richard gorey 21st century rev 016

MSc in Pharmaceutical Business and Technology

Module:

21st Century Dynamics & Emerging Trends

Lecturer:

Paul Blunnie

Assignment by:

Richard Gorey Student No. 2917956

MSc in Pharmaceutical Business and Technology Module: 21

st Century Dynamics & Emerging Trends

Assignment by Richard Gorey - Student No. 2917956

Richard Gorey 20/02/2016

Assignment Document:

MSc in Pharmaceutical Business and Technology

21st

Century Dynamics & Emerging Trends

Semester 1 Assignment (100%)

Lecturer: Paul Blunnie

Please prepare a 3000 word assessment of the evolving subject of Personalised Medicine. The assignment should

include a consideration of the following key elements with examples from the literature. Please refer to the recent

publication by the FDA on Personalised Medicine- copy attached.

Element 1: Why is the percentage of patients for whom drugs are ineffective so high ( 38% in Depression - 75% in

Cancer)- a 2001 study showed that the response rates of patients to medications from different therapeutic classes

ranged from ~80% (analgesics) to ~25% (oncology). In addition, an estimated 2.2 million adverse drug reactions occur

each year in the United States, including more than 100,000 deaths.-see Fig 2 Page 12 of attachment.

Element 2: A consideration of why many drugs under development never reach the stage of being submitted to FDA in an

application requesting approval for marketing. High attrition rates stem largely from failure of drugs to meet expected

efficacy levels, to demonstrate improved outcomes over a comparator drug, or to demonstrate sufficient safety to justify

their use. Improving our understanding of the underlying causes of variability in patient response should catalyse an

increase in the numbers of drugs that are shown to be safe and effective and make it to the market.

Element 3: What personalised medicine seeks to accomplish.

Element 4. The success of many personalised medicines fundamentally depends on the identification of biomarkers and

the successful development of diagnostic tests that can be used to accurately stratify the patient population. Illustrate how

this is so.

The publication gives you a very solid working document with excellent research references so there should be no lack of

resources for you.

Please submit a softcopy on Moodle by Friday Feb. 12th. (EDIT RG: DEADLINE CHANGED TO 20/02/2016)

Paul Blunnie





Assignment Paper:

Assessment of the Evolving

Subject of Personalised Medicine





Table of Contents

Assumptions & Considerations: 5

1. Assignment Paper - Element 1 of 4 6




References 25

The blockbuster model is “Broken”

"The challenge for us as an industry …is to move more from a blockbuster model to a targeted model”

Sidney Taurel; Chairman, President and CEO, Eli Lilly and Company (2007)





Assumptions & Considerations

The Author (of this assignment paper) assumes and considers the following;

1. As per the assignment document, this assignment paper considers and refers to the publication

by the FDA; Paving the Way for Personalised Medicine - FDA’s Role in a New Era of Medical

Product Development - October 2013

2. This assignment paper also considers and refers to other sources and material.

3. The Author has outlined some of the positive effects which personalised medicine addresses

regarding the issues raised in elements 1 to 4 of the assignment paper.

4. The assignment document instructs the student to prepare a 3000 word assessment of the

evolving subject of Personalised Medicine. The Author (of this assignment paper) assumes that

“3000 words” is an approximation i.e., not an upper or lower limit. The word count of this paper

is approximately 5,150 words, including a copy of the assignment document and the assignment

paper references section.




Richard Gorey of 26 20/02/2016

1. Assignment Paper - Element 1 of 4:

Element 1: Why is the percentage of patients for whom drugs are ineffective so high ( 38% in Depression

- 75% in Cancer)- a 2001 study showed that the response rates of patients to medications from different

therapeutic classes ranged from ~80% (analgesics) to ~25% (oncology). In addition, an estimated 2.2

million adverse drug reactions occur each year in the United States, including more than 100,000 deaths.-

see Fig 2 Page 12 of attachment.

1.1. Why is the percentage of patients for whom drugs are ineffective so high ( 38% in

Depression - 75% in Cancer)- a 2001 study showed that the response rates of patients to

medications from different therapeutic classes ranged from ~80% (analgesics) to ~25%

(oncology)

Every person has a unique variation of the human genome.(1) Although most of the variation

between individuals has no effect on health, an individual's health stems from genetic

variation with behaviors and influences from the environment.(2)(3)

One way that biological variation among people makes itself clear, is responsiveness to drugs,

e.g.; ADHD medicine only works for one of ten preschoolers, cancer drugs are effective for

25% of patients, and depression drugs work with 6 out of 10 patients.(4)

A 2001 study showed that the response rates of patients to medications from different

therapeutic classes ranged from ~80% (analgesics) to ~25% (oncology). (5)





Figure 1

Figure 1 shows the percentage of patients for whom drugs are ineffective. (Source of data:

Spear, B.B., Heath-Chiozzi, M., & Huff, J. (2001). Clinical application of pharmacogenetics.

TRENDS in Molecular Medicine, 7(5), 201-204.) (Note that lack of efficacy in a given patient

may reflect a complex interaction of factors and can also result from inadequate or

inappropriate dosing regimens of a drug that would otherwise be effective, as well as lack of

adequate patient compliance.) (5)

Human Variation:

The bases of human beings are are 99.9% similar

The remaining 0.1% (~3M bases) makes a person unique

o Different attributes / characteristics / traits, e.g.:

How a person looks

Diseases a person develops

These variations can be:





o Harmless (variation in phenotype)

o Harmful (diabetes, cancer, heart disease, Huntington's disease, and hemophilia )

o Latent (variations found in coding and regulatory regions, are not harmful on their

own, and the change in each gene only becomes apparent under certain conditions

e.g. susceptibility to lung cancer) (6)

The safety and effectiveness of a health product may vary from one individual to the next as a

result of genetic and environmental factors, as well as the interaction of these factors. As a

result, there is considerable room for improvement in overall efficacy rates for many products.

(5)

By further elucidating why some patients respond or do not respond to a drug, and why some

experience adverse reactions while others do not, we may be able to use this information to

tailor drug indications to certain populations, thus improving safety and efficacy of drugs by

specifying the population(s) in which they should be used. (5)

1.2. An estimated 2.2 million adverse drug reactions occur each year in the United States,

including more than 100,000 deaths.

Although they are generally safe and effective in most instances, current empirical approaches

to pharmaceutical therapy contribute to an estimated 3 million incorrect or ineffective drug

prescriptions annually. One study found that approximately 2.2 million people per year in the

United States experienced an adverse drug reaction (ADR) during a hospital stay or were

admitted to the hospital for an ADR. This study also reported that ADRs account for

approximately 106,000 deaths per year, which would rank ADRs between the fourth and sixth

leading causes of death in the United States, depending on whether liberal or conservative

estimates are used. The economic burden associated with drug-related morbidity and

mortality is substantial, with annual costs estimated earlier this decade at more than $177

billion. ADRs also are the leading cause of market withdrawals of drugs. (7)





Few prescribed medications are effective for all who use them, and most ADRs are caused by

an exaggerated effect of a drug on the human body. Drug response can be influenced by

genetically mediated variations that affect the metabolism, transport, distribution, absorption,

and excretion of a drug. Although ADRs can result from a variety of factors, genetic variations

of drug-metabolising enzymes have been highly correlated with ADRs in some instances. (7)

Pharmacogenomics (PGx) is the study of how genes affect a person's response to drugs. This

relatively new field combines pharmacology (the science of drugs) and genomics (the study of

genes and their functions) to develop effective, safe medications and doses that will be

tailored to a person's genetic makeup. (13)

One of the most anticipated potential benefits of PGx is the reduction of ADRs. In vitro

diagnostic tests may be useful in identifying individuals who are more likely to experience

ADRs from particular drugs because of genetic variations in drug targets in the body or in the

enzymes that metabolise drugs. Achieving even modest reductions in the rate of ADRs could

result in substantial improvements in health outcomes and reductions in health care costs. (7)

One group of drug-metabolising enzymes that figures prominently in contemporary and future

PGx applications is cytochrome P450 (CYP450). This enzyme metabolises many of the most

widely prescribed drugs used in the United States, including Adderall®

(amphetamine/dextroamphetamine), Coreg® (carvedilol), Effexor® (venlafaxine), Inderal®

(propranolol), Paxil® (paroxetine), Prozac® (flouxetine), Risperdal® (risperidone), Strattera®

(atomoxetine), Toprol® (metoprolol), Tussionex® (chlorpheniramine and hydrocodone), and

Zofran® (ondansetron). A variant of the CYP2D6 gene, which affects expression of the CYP450

enzyme, is associated with slower metabolism of these drugs and is prevalent at differing rates

among various population groups. The CYP2D6 gene is associated with slower drug

metabolism among approximately 5% to 10% of Caucasians, 1% to 3% of Hispanics, 2% to 5%

of Asians, and 2% to 7% of African Americans. As is the case with similar genetically





determined metabolic traits, conventional racial and ethnic designations are inadequate

markers, and differences in drug metabolism among these groups may be more accurately

identified with PGx testing. (7)






Element 2: A consideration of why many drugs under development never reach the stage of being

submitted to FDA in an application requesting approval for marketing. High attrition rates stem largely

from failure of drugs to meet expected efficacy levels, to demonstrate improved outcomes over a

comparator drug, or to demonstrate sufficient safety to justify their use. Improving our understanding of

the underlying causes of variability in patient response should catalyse an increase in the numbers of

drugs that are shown to be safe and effective and make it to the market.

The FDA determines that products are safe and effective before marketing through a careful evaluation

of benefits and risks that considers the available scientific data in the context of the underlying condition

or disease. (5)

Many drugs under development never reach the stage of being submitted to FDA in an application

requesting approval for marketing. High attrition rates stem largely from failure of drugs to meet

expected efficacy levels, to demonstrate improved outcomes over a comparator drug, or to demonstrate

sufficient safety to justify their use. Improving our understanding of the underlying causes of variability

in patient response should catalyse an increase in the numbers of drugs that are shown to be safe and

effective and make it to the market. (5)





Figure 2

Figure 2 shows the probability of success from stage of development. This figure shows the probability of

drugs successfully making it to market according to key milestones in the development process. (Source:

Arrowsmith, J. (2012). A decade of change. Nature Reviews Drug Discovery, 11, 17-18.) (5)

Through use of its technologies, personalised medicine may achieve FDA approval for marketing in areas

where traditional drugs failed.





Figure 3

Figure 3 shows a typical personalised medicine route to market including methods by which this process

may be expedited, e.g., efficient and speedy clinical trials. (12)

By further elucidating why some patients respond or do not respond to a drug, and why some

experience adverse reactions while others do not, the FDA may be able to use this information to tailor

drug indications to certain populations, thus improving safety and efficacy of drugs by specifying the

population(s) in which they should be used. (5)

Next-Generation Sequencing (NGS) enables researchers to study biological systems at a level never

before possible. Today's complex genomic research questions demand a depth of information beyond

the capacity of traditional DNA sequencing technologies. Next-generation sequencing has filled that gap

and become an everyday research tool to address these questions. (8)





The FDA’s current premarket review approaches for evaluating a test’s analytical and clinical

performance are designed around the more traditional one-test, one-disease paradigm. However, the

massive amount of data produced in next generation sequencing and the multitude of possible diseases

and conditions which a single genomic sequence might identify, present new challenges for the FDA, and

clearly requires thinking through new approaches that will enable the FDA to fulfil its mission of

protecting and promoting public health. As the FDA evaluates its options and considers what approach it

should take going forward, its work is informed by some real world experience, which it has already had

with the technology. (9)

In late 2013, the FDA granted marketing authorisation to the first NGS sequencing test system, Illumina’s

MiSeqDx instrument platform and two tests that detect DNA changes in the gene that causes cystic

fibrosis (CF). The MiSeqDx compares the patient’s genomic sequence to a reference sequence and

indicates any differences between the patient and the reference sequence. The FDA’s review of these

products employed a practical approach. For analytical performance, the FDA looked at how accurately

the instrument sequenced a representative set of genetic variants across the genome rather than

requiring data on every possible variant. Doing so avoided years of data gathering and unnecessary delay

in the public’s access to the benefits of this technology while still assuring its accuracy and reliability. (9)

The FDA employed similar flexibility in assessing the two CF tests. The FDA did not require that the

company collect new, independent data supporting each mutation’s association with disease in order to

demonstrate clinical performance. (9)

Instead, the company was able to leverage existing information by referring to a well-curated, shared

database of CF mutations to demonstrate its tests’ clinical validity. This database is really a model of

patient participation, created with support from the Cystic Fibrosis Foundation. With Illumina as the

trailblazer, subsequent sequencing platforms of the same type are exempt from premarket review. The

FDA’s primary focus will be the tests that are developed and performed on these platforms. (9)

The FDA is committed to regulating medical products based on the most advanced scientific information

available. It intends use the breakthroughs resulting from NGS technology to accelerate development of





a practical and nimble approach that will allow medical advances in the field of genomics to be

implemented as soon as possible, using its regulatory flexibility and the power of the information placed

into high-quality databases. The FDA published a preliminary discussion paper on how to optimise its

regulation of NGS technology to assure that tests are not only safe and effective, but are available for

patients as soon as possible. (9)

NGS technology is integral to the future of personalised medicine. Whatever approach FDA ultimately

adopts must be selected with care to ensure continued innovation in this still evolving technology and

the advancement of the public health. (9)

President Obama’s 2016 budget proposal included a request for an additional $10 million for FDA work

on Next Generation Sequencing. This funding, which is part of the Precision Medicine Initiative, will help

the FDA acquire the additional tools and expertise that it needs to develop new approaches and to

advance the development of high quality, curated databases akin to the one that was used for the

review of the two Illumina tests for cystic fibrosis. (9)

Ultimately, The FDA’s actions and policies must be based on the best available science, while ensuring

that these breakthrough technologies are part of a foundation of basic medical care by doctors and

other providers that are safe, effective, and focus on meeting the needs of individual patients. (9)

“As the field advances, we expect to see more efficient clinical trials based on a more thorough

understanding of the genetic basis of disease. We also anticipate that some previously failed medications

will be recognised as safe and effective and will be approved for subgroups of patients with specific

genetic markers.” - Margaret Hamburg, M.D.Commissioner, U.S. Food and Drug Administration, Francis

Collins, M.D., Ph.D.Director, National Institutes of Health - The Case for Personalised Medicine 3rd

Edition - Personalised Medicine Coalition (9)






Element 3: What personalised medicine seeks to accomplish.

By using molecular analysis to achieve optimum medical outcomes in the management of a patient’s

disease or disease predisposition, personalised medicine promises to introduce a new standard of

healthcare. (10)

The concept of personalised medicine, that medical care can be tailored to the genomic and molecular

profile of the individual has repercussions that extend far beyond the technology that makes it possible.

The adoption of personalised medicine will require changes in healthcare infrastructure, diagnostics and

therapeutics business models, reimbursement policy from government and private payers, and a

different approach to regulatory oversight. Personalised medicine will shift medical practices upstream

from the reactive treatment of disease, to proactive healthcare management including screening, early

treatment, and prevention, and will alter the roles of both physician and patient. It will create a greater

reliance on electronic medical records and decision support systems in an industry that has a long history

of resistance to information technology. Personalised medicine requires a systems approach to

implementation. However, in a healthcare economy that is highly decentralised and market driven, it is

incumbent upon the stakeholders themselves to advocate for a consistent set of policies and legislation

that pave the way for the adoption of personalised medicine. (14)

Personalised medicine seeks to:

Detect disease at an earlier stage, when it is easier to treat effectively

Enable the selection of optimal therapy and reduce trial-and error prescribing

Reduce adverse drug reactions

Increase patient compliance with therapy

Improve the selection of targets for drug discovery

Reduce the time, cost, and failure rate of clinical trials

Revive drugs that failed clinical trials or were withdrawn from the market

Avoid withdrawal of marketed drugs

Shift the emphasis in medicine from reaction to prevention





Reduce the overall cost of healthcare

(12)

Figure 4

Figure 4 shows the old paradigm for treatment of disease, i.e., reactive medical care, which treats

symptoms and is a costly, trial and error treatment. (12)





Figure 5

Figure 5 shows the personalised medicine paradigm for health management, i.e., efficient medical care,

which includes molecular screening, early detection, rapid effective treatment and improved quality of

care. (12)





Figure 6

Figure 6 shows the personalised medicine paradigm for preventive medical care, which incorporates

predisposition guides for prevention, identification of molecular markers and healthcare cost reduction.

(12)






Element 4; The success of many personalised medicines fundamentally depends on the identification of

biomarkers and the successful development of diagnostic tests that can be used to accurately stratify the

patient population. Illustrate how this is so.

Biomarkers are defined as any molecule derived from a biological sample that can indicate current

disease status, evaluate progression of the disease, or assess potential responsiveness to a particular

medication. (11)

There is significant interest in the prediction and early detection of disease through the analysis of

biological markers, or biomarkers, which have the potential to significantly improve clinical outcomes.

(11)

Biomarkers come in many forms including DNA mutations, proteins, and messenger RNA (mRNA)

transcripts. For example, ratios of aspartate/alanine aminotransferase are used as a reliable biomarker

for liver fibrosis, protein levels of S100-beta are used as a biomarker of treatment response for

malignant melanoma, while mutations of the genes BRCA1 and BRCA2 are well known biomarkers

predicting the development of breast cancer. DNA methylation is also a well-studied biomarker.

Methylated cytosine residues have been associated with several diseases, including cancer and

neurological disorders. (11)

Over the years, non-coding RNAs (ncRNAs) have become the focus of biomarker research, an approach

that has been favorably used in the investigation of response to treatment for several medical

conditions. There are several types of ncRNAs, of which microRNAs (miRNAs) are the best known and the

most frequently assessed for their potential role as biomarkers. MiRNAs have been proposed as

molecular biomarkers in cancer, liver and cardiovascular disease, and central nervous system disorders,

among many others. MiRNAs are small ncRNAs molecules that follow a well characterized biogenesis

pathway that includes processing through the DGCR8/ DROSHA, Exportin-5, Dicer and RISC molecular

complexes . Through post-transcriptional activity, these small, single-stranded, 19–25-base RNA





transcripts regulate the expression of numerous genes. Binding of the miRNA to the complementary

sequence of a target mRNA relies on recognition of the seed region, the 2–8 nucleotides located at the

3′end of the miRNA, which leads to either mRNA degradation or translational repression. (11)

Other ncRNA species such as PIWI-interacting RNAs (piRNAs), small nucleolar RNAs (snoRNAs), small

nuclear RNAs (snRNAs) and long non-coding RNAs are also gaining support as key components of cellular

regulation, and thus might be potentially assessed as biomarkers of disease. PiRNAs are small ncRNAs of

24–31 nt length. In contrast to miRNAs, these are Dicer-independent and interact with the PIWI

subfamily of Argonaute proteins involved in the regulation of genome stability. PIWI proteins are

involved in gene regulation through RNA degradation and have been linked to DNA methylation. In

addition, piRNAs have been reported as potential biomarkers for bladder, breast, and gastric cancers.

SnoRNAs are key components of the small ribonucleoproteins (snoRNPs) which are responsible for

sequence-specific 2′-O-Methylation of ribosomal RNA (rRNA). SnoRNAs have been shown to participate

in post-transcriptional regulation of rRNA by targeting snoRNPs in the nucleus. In addition, snoRNAs have

been proposed as potential biomarkers for several forms of human cancers. Long non-coding RNAs are

another class of ncRNAs that have gained a lot of attention recently as potential biomarkers. They

comprise a heterogeneous group of ncRNAs larger than 200 nt, which includes long non-coding RNAs

(lncRNAs), large intergenic non-coding RNAs (lincRNAs) and transcribed ultraconserved regions (T-UCRs),

among others. LncRNAs are known to regulate DNA methylation by recruiting chromatin remodeling

complexes. LincRNAs have been associated with active transcription in regions of transcriptional

elongation. Finally, while the function of T-UCRs is still unknown, they have been demonstrated to

interact with microRNAs and might have a role in the development of disease. T-UCRs have been

recently postulated as potential diagnostic and prognostic biomarkers in colorectal cancer patients. (11)

While any ncRNA is a putative biomarker, miRNAs have received the most attention because they

possess several features that render them especially powerful :

i.) they are highly conserved, and evolutionary complexity correlates with miRNA complexity, which

suggests an important biological function;





ii.) there are a relatively small number of individual miRNAs with a large dynamic range of

expression;

iii.) they are secreted into circulation and can be measured in all biological fluids;

iv.) they are not easily degraded and are thus highly stable in clinical samples;

v.) they are involved in pathway regulation, as one miRNA can target many genes, and a single gene

can be regulated by many different miRNAs;

vi.) miRNAs show tissue and cell specific expression profiles; and

vii.) there is a large body of literature supporting their role in the pathophysiology of disease. (11)

Most ncRNA quantification studies performed to date rely on qRT-PCR, in situ hybridization, or

microarray techniques. These methods have several strengths, but also contain some important

limitations. These include: the number of miRNA molecules that can be analyzed simultaneously, the

amount of RNA required for the analysis of multiple targets, the quality and source of the RNA, the

sensitivity of detection, and the need for previous knowledge of targets. Next generation sequencing

(NGS) provides researchers with a powerful tool for the detection of RNA molecules in biological

samples. NGS offers methodological advantages such as increased throughput, decreased RNA input,

consistency and quality of data, higher detection depth, analysis of all RNA populations, and discovery of

novel molecules. Furthermore, length of protocols, sequencing time, and prices are continuously

dropping, making NGS an ideal tool for biomarker research. (11)

While scientific discoveries across multiple fields have led to an explosion of biological information, the

development of diagnostics and their translation into clinical practice pose a number of scientific and

regulatory challenges. Inadequate performance of a diagnostic test that is used to guide treatment

decisions can have severe therapeutic consequences. For example, with an incorrect diagnostic result, an

unsuitable drug may be given to a patient who will, as a result, be harmed or will not benefit, because

the drug will cause an other wise avoidable adverse event, will be ineffective for that patient, or both. (5)





Diagnostic tests are intended to measure (as in the case of in vitro diagnostics), or evaluate (as in the

case of electrocardiogram tracings or imaging technologies), an indicator of a normal biological process,

pathogenic process, or response to a therapeutic intervention. In the case of in vitro diagnostic test

development, biomarker discovery and evaluation of the biomarker are critical initial steps. If the

biomarker is not significantly correlated with the clinical state – for example, a particular genetic

mutation with a disease – a diagnostic test that measures that biomarker will not produce meaningful

results for that disease. (5)

Diagnostic tests generally fall under the FDA’s medical device authority and are classified and regulated

in a risk-based manner. Risk determination includes the risk of an erroneous result, and the harm to a

patient that might be incurred based on an incorrect test result when the test is used as intended.

Diagnostic test results can be incorrect in two major ways: they can report a positive result when the

result is actually negative (false positive), or they can report a negative result when the actual result was

positive (false negative). Tests that measure the amount of a substance can report values that are falsely

high or low. False test results and their consequences are evaluated for their risk of harm to patients. For

example, a false positive test result that could lead to a patient undergoing an invasive medical

procedure or a therapy with toxic side effects would generally be considered high risk. Similarly, a false

negative test result that might alter medical management and delay appropriate intervention for a life-

threatening condition might also be considered high risk. (5)

In evaluating a diagnostic device, FDA looks at its analytical validity as well as its clinical validity.

Analytical validity refers to how well the test measures what it is supposed to measure, whereas clinical

validity looks at how well the test predicts who has or does not have a disease or condition for which it is

being tested. In personalised medicine, where the diagnostic test is often a biomarker-based assay, such

as a genetic test, the clinical validity of the test refers to how well the test works in helping to identify

people who will or will not respond to a therapy (or who will or will not suffer adverse consequences).

(5)

In addition to analytical and clinical validity, stakeholders in personalised medicine are also interested to

know the clinical utility of new diagnostics. “Clinical utility” is a term that describes the relevance and





usefulness of an intervention in patient care; in other words, how much value does it add? When a

diagnostic test informs the use of a medical treatment, the test has clinical utility if its use improves the

treatment outcome. While the accuracy of a diagnostic test used to individualise treatment or an

intervention is evaluated by measuring its analytical and clinical validity, the usefulness of the test is

typically evaluated by its clinical utility. There is considerable debate about the methods of

demonstrating clinical utility and the level of evidence – in terms of quantity, quality, and type – that

should be obtained for any new diagnostic test to be introduced into routine clinical practice. (5)

Many of the diagnostic tests used in personalised medicine are in vitro diagnostic devices (IVDs), also

called clinical laboratory tests, which test body substances from patients for alterations in levels of

biomarkers (e.g., proteins) and the presence/absence of genetic susceptibility biomarkers. The

development and validation of IVDs for use in guiding therapeutic treatment pose a number of particular

challenges. First, the sheer pace of the development of IVDs over the past decade has been staggering.

Volumes of information arising out of the human genome project combined with a dramatic decrease in

costs of DNA sequencing, for example, are giving way to an explosion of publications linking particular

genetic markers to diseases or conditions and a rapid application of this information in the development

of new molecular diagnostic tests. How best to integrate rapidly evolving genomic information into

clinical care while ensuring safety and efficacy is a topic of considerable public debate and discussion. For

the FDA, the evaluation of these tests, and the development of standards for levels of evidence required

to demonstrate the validity of the test, are especially complicated when the meaning of a given genetic

association may be poorly understood or change over time. Moreover, the complexity of these tests is

ever evolving, as single marker tests have given way to tests that measure multiple markers

simultaneously, such as complex gene panels. Extensive DNA and RNA sequencing across multiple genes

or the whole genome are already being used in clinical practice. (5)





References

1. Dudley, J; Karczewski, K. (2014). Exploring Personal Genomics. Oxford: Oxford University Press.

2. "Personalized Medicine 101: The Science". Personalized Medicine Coalition. Retrieved 26 April 2014.

3. Lu, YF; Goldstein, DB; Angrist, M; Cavalleri, G (24 July 2014). "Personalized medicine and human

genetic diversity". Cold Spring Harbor perspectives in medicine 4 (9): a008581.

doi:10.1101/cshperspect.a008581. PMID 25059740.

4. "When Healthcare and Computer Science Collide". University of Illinois at Chicago. University of

Illinois at Chicago Health Information Management Department.

5. Paving the Way for Personalized Medicine - FDA’s Role in a New Era of Medical Product

Development - October 2013

6. Dumontier::BIOL4301:Personalized Medicine Personalized Medicine Michel Dumontier, Ph.D.

Associate Professor of Bioinformatics Department of Biology, Institute of Biochemistry, School of

Computer Science Carleton University Ottawa Institute for Systems Biology Ottawa-Carleton Institute

for Biomedical Engineering Nov 18, 2010

7. Realizing the Potential of Pharmacogenomics: Opportunities and Challenges - Report of the

Secretary’s Advisory Committee on Genetics, Health, and Society – 2008

8. http://www.illumina.com/technology/next-generation-sequencing.html

9. “Optimizing FDA's Regulatory Oversight of Next Generation Sequencing Diagnostic Tests” - Remarks

by Margaret A. Hamburg, M.D. Commissioner of Food and Drugs - National Institutes of Health -

February 20, 2015

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The Case for Personalized Medicine - Personalized Medicine Coalition

11. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4487992/

Biomarker discovery: quantification of microRNAs and other small non-coding RNAs using next

generation sequencing - Juan Pablo Lopez, Alpha Diallo, Cristiana Cruceanu, Laura M. Fiori, Sylvie

Laboissiere, Isabelle Guillet, Joelle Fontaine, Jiannis Ragoussis, Vladimir Benes, Gustavo

Turecki, and Carl Ernst

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https://www.aacc.org/~/media/files/divisions/lvd/personalized_medicine_lvd_division.pdf?la=en

http://www.ncbi.nlm.nih.gov/pubmed/?term=Lopez%20JP%5Bauth%5D

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http://www.ncbi.nlm.nih.gov/pubmed/?term=Guillet%20I%5Bauth%5D

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http://www.ncbi.nlm.nih.gov/pubmed/?term=Ragoussis%20J%5Bauth%5D

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http://www.ncbi.nlm.nih.gov/pubmed/?term=Ernst%20C%5Bauth%5D





Personalized Medicine: The Changing Landscape of Healthcare - American Association of Clinical

Chemistry Annual MeetingSan Diego, California July 14th, 2007 Edward Abrahams, Ph.D.Executive

Director Personalized Medicine Coalition

13. http://ghr.nlm.nih.gov/handbook/genomicresearch/pharmacogenomics

What is pharmacogenomics?

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The Personalized Medicine Coalition: goals and strategies.

Abrahams E1, Ginsburg GS, Silver M.

Personalized Medicine Coalition, Washington, DC 20005, USA.

http://ghr.nlm.nih.gov/handbook/genomicresearch/pharmacogenomics

http://www.ncbi.nlm.nih.gov/pubmed/?term=Abrahams%20E%5BAuthor%5D&cauthor=true&cauthor_uid=16336000

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http://www.ncbi.nlm.nih.gov/pubmed/?term=Silver%20M%5BAuthor%5D&cauthor=true&cauthor_uid=16336000

richard gorey 21st century rev 016

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