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Personalized Medicine Market Overview 2024-2025 & 2030: Oncology, Neurology, and Cardiology Lead Expansion, ext-Generation Sequencing and AI Revolutionize Tailored Treatment Approaches  Yahoo Finance

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Leading Driver In The Jetrea Market 2025: Rise Of Personalized Medicine Fueling Growth  EIN News

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Leading Driver In The Jetrea Market 2025: Rise Of Personalized Medicine Fueling Growth - EIN News

Innovations in core-shell nanoparticles advance drug delivery and precision medicine  Phys.org

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Innovations in core-shell nanoparticles advance drug delivery and precision medicine - Phys.org

Abstract

There is a great deal of hype surrounding the concept of personalized medicine. Personalized medicine is rooted in the belief that since individuals possess nuanced and unique characteristics at the molecular, physiological, environmental exposure and behavioral levels, they may need to have interventions provided to them for diseases they possess that are tailored to these nuanced and unique characteristics. This belief has been verified to some degree through the application of emerging technologies such as DNA sequencing, proteomics, imaging protocols, and wireless health monitoring devices, which have revealed great inter-individual variation in disease processes. In this review, we consider the motivation for personalized medicine, its historical precedents, the emerging technologies that are enabling it, some recent experiences including successes and setbacks, ways of vetting and deploying personalized medicines, and future directions, including potential ways of treating individuals with fertility and sterility issues. We also consider current limitations of personalized medicine. We ultimately argue that since aspects of personalized medicine are rooted in biological realities, personalized medicine practices in certain contexts are likely to be an inevitability, especially as relevant assays and deployment strategies become more efficient and cost-effective.

Keywords: Precision medicine, biomarkers, patient monitoring, genomics

The application of emerging, high-throughput, data-intensive biomedical assays, such as DNA sequencing, proteomics, imaging protocols, and wireless monitoring devices, has revealed a great deal of inter-individual variation with respect to the effects of, and mechanisms and factors that contribute to, disease processes. This has raised questions about the degree to which this inter-individual variation should impact decisions about the optimal way to treat, monitor, or prevent a disease for an individual. In fact, it is now widely believed that the underlying heterogeneity of many disease processes suggests that strategies for treating an individual with a disease, and possibly monitoring or preventing that disease, must be tailored or personalized to that individual's unique biochemical, physiological, environmental exposure, and behavioral profile. A number of excellent reviews on personalized medicine have been written, including a growing number of textbooks on the subject meant for medical students and clinicians. It should be noted that although many use the term personalized medicine interchangeably with the terms individualized and precision medicine (as we do here), many have argued that there are some important, though often subtle, distinctions between them.(1, 2))

There are a number of challenges associated with personalized medicines, especially with respect to obtaining their approval for routine use from various regulatory agencies. In addition, there have many issues associated with the broad acceptance of personalized medicines on the part of different health care stakeholders, such as physicians, health care executives, insurance companies, and, ultimately, patients. Almost all of these challenges revolve around a need to prove that personalized medicine strategies simply outperform traditional medicine strategies, especially since many tailored or personalized therapies, such as autologous CAR-T cell transplant therapies for certain types of cancer(3) and mutation-specific medicines such as ivacaftor to treat cystic fibrosis (4, 5), can be very expensive(6). In this review we consider the history and motivation of personalized medicine and provide some context on what personalized medicines strategies have emerged in the last few decades, what limitations are slowing their advance, and what is on the horizon. We also consider strategies for proving that personalized medicine protocols and strategies can outperform traditional medicine protocols and strategies. Importantly, we distinguish examples and challenges associated with personalized disease prevention, personalized health monitoring, and personalized treatment of overt disease.

There is much in the history of western medicine that anticipates the emergence of personalized medicine. For reasons of brevity, we will not focus on all of these events, but rather only a few that we feel encompass the most basic themes behind personalized medicine. More than a century ago Archibald Garrod, an English physician, began studying in earnest diseases that would later become known as inborn errors of metabolism. Garrod studied a number of rare diseases with overt, visible phenotypic manifestations including alkaptonuria, albinism, cystinuria and pentosuria. Of these, his focused work on alkaptonuria led to some notoriety when he observed that some members of families exhibiting alkaptonuria showed measurably outlying values for certain basic biochemical assays, e.g., from urine, relative to the values of family members who did not possess alkaptonuria. This led him to conclude that alkaptonuria was due to a specific altered course of metabolism among affected individuals, which was subsequently proven correct.(7) Further, in considering other rare diseases like alkaptonuria, Garrod argued that the thought naturally presents itself that these [conditions] are merely extreme examples of variation of chemical behavior which are probably everywhere present in minor degrees and that just as no two individuals of a species are absolutely identical in bodily structure neither are their chemical processes carried out on exactly the same lines. This more than hints at his belief that, at least with respect to metabolism, humans vary widely and that these differences in metabolism could help explain overt phenotypic differences between individuals, such as their varying susceptibilities to diseases and the ways in which they manifest diseases.(8, 9)

Garrod was working in the backdrop of a great deal of debate about the emerging field of genetics. Although the specific entities we now routinely refer to as genes (i.e., stretches of DNA sequence that code for a protein and related regulatory elements), were unknown to Garrod and his contemporaries, he and others often referred to factors influencing disease possessed by certain individuals that were consistent with the modern notion of genes. Claims about the very presence of such factors were born out of discussions rooted in the findings of Mendel (later, it would be shown that many of the metabolic outliers Garrod observed in people with diseases like alkaptonuria were due to defects in genes possessed by people with those diseases). Mendel observed consistent connections between the emergence of very specific phenotypes only when certain breeding protocols were followed in peas that anticipated the modern field of genetics.(10) Essentially, as discussed in an excellent book by William Provine,(11) many in the research community at the time debated how genes or factors of the type Garrod and others were considering could explain the broad variation in phenotypic expression observed in nature. One group of academics and researchers, referred to as the Mendelians in the historical literature, which included William Bateson and Hugo de Vries, focused on the discrete nature of the factors likely to be responsible for many observable inheritance patterns (such as those of focus in Mendel's studies and observations like Garrod's in the context of rare disease). In opposition to the Mendelians were the Biometricians, represented most notably by Karl Pearson, whose focus on continuous or graded phenotypes, like height, gave them concerns about how to reconcile such continuous variation with the overtly discrete (either/or) factors and inheritance patterns considered by the Mendelians and researchers like Garrod.

The Mendelian vs. Biometrician debate was resolved to a great extent by the statistician Ronald Fisher in a series of seminal papers. Fisher argued that one could reconcile continuous phenotypic variation with discrete, heritable factors that contribute to this variation by suggesting that many factors (i.e., genes) might contribute in a small way to a particular phenotype. The collective effect, or sum total, of these factors could then create variation in phenotypes that give the appearance of continuity in the population at large (e.g., an individual who inherited only 1 of 25 genetic variants known to increase height would be shorter on average than someone who inherited 10 or 12, and much shorter, relatively speaking, than an individual who inherited 22 or 25).(12) The belief that there might be many genes that contribute to phenotypic expression broadly, some with more pronounced effects and some with less pronounced effects, that interact and collectively contribute to a phenotype in a myriad of ways, has been validated through the application of modern high-throughput genetic technologies such as genotyping chips and DNA sequencing. As a result, much of the contemporary focus on personalized medicine is rooted in the findings of genetic studies, as it has been shown that individuals do in fact vary widely as each individual possesses subsets of literally many millions of genetic variants that exist in the human population as a whole. In addition, subsets of these genetic variants may have arisen as de novo mutations and hence may be unique to an individual. These extreme genetic variation explains, in part, why individuals vary so much with respect to phenotypes, in particular their susceptibilities to disease and their responses to interventions.(13) It should be emphasized that although personalized medicine has its roots in the results of genetic studies, it is widely accepted that other factors, e.g., environmental exposures, developmental phenomena and epigenetic changes, and behaviors, all need to be taken into account when determining the optimal way to treat an individual patient (see Figure 1).(14-16)

Graphical depiction of elements in need of integration and assessment in pursuing truly personalized medicine. Access to health care is important since some individuals may not be able to access expertise and technologies due to geographic or economic barriers and therefore interventions might need to be crafted for those individuals with this in mind. Inherited genetic information is really only predictive or diagnostic in nature however somatic changes to DNA can provide valuable insight into pathogenic processes. Tissue biomarkers (e.g., routine blood-based clinical chemistry panels) are useful for detecting changes in health status, as are imaging and radiology exams as well as data collected routinely via wireless monitors. Environmental exposures and behaviors can really impact the success of an intervention and exhibit great inter-individual variability. Epigenetic phenomena reshape gene function based on exposures and developmental or stochastic phenomena and should be monitored as well as indicators of a health status change.

Another, sadly more obscure, publication was also prescient for personalized medicine, although this publication bears more on the need for clinical practices that are consistent with personalized medicine rather than a scientific justification of personalized medicine. More than 60 years ago Hogben and Sim considered how clinical practice needs to pay attention to nuanced characteristics of patients in order to determine an appropriate intervention for them.(17-19) Although more will be discussed about their paper in the section on Testing Personalized Medicines, suffice it to say that the authors believed that in order to determine an optimal course of action for an individual patient in the absence of any a priori understanding of how best to treat that patient given his or her characteristics or profile, a number of items would need to be obtained. Thus, greater information about that patient would have to be gathered, a plan to vet the utility of an intervention chosen on the basis of that information would have to be pursued, and a strategy for incorporating the results of the patient-oriented study into future care would have to be crafted. Although simple in theory, the practical issues surrounding gathering more information about a patient and pursuing an the empirical assessment of a personalized intervention can be daunting. For example, questions surrounding how one can know that a chosen intervention works unless meticulous patient follow-up information is kept, how one would know if a patient satisfied with what they are experiencing with the intervention, and how one could assess the difference between other interventions that could have been chosen and the chosen personalized intervention, would all need to be addressed. In fact, practical issues surrounding the implementation of personalized medicine that Hogben and Sim considered are often overlooked in contemporary discussions about personalized medicine, especially since different technologies for profiling patients are constantly being developed and refined, and more and more evidence for inter-individual variation in factors associated with diseases (from technologies such as DNA sequencing, proteomics, sophisticated imaging protocols, etc.) is emerging.

There have been a great many examples of interventions tailored to individual patient profiles, virtually all of them based on genetic profiles. Before providing a few classic examples, it should be emphasized that personalized medicine can be practiced not only for the treatment of disease, but also for the early detection and prevention of disease. We provide some historical examples of personalized disease treatments here and consider early detection and prevention in the next section, as developments in personalized disease detection and prevention are much more recent.

The human body deals with traditional pharmacotherapies (i.e., drugs) to treat disease in two general ways. Initially, the body must respond to a drug. This response occurs in steps, with the first step involving the absorption of the drug by the body. The drug must then be distributed throughout the body (during this process the drug might be biotransformed or metabolized into useful components) and then begin to elicit effects. Finally, any remaining drug or drug components are excreted. These processes are often lumped under the heading of pharmacokinetics and collectively referred to as the ADME of a drug (Absorption, Distribution, Metabolism and Excretion). Pharmacokinetic activity is often under the control of a unique set of genes (e.g., drug metabolizing enzymes) that could harbor naturally-occurring genetic variants (or polymorphisms) that influence their function and hence how the body ultimately deals with a particular drug. Once a drug is within the body, how it interacts with its target (typically a gene or protein encoded by a gene) to elicit an effect is known as its pharmacodynamic properties. These properties include the affinity the drug has for its target(s), the drug's ability to modulate the target(s) (or its efficacy), and the potency of the drug, or how much of the drug is needed to induce a certain change in its target. Pharmacodynamic properties of a drug are also under genetic control.

Many early examples of personalized medicines were associated with genetically-mediated pharmacokinetic aspects of drugs. This was due in part to the biomedical science community's understanding of drug metabolizing enzymes and the role they play in the body's response to drugs. An excellent introduction to pharmacogenetic properties of drugs, as well genetic variants in genes that influence the efficacy and side effects of drugs (especially with respect to genetic variants in drug metabolizing enzymes) is the book by Weber.(20) Warfarin is a widely used blood thinning medication that, if not dosed properly, could cause a potentially life-threatening adverse drug reaction. Warfarin targets a particular gene, VKORC1, and is metabolized in part by the gene CYP2C9. Naturally-occurring genetic variation in both the VKORC1 and CYP2C9 genes leads to variation in the pharmacodynamic and pharmacokinetic properties of Warfarin across individuals, ultimately creating variation in individuals' responses to warfarin. The US Food and Drug Administration has therefore recommended that dosing for warfarin take into consideration an individual's genotype (i.e., the dose must be personalized to an individual based on the specific genetic variants they possess in the VKORC1 and CYP2C9 genes).(21)

Another classic example of a drug that should only be provided to individuals with a certain genetic profile is primaquine (PQ). PQ has been used to manage malaria with some success in parts of the world where malaria is endemic. However, military doctors working in the past observed that some of the soldiers they treated for malaria that were provided the drug became jaundiced and anemic, and ultimately exhibited symptoms of what would later be termed acute haemolytic anaemia (AHA). It was later shown that the individuals exhibiting AHA after PQ administration carried variants in the G6PD gene.(22) Current clinical practice with PQ therefore calls for the genotyping of individual patients to see if they carry relevant variants in the G6PD gene that might discourage PQ use for them.

A final, often-cited example of a personalized medicine is the drug imatinib.(23) Imatinib is used to treat chronic myelogenous leukemia (CML). Imatinib inhibits an enzyme, tyrosine kinase, that is increased by the formation of a fusion of two genomic regions, one encompassing the Abelson proto-oncogene (abl) and the other the breakpoint cluster region (bcr). This fusion event arises in many tumors contributing to the development of CML and is referred to as the bcr-abl fusion or Philadelphia chromosome. However, not all individuals with CML have tumors harboring the bcr-abl fusion mutation. Therefore, imatinib is typically given only to individual CML patients with this fusion event.

Drugs like warfarin, PQ and imatinib that appear to only work or only work without side effects when a patient possesses a certain genetic profile, have generated tremendous interest in identifying factors, such as genetic variants, that influence an individual patient's response to any number of drugs and interventions. This interest in crafting personalized medicines to treat diseases has expanded into personalized disease surveillance (i.e., early detection protocols) and personalized disease prevention strategies as well. We briefly describe a few very recent examples of this activity.

Instead of developing a drug and then identifying factors that mitigate its efficacy or side effects through observational studies on individuals provided the drug, as with warfarin, PQ and imatinib, there are now attempts to identify, e.g., genetic profiles possessed by patients and then craft therapies that uniquely target those profiles. For example, the drug ivacaftor mentioned earlier was designed to treat individuals with cystic fibrosis (CF) that have very specific pathogenic mutations in the gene CFTR.(4) The CFTR gene has many functions, but one set of functions is dictated by a gate-like structure in the CFTR gene's encoded protein that can open and close to control the movement of salts in and out of cells. If the CFTR gene is dysfunctional, then the gate is closed, causing a build-up of mucus and other material in the lungs. Different mutations in the CFTR gene cause different types of dysfunction. For example, some mutations simply cause the CFTR gene to not produce anything, whether the gate is open or not. Other mutations cause the gate mechanism to dysfunction. Ivacaftor is designed to open the gate for longer periods of time in the presence of certain mutations that tend to cause the gate to be closed. Therefore, ivacaftor is only useful for the small subset of CF patients whose CFTR mutations lead to this specific gating problem. Connections between genetic variants and drug efficacy and side effects are growing in number, and in fact the US FDA provides a list of approved drug-based interventions that require a test to determine their appropriateness for an individual: https://www.fda.gov/Drugs/ScienceResearch/ucm572698.htm. Other publications consider the practical implications of approved personalized medicine interventions, such as the report produced by the Personalized Medicine Coalition (PMC).(24)

A second example involves the emerging set of cancer treatments known as immunotherapies.(25) Although there are many types of immunotherapies, all of them seek to prime or trigger an individual's own immune system to attack a cancer. One type of immunotherapy exploits potentially unique sets of genetic alterations that arise in a cancer patient's tumor cells, known as neo-antigens, which are often capable of raising an immune response if recognized properly by the host's immune cells. Essentially, this type of immunotherapy works by harvesting cells from a patient that mediate that patient's immune reactions, such as T cells, then modifying those cells to specifically recognize and target the neo-antigens found to be present in the patient's tumor. These modified cells are then put back in the patient's body so these cells can attack the tumor cells giving off the neo-antigen signals. Cytotoxic T cell therapies like this, as well as immunotherapies in general, have had notable successes, but can be very patient-specific for two reasons. First, the neo-antigen profile of a patient might be very unique, such that cytotoxic T cells made to recognize and attack a specific set of neo-antigens will not work in someone whose tumor does not have those neo-antigens. Second, if autologous constructs are used, then the patient's own T cells are modified, and hence not likely to work as well in another patient, although attempts to make allogeneic constructs in which one individual's T cells are modified and introduced into another patient's body are being pursued aggressively.(25)

If an individual is susceptible to a disease, or susceptible to recurrence of a disease, then that individual should be monitored. It is now believed that such monitoring should be pursued with use of personal thresholds, as opposed to population thresholds, to make claims about evidence or signs of disease or a pathogenic process.(26) Population thresholds are derived from epidemiologic data and population surveys and include, for example, cholesterol levels > 200 being an indicator for risk of heart disease, or systolic blood pressure > 140 being an indicator of hypertension, risk of stroke or heart disease. Personal thresholds are established from legacy values of a measure collected on an individual over time that used to gauge how deviant future values of that measure might be for that individual. Significant deviations from historical or average legacy values are taken as a sign of a health status change, irrespective of whether or not those values are beyond a population threshold.(27) As an example, Drescher et al.(26) explored the utility of personal thresholds applied to longitudinal CA125 levels collected on a number of women, a subset of whom developed ovarian cancer. The authors found that in all but one instance, the application of personal thresholds would have captured the presence of ovarian cancer at the same time as, or importantly earlier than, the application of population thresholds. Further, the authors showed that the use of personal thresholds could have captured the ovarian cancer almost a year earlier, on average, then the use of population thresholds. As the costs and convenience associated with monitoring assays and technologies improves (i.e., they become cheap and non-intrusive, if not transparent, to an individual user, say through an easily implantable wireless device), then the use of personal thresholds will likely become the rule rather than the exception in health monitoring protocols.

The use of genetic information to develop personalized disease prevention strategies is now well established in the scientific community, but not yet widely adopted in clinical practice. There are many excellent examples of how the use of genetic information can lead to both a decreased risk of disease development as well as decreased complications from standard treatment and screening strategies. A prime example relates to colorectal cancer, which remains the third leading cause of cancer deaths despite being a highly preventable illness. In 2012 Liao et al. reported an improvement in overall survival and a decreased risk for cancer-specific deaths in patients taking postoperative aspirin if they exhibited a somatic mutation in the PIK3CA gene in their colorectal cancers compared with patients whose cancers had the wild-type PIK3CA gene.(28) In 2015, Nan et al. reported varying effects of aspirin use on risk for development of colorectal cancer depending on an individual's genotype, with individuals possessing different genotypes having either lower, higher or no change in their risk of colorectal cancer development with aspirin use.(29) Given that aspirin use can have serious side effects associated with intestinal and intracranial bleeding, it would be ideal to limit the use of this medication for those individuals predicted to have a side effect, based on genotype.

As another example, in 2018, Jeon et al. reported the use of expanded risk prediction models for determining when to begin colorectal cancer screening. Currently the guidelines use only age and family history as variables. Jeon et al. showed that by using information about an individual's environmental exposure and genetic profile, specifically the presence of colorectal cancer associated genetic variants, recommendations for when to start screening could change by 12 years for men and 14 years for women.(30) The accuracy of relevant predictions about an individual's risk for colorectal cancer has been studied and suggests that the area under the curve (AUC) value for a model including environmental and genetic factors, where an AUC of 1.0 would suggest a model with perfect predictive accuracy, was 0.63 for men and 0.62 for women. This is compared to an AUC value of 0.53 (men) 0.54 (women) when only family history information was considered. Although there is still room for improvement given the AUCs were only 0.62 for the model with patient environmental exposure and genetic information, the considerable improvement over models that did not include genetic or environmental information justifies their use.

Although we have argued that personalized medicine is rooted in a great number of legacy insights and historical precedents, mostly related to genetics and rare diseases, its recognition as a paradigm that should be embraced broadly by the biomedical research and clinical communities is relatively recent. This suggests that not enough time has elapsed since the time of this recognition for researchers to show that personalized medicine actually works in a wide enough variety of settings to motivate its broad adoption. In this light, questions of how the community can vet or test the utility of personalized medicine arise. We describe three emerging strategies for vetting personalized medicines below, including N-of-1 clinical trials, intervention-matching trials, and adaptive clinical trials, and argue that although these strategies borrow elements from traditional randomized clinical trials (RCTs), they deviate significantly from historical population-based RCTs that were prominent in the past.

If there is no reason to believe that any one of a set of different interventions matches an individual's profile (e.g., genomic, behavioral, etc.) better than others, then there is equipoise among those interventions. In this case it becomes an empirical question as to which intervention might be optimal for the individual in question. Trials focusing on an individual's response to different interventions to determine an optimal intervention are referred to as N-of-1 or single subject trials. N-of-1 trials often exploit a simple cross-over design or even a repeated crossover designs, such as ABABAB designs, where A and B refer to different interventions, and the sequence ABABAB refers to the order in which the interventions are provided to a patient. Alternating interventions, and collecting data on the individual's response to those interventions, allows comparisons of those interventions (for example between a test intervention and a comparator, or placebo, intervention. Randomization, blinding, washout periods, multiple endpoints, and many other design elements can be used in N-of-1 trials.(27, 31, 32)

N-of-1 trials involving the provision of different interventions in sequence to an individual and evaluating outcomes for each, need to accommodate serial correlation between the observations, as well as possible carry-over effects from one intervention to another, but these issues can largely be overcome with appropriate analytical methods and study design.(32) Cross-over based N-of-1 trials are impractical, if not unethical, in settings where an individual is suffering from an acute or life-threatening condition, since switching from one intervention to another may exacerbate that individual's condition. However, sequential N-of-1 designs, in which measures are continuously monitored in real time to determine if an intervention is causing harm or working, have been proposed for these situations.(27) Given that the focus of an N-of-1 trial is on the identification of an optimal intervention for an individual, rather than on the average response to an intervention in the population at large (which is often the focus of traditional RCTs), they may be most appropriate to conduct in actual clinical practice when a physician is faced with equipoise, as considered by Hogen and Sim.(33, 34)

If evidence is found that certain features in individual patients' profiles can be used to identify interventions that might work for each of them, then a question arises as to how to test that the hypothesis that providing interventions to those individuals based on these matches leads to better outcomes than providing those individuals interventions based on some other scheme or strategy. One could test each individual match, but this may require pursuing many small clinical trials, which may be logistically complicated and hard to find financial support and infrastructure to implement. As an alternative, one could test an entire matching strategy against an alternative way of providing interventions (e.g., giving everyone the same intervention). This is more or less the motivation behind basket and umbrella trials currently in use, primarily in oncology settings.(35, 36) In oncology contexts, basket and umbrella trials enroll multiple individual patients into a trial knowing that they each might have unique features in their profile that could indicate that different interventions are appropriate. Basket trials enroll individuals without regard to the specific tissue affected by cancer (e.g., lung, breast and colorectal cancer patients can be enrolled) whereas umbrella trials only consider a single tissue (only lung cancer patients are enrolled). Each patient's tumor is profiled, usually via DNA sequencing. The tumor genome is analyzed to see if there are actionable driver perturbations in the tumor, such as mutations affecting particular genes, that are likely contributing to the growth of the tumor. If the mechanisms of action of a group of interventions (i.e., cancer drugs) are understood well enough, it may be possible to match those drugs to the perturbations in the tumor (e.g., if the EGFR gene is mutated and overexpressed in the tumor, then using a drug like cetuximab, which inhibits the EGFR gene, would be logical). Thus, each patient is steered towards a particular intervention basket (e.g., the EGFR inhibitor basket). The trial then seeks to test the hypothesis that the use of the different intervention baskets based on the matching scheme results in better outcomes than interventions provided to individual patients based on some other scheme that does not involve tumor profiling and matching.

If the trial is a failure (i.e., the matching scheme doesn't lead to better outcomes than something else), then an argument could be made that the matching scheme was flawed and not necessarily that the interventions considered in the trial are flawed. It would also be wrong to assume that the concept of personalized medicine is flawed as a result of a failure of a basket or bucket trial if in fact the matching scheme was found to be flawed. Some basket trials only have a single basket and no comparison group, but rely on determining which patient profiles appear to be associated with better outcomes for the intervention being tested.(37) Intervention matching schemes are likely to become the rule rather than the exception in medicine, especially since the introduction of computational environments like IBM's Watson system. Essentially, Watson is system that includes a very large database extracted in part from the vast medical literature, providing links between information about a patient (e.g., genetic profiles, age, sex, etc.) to outcomes (such as drug response). These links have been enhanced by leveraging statistical methods to further assess relationships between patient profiles and outcomes. For example, Watson has been trained to identify and establish links about perturbations often observed in a tumor and how those perturbations might be combatted by available drugs and interventions generally. Thus, if Watson was provided a patient profile, it could look up the best possible intervention given the current state of the science reflected in the literature and Watson's methods for establishing links between profiles and outcomes. The use of IBM's Watson system in actual clinical settings has led to discussions about how best to test and deploy such as a system as a way of supporting, as opposed to replacing, physicians' decisions about an intervention choice for individual patients.(38)

Adaptive and sequential clinical trials have been used for decades but their consideration and use in personalized medicine contexts is much more recent.(35) Essentially, adaptive trials have as one of their focal points a desire to minimize the amount of time a patient is on what is likely to be an inferior therapy. In the context of personalized medicine, if there is equipoise with respect to available interventions or between an untested and a conventional intervention for an individual patient, then the evaluation of the effects of each intervention on an individual to determine the best one for that individual (as in a very elaborate N-of-1 study) might be impractical or cause more harm than good. This is the case because some, if not all, of the interventions might not actually benefit that individual. In this light, it makes sense to implement studies in which biomarkers reflecting response or adverse effects are collected on an individual trial participant and monitoring of those biomarkers is pursued to determine if there are signs an intervention is not working. If there are, e.g., signs that an intervention is not working, the individual could cross-over to a new intervention. Although adaptive designs can be difficult to implement given their real-time evaluation and updating components, and can also produce data that might be more complicated to analyze than data from fixed, non-adaptive trials, they are often seen as more ethical. In addition, adding adaptive components to N-of-1 and aggregated N-of-1 trials as well as intervention-matching trials is possible. Although there are a growing number of papers describing adaptive trials, the work of Murphy and colleagues has received a great deal of attention because of its focus on minimizing the amount of time a patient is on an inferior treatment.(39-41)

There are a number of very recent research and clinical activities that are charting new territory for personalized medicine. We focus on four of these activities in the following, providing a brief overview of each. These activities include the use of patient-derived cell and organoid avatars for determining the best therapies for that patient, the use of intense individualized diagnostic and monitoring protocols to detect signs of disease, the development of personalized digital therapeutics, and the use of personalized medicine approaches in treating patients with fertility issues.

It is now possible to harvest cells from individuals and use pluripotency induction (i.e., induced pluripotent stem cell or iPSC) methods on those cells to generate additional cell types of relevance to a patient's condition without having to directly biopsy the affected tissue. This allows researchers to essentially develop a disease in a dish cellular model of a patient's condition.(42-44) These in vitro cellular avatars can be studied to identify key molecular pathologies that might give an indication as to how best to treat an individual patient of interest. The use of iPSC technologies in this manner can be extended with a few additional, very recently developed, technologies to create even better models of an individual's condition. For example, if the patient has a known mutation causing his or her condition, it is possible to use assays based on, e.g., Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) and related constructs to create isogenic cells in which some cells have the mutation in question and some do not. Comparison of these cells allows direct insight into the effects of the mutation while controlling for all relevant genetic background effects associated with the patient's genome.(45, 46) In addition, it is possible to create partial organs or organoids from cells obtained from an individual.(47) Organoids can provide greater insight into molecular pathologies associated with an individual patient's condition since they can model cell:cell interactions and more global tissue function.(48)

To achieve truly personalized medical care, the use of patient avatars derived from their own cells could be integrated with other pieces of information about a patient, as well as protocols for acting on that information. Schork and Nazor describe the motivation and integration of different aspects of patient diagnosis, intervention choice, and monitoring, using, among other things, patient avatars.(49) One important aspect of the use of cell-based patient avatars in personalized medicine is that they can accommodate personalized drug screening: literally testing thousands of drugs and compounds against a patient's cells (or organoids, possibly modified with CRISPR technologies) to identify drugs or compounds that uniquely correct the patient's molecular defects. If the drug or compound has actually been approved for use, possibly for another condition, then it could be tested for efficacy with the patient in question under an approved drug repurposing protocol. The use of patient-derived cells in personalized drug screening initiatives has shown some success in cancer settings, as tumor biopsies can yield appropriate material for drug screening.(48, 50) The biggest concern with this approach revolves around the question of whether or not the in vitro models capture relevant in vivo pathobiology and drug response information that may impact a patient's response to a chosen drug. A more direct strategy for in vivo experimental cancer intervention choice could involve implanting a device into a patient's tumor in vivo and then delivering different drugs through that device to see which ones have an effect.(51, 52)

The availability of inexpensive genotyping and sequencing technologies is allowing individuals and their health care providers to assess their genetically-mediated risk for disease and/or make a genetic diagnosis if they are already diseased. In addition, given the availability of health monitoring devices, online-ordered blood-based clinical assays, inexpensive imaging devices, etc. it is possible to continuously, or near continuously, monitor aspects of an individual's health (see Figure 1 and see the articles associated with the quantified self movement: http://quantifiedself.com)(53, 54). With this in mind, combining genetic risk or diagnostic assessment with intense health monitoring makes sense. A number of individuals with unique diseases and conditions have benefitted from a genetic diagnosis, as it uncovered potential genetically-mediated pathogenic mechanisms or revealed potential targets for pharmacotherapies for them.(49) In addition, a number of individuals have monitored their health intensely for the express purpose of identifying signs of a health status changes, some of which might be attributable to genetic susceptibilities.(55) Table 1 lists examples of published studies exploring the utility of genetic assays in generating a diagnosis for individuals with idiopathic conditions (or what have been referred to as diagnostic odysseys) as well as published studies exploring the utility of near continuous monitoring to identify evidence for a health status change in an individual. Such diagnoses and monitoring are highly personalized by definition.(15, 16, 56)

Monitoring individuals for health status changes is not trivial, however, if the measures being collected have not been evaluated in a population. This is because there will be no established norms that can be contrasted to the measures collected on an individual to know if those measures are abnormal. However, the community is quickly recognizing the utility of establishing personal thresholds for measures as opposed to population thresholds, as discussed in the Personalizing Early Detection Strategies section above (26, 27) As noted, population thresholds are established from epidemiologic and population survey data and include often-used thresholds for determining disease status such as a cholesterol level greater than 200 for heart disease or a systolic blood pressure greater than 140 mmHg for hypertension. Personal thresholds are established from longitudinal or legacy values of a measure collected on an individual and may be unique to the individual in question and their use in some settings suggests that they work better than population thresholds.(26)

The ubiquity of smart phones has attracted the interest of many researchers in the health professions as a vehicle for not only collecting health data through various apps but also to provide advice, feedback, coaching, imagery, music, text-messages, or connections with other resources, that could benefit an individual with a particular condition or disease. This has led to the emergence of the concept of a digital therapeutic: a smart phone app designed to treat and bring relief to an individual affected by a medical or psychological condition.(57) The content provided by a digital therapeutic app to an individual could vary depending on what is learned about that individual and his or her response to content provided in the app. In this way, the app can be personalized.(58) Many digital therapeutics have undergone evaluation for their ability to engage users and benefit them.(59) The US Food and Drug Administration (FDA) has created guidelines for registering digital therapeutics as bona-fide, insurance-reimbursable, approved health technologies, and has begun evaluating and approving many of them. The first approved digital therapeutic an app for substance abuse was approved by the FDA in 2017.(60) How easily digital therapeutics will be assimilated into the care stream is an open question.(61)

Personalized medicine strategies and approaches can be applied to treatments for fertility, as many researchers have proposed. For example, it has been suggested that one could leverage real world data of the type collected routinely on patients visiting reproductive medicine and fertility clinics (from, e.g., Electronic Medical Record (EMR) systems established at many hospitals and clinics), and use these data to in analyses exploring patterns, patient subgroups and individual patient profiles that could shed light on variation in fertility rates, responses to interventions to enhance fertility, etc. The results of these analyses could then guide future care for patients with fertility issues.(62) In the context of the use of digital medicine, proposals to develop smart phone apps that could provide personalized coaching content to enhance pregnancy have been put forth.(63). Genetic variants known to influence fertility have also been identified and could be used to support diagnoses or personalized intervention plans.(64)(65) Finally, adaptive trial designs have been proposed that could be used to assess the utility of personalized approaches to raising awareness about time to conception and fertility.(66)

In addition to these more traditional approaches to personalizing fertility interventions, there are a number of emerging strategies to enhance fertility in women that go beyond traditional ways of stimulating ovaries.(67) For example, it is now possible to cryopreserve a set oocytes and ovarian tissue samples from a woman and then implant them in her at a later time that may suit her desire to become pregnant.(68) Such a procedure would be highly personalized, since it would work with an individual's own cells and accommodate her preferences for becoming pregnant. However, this procedure would only work if the preserved tissues were viable and not damaged, although relevant cells in those tissues could, in theory, be corrected for genetic defects using gene editing techniques.(69) A more futuristic and controversial personalized fertility intervention, involves the concept that one could use cell reprogramming technologies to generate sperm and egg cells from other cells obtained from an individual (e.g., skin cells) that could be edited to generate de novo gametes for fertilization a concept known as in vitro gametogenesis.(70)

Personalized Medicine, or the practice of characterizing an individual patient on a number of levels (e.g., genomic, biochemical, behavioral, etc.) that might shed light on their response to an intervention, and then treating them accordingly, is a necessity given the fact that clinically meaningful inter-individual variation has, and will continue to be, identified. The availability of modern biomedical technologies such as DNA sequencing, proteomics, and wireless monitoring devices, has enabled the identification of this variation, essentially exposing the need for the personalization of medicine at some level. The future challenges associated with this reality will be to not only improve the efficiency in the way in which individuals are characterized, but also in the way in personalized medicines are crafted and vetted to show their utility. This is not to say that interventions that work ubiquitously (i.e., the traditional single agent block buster drugs) should be ignored if identified, but rather that they might be very hard to identify going forward.

There are a few other issues associated with personalized medicine that may hard to overcome in the near term. For example, the need for large data collections in order to identify factors that discriminate groups of individuals that might benefit more from one or another intervention, could create concerns about privacy and the data about those individuals possibly being used for nefarious purposes.(71-73) Fortunately, this issue is not necessarily unique to health care settings, whether current or future, as it has plagued many other industries including the banking, marketing, and social media industries. Strategies exploited in these other industries could be used in health care settings as well. In addition, developing more efficient ways of developing personalized medicines (for example, with respect to cell replacement therapies or mutation-specific drugs that work for a small fraction of patients) is crucial to meet the demands of all patients. Also, paying for personalized medicine practices in the future may be complicated given that they might be initially more expensive.(74) Finally, in order for various stakeholders to embrace personalized medicine, better strategies to educate and train health care professionals about personalized medicine must be developed and implemented.

Dr. Schork and his lab are funded in part by US National Institutes of Health Grants UL1TR001442 (CTSA), U24AG051129, U19G023122, as well as a contract from the Allen Institute for Brain Science (note that the content of this manuscript is solely the responsibility of the authors and does not necessarily represent the official views of the NIH).

LHG and NJS have no conflicts to declare with respect to this article.

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Personalized Medicine: Motivation, Challenges and Progress

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Commensurate with technological improvements and the subsequent rise in genetic research and genetics-based tests and treatments, the term personalized medicine is being used more frequently. But what does it mean? This explainer outlines what personalized medicine is, its advantages and some concerns that have been raised in relation to it.

Also known as precision medicine, personalized medicine is a rapidly expanding field of practice that uses an individuals genetic profile to guide decisions about the prevention, diagnosis, and treatment of disease.

Although the concept of personalized medicine began in the 1990s, following advances in DNA sequencing technology, it pretty much remained just a concept and was rarely applied. Since then, continued advances have generated enormous amounts of new information. The discovery of genes, proteins, and pathways has enabled studies into the genetics underlying both rare and common diseases and aided the identification of new drug targets.

Whats good about personalized medicine

For a long time, the practice of medicine has largely been reactive, waiting for the onset of disease before treating or curing it. But were all unique in terms of genetic makeup, environment, and lifestyle factors. Our growing understanding of genetics and genomics the study of all of a persons genes and how they drive health, disease and treatment in individual people offers an opportunity to step away from a one size fits all approach based on broad population averages and adopt an individualized approach.

In addition to advances in the field of genomics, developments in the fields of science and technology play a crucial role in personalized medicine. For example, the development of high-resolution analytics, biotech research and chemistry, and the ability to decipher molecular structures, signaling pathways, and protein interactions that underpin the mechanisms of gene expression.

Personalized medicine is about more than prescribing the best drugs, although thats a large part of it. Proponents say it would shift medicines emphasis from reaction to prevention, better predict disease susceptibility and improve diagnosis, produce more effective drugs and reduce adverse side effects, and eliminate the inefficiency and cost of adopting a trial-and-error approach to healthcare.

Goetz & Schork

Weve already seen personalized medicine positively impact patient care in patients with diseases like breast cancer, melanoma, and cardiovascular disease. And the use of patient-derived cell and organoid avatars as disease models to identify beneficial treatments provides truly personalized medicine to individual patients. Then theres CRISPR, technology that allows genetic material to be added, removed, or altered at particular locations in the genome as a direct way of treating genetic and other conditions.

Concerns about personalized medicine

Despite its numerous benefits, the adoption of a personalized medicine approach raises several issues. For it to reach peak efficiency, a lot of genomic data must be collected from a large and diverse section of the population, and its critical that participants privacy and confidentiality are protected. Privacy issues extend to the collection, storage and sharing of that information.

Extensive changes to the healthcare system, including ethical changes, are likely needed to overcome the ethical obstacles of personalized medicine use, including knowledge gap and informed consent, privacy and confidentiality, and the availability of healthcare services. Social benefit versus science development and individual benefit needs to be considered and balanced. And there are concerns that data collected might be used unethically, such as insurance companies not offering some policies to people with a certain genetic predispositions.

Legally, a physician is negligent when they fail to follow generally accepted practice. In personalized medicine, when the interpretation of genetic information is at issue, there may be no generally accepted practice or standard. It begs the question, at what point does clinical genetic knowledge become a standard of practice?

Cost is another relevant factor. While the expense associated with large-scale DNA sequencing is decreasing, its still expensive. And drugs developed based on molecular or genetic variations are likely to be costly. Further, massive amounts of data require massive infrastructure changes, including changes to the mechanisms of data collection, storage and sharing, all of which require investment.

Will personalized medicine happen?

Personalized medicine is already happening in the form of things like CRISPR, mRNA vaccines and the large-scale genome sequencing of newborns. Its the kind of future that was envisioned when the Human Genome Project was first completed 20 years ago, and it certainly has its benefits.

However, the widespread adoption of personalized medicine may prove more difficult than first imagined. In addition to the concerns already mentioned, there needs to be a change in public attitudes and the way medical professionals, patients and health regulators view the approach. It may require a new approach to the way drugs are tested and a willingness to embrace risk.

Nonetheless, the potential benefits are so great and the march of technology and knowledge so inexorable that it is a near certainty that personalized medicine will continue to develop and become standard in healthcare systems at some point in the future it's just a matter of how fast the advances are made and how soon the aforementioned hurdles can be overcome as to exactly when this might be.

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Personalized medicine: The pros, cons and concerns - New Atlas

Abstract

The convergence of artificial intelligence (AI) and precision medicine promises to revolutionize health care. Precision medicine methods identify phenotypes of patients with lesscommon responses to treatment or unique healthcare needs. AI leverages sophisticated computation and inference to generate insights, enables the system to reason and learn, and empowers clinician decision making through augmented intelligence. Recent literature suggests that translational research exploring this convergence will help solve the most difficult challenges facing precision medicine, especially those in which nongenomic and genomic determinants, combined with information from patient symptoms, clinical history, and lifestyles, will facilitate personalized diagnosis and prognostication.

In a recent National Academy of Medicine report about the current and future state of artificial intelligence (AI) in health care, the authors noted unprecedented opportunities to augment the care of specialists and the assistance that AI provides in combating the realities of being human (including fatigue and inattention) and the risks of machine error. Importantly, the report notes that whereas care must be taken with the use of these technologies, much promise exists.1 The digitization of healthrelated data and the rapid uptake in technology are fueling transformation and progress in the development and use of AI in healthcare.2, 3, 4 However, multimodal data integration, security, federated learning (which requires fundamental advances in areas, such as privacy, largescale machine learning, and distributed optimization), model performance, and bias may pose challenges to the use of AI in health care.5

Three main principles for successful adoption of AI in health care include data and security, analytics and insights, and shared expertise. Data and security equate to full transparency and trust in how AI systems are trained and in the data and knowledge used to train them. As humans and AI systems increasingly work together, it is essential that we trust the output of these systems.

Analytics and insights equate to purpose and people where augmented intelligence and actionable insights support what humans do, not replace them. AI can combine input from multiple structured and unstructured sources, reason at a semantic level, and use these abilities in computer vision, reading comprehension, conversational systems, and multimodal applications to help health professionals make more informed decisions (e.g., a physician making a diagnosis, a nurse creating a care plan, or a social services agency arranging services for an elderly citizen). Shared expertise equates to our complementary relationship with AI systems, which are trained by and are supporting human professionals, leading to workforce change, which leads to new skills. The ability to create cuttingedge AI models and build highquality business applications requires skilled experts with access to the latest hardware.

A vast amount of untapped data could have a great impact on our healthyet it exists outside medical systems.6 Our individual health is heavily influenced by our lifestyle, nutrition, our environment, and access to care. These behavioral and social determinants and other exogenous factors can now be tracked and measured by wearables and a range of medical devices. These factors account for about 60% of our determinants of health (behavioral, socioeconomical, physiological, and psychological data), our genes account for about 30%, and last our actual medical history accounts for a mere 10%.6 Over the course of our lifetimes, we will each generate the equivalent of over 300 million books of personal and healthrelated data that could unlock insights to a longer and healthier life.7

The phenomenon of big data can be described using the five Vs: volume, velocity, variety, veracity, and value. Volume refers to the vast amount of complex and heterogenous data, which makes data sets too large to store and analyze using traditional database technology. Velocity refers to the speed at which new data are generated and moves around. Variety refers to the different types of structured, semistructured, and unstructured data, such as social media conversations and voice recordings. Veracity refers to the certainty, accuracy, relevance, and predictive value of the data. Value refers to the conversion of data into business insights. Whereas the volume, variety, velocity, and veracity of data are contributing to the increasing complexity of data management and workloadscreating a greater need for advanced analytics to discover insightsmobile devices have made technology more consumable, creating user demand for interactive tools for visual analytics.

Big data analytics and AI are increasingly becoming omnipresent across the entire spectrum of health care, including the 5 Ps spanning: payer, provider, policy maker/government, patients, and product manufacturers. Up to 10% of global health care expenditure is due to fraud and abuse and AIbased tools help mitigate fraud, waste, and abuse in payer programs.8 Reliable identification of medical coding errors and incorrect claims positively impacts payers, providers, and governments by saving inordinate amounts of money, time, and efforts.9 As an example, IBM DataProbe, an AIbased business intelligence tool, was able to detect and recover US $41.5million in feeforservice payments over a 2year period in Medicaid fraud for Iowa Medicaid Enterprise.10 In the provider space, AI is used for evidencebased clinical decision support,11 detection of adverse events, and the usage of electronic health record (EHR) data to predict patients at risk for readmission.12 Healthcare policymakers and government use AIbased tools to control and predict infections and outbreaks. An example is FINDER, a machinelearned model for realtime detection of foodborne illness using anonymous and aggregated web search and location data.13 Another example is the integrated data hub and caremanagement solution using IBM Connect360 and IBM Watson Care Manager that Sonoma County, California government agencies used to transform health and healthcare for socially disadvantaged groups and other displaced individuals during a time of communitywide crisis.14 This solution enabled integration of siloed data and services into a unified citizen status view, identification of clinical and social determinants of health from structured and unstructured sources, construction of algorithms to match clients with services, and streamlining of care coordination during the 2017 and 2019 Sonoma County wildfires. With the advent of the global pandemic coronavirus disease 2019 (COVID19) in early 2020, such a model can be used to predict atrisk populations, and potentially provide additional risk information to clinicians caring for atrisk patients.15 The use of AI for patients and life sciences/healthcare products are addressed extensively in the sections that follow.

AI is not, however, the only datadriven field impacting health and health care. The field of precision medicine is providing an equal or even greater influence than AI on the direction of health care16 and has been doing so for more than a decade.17 Precision medicine aims to personalize care for every individual. This goal requires access to massive amounts of data, such as data collected through the United Kingdoms UK Biobank and the All of Us project, coupled with a receptive health care ecosystem willing to abandon the conventional approach to care in favor of a more highly individualized strategy. The convergence of these fields will likely accelerate the goals of personalized care and tightly couple AI to healthcare providers for the foreseeable future. In the sections that follow, we will briefly summarize the capabilities of existing AI technology, describe how precision medicine is evolving, and, through a series of examples, demonstrate the potentially transformative effect of AI on the rate and increasing breadth of application for precision medicine.

The past 10years have seen remarkable growth and acceptance of AI in a variety of domains and in particular by healthcare professionals. AI provides rich opportunities for designing intelligent products, creating novel services, and generating new business models. At the same time, the use of AI can introduce social and ethical challenges to security, privacy, and human rights.1

AI technologies in medicine exist in many forms, from the purely virtual (e.g., deeplearningbased health information management systems and active guidance of physicians in their treatment decisions) to cyberphysical (e.g., robots used to assist the attending surgeon and targeted nanorobots for drug delivery).18 The power of AI technologies to recognize sophisticated patterns and hidden structures has enabled many imagebased detection and diagnostic systems in healthcare to perform as well or better than clinicians, in some cases.19 AIenabled clinical decisionsupport systems may reduce diagnostic errors, augment intelligence to support decision making, and assist clinicians with EHR data extraction and documentation tasks.20 Emerging computational improvements in natural language processing (NLP), pattern identification, efficient search, prediction, and biasfree reasoning will lead to further capabilities in AI that address currently intractable problems.21, 22

Advances in the computational capability of AI have prompted concerns that AI technologies will eventually replace physicians. The term augmented intelligence, coined by W.R. Ashby in the 1950s,23 may be a more apt description of the future interplay among data, computation, and healthcare providers and perhaps a better definition for the abbreviation AI in healthcare. A version of augmented intelligence, described in the literature in Friedmans fundamental theorem of biomedical informatics,24 relates strongly to the role of AI in health care (depicted in Figure1). Consistent with Friedmans description of augmented intelligence, Langlotz at Stanford stated that Radiologists who use AI will replace radiologists who dont.25

A version of the Friedmans fundamental theorem of informatics describing the impact of augmented intelligence. The healthcare system with AI will be better than the healthcare system without it. AI, artificial intelligence.

An AI system exhibits four main characteristics that allow us to perceive it as cognitive: understanding, reasoning, learning, and empowering.26 An AI system understands by reading, processing, and interpreting the available structured and unstructured data at enormous scale and volume. An AI system reasons by understanding entities and relationships, drawing connections, proposing hypotheses, deriving inferences, and evaluating evidence that allows it to recognize and interpret the language of health and medicine. An AI system learns from human experts and realworld cases by collecting feedback, learning from outcomes at all levels and granularities of the system, and continuing to improve over time and experience. An AI system empowers and interacts clinicians and users by providing a more integrated experience in a variety of settings, combining dialog, visualization, collaboration, and delivering previously invisible data and knowledge into actionable insights. In contrast, humans excel at common sense, empathy, morals, and creativity.

Augmenting human capabilities with those provided by AI leads to actionable insights in areas such as oncology,27 imaging,28 and primary care.29 For example, a breast cancer predicting algorithm, trained on 38,444 mammogram images from 9,611 women, was the first to combine imaging and EHR data with associated health records. This algorithm was able to predict biopsy malignancy and differentiate between normal and abnormal screening results. The algorithm can be applied to assess breast cancer at a level comparable to radiologists, as well has having the potential to substantially reduce missed diagnoses of breast cancer.30 This combined machinelearning and deeplearning model trained on a dataset of linked mammograms and health records may assist radiologists in the detection of breast cancer as a second reader.

The field of precision medicine is similarly experiencing rapid growth. Precision medicine is perhaps best described as a health care movement involving what the National Research Council initially called the development of a New Taxonomy of human disease based on molecular biology, or a revolution in health care triggered by knowledge gained from sequencing the human genome.31 The field has since evolved to recognize how the intersection of multiomic data combined with medical history, social/behavioral determinants, and environmental knowledge precisely characterizes health states, disease states, and therapeutic options for affected individuals.32 For the remainder of this paper, we will use the term precision medicine to describe the health care philosophy and research agenda described above, and the term personalized care to reflect the impact of that philosophy on the individual receiving care.

Precision medicine offers healthcare providers the ability to discover and present information that either validates or alters the trajectory of a medical decision from one that is based on the evidence for the average patient, to one that is based upon individuals unique characteristics. It facilitates a clinicians delivery of care personalized for each patient. Precision medicine discovery empowers possibilities that would otherwise have been unrealized.

Advances in precision medicine manifest into tangible benefits, such as early detection of disease33 and designing personalized treatments are becoming more commonplace in health care.34 The power of precision medicine to personalize care is enabled by several data collection and analytics technologies. In particular, the convergence of highthroughput genotyping and global adoption of EHRs gives scientists an unprecedented opportunity to derive new phenotypes from realworld clinical and biomarker data. These phenotypes, combined with knowledge from the EHR, may validate the need for additional treatments or may improve diagnoses of disease variants.

Perhaps the most wellstudied impact of precision medicine on health care today is genotypeguided treatment. Clinicians have used genotype information as a guideline to help determine the correct dose of warfarin.35 The Clinical Pharmacogenetics Implementation Consortium published genotypebased drug guidelines to help clinicians optimize drug therapies with genetic test results.36 Genomic profiling of tumors can inform targeted therapy plans for patients with breast or lung cancer.34 Precision medicine, integrated into healthcare, has the potential to yield more precise diagnoses, predict disease risk before symptoms occur, and design customized treatment plans that maximize safety and efficiency. The trend toward enabling the use of precision medicine by establishing data repositories is not restricted to the United States; examples from Biobanks in many countries, such as the UK Biobank,37 BioBank Japan,38 and Australian Genomics Health Alliance39 demonstrate the power of changing attitudes toward precision medicine on a global scale.

Although there is much promise for AI and precision medicine, more work still needs to be done to test, validate, and change treatment practices. Researchers face challenges of adopting unified data formats (e.g., Fast Healthcare Interoperability Resources), obtaining sufficient and high quality labeled data for training algorithms, and addressing regulatory, privacy, and sociocultural requirements.

AI and precision medicine are converging to assist in solving the most complex problems in personalized care. Figure2 depicts five examples of personalized healthcare dogma that are inherently challenging but potentially amenable to progress using AI.40, 41, 42

Dimensions of synergy between AI and precision medicine. Both precision medicine and artificial intelligence (AI) techniques impact the goal of personalizing care in five ways: therapy planning using clincal, genomic or social and behavioral determinants of health, and risk prediction/diagnosis, using genomic or other variables.

Genomeinformed prescribing is perhaps one of the first areas to demonstrate the power of precision medicine at scale.43 However, the ability to make realtime recommendations hinges on developing machinelearning algorithms to predict which patients are likely to need a medication for which genomic information. The key to personalizing medications and dosages is to genotype those patients before that information is needed.44

This use case was among the earliest examples of the convergence between AI and precision medicine, as AI techniques have proven useful for efficient and highthroughput genome interpretation.45 As noted recently by Zou and colleagues,46 deep learning has been used to combine knowledge from the scientific literature with findings from sequencing to propose 3D protein configurations, identify transcription start sites, model regulatory elements, and predict gene expression from genotype data. These interpretations are foundational to identifying links among genomic variation and disease presentation, therapeutic success, and prognosis.

In medulloblastoma, the emergence of discrete molecular subgroups of the disease following AImediated analysis of hundreds of exomes, has facilitated the administration of the right treatment, at the right dosage, to the right cohort of pediatric patients.47 Although conventional treatment of this disease involved multimodal treatment, including surgery, chemotherapy, and whole brain radiation, precision genomics has enabled treatment of the wingless tumor subgroup, which is more common in children, with chemotherapy aloneobviating the need for radiation.48 Avoiding radiotherapy is particularly impactful for mitigating potential neurocognitive sequelae and secondary cancers from wholebrain radiation among disease survivors.49, 50

The initial successful paradigm of AI in imaging recognition has also given rise to radiogenomics. Radiogenomics, as a novel precision medicine research field, focuses on establishing associations between cancer imaging features and gene expression to predict a patients risk of developing toxicity following radiotherapy.51, 52, 53 For example, Chang et al. proposed a framework of multiple residual convolutional neural networks to noninvasively predict isocitrate dehydrogenase genotype in grades IIIV glioma using multiinstitutional magnetic resonance imaging datasets. Besides, AI has been used in discovering radiogenomic associations in breast cancer,52 liver cancer,54 and colorectal cancer.53 Currently, limited data availability remains the most formidable challenge for AI radiogenomics.51

Knowing the response to therapy can help clinicians choose the right treatment plan. AI demonstrates potential applications in this area. For example, McDonald et al. trained a support vector machine using patients gene expression data to predict their response to chemotherapy. Their data show encouraging outcomes across multiple drugs.55 Sadanandam et al. proposed approaches of discovering patterns in gene sequences or molecular signatures that are associated with better outcomes following nontraditional treatment. Their findings may assist clinicians in selecting a treatment that is most likely to be effective.56 Although tremendous progress has been made using AI techniques and genomics to predict treatment outcome, more prospective and retrospective clinical research and clinical studies still need to be conducted to generate the data that can then train the algorithms.

Incorporating environmental considerations into management plans require sufficient personal and environmental information, which may affect a patients risk for a poor outcome, knowledge about care alternatives, and conditions under which each alternative may be optimal.

One such example has been the challenge of identifying homelessness in some patients.57, 58, 59 These patients may require care in varying locations over a short period, requiring frequent reassessments of patient demographic data. Related issues, such as transportation, providing medications that require refrigeration, or using diagnostic modalities that require electricity (for monitoring), need to be modified accordingly.

Another environmental consideration is the availability of expertise in remote locations, including the availability of trained professionals at the point of need. AI has provided numerous examples of augmenting diagnostic capabilities in resourcepoor locations, which may translate into better patient classification and therefore more personalized therapy planning. Examples include the use of deep learning to identify patients with malaria60 and cervical cancer,61 as well as predicting infectious disease outbreaks,62 environmental toxin exposure,63 and allergen load.64

Finally, in addition to genomic considerations and social determinants of health, clinical factors are imperative to successful therapy planning. Age, comorbidities, and organ function in particular predicate treatment considerations and AI has emerged as a central pillar in stratifying patients for therapy. In one study, machine learning classifiers were used to analyze 30 comorbidities to identify critically ill patients who will require prolonged mechanical ventilation and tracheostomy placement.65 Other studies have used AI algorithms to analyze bedside monitored adverse events and other clinical parameters to predict organ dysfunction and failure.66, 67

Actress Angelina Jolies response to her inheritance of the BRCA gene illustrates the potential impact of more advanced genomic information on disease risk and prevention options.68 This case is not novel; the case of Woodie Guthrie and Huntingtons disease disclosed a similar conundrum for health care.69 Although the ethics of genetic testing without a clear cure continues to be debated, the broad availability of genetic information offered by nextgeneration sequencing and directtoconsumer testing renders personalized prevention and management of serious diseases a reality.70

Cardiovascular medicine is an area with a long history of embracing predictive modeling to assess patient risk.71 Recent work has uncovered methods to predict heart failure72 and other serious cardiac events in asymptomatic individuals.73 When combined with personalized prevention strategies,74, 75 these models may positively impact disease incidence and sequela. Complex diseases, such as cardiovascular disease, often involve the interplay among gender, genetic, lifestyle, and environmental factors. Integrating these attributes requires attention to the heterogeneity of the data.76 AI approaches that excel at discovering complex relationships among a large number of factors provide such opportunities. A study from Vanderbilt demonstrated early examples of combining EHR and genetic data, with positive results in cardiovascular disease prediction.77 AIenabled recognition of phenotype features through EHR or images and matching those features with genetic variants may allow faster genetic disease diagnosis.78 For example, accurate and fast diagnosis for seriously ill infants that have a suspected genetic disease can be attained by using rapid wholegenome sequencing and NLPenabled automated phenotyping.79

Automated speech analytics have benefited from improvements in the technical performance of NLP, understanding, and generation. Automated speech analytics may provide indicators for assessment and detection of earlystage dementia, minor cognitive impairment, Parkinsons disease, and other mental disorders.80, 81, 82, 83 Efforts also are underway to detect changes in mental health using smartphone sensors.84

AIassisted monitoring may also be used in realtime to assess the risk of intrapartum stress during labor, guiding the decision of cesarean section vs. normal vaginal deliveries, in an effort to decrease perinatal complications and stillbirths.85 This exemplifies realtime AIassisted monitoring of streaming data to reduce manual error associated with human interpretation of cardiotocography data during childbirth.

AI is also being used in the detection and characterization of polyps in colonoscopy.86 Wider adaptation of AI during endoscopy may lead to a higher rate of benign adenoma detection and reduction of cost and risk for unwarranted polypectomy.87 AImediated image analysis aimed at improving disease risk prediction and diagnosis will likely continue to increase in use for detection of diabetic retinopathy88 and metastasis in cancer,89 as well as for identification of benign melanoma.90 AIbased image analysis has become a part of a directtoconsumer diagnostic tool for anemia as well.91

The widespread use of home monitoring and wearable devices has long been accompanied by the expectation that collected data could help detect disease at an earlier state. Indeed, these advances have fueled new, noninvasive, wearable applications for monitoring and detecting specific health conditions, such as diabetes, epilepsy, pain management, Parkinsons disease, cardiovascular disease, sleep disorders, and obesity.92 Digital biomarkers are expected to facilitate remote disease monitoring outside of the physical confines of a hospital setting and can support decentralized clinical trials.93 Wearable tools that provide continuous multidimensional measurements of preselected biomarkers would enable the detection of minimum residual disease and monitor disease progression.94 In the field of cancer care, evolving technology using wearable devices continuously analyzes circulating tumor cells to screen for relapsed disease.95

We have observed increasing efforts to implement AI in precision medicine to perform tasks such as disease diagnosis, predicting risk, and treatment response. Although most of these studies showed promising experimental results, how AI improved health care is not fully demonstrated. In reality, the success of transforming an AI system to a realworld application not only depends on the accuracy but also relies on the capability of working accurately in a reliable, safe, and generalizable manner.5 For example, the difference among institutions in coding definitions, report formats, or cohort diversity, may result in a model trained using onesite data to not work well in another site (https://www.bmj.com/content/368/bmj.l6927). Here, we highlighted three main challenges that would impact the success of transitions to realworld healthcare.

Fairness and bias. The health data can be biased while building and processing the dataset (e.g., a lack of diverse sampling, missing values, and imputation methods; https://datasociety.net/library/fairnessinprecisionmedicine/). An AI model trained on the data might amplify the bias and make nonfavorable decisions toward a particular group of people characterized by age, gender, race, geographic, or economic level. Such unconscious bias may harm clinical applicability and health quality. Thus, it is crucial to detect and mitigate the bias in data and models. Some potential solutions include improving the diversity of the data, such as the All of Us program that aimed to recruit participants with diverse backgrounds. AI communities also proposed several techniques to fight against bias (https://arxiv.org/abs/1908.09635). IBM has developed an online toolkit (AI Fairness 360) that implemented a comprehensive set of fairness metrics to help researchers examine the bias among datasets and models, and algorithms to mitigate bias in classifiers (https://doi.org/10.1147/JRD.2019.2942287). However, fairness and protected attributes are closely related to the domain context and applications. More work is needed in biomedical research to define and explore the fairness and bias in AI models trained with historical patient data. To address the challenge, a collaborative effort that involves the AI and biomedical community is needed.

Socioenvironmental factors. The environmental factors and workflows where the AI model would be deployed may impact model performance and clinical efficacy. A recent prospective study carried out by Google Health evaluated an AI system for screening diabetic retinopathy in a real clinical environment. The AI system was developed to augment diabetic retinopathy screening by providing intime assessment, before this the process may take several weeks. Despite a specialistlevel accuracy (>90% sensitivity and specificity) achieved on retrospective patient data; however, the system has undergone unexpected challenges when applied to Thailand clinics (https://doi.org/10.1145/3313831.3376718). For example, the variety of conditions and workflows in clinics impaired the quality of the images that did not meet the system' high standards, resulting in a high rejection rate of images. The unstable internet connection restricted the processing speed of the AI models and caused a longer waiting time for the patients. Travel and travel costs may deter participants from remaining in the study. Such prospective studies highlighted the importance of validating the AI models in the clinical environment and considering an iteration loopthat collects users feedback as new input for learning and system improvement96 before applying the AI system widely. Of note, in healthcare, obtaining such feedback would take a long time at a high cost. It may take a longer time to evaluate a therapys effect and associated longterm health outcomes than what is required to validate whether a product is appealing to a customer. There is a need to explore other ways to facilitate creation of highperforming AI systems, for example, generating synthetic data that carries similar distributions and variances as the realworld data, or leveraging a simulated environment. Early examples by groups, such as Baowaly and colleagues,89 demonstrate much promise, but more AI research efforts are needed.

Data safety and privacy. Data is crucial to an AIdriven system. As AI and precision medicine are converging, data (e.g., genomics, medical history, behaviors, and social data that covers peoples daily lives) will be increasingly collected and integrated. Individuals concerns for data privacy are closely related to trust when they use AIenabled services. Building a safe and wellcontrolled ecosystem for data storage, management, and sharing is essential, requiring new technology adoptions, and collaborations, as well as the creation of new regulations and business models.

The training of AI methods and validation of AI models using large data sets prior to applying the methods to personal data may address many of the challenges facing precision medicine today. The cited examples reinforce the importance of another potential use of augmented intelligence, namely that of the role of technology in the hands of consumers to help communicate justintime risk or as an agent of behavior change. Although most studies to date are small and the data are limited, the ability to identify atrisk patients will translate into personalized care when identification is combined with strategies to notify and intervene. Researchers are actively pursuing the use of mobile apps, wearables, voice assistants, and other technology to create personspecific interfaces to intelligent systems. A review of these approaches is beyond the scope of this paper.

Active research in both AI and precision medicine is demonstrating a future where healthrelated tasks of both medical professionals and consumers are augmented with highly personalized medical diagnostic and therapeutic information. The synergy between these two forces and their impact on the healthcare system aligns with the ultimate goal of prevention and early detection of diseases affecting the individual, which could ultimately decrease the disease burden for the public at large, and, therefore, the cost of preventable health care for all.

This work was funded by a partnership between IBM Watson Health and Vanderbilt University Medical Center.

Drs. Weeraratne, Rhee, and Snowdon are employed by IBM Watson Health. All other authors declared no competing interests for this work.

The authors thank Karlis Draulis for his assistance with the figures.

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Precision Medicine, AI, and the Future of Personalized Health Care

Comprehensive Genomic Profiling at Diagnosis Extends Survival in Patients with Advanced Cancer  Inside Precision Medicine

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Comprehensive Genomic Profiling at Diagnosis Extends Survival in Patients with Advanced Cancer - Inside Precision Medicine

Why precision medicine results in more effective health care, treatment plans  The Business Journals

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Why precision medicine results in more effective health care, treatment plans - The Business Journals

More Precise Classifications of NonClear Cell RCC Are Required to Improve Personalized Treatment  OncLive

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More Precise Classifications of NonClear Cell RCC Are Required to Improve Personalized Treatment - OncLive

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Bahrain aims to provide residents with personalized healthcare - Healthcare IT News

Precision Medicine Market Is Expected To Reach Revenue Of  GlobeNewswire

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New Graduates Leverage Genomics Education in Clinical and Research Settings  University of Colorado Anschutz Medical Campus

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New Graduates Leverage Genomics Education in Clinical and Research Settings - University of Colorado Anschutz Medical Campus

Editors Note: This is the second article in a two-part series exploring the promise and limitations of the field of personalized medicine. The first part focused on advances and innovation in the field.

In the mid-1990s, researchers identified two gene mutations that are key to predicting genetic susceptibility to breast cancer: BRCA1 and BRCA2. In 1996, the BRCA1/2 mutation screening became the first genetic test for cancer risk available as a clinical service.

This genetic screening was an early innovation in a field that has come to be known as personalized medicine, which can be applied across a variety of medical specialties. Its defining characteristic is that a patients health care team takes into consideration a wide range of factors such as genetics, lifestyle, diet, specifics of disease presentation, and living environment when deciding on an individualized prevention or treatment plan.

With the advent of personalized medicine, including genetic screening as well as more targeted cancer drugs and therapies, the death rate for breast cancer in the United States declined by 43% from 1989 to 2020, according to the American Cancer Society (ACS). But even as mortality from breast cancer has decreased overall, there are statistics that highlight inequities in outcomes. Despite Black women having a lower incidence of breast cancer than non-Hispanic White women, Black women of all ages die from breast cancer at a 40% higher rate than non-Hispanic White women, and Black women under 50 years old die of breast cancer at twice the rate of non-Hispanic White women in the same age category.

Research shows that Black women get the BRCA1/2 screening less often than White women, at least in part because it is offered to them less frequently. One 2017 study found that, of women under 50 years old diagnosed with invasive breast cancer in Florida, 85.7% of the White women in the study were referred for genetic testing, while only 37% of the Black women were.

This is just one example of the inequities that some medical researchers and health equity advocates say severely limit the benefits of personalized medicine, even as technology advances.

[Personalized medicine] products are informative and are having an impact in certain communities, but its not equitable across all communities, says Rick Kittles, PhD, senior vice president for research at Morehouse School of Medicine, a historically Black medical college (HBCU) in Atlanta.

In the United States, people who are Black, Hispanic or Latino, American Indian or Alaska Native, people with low incomes, people who are uninsured or underinsured, and those who live in rural areas, as well as others who have been marginalized, face multiple barriers to personalized medicine. These barriers include a lack of inclusion of diverse genetics in research, the high cost of genetic testing and technology used in personalized medicine, and a lack of awareness of and education about personalized medicine among health care providers outside of urban medical centers. Some sociologists hypothesize that advances in medical innovation may, in fact, exacerbate existing inequities because people with economic and educational advantage are more likely to access care that improves lives and reduces mortality, while those from marginalized communities are left behind.

Its a problem that several academic medical centers are seeking to address with a range of strategies, from expanding personalized medicine research at HBCU medical schools to engaging community partners for research recruitment.

The field of human genetics has grown exponentially since the 2003 completion of the Human Genome Project, an international research effort that mapped the gene pairs that make up human DNA. The endeavor found that all humans share 99.9% of the same genome, with the other 0.1% accounting for all genetic diversity among individuals. Within that 0.1% are the wide variety of heritable traits, from physical characteristics to genetic mutations that cause or increase risk for certain diseases.

And yet, in the more than 6,000 genome-wide association studies (when researchers scan the genomes of large populations to try to identify genetic variations associated with diseases) that have been published internationally over the last two decades, 90% of all people analyzed were of European descent, according to a 2023 article in the Human Molecular Genetics journal.

This means that researchers have very little understanding of heritable disease risk for the vast majority of the worlds population when it differs from the variations seen in people of European descent.

Kittles, who is a genetic epidemiologist by trade, joined Morehouse in 2022 to lead the medical schools expanding efforts to advance medical research focusing on the inclusion of people from groups that have historically been excluded from clinical research and underserved in health care.

Among his faculty recruits is Melissa B. Davis, PhD, a genetics researcher focused on racial disparities in cancer who will lead the schools new Institute of Genomic Medicine. Davis previous work includes identifying two genes found in women of African ancestry that may increase their likelihood of developing an aggressive form of breast cancer, much like the BRCA1/2 gene.

For women of color who get tested [for BRCA 1/2], the benefit of that test is not equitable and in many cases the tests come back unknown, Kittles says. Thats because those variants [that are found in people of African descent] are not in databases Its a glaring, prime example of where we are in precision medicine right now.

The research expansion at Morehouse is funded by an $11.5 million grant from the Chan Zuckerberg Initiative (CZI, created by Facebook founder Mark Zuckerberg and his wife, Priscilla Chan) and is part of the charitable foundations larger Accelerate Precision Health program. CZI has granted equal funds to each of the nations three other HBCU medical schools: Charles Drew University College of Medicine in Los Angeles; Howard University College of Medicine in Washington, D.C.; and Meharry Medical College in Nashville.

When we think about the science we want to support, [we ask,] Who does the science? What science is being done? Who does the science serve? says Bil Clemons, PhD, science program officer for Diversity, Equity, and Inclusion in Science for CZI. Fundamentally, Is the science that were doing inclusive of everyone?

Most of the funding from CZI has gone to hiring faculty at HBCU medical schools to bolster their capacity to expand their research footprint over time, but its also funded the creation of new programs to train genetic counselors at Charles Drew University College of Medicine.

Kittles says that CZIs funding is instrumental to advancing research into genetic diversity and health disparities at HBCU medical schools, particularly because these institutions have often been overlooked for federal and philanthropic funding in the past.

That creates a disparity that not only limits the research impact of those institutions, but also the health of the communities that they serve, says Kittles. So much so that while all HBCUs have strong teaching experience, the development of research has been hampered because of the lack of funding and the ability to bring in talent who want to do research. The sustainability of research is limited because of that history.

In turn, thats set back progress in reducing health disparities, especially in Black communities and other communities of color, Kittles says, because HBCU medical schools tend to have more trust and access to those communities than many other medical centers.

Many academic medical centers historically have had a very strong disconnect with disparate communities, Kittles explains. The bulk of their research and the bulk of their patients are not diverse And so, when they do research, theyre limited in terms of their touch.

In addition to the efforts at the HBCU medical schools, dozens of medical centers are participating in the National Institutes of Health (NIH) All of Us research program, the goal of which is to build one of the largest and most diverse health databases in the world.

The All of Us program is studying patients social determinants of health, a phrase that refers to the various factors such as environment, socioeconomic status, access to healthy food, and access to health care that can affect health.

The NIH has funded and partnered with more than a dozen organizations to expand their reach into the communities that are historically underrepresented in biomedical research, including the American Association on Health and Disability; the National Alliance for Hispanic Health; and the National Baptist Convention, USA Inc.; among others. These organizations use their connections within marginalized communities to enroll and retain participants in the program. As of September 2021, the partners had helped enroll more than 400,000 participants, 80% of whom are from communities that are historically underrepresented in research. The study aims to provide a holistic picture of health by collecting samples of blood, urine, and saliva; physical measurements; electronic health records; health and family medical histories; information about lifestyles and communities; and data from wearable technologies, such as smartwatches, according to the NIH.

And while this and other endeavors are a step forward, Kittles says that all academic medical centers have a responsibility to resolve inequities in their own communities in order to truly make progress in advancing accessibility to personalized medicine.

In my career, Ive been at resource-rich [institutions], and resource-poor [institutions], and what I call community-rich and community-poor. Some had strong relationships with the community, and others had no trust from communities around them, says Kittles. When we talk about health equity, there has to be a commitment that goes beyond the window dressings and the social media tags that you see Part of that is bringing individuals into the institution that represent the communities that you want to benefit.

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Personalized medicine is coming, but who will have access to it?

Also called: precision medicine or individualized medicine(Showmore)

personalized medicine, field of medicine in which decisions concerning disease prevention, diagnosis, and treatment are tailored to individual patients based on information derived from genetic and genomic data. Personalized medicine centres on the concept that information about a patients genes and genome allows physicians to make more informed and effective decisions about a patients care.This idea essentially is an extension of conventional medicine, in which one strategy is applied across all patients, without tailoring to personal genetic and genomic information.

The concept of personalized medicine, although not novel at the time, materialized in the 1990s, following advances in DNA sequencing technology, including automation and increased throughput. Out of those advances came efforts such as the Human Genome Project (HGP; 19902003), in which sequences of more than three billion base pairs of the human genome were elucidated and made available to researchers worldwide. Likewise, the International HapMap Project (200210), which identified genetic variations that contribute tohuman disease, provided researchers with the information needed to associate gene variants with specific diseases and disorders.

Those advances cast light on phenomena in medicine that had been observed for yearsfor example, that certain drugs are more effective in some patients and that, in response to certain medications, some patients experience unusually severe side effects. Progress in understanding the molecular factors underlying the influence of individual genetic constitution on disease and therapeutics was greatly aided by developments in pharmacogenetics and pharmacogenomicsthe study of genetic causes behind differences in how individuals respond to drugs and the study of how multiple variations within the genome affect responses to drug treatments, respectively. Using data derived from pharmacogenetics and pharmacogenomics, researchers were able to develop more objective and accurate tests fordisease diagnosis and for predicting how patients would respond to therapeutic agents. In some cases, researchers found, using genetic and other molecular data to inform diagnosis and treatment, that the development or outcome of certain diseases could be modified.

The emergence of personalized medicine was further facilitated by developments in the area of health information technology, which entails electronic processing and storage of patient data, and in the clinical uptake of personalized medicine, particularly through translational and clinical research. Advances in those areasespecially the implementation of electronic health records (EHRs), which store data on patient history, medications, test results, anddemographicswere critical to the integration of data derived from genetics and genomics research and clinical settings.

Personalized medicine is used in various ways to facilitate the prevention, diagnosis, and treatment of disease. For example, physicians can use information on family history of disease to assess a patients risk for a disease. In certain instances, family history can be used to determine whether a patient should undergo genetic testing and, based on that information, whether the individual would benefit from specific preventive measures. In the case of individuals with a family history of Lynch syndrome (a cause of hereditary colorectal cancer), for instance, detection of the causative mutation through genetic testing can be used to inform decisions about screening. For persons who carry the mutation, frequent and routine screening for evidence of precancerous lesions in the colon allows for early disease detection, which can be a lifesaving measure. Similarly, tests capable of detecting mutations in multiple genes at one time can assist in the early diagnosis of hereditary forms of breast cancer, ovarian cancer, and prostate cancer.

The term personalized medicine is sometimes considered to be synonymous with targeted therapy, a form of treatment centred on the use of drugs that target specific molecules involved in regulating the growth and spread of cancer.Among the first successful targeted therapies was the anticancerdrug imatinib, which istailored to patients with chronic myelogenousleukemia(CML) who carry anenzymecalled BCR-ABLtyrosinekinase, a protein produced by a cytogenetic abnormality known as the Philadelphiachromosome. Imatinib blocks the proliferation of CML cells that possess themutated kinase, effectively reversing the abnoramalitys cancerous effects.

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Another example of personalized medicine applied to therapeutics is the use of genotyping to identify variations in enzymes that alter a patients sensitivity to the commonly prescribed anticoagulant drug warfarin. Information about variations in warfarin-metabolizing enzymes can be used to help guide decisions about the amount of the drug that a patient needs to receive in order to achieve the desired effect.

Personalized medicine faces significant challenges. For example, compared with the HGP reference sequence of the human genome, each individual persons genome houses roughly three to five million variations. Thus, attributing disease causation or therapeutic response to a given genetic variant requires careful analysis and interpretation across multiple disciplines. Moreover, genomes vary across geographic and ethnic populations and are influenced by environmental factors; thus, an individual variation identified within a given population may have very different impacts on disease in another population, based on ethnic or geographic factors.

Technological issues also continue to challenge advances in personalized medicine. The structure of EHR data, for example, can impact its utility. Access to and analysis of genomic data in EHRs may be limited by the presentation of genomic test results as a summary that includes relevant observations but excludes raw data and by the lack of information on details such as patient lifestyle and behaviour, which are essential to the accurateinterpretation of genomic information.

Various ethical issues are associated with personalized medicine. Of particular concern is that the majority of genomic studies historically have focused on populations of European descent, with significant underrepresentation of racial and ethnic minorities. This unevenness in representation can impact algorithms used to guide decisions about drug selection and dosing regimens, potentially resulting in ineffective treatment and poorer outcomes for patients whose genetic backgrounds and lifestyles differ from more thoroughly studied groups.

Other ethical issues surround privacy and security concerns, mainly involving the use of EHRs. For example, a breachin an EHR system could result in the release of personal information and health data as well as information about health care providers.Personalized medicine also carries high costs and therefore is potentially inaccessible for patients who lack health insurance and financially out of reach for less-developed countries with limited health resources.

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