot of controversy regarding patent protection for diagnostic tools, genes, and biomarkers.<ref name=eighteen>{{cite web|title=Intellectual Property Issues Impacting the Future of Personalized Medicine|url=http://www.aipla.org/committees/committee_pages/Biotechnology/diagnostics/SharedDocuments/Personalized.Med.IP.docx|publisher=American Intellectual Property Law Association|access-date=April 26, 2014}}{{dead link|date=November 2016}}</ref> 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.<ref name=eighteen />
===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".<ref name="PMC_C_2014" /> 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.<ref>{{cite journal | vauthors = Weldon CB, Trosman JR, Gradishar WJ, Benson AB, Schink JC | title = Barriers to the use of personalized medicine in breast cancer | journal = Journal of Oncology Practice | volume = 8 | issue = 4 | pages = e24-31 | date = July 2012 | pmid = 23180995 | pmc = 3396824 | doi = 10.1200/jop.2011.000448 }}</ref>
===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.<ref name="PMC_C_2014" />
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.<ref name="Machine learning in medicine: Addre"/> 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.<ref name="PMC_C_2014" /> On February 19, 2015, FDA issued a press release titled: "FDA permits marketing of first direct-to-consumer genetic carrier test for Bloom syndrome.<ref name="PMC_101" />
=== 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.<ref>{{cite journal | vauthors = Char DS, Shah NH, Magnus D | title = Implementing Machine Learning in Health Care - Addressing Ethical Challenges | journal = The New England Journal of Medicine | volume = 378 | issue = 11 | pages = 981–983 | date = March 2018 | pmid = 29539284 | pmc = 5962261 | doi = 10.1056/NEJMp1714229 }}</ref>
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.<ref>{{cite journal | vauthors = Chernew ME, Landrum MB | title = Targeted Supplemental Data Collection - Addressing the Quality-Measurement Conundrum | journal = The New England Journal of Medicine | volume = 378 | issue = 11 | pages = 979–981 | date = March 2018 | pmid = 29539286 | doi = 10.1056/NEJMp1713834 }}</ref> 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.<ref>{{cite journal | vauthors = Gijsberts CM, Groenewegen KA, Hoefer IE, Eijkemans MJ, Asselbergs FW, Anderson TJ, Britton AR, Dekker JM, Engström G, Evans GW, de Graaf J, Grobbee DE, Hedblad B, Holewijn S, Ikeda A, Kitagawa K, Kitamura A, de Kleijn DP, Lonn EM, Lorenz MW, Mathiesen EB, Nijpels G, Okazaki S, O'Leary DH, Pasterkamp G, Peters SA, Polak JF, Price JF, Robertson C, Rembold CM, Rosvall M, Rundek T, Salonen JT, Sitzer M, Stehouwer CD, Bots ML, den Ruijter HM | title = Race/Ethnic Differences in the Associations of the Framingham Risk Factors with Carotid IMT and Cardiovascular Events | journal = PLOS ONE | volume = 10 | issue = 7 | article-number = e0132321 | date = 2 July 2015 | pmid = 26134404 | pmc = 4489855 | doi = 10.1371/journal.pone.0132321 | bibcode = 2015PLoSO..1032321G | doi-access = free }}</ref>
=== 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.<ref>{{cite journal | vauthors = Yngvadottir B, Macarthur DG, Jin H, Tyler-Smith C | title = The promise and reality of personal genomics | journal = Genome Biology | volume = 10 | issue = 9 | page = 237 | date = 2009 | pmid = 19723346 | pmc = 2768970 | doi = 10.1186/gb-2009-10-9-237 | doi-access = free }}</ref> 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.<ref name="Fernald_2011">{{cite journal | vauthors = Fernald GH, Capriotti E, Daneshjou R, Karczewski KJ, Altman RB | title = Bioinformatics challenges for personalized medicine | journal = Bioinformatics | volume = 27 | issue = 13 | pages = 1741–8 | date = July 2011 | pmid = 21596790 | pmc = 3117361 | doi = 10.1093/bioinformatics/btr295 }}</ref> 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.<ref name="Fernald_2011" /> 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.<ref name="Fernald_2011" /> 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<ref>{{cite web |title=Personalised and precise healthcare means a better quality of life for patients |url=https://www.futureproofinghealthcare.com/en |website=future proofing healthcare. |access-date=17 November 2022 |archive-date=17 November 2022 |archive-url=https://web.archive.org/web/20221117093945/https://www.futureproofinghealthcare.com/en |url-status=dead }}</ref> 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.<ref>{{cite news |title=Building long term sustainable and personalised healthcare systems |url=https://events.economist.com/events-conferences/building-long-term-sustainable-and-personalised-healthcare-systems/ |access-date=17 November 2022 |publisher=Economist |date=28 January 2021}}</ref><ref>{{cite news |title=Ireland performs poorly on international index of personalised healthcare |url=https://www.imt.ie/features-opinion/ireland-performs-poorly-on-international-index-of-personalised-healthcare-09-09-2021/ |access-date=17 November 2022 |publisher=Irish Medical Times |date=9 September 2021}}</ref>
==Systems medicine== Systems medicine is an interdisciplinary field of study that looks at the systems of the human body as part of an integrated whole, incorporating biochemical, physiological, and environment interactions. Systems medicine draws on systems science and systems biology, and considers complex interactions within the human body in light of a patient's genomics, behavior and environment.<ref>{{cite journal | vauthors = Federoff HJ, Gostin LO | title = Evolving from reductionism to holism: is there a future for systems medicine? | journal = JAMA | volume = 302 | issue = 9 | pages = 994–6 | date = September 2009 | pmid = 19724047 | doi = 10.1001/jama.2009.1264 | s2cid = 219774 }}</ref>
The earliest uses of the term ''systems medicine'' appeared in 1992, in an article on systems medicine and pharmacology by T. Kamada.<ref>{{cite journal | vauthors = Kamada T | title = System biomedicine: a new paradigm in biomedical engineering | journal = Frontiers of Medical and Biological Engineering | volume = 4 | issue = 1 | pages = 1–2 | date = 1992 | pmid = 1599879 }}</ref>
An important topic in systems medicine and systems biomedicine is the development of computational models that describe disease progression and the effect of therapeutic interventions.<ref name="pmid16552630">{{cite journal | vauthors = de Winter W, DeJongh J, Post T, Ploeger B, Urquhart R, Moules I, Eckland D, Danhof M | title = A mechanism-based disease progression model for comparison of long-term effects of pioglitazone, metformin and gliclazide on disease processes underlying Type 2 Diabetes Mellitus | journal = Journal of Pharmacokinetics and Pharmacodynamics | volume = 33 | issue = 3 | pages = 313–43 | date = June 2006 | pmid = 16552630 | doi = 10.1007/s10928-006-9008-2 | s2cid = 21941015 }}</ref> <ref>{{cite journal | vauthors = Tiemann CA, Vanlier J, Oosterveer MH, Groen AK, Hilbers PA, van Riel NA | title = Parameter trajectory analysis to identify treatment effects of pharmacological interventions | journal = PLOS Computational Biology | volume = 9 | issue = 8 | article-number = e1003166 | date = Aug 2013 | pmid = 23935478 | pmc = 3731221 | doi = 10.1371/journal.pcbi.1003166 | bibcode = 2013PLSCB...9E3166T | doi-access = free }}</ref>
More recent approaches include the redefinition of disease phenotypes based on common mechanisms rather than symptoms. These provide then therapeutic targets including network pharmacology<ref>{{cite journal | vauthors = Oettrich JM, Dao VT, Frijhoff J, Kleikers P, Casas AI, Hobbs AJ, Schmidt HH | title = Clinical relevance of cyclic GMP modulators: A translational success story of network pharmacology | journal = Clinical Pharmacology and Therapeutics | volume = 99 | issue = 4 | pages = 360–2 | date = April 2016 | pmid = 26765222 | doi = 10.1002/cpt.336 | s2cid = 40005254 | url = https://cris.maastrichtuniversity.nl/en/publications/501d0909-5e77-427a-944a-a7b3cfae2d35 }}</ref> and drug repurposing.<ref>{{cite journal | vauthors = Langhauser F, Casas AI, Dao VT, Guney E, Menche J, Geuss E, Kleikers PW, López MG, Barabási AL, Kleinschnitz C, Schmidt HH | title = A diseasome cluster-based drug repurposing of soluble guanylate cyclase activators from smooth muscle relaxation to direct neuroprotection | journal = npj Systems Biology and Applications | volume = 4 | issue = 1 | article-number = 8 | date = 2018-02-05 | pmid = 29423274 | pmc = 5799370 | doi = 10.1038/s41540-017-0039-7 }}</ref> Since 2018, there is a dedicated scientific journal, ''Systems Medicine''.<ref>{{Cite journal| vauthors = Baumbach J, Schmidt HH |date=2018|title=The End of Medicine as We Know It: Introduction to the New Journal, Systems Medicine|journal=Systems Medicine|volume=1|pages=1–2|doi=10.1089/sysm.2017.28999.jba|doi-access=free}}</ref>
=== Fundamental schools of systems medicine === Essentially, the issues dealt with by systems medicine can be addressed in two basic ways, molecular (MSM) and organismal systems medicine (OSM):<ref>{{cite journal |last1=Tretter |first1=F |last2=Löffler-Stastka |first2=H |title=Medical knowledge integration and "systems medicine": Needs, ambitions, limitations and options. |journal=Medical Hypotheses |date=December 2019 |volume=133 |article-number=109386 |doi=10.1016/j.mehy.2019.109386 |pmid=31541780|s2cid=202718643 }}</ref><ref name="Tretter_2021">{{cite journal |last1=Tretter |first1=F |last2=Wolkenhauer |first2=O |last3=Meyer-Hermann |first3=M |last4=Dietrich |first4=JW |last5=Green |first5=S |last6=Marcum |first6=J |last7=Weckwerth |first7=W |title=The Quest for System-Theoretical Medicine in the COVID-19 Era. |journal=Frontiers in Medicine |date=2021 |volume=8 |article-number=640974 |doi=10.3389/fmed.2021.640974 |pmid=33855036|pmc=8039135 |doi-access=free }}</ref>
==== Molecular systems medicine (MSM) ==== This approach relies on omics technologies (genomics, proteomics, transcriptomics, phenomics, metabolomics etc.) and tries to understand physiological processes and the evolution of disease in a bottom-up strategy, i.e. by simulating, synthesising and integrating the description of molecular processes to deliver an explanation of an organ system or even the organism in its whole.
==== Organismal systems medicine (OSM) ==== This branch of systems medicine, going back to the traditions of Ludwig von Bertalanffy's systems theory and biological cybernetics is a top-down strategy that starts with the description of large, complex processing structures (i.e. neural networks, feedback loops and other motifs) and tries to find sufficient and necessary conditions for the corresponding functional organisation on a molecular level.
A common challenge for both schools is the translation between the molecular and the organismal level. This can be achieved e.g. by affine subspace mapping and sensitivity analysis, but also requires some preparative steps on both ends of the epistemic gap.<ref name="Tretter_2021"/>
== List of research groups == {| class="wikitable sortable" |- ! Country ! University / Institute ! Department / Center / Program / Network ! Participants |- |Austria |University of Vienna |Centre for Organismal Systems Biology (COSB)<ref>{{cite web |title=Organismal Systems Biology |url=https://lifesciences.univie.ac.at/research/thematic-areas/organismal-systems-biology/ |website=lifesciences.univie.ac.at |access-date=17 April 2021 |language=en |archive-date=7 January 2022 |archive-url=https://web.archive.org/web/20220107034328/https://lifesciences.univie.ac.at/research/thematic-areas/organismal-systems-biology/ |url-status=dead }}</ref> | |- |Ireland |Royal College of Surgeons in Ireland |Medical Systems Biology<ref>{{cite web | url = http://systemsbiology.rcsi.ie/ | publisher = Royal College of Surgeons in Ireland | title = Medical Systems Biology | access-date = 19 November 2025 | archive-date = 22 August 2019 | archive-url = https://web.archive.org/web/20190822003712/http://systemsbiology.rcsi.ie/ | url-status = dead }}</ref> | |- |Luxembourg |Luxembourg Centre for Systems Biomedicine |Computational Biology group<ref>{{cite web | url = http://wwwen.uni.lu/lcsb/research/computational_biology | title = Computational Biology group | date = 16 March 2024 | publisher = Luxembourg Centre for Systems Biomedicine }}</ref> | |- |Netherlands |Eindhoven University of Technology (TU/e) |Department of Biomedical Engineering, Computational Biology Group (CBio)<ref>{{cite web | url = https://www.tue.nl/cbio/ | title = Computational Biology Group | publisher = Eindhoven University of Technology }}</ref> |Natal van Riel |- |USA |Institute for Systems Biology (ISB) | |Leroy Hood, Alan Aderem, Ruedi Aebersold |- |Germany |Helmholtz Association of German Research Centres |Department of Systems Immunology<ref>{{cite web | url=https://www.helmholtz.de/en/about_us/networks_and_cooperation/helmholtz_alliances/systems_biology/networks/systems_biology_at_the_helmholtz_centre/ | title=Systems Biology at the Helmholtz Centre for Infectious Diseases, Braunschweig | publisher=Helmholtz Centre for Infection Research | access-date=19 November 2025 | archive-date=14 August 2020 | archive-url=https://web.archive.org/web/20200814172342/https://www.helmholtz.de/en/about_us/networks_and_cooperation/helmholtz_alliances/systems_biology/networks/systems_biology_at_the_helmholtz_centre/ | url-status=dead }}</ref> |Esteban Hernandez-Vargas |- |Netherlands |Utrecht University University Medical Center Utrecht
Maastricht University |Laboratory of Translational Immunology<ref>{{Cite web | url=http://www.umcutrecht.nl/nl/Ziekenhuis/Zorgverleners/Radstake-TRDJ | title=Radstake TRDJ}}</ref> Utrecht Center for Quantitative Immunology<ref>{{Cite web | url=http://theory.bio.uu.nl/ucqi/ |title = Utrecht Center for Quantitative Immunology}}</ref>
Pharmacology and Personalised Medicine<ref>{{Cite web|url=https://ppm.mumc.maastrichtuniversity.nl/pharmacology-personalised-medicine-1|title=Pharmacology & Personalised Medicine {{!}} Pharmacology & Personalised Medicine|website=ppm.mumc.maastrichtuniversity.nl|language=en|access-date=2017-02-02|archive-date=12 December 2021|archive-url=https://web.archive.org/web/20211212153702/https://ppm.mumc.maastrichtuniversity.nl/pharmacology-personalised-medicine-1|url-status=dead}}</ref> |Prof. Timothy Radstake, Dr. Aridaman Pandit
Prof. Harald H.H.W. Schmidt |- |Israel |Weizmann Institute of Science |Department of Molecular Cell Biology<ref>{{Cite web|url=http://www.weizmann.ac.il/mcb/|title=Molecular Cell Biology, Weizmann Institute of Science|website=www.weizmann.ac.il|access-date=2019-05-05}}</ref> Systems Medicine course<ref>{{Cite web|url=https://www.weizmann.ac.il/mcb/UriAlon/download/systems-medicine-course-2019|title=Systems Medicine course 2019 {{!}} Uri Alon|website=www.weizmann.ac.il|date=17 April 2019|access-date=2019-05-05|archive-date=26 February 2021|archive-url=https://web.archive.org/web/20210226125207/https://www.weizmann.ac.il/mcb/UriAlon/download/systems-medicine-course-2019|url-status=dead}}</ref> |Uri Alon<ref>{{cite journal | vauthors = Karin O, Swisa A, Glaser B, Dor Y, Alon U | title = Dynamical compensation in physiological circuits | journal = Molecular Systems Biology | volume = 12 | issue = 11 | article-number = 886 | date = November 2016 | pmid = 27875241 | pmc = 5147051 | doi = 10.15252/msb.20167216 }}</ref><ref>{{cite journal | vauthors = Karin O, Alon U | title = Biphasic response as a mechanism against mutant takeover in tissue homeostasis circuits | journal = Molecular Systems Biology | volume = 13 | issue = 6 | article-number = 933 | date = June 2017 | pmid = 28652282 | pmc = 5488663 | doi = 10.15252/msb.20177599 }}</ref><ref>{{Cite book |last=Alon |first=Uri |url=https://www.taylorfrancis.com/books/9781003356929 |title=Systems Medicine: Physiological Circuits and the Dynamics of Disease |date=2023-10-11 |publisher=Chapman and Hall/CRC |isbn=978-1-003-35692-9 |edition=1 |location=Boca Raton |language=en |doi=10.1201/9781003356929}}</ref> |- |Norway |Haukeland University Hospital |Neuro-SysMed<ref name="neurosysmed">[https://www.forskningsradet.no/prognett-fkb/Nyheter/Norges_forste_forskningssenter_for_klinisk_behandling_tildelt/1254037899980/p1254032200593 Norges første forskningssenter for klinisk behandling tildelt] {{webarchive|url=https://web.archive.org/web/20190417125308/https://www.forskningsradet.no/prognett-fkb/Nyheter/Norges_forste_forskningssenter_for_klinisk_behandling_tildelt/1254037899980/p1254032200593|date=April 17, 2019}} Annonsering 11/2018.</ref> |Kjell-Morten Myhr, Charalampos Tzoulis |- |}
== See also == * Biocybernetics * Medical cybernetics * Personal genomics * Phenotypic screening * Systems biology * Systems pharmacology * Systems science * List of medical abbreviations
== References == {{Reflist}}
== External links == {{wikibooks|Internal Medicine|Precision Medicine}} * [https://www.cadth.ca/10-trends-shaping-future-health-care-canada-2023 2023 Watch List: Top 10 Precision Medicine Technologies and Issues] {{Webarchive|url=https://web.archive.org/web/20240713133801/https://www.cadth.ca/10-trends-shaping-future-health-care-canada-2023 |date=13 July 2024 }}, Canadian Drug Agency, 2024
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