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Agility Suite of Solution Genomic IT

Agility Suite of Solution Genomic IT

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  • HHS 2012 regulatory for EHR / nexus of Bio & HIT
  • Genomic Informatics - http://www.warfarindosing.org/Source/Home.aspx
  • Genome Base Pairs
  • Dr. Higgins - It’s not just genomic scientists that are dealing with enormous amounts of DNA sequence data – the clinician will soon be next. However, the explosion in linking disease with genetics, and the realization that the FDA will require gene testing prior to the prescription of potentially hundreds of drugs, will challenge the storage capacity required for clinical data. In addition, recent studies have shown the utility of whole genome analysis (6 billion bases of DNA per individual), discovering a cause of Charcot-Marie-Tooth disease, one of the most commonly inherited neurological disorders, which affects approximately 1 in 2500 people. In addition, other disease-causing genes have also recently been found by looking in families that inherit a variety of different types of mutations.This is no longer the “spit-test” parties and subsequent Single Nucleotide Polymorphism (“SNiP”) analysis that Direct-to-Consumer companies offer to consumers, often only showing modest increased risk for disease prediction (typically ranging from 1.3 – 1.8 fold greater than another individual).Ironically, the mutations that lead to Adverse Drug Events often convey a 100 – 10000+ fold greater risk for that patient.“There will be an explosion of family sequencing that will identify disease genes,” Dr. Leroy Hood, Director of the Institute for Systems Biology, said in a recent interview. “My prediction is that most of us will have our genome sequences done, included as part of our medical records, and it will be an important part of predictive medicine.” This suggests that “all Healthcare IT systems will soon be overwhelmed by patient genomic and pharmacogenomic data.”As stated by Dr. George Church, Professor of Genetics at Harvard Medical School and Director of the Center for Computational Genetics, said in a Newsweek interview, - The message is not "Here's your destiny. Get used to it!" Instead, it's "Here's your destiny, and you can do something about it!" Diseases result from a combination of genetic vulnerability and lifestyle. If you know you have high risk of certain diseases, it's in your interest to know and practice the lifestyle that reduces your risk—and the younger, the better.”The latest research findings have started a “tsunami” in looking at the entire genome in patients with both Mendelien traits and those with common, complex diseases. This has created a need for more computing resources. For example, Washington University’s current scientific and clinical genomic data center, a 16,000-square-foot facility that houses approximately 5,000 processors and more than 5 petabytes of disk storage, is nearly 90 percent full. The University just received a $14M grant from the National Institutes of Health to increase storage capacity.At least 30% of disease has a primary genetic cause, yet we are still in the discovery phase of genomic medicine. Given the exponential rate of change in disease-gene causation & individual variation related to healthcare, we estimate that within 5 years, genetic testing will be the most common laboratory test in several specialties, including oncology, cardiovascular disease, Type II diabetes, psychiatry & neurology.
  • http://Single Nucleotide Polymorphism (“SNiP”) analysis that Direct-to-Consumer companies offer to consumers, often only showing modest increased risk for disease predictiowarfarindosing.org/Source/Home.aspxDr. Higgins - It’s not just genomic scientists that are dealing with enormous amounts of DNA sequence data – the clinician will soon be next. However, the explosion in linking disease with genetics, and the realization that the FDA will require gene testing prior to the prescription of potentially hundreds of drugs, will challenge the storage capacity required for clinical data. In addition, recent studies have shown the utility of whole genome analysis (6 billion bases of DNA per individual), discovering a cause of Charcot-Marie-Tooth disease, one of the most commonly inherited neurological disorders, which affects approximately 1 in 2500 people. In addition, other disease-causing genes have also recently been found by looking in families that inherit a variety of different types of mutations.This is no longer the “spit-test” parties and subsequent n (typically ranging from 1.3 – 1.8 fold greater than another individual).Ironically, the mutations that lead to Adverse Drug Events often convey a 100 – 10000+ fold greater risk for that patient.“There will be an explosion of family sequencing that will identify disease genes,” Dr. Leroy Hood, Director of the Institute for Systems Biology, said in a recent interview. “My prediction is that most of us will have our genome sequences done, included as part of our medical records, and it will be an important part of predictive medicine.” This suggests that “all Healthcare IT systems will soon be overwhelmed by patient genomic and pharmacogenomic data.”As stated by Dr. George Church, Professor of Genetics at Harvard Medical School and Director of the Center for Computational Genetics, said in a Newsweek interview, - The message is not "Here's your destiny. Get used to it!" Instead, it's "Here's your destiny, and you can do something about it!" Diseases result from a combination of genetic vulnerability and lifestyle. If you know you have high risk of certain diseases, it's in your interest to know and practice the lifestyle that reduces your risk—and the younger, the better.”The latest research findings have started a “tsunami” in looking at the entire genome in patients with both Mendelien traits and those with common, complex diseases. This has created a need for more computing resources. For example, Washington University’s current scientific and clinical genomic data center, a 16,000-square-foot facility that houses approximately 5,000 processors and more than 5 petabytes of disk storage, is nearly 90 percent full. The University just received a $14M grant from the National Institutes of Health to increase storage capacity.At least 30% of disease has a primary genetic cause, yet we are still in the discovery phase of genomic medicine. Given the exponential rate of change in disease-gene causation & individual variation related to healthcare, we estimate that within 5 years, genetic testing will be the most common laboratory test in several specialties, including oncology, cardiovascular disease, Type II diabetes, psychiatry & neurology.
  • Dr. Higgins - It’s not just genomic scientists that are dealing with enormous amounts of DNA sequence data – the clinician will soon be next. However, the explosion in linking disease with genetics, and the realization that the FDA will require gene testing prior to the prescription of potentially hundreds of drugs, will challenge the storage capacity required for clinical data. In addition, recent studies have shown the utility of whole genome analysis (6 billion bases of DNA per individual), discovering a cause of Charcot-Marie-Tooth disease, one of the most commonly inherited neurological disorders, which affects approximately 1 in 2500 people. In addition, other disease-causing genes have also recently been found by looking in families that inherit a variety of different types of mutations.This is no longer the “spit-test” parties and subsequent Single Nucleotide Polymorphism (“SNiP”) analysis that Direct-to-Consumer companies offer to consumers, often only showing modest increased risk for disease prediction (typically ranging from 1.3 – 1.8 fold greater than another individual).Ironically, the mutations that lead to Adverse Drug Events often convey a 100 – 10000+ fold greater risk for that patient.“There will be an explosion of family sequencing that will identify disease genes,” Dr. Leroy Hood, Director of the Institute for Systems Biology, said in a recent interview. “My prediction is that most of us will have our genome sequences done, included as part of our medical records, and it will be an important part of predictive medicine.” This suggests that “all Healthcare IT systems will soon be overwhelmed by patient genomic and pharmacogenomic data.”As stated by Dr. George Church, Professor of Genetics at Harvard Medical School and Director of the Center for Computational Genetics, said in a Newsweek interview, - The message is not "Here's your destiny. Get used to it!" Instead, it's "Here's your destiny, and you can do something about it!" Diseases result from a combination of genetic vulnerability and lifestyle. If you know you have high risk of certain diseases, it's in your interest to know and practice the lifestyle that reduces your risk—and the younger, the better.”The latest research findings have started a “tsunami” in looking at the entire genome in patients with both Mendelien traits and those with common, complex diseases. This has created a need for more computing resources. For example, Washington University’s current scientific and clinical genomic data center, a 16,000-square-foot facility that houses approximately 5,000 processors and more than 5 petabytes of disk storage, is nearly 90 percent full. The University just received a $14M grant from the National Institutes of Health to increase storage capacity.At least 30% of disease has a primary genetic cause, yet we are still in the discovery phase of genomic medicine. Given the exponential rate of change in disease-gene causation & individual variation related to healthcare, we estimate that within 5 years, genetic testing will be the most common laboratory test in several specialties, including oncology, cardiovascular disease, Type II diabetes, psychiatry & neurology.
  • Currently, it costs about $5,000 to sequence an entire human genome; That cost will be lowered to about $100 per patient by the year 2012, making it feasible to sequence individual patient genomes & store them in the EHR. Upside for Agility = the cost of storage and of the analyzed genome will be significant.Market Size; The market for Personalized Medicine in the United States is already $232 billion, and it is projected to grow 11% annually, according to a new report published today by PricewaterhouseCoopers LLP. The core diagnostic and therapeutic segment of the market – comprised primarily of pharmaceutical, medical device & diagnostics companies – is estimated at $24 billion and is expected to grow by 10 percent annually, reaching $42 billion by 2015.NOTE: personalized medical care portion of the market – including telemedicine, health information technology & disease management services offered by traditional health & technology companies – is estimated at $4 billion to $12 billion and could grow tenfold to over $100 billion by 2015 if telemedicine takes off.The related nutrition and wellness market – including retail, complementary and alternative medicine offered by consumer products, food and beverage, leisure and retail companies – is estimated at $196 billion and is projected to grow 7 percent annually to over $290 billion by 2015.
  • **In the United States, a Pharmacy Benefit Manager (PBM) is a third party administrator of prescription drug programs. They are primarily responsible for processing and paying prescription drug claims. They also are responsible for developing and maintaining the fomrulary, contracting with pharmacies, and negotiating discounts and rebates with drug manufacturers. Today, more than 210 million Americans nationwide receive drug benefits administered by PBMs. Fortune 500 employers, and public purchasers (Medicare Part D, the Federal Employees Health Benefits Program) — provide prescription drug benefits to the vast majority of American workers and retirees. The market is dominated by 3 large players – MEDCO, CAREMARK, and EXRESS SCRIPTS.Core BPM Services: Pharmacy networks — PBMs build networks of retail pharmacies to provide consumers convenient access to prescriptions at discounted rates. PBMs monitor prescription safety across all of the network pharmacies, alerting pharmacists to potential drug interactions even if a consumer uses multiple pharmacies.Mail service pharmacies — PBMs provide highly efficient mail-service pharmacies that supply home-delivered prescriptions with great accuracy and safety and at a substantial savings. In a 2005 report, the FTC determined that PBM-owned mail-order pharmacies offer lower prices on prescription drugs than retail pharmacies and non-PBM owned mail pharmacies; are very effective at capitalizing on opportunities to dispense generic medications; and have incentives closely aligned with their customers: the third-party payers who fund prescription drug care. The Government Accountability Office (GAO) has also found that mail-order pharmacies in the Federal Employees Health Benefits Program (FEHBP) offer substantial savings, especially when compared to retail pharmacies. According to January 2003 GAO report examining cost savings with mail-order pharmacies under FEHBP, the average mail-order pharmacy price for prescription drugs was 27 percent lower for brand name drugs and 53 percent lower for generic drugs than the price paid to retail pharmacies by cash-paying customers.According to GAO, “enrollees in the plans reviewed had wide access to retail pharmacies, coverage of most drugs, and benefited from cost savings generated by the PBMs. Enrollees typically paid lower out-of-pocket costs for prescriptions filled through mail-order pharmacies and benefited from other savings that reduced plans’ costs and therefore helped to lessen rising premiums.Formularies — PBMs use panels of independent physicians, pharmacists, and other clinical experts to develop lists of drugs approved for reimbursement in order to encourage clinically appropriate and cost-effective prescribing; PBM clients always have the final say over what drugs are included on the formulary that they offer to their employees or members.Plan design — PBMs advise their clients on ways to structure drug benefits to encourage the use of lower cost drug alternatives — such as generics — when appropriate.This is done by setting plans up with different copay tiers, in this case the client will apply a lower copay for generic drugs than it would for brand drugs. This forces the particiapents to use generic drugs, because the cost is much less than a brand drug. Plan designs can also be set up with Maximum Allowed Benefit (MAB) for clients that need to limit drug spend for financial reasons. The PBMs’ role is advisory only; the client retains all responsibility for establishing the plan design.Electronic prescribing (E-prescribing) — PBMs have pioneered the use of cutting-edge e-prescribing technology, which provides physicians with clinical and cost information on prescription options that allows them to better counsel consumers on which medications—including various lower cost options—will be the safest and most affordable choices. PBMs led the effort to increase the use of e-prescribing in Medicare.Financial incentives for physicians to adopt health information technology (HIT) included in the recent economic stimulus bill will increase the number of prescribers using e-prescribing to more than 75 percent over the next five years—nearly double the rate of use anticipated after passage of last year’s e-prescribing legislation.Research has found that e-prescribing will help prevent 3.5 million harmful medication errors and save the federal government $22 billion in drug and medical costs over the next 10 years, offsetting the projected $19 billion in federal outlays to modernize the nation’s HIT infrastructure under the American Recovery and Reinvestment Act (ARRA).Manufacturer discounts — PBMs pool purchasing power to negotiate substantial discounts from pharmaceutical manufacturers in order to lower benefit costs for clients and consumers.Clinical management — PBMs use a variety of tools such as drug utilization review and disease management to encourage the best clinical outcomes for patients.***CRO – Clinical Research Organization - A clinical trial is a research project, involving patients or healthy individuals, to test new treatment modalities or medicine. Clinical trials help doctors find out if these treatments are safe, if they have any side effects, and if they are better than treatment options already available. Types f trials: Treatment trials test experimental treatments, new combinations of drugs, or new approaches to surgery or radiation therapy. Prevention trials look for better ways to prevent disease in people who have never had the disease or to prevent a disease from returning. These approaches may include medicines, vitamins, vaccines, minerals, or lifestyle changes. Diagnostic trials are conducted to find better tests or procedures for diagnosing a particular disease or condition Screening trials test the best way to detect certain diseases or health conditions. Quality of Life trials (or Supportive Care trials) explore ways to improve comfort and the quality of life for individuals with a chronic illness.
  • GINA = On May 21st, President Bush signed into law the Genetic Information Nondiscrimination Act (GINA), which prohibits U.S. insurance companies and employers from discriminating on the basis of information derived from genetic tests. GINA passed both houses of Congress with a vote in the U.S. House of Representatives of 414 to 1. The bill had passed in the House twice before, most recently last year when the vote was 420 to 3. The U.S. Senate unanimously passed the current bill after compromises were reached on areas of disagreement that had held up its passage for several months.GINA protects Americans from discrimination based on information derived from genetic tests. It forbids insurance companies from discriminating through reduced coverage or pricing and prohibits employers from making adverse employment decisions based on a person’s genetic code. In addition, insurers and employers are not allowed under the law to request or demand a genetic test. A 2001 study by the American Management Association showed that nearly two-thirds of major U.S. companies require medical examinations of new hires. In addition, 14% conduct tests for susceptibility to workplace hazards, 3% for breast and colon cancer, and 1% for sickle cell anemia; 10% collect information about family medical history. “Because of this legislation, Americans will be free to undergo genetic testing for diseases such as cancer, heart disease, diabetes, and Alzheimer’s without fearing for their job or health insurance,” said House speaker Nancy Pelosi (D-Calif.) in a statement. Increased genetic testing makes it more likely that researchers will come up with early, lifesaving therapy for a wide range of diseases with hereditary links, lawmakers said. Genetic testing also will help doctors catch problems early, perhaps leading to preventive treatment and lower costs.
  • The foundation of Personalized Medicine – Genomic & Genetic Medicine; Understanding Molecular Medicine, through both laboratory and imaging techniques, deepens our ability to detect, diagnose and treat disease. Genomics / Genetics, the study of genes / heredity, is the most common area of study since it involves a stable, albeit large, data set. Genomic data is particularly useful in identifying certain diseases, unveiling risk factors for other diseases, and predicting how well certain drugs will work in humans. This last area, called pharmacogenomics, is an increasingly popular area of study involving both drug effectiveness (efficacy) and drug side effects (contraindications). This is a critical field since many medications are only effective in 50% of the population and cause side effects in another large percentage, but we don’t know ahead of time how individuals will react. Being able to test for gene differences ahead of time will both improve effectiveness and decrease side effects for patients. Other important areas of study include proteomics and metabolomics. While genetic markers are stable, these biomarkers are constantly changing as a function of both genetic and environmental exposures. They are particularly important in diagnosing diseases and calibrating treatment regimens. Dr. Higgins: “There are established Human Geneticists (M.D.s) who have been studying rare Mendelien disorders for over one hundred years. These are disorders like Huntington’s disease or Cystic Fibrosis in which 25-50% of the offspring typically inherit the disease, and up until recent DNA sequencing, no one knew thecause of these diseases. We still do not have a cure for most of these diseases. It is now recognized that, following the sequencing of the human genome, many small single mutations (called Single Nucleotide Polymorphisms or “SNiPs”) in the genome that form patterns that are diagnostic for more common, complex diseases like Type II Diabetes and Coronary Artery Disease. It appears that most human disease has a distinct genomic component. Finally, it is now known that the difference in the side effects and action of most drugs are aconsequence of individual genetic variation between patients – this leads to “Personalized Medicine”, where some drugs now require a “gene test” before they can be prescribed. Pharmacogenomics information is contained in about ten percent of labels for drugs approved by the FDA. Note: There are also breakthroughs in transciptomics, proteomics and metabolomics which are diagnostic markers of disease, and could be stored in the EHR. For example, a few (3) markers from cerebrospinal fluid, or many (18) proteins sampled from blood can predict Alzheimer’s disease.“Personalized Medicine” refers to the tailoring of medical treatment to the individual characteristics of each patient…to classify individuals into subpopulations that differ in their susceptibility to a particular disease or their response to a specific treatment.  Preventative or therapeutic interventions can then be concentrated on those who will benefit, sparing expense and side effects for those who will not.”Diagnosing and Predicting Disease and Disease SusceptibilityAll diseases have a genetic component, whether inherited or resulting from the body's response to environmental stresses like viruses or toxins. The successes of the HGP have even enabled researchers to pinpoint errors in genes--the smallest units of heredity--that cause or contribute to disease.  The ultimate goal is to use this information to develop new ways to treat, cure, or even prevent the thousands of diseases that afflict humankind. But the road from gene identification to effective treatments is long and fraught with challenges. In the meantime, biotechnology companies are racing ahead with commercialization by designing diagnostic tests to detect errant genes in people suspected of having particular diseases or of being at risk for developing them.  An increasing number of gene tests are becoming available commercially, although the scientific community continues to debate the best way to deliver them to the public and medical communities that are often unaware of their scientific and social implications. While some of these tests have greatly improved and even saved lives, scientists remain unsure of how to interpret many of them. Also, patients taking the tests face significant risks of jeopardizing their employment or insurance status*. And because genetic information is shared, these risks can extend beyond them to their family members as well.  *Passing of the 2008 Genetic Information Non-descrimination Act should protect against such discrimination. May 2008. Disease InterventionExplorations into the function of each human gene--a major challenge extending far into the 21st century --will shed light on how faulty genes play a role in disease causation. With this knowledge, commercial efforts are shifting away from diagnostics and toward developing a new generation of therapeutics based on genes. Drug design is being revolutionized as researchers create new classes of medicines based on a reasoned approach to the use of information on gene sequence and protein structure function rather than the traditional trial-and-error method. Drugs targeted to specific sites in the body promise to have fewer side effects than many of today's medicines.  The potential for using genes themselves to treat disease--gene therapy--is the most exciting application of DNA science. It has captured the imaginations of the public and the biomedical community for good reason. This rapidly developing field holds great potential for treating or even curing genetic and acquired diseases, using normal genes to replace or supplement a defective gene or to bolster immunity to disease (e.g., by adding a gene that suppresses tumor growth).
  • Today applied medicine involves disease prevention (e.g. guidance on diet & exercise habits), early detection of problems (e.g. Colonoscopy, Mammograms, Physicals), and treating a problem that has occurred. However, we are limited in each of these areas. For preventive care, our guidance could be more specific & could carry more weight if we had genetic testing to help a patient understand their risk. For early detection, we must rely on “macro” level events, such as visualizing polyps, feeling masses, imaging structural distortions, and tracking non-specific biomarkers (e.g. PSA). The field of molecular detection will help us to more quickly identify diseases and thus have more success in treating them before they spread or cause excessive damage. For disease treatment, we know that not all medications or other treatment options work for all patients. Molecular medicine has the ability to help us choose the best treatment regimens, as well as more easily follow the progression of disease over time. By The Numbers: The ambitions of healthcare providers have not changed in the past hundred years, but the capabilities certainly have. We are now unlocking the secrets of health at a molecular level – which includes not only why some people get diseases, but also how to prevent or cure them. Knowing this information is only valuable in the context of making it available for the right patient at the right time. There are 3 billion letters in the human DNA code. While more than 99.9 % of DNA is identical between any two humans, about one in every 1200 amino acid pairs varies from one person to another. These single nucleotide polymophisms (SNPs or SNiPs) are what differentiates us from each other. Furthermore, haplotypes are a set of SNPs on a single chromatid that are statistically associated. It is thought that these associations, and the identification of a few alleles of a haplotype block, can unambiguously identify all other polymorphic sites in its region. The causes of common disease are very complex. We know that both environment and genetics play important roles. Furthermore, most diseases, as well as responses to medicines, involve the interaction of multiple genes. Practical Use TodayBRCA1 gene mutations are associated with increased breast, ovarian, and possibly prostate, and colon cancers; while BRCA2 gene mutations are associated with breast, pancreatic, gallbladder, and stomach cancers. BRCA positive patients should get more aggressive monitoring, and some opt for even more aggressive measures including bilateral mastectomies and oopherectomies to minimize their future risk of breast and ovarian cancer. Several genes affecting the Cytochrome P450 pathway determine a patient’s ability to metabolize a large variety of medications. Depending on results, a patient is usually put into four categories of metabolism: Normal, Ultrarapid, Intermediate, and Poor. The ultrarapid group may therefore not respond well to normal dosages, while the Intermediate and Poor groups may suffer side effects related to the drug levels getting too high.About 20-30% of women with breast cancer are HER-2 positive (meaning they have too many copies of the HER-2 gene, and thus too much HER2 protein). These breast cancers are often more aggressive and harder to treat. Herceptin is a monoclonal antibody that specifically targets the HER-2 genes and thus is only effective for HER-2 positive cancers. The FDA therefore requires that molecular testing confirm elevated HER-2 levels before allowing physicians to prescribe Herceptin.
  • Dr. Higgins There are several approaches to storing genomic data.If you store the entire genome of a patient - that's about 6 GB. Without annotation, if you multiply that by the 5M EHRs in the Microsoft EHR, that's 30 PB.Since 99.9% of human genomic data is identical, you can use a ‘sparse matrix’ approach, in which you only store the differences in genomes between individual patients. However, this requires another layer of analysis to understand where the genomic sequences differ.Finally, and most importantly at this moment in time, we have been using microarray data (from DNA, RNA or protein). In the case of the human genome there are about 6M Single Nucleotide Polymorphisms (SNP), that is, single base changes (A, C, G or T), that differ between individuals. These can be predictive for common diseases, as stated above.Some challenges:On the data storage side: For a few patients or samples, it may be possible to store primary data, but the cost of professional storage, backup, maintenance, electricity and transfer is huge. Here, we are not talking about the high street hard disks, but the professional raid arrays which store data reliably.The Sanger Hinxton Institute has one of the largest data centers in the world, and they have just now completed the majority of 1000 genomes project and few other genomics projects. The storage requirement is sky-rocketing. Imagine the real time data, where the patients are unlimited - ie several On the data transfer side: Moving huge data across places would also lead data congestion. For the large hadron collider data, they have built their dedicated network. But, most rely on the internet. We can have one facility (per city or state or region wise) which is common for several hospitals. If the patient is moving within region for a different hospital, the same facility would be able to serve without moving data. This also aids in cost-cutting and maintenance issues. On the sample side: It clearly depends on a case by case basis. If you have an important, hard-to-get sample, deleting the primary data is not good. But, in many cases where the patient sample (nowadays, we can have cheap/efficient saliva kits - no need for blood samples) can be re-obtained or stored, AND you vaguely know that the data analysis is required only once, at least the primary data can be deleted.Again, moving them across hospitals is not a viable option in the long time. Having a facility integrated with data center (regional basis) is the option. Classifying samples based on various factors, and applying rules is a good option.On the data format side: This should evolve a lot in (gen)-omics. There are several new data format types/potentials on the informatics side, which would reduce the size of data (without loss) on a per genome basis. This will again not help us in keeping _all_ data, but would let us store more (millions) patients'.
  • It is estimated that less than 10% of disease-causing mutations have been discovered in the human genome. These range from monogenic diseases like Huntington’s disease and Cystic Fibrosis, to mutations that cause variability in drug response between individuals, to variation in Copy Number that lead to diseases like childhood Autism, to Single Nucleotide Polymorphisms (SNPs) that may form patterns that contribute to common, complex diseases like Type II Diabetes and Coronary Artery Disease.
  • Dr. Leavitt (Original ONC Chief 2007) “Personalized health care will combine the basic scientific breakthroughs of the human genome with computer-age ability to exchange and manage data…. Increasingly it will give us the ability to deliver the right treatment to the right patient at the right time – every time.”
  • Dr. Leavitt (Original ONC Chief 2007) “Personalized health care will combine the basic scientific breakthroughs of the human genome with computer-age ability to exchange and manage data…. Increasingly it will give us the ability to deliver the right treatment to the right patient at the right time – every time.”We stand at the dawn of a new understanding of disease…Nature 409, 860 - 921 (2001)Initial sequencing and analysis of the human genome International Human Genome Sequencing ConsortiumThe human genome holds an extraordinary trove of information about human development, physiology, medicine andevolution. Here we report the results of an international collaboration to produce and make freely available a draft sequence of thehuman genome. We also present an initial analysis of the data, describing some of the insights that can be gleaned from the sequence.
  • Services / Software Nexus Bioinformatics & PharmacogenomicsKnowledge DiscoveryIdentification of potential drug targetsGenomicsProteomicsCheminformaticsDrug target modelingDrug optimizationSimulating drug effects on pathwaysEstimating toxicological effects
  • We will use "personalized medicine" as a conveyer of genomic informatic data (and management) to an existing EHR.  Agility does this leveraging interoperability standards, a unified medical language and a layer of intelligent clinical content (for existing clinical decision support systems, CPOE solutions and as an easy add on for medication Management "eMAR" and or Order Management Applications.Critical Data Needed:Need for data standardizationGenomic data + traditional clinical dataNeed to effectively deliver complex information in busy practice settings E.g., primary careNeed for widely deployable CDS systemsIn settings with different EHR systems (or none at all)
  • http://www.ornl.gov/sci/techresources/Human_Genome/faq/seqfacts.shtmlQuality Analysis: This step is crucial as it means we can have  confidence in the results obtained in subsequent analyses, excluding the risk that  final conclusions result from poor quality  biases in the data. Biostatisticsis the application of computational and statistical techniques to biology, in order to give statistical credibility to  data: It shows to what extent  an observation  is the result of  experimental design and  not a consequence of  chance. Data Mining aims to further analyze the biostatistical results (for example, differentially expressed genes, single nucleotide polymorphisms, portions of chromosomes  showing  copy number variations), in order to extract the most relevant biological information (inference of pathways, gene ontology, functional annotation,...). Genome Analysis and Annotation: Through strong partnerships, we provide tailored  automated genome annotation tools for prokaryotic and eukaryotic genomes.
  • Sequencing - http://www.illumina.com/Over the past decade, DNA sequencing throughput has increased over 50-fold. Advances in DNA sequencing have enabled logarithmic growth of data points and breadth in coverage of an individual genome. High-throughput sequencing holds great promise for population-wide analysis that may influence treatment of human diseases, development of prognostic genetic biomarkers, elucidation of somatic cancer-generating mutations, or viral drug-resistance. However, considerable collection, processing, and analysis costs remain and still impede studies involving multiple samples. If the cost of a single genome analysis were lowered, without jeopardizing performance standards, many previously impractical studies could become an everyday reality. The key challenges in achieving this goal are miniaturization of the reactions and increase in quantity and density of reactions. The same volume previously occupied by a single reaction now contains millions of reactions, proportionally decreasing per-reaction cost. New technologies aim to achieve price-performance points that bring the cost to process a human genome on par with the cost of the bacterial genome, ultimately aiming for a coveted goal of $1,000 per genome. Most of the current applications are based on the comparison of already sequenced genomes. Re-sequencing of an entire genome can be successfully accomplished with short reads of about 30 bp, as long as the coverage is somewhere between 25–30x. This paradigm shift gave a boost to short-read technologies.
  • Reduce the cost of storage & emphasize DICOM expertise, ability to work within HIPAA guidelines. Integration expertise, & initial focus on research to garner tactical grants.
  • Genomic Informatics - http://www.warfarindosing.org/Source/Home.aspx

Agility v7.0-rro Agility v7.0-rro Presentation Transcript

  • Rex Osborn HealthCare Informatics Idea Architect Agility Health International 1
  • Agility Brand Suites 3
  • Introduction A practical bridge between BioInformatics & HealthCare IT… 4
  • I. The Idea II. The Market III. Patentable Technology IV. Defining Personalized Medicine V. Relevance of Personalized Medicine VI. Challenges of Personalized Medicine VII.Pharmacogenomics VIII.Cost Impetus IX. Closed Loop Medication / Genome enabled X. Sequencing to DICOM 5
  • The Idea Today bioinformatics & healthcare information technology (HIT) does not provide a practical nexus. The scientific & clinician genomic model is dissimilar & does NOT take into consideration; clinician workflow, HIT standards, CPOE & the rightful place within ―closed-loop-medication‖ to ensure patient safety. Without bridging bioinformatic & HIT - ―Personalized Medicine‖ will never be realized. Challenges in the exponential size of genomic data & controversy in how to manage that data is the gateway for credibility & revenue. Agility Genomics has a plan and engineering approach , as well as the team to bridge the Pharmacogenomic components of bioinformatics to the EHR. Making sequenced DNA clinically relevant. Furthermore we enable Personalized Medicine by providing clinically germane genome data to the patient’s PHR. Today intelligent storage, tomorrow the management of secondary data. 6 Practical interoperability; BioInformatics & the EHR
  • The Idea We propose a new business that provides “sequenced” Genomic storage, management & clinical decision content for CPOE / EHR solutions; Bridging BioInformatics capacity to manage a sequenced genome into a practical HL7 feed for clinicians EHR Phase I. Genomic Data Storage (DICOM / HL7) – National Library of Medicine / National Institute Health Genomic Program / Grant (Genome raw data to DICOM) Phase II. Apply Intelligent HSM & HIPAA encryption to genetic data elements Phase III. Develop genomic ―ontology‖ leveraging standards to initiate parsing & aggregation of structured data (HealthLanguage) Phase IV. Develop clinical decision support (CDS) mechanism to convey Pharmacogenomic data @ the point of care (CPOE) (www.warfarindosing.org) Phase V. Commercialize DNA archive & analysis for forensic products (Services: storage, integration & aggregation to HL7) 7 Clinical Annotation of Patient Genomic Data within the EHR
  • The Approach STORAGE OF WHOLE PATIENT GENOMES ACCESSIBLE VIA YOUR EHR  DICOM-based format w/ HL7 relevant data (MPI, PIX, PID etc)  Fast transfer in & out of EHR  Parsing, aggregation & mining for public / research / private output PHARMACOGENOMIC CLINICAL DECISION SUPPORT (CDS)  Generic algorithms for the EHR  Tethered, secure service  Drug – Genome clinical content for critical decision making @ POC ―CPOE‖ NOSOLOGY-BASED GENOME SEARCH FUNCTIONS: EHR  Provides clinician quick search to identify disease mutations  Will also offer OTS applications for genome analysis – M&A  Disease predisposition characteristic availability Agility sells software, services & secure cloud access through EHR & PHR suppliers. Leveraging our work with research institutions sharing, storing and managing genetic data packets. Our adherence to HIPAA, GINA, DICOM, HL7, HIPAA will enable a practical path to Personalized Medicine (healthcare tailored to each individual's genetic makeup). www.warfarindosing.org 8
  • The Market Market Impetus: According to the report: Currently, it costs about $1,000 to sequence an entire human genome; That cost will be lowered to about $100 per patient by the year 2012, making it feasible to sequence individual patient genomes & store them in the EHR. Upside for Agility = the cost of storage and of the analyzed genome will be significant. The core diagnostic and therapeutic segment of the market – comprised primarily of pharmaceutical, medical device & diagnostics companies – is estimated at $24 billion and is expected to grow by 10 percent annually, reaching $42 billion by 2015. Market Size; The market for Personalized Medicine in the United States is already $232 billion, and it is projected to grow 11% annually, according to a new report published today by PricewaterhouseCoopers LLP The personalized medical care portion of the market – including telemedicine, health information technology & disease management services offered by traditional health & technology companies – is estimated at $4 billion to $12 billion and could grow tenfold to over $100 billion by 2015 if telemedicine takes off. The related nutrition and wellness market – including retail, complementary and alternative medicine offered by consumer products, food and beverage, leisure and retail companies – is estimated at $196 billion and is projected to grow 7 percent annually to over $290 billion by 2015. 9
  • Market Focus Initial Secondary Tertiary Future 10
  • Patentable Technology 1) A DICOM Method for Storage of Human Genomic Data 2) Searchable Human Genomic Data by Nosology 3) Generalized, Allele-Specific Algorithms for Pharmacogenetic Clinical Decision Support (CDS) 4) Fast Transfer of Genomic Data to an XML-based Electronic Health Record (EHR) 5) Genomic-based XDS clinical connectivity (associate relevant clinical documentation) 6) Clinical content with ―ontology‖ for CPOE CDS solutions (conveyance method) 7) Genomic data encryption & access audit (HIPAA based) that meets GINA (2008 Genomic Legislation) 8) Patent transition methodology moving ―sequenced DNA‖ raw data to DICOM stored for conveyance to output of HL7 message relaying clinical content to CDS / CPOE / EHR 11
  • Defining Personalized Medicine Personalized Medicine—also called Genome-based or Genomic Medicine Personalized Medicine analyzes a person’s molecular information to determine their propensity for developing certain diseases, including a disease’s potential onset & course. It also determines a person’s susceptibility for responding to different drug therapies, by determining the ability to metabolize drugs &/or likelihood of experiencing adverse drug reactions (ADR). The term personalized medicine is often used in place of the scientific term Pharmacogenomics. Bioinformatics is using computers to solve problems in biology. Bioinformatics is a scientific discipline that encompasses all aspects of biological information acquisition, processing, storage, distribution, analysis and interpretation. Bioinformatics combines the tools of Biology, Chemistry, Mathematics, Statistics & Computer Science to understand and model biological processes. Genetics is the study of heredity & genetic medicine, it examines the role of individual genes as they relate to biology and medicine. Genomic medicine makes use of our own personal (thus the name, personalized medicine) genome - our individual genetic structure encoded by the nucleotide sequences, etc. to determine an individuals vulnerability to disease and responsiveness to medication. Personalized Medicine is the Outcome of Genomic Medicine 12
  • Relevance of Personalized Medicine Goals of Personalized Medicine allowing clinicians to…  Predict Disease pre-symptomatically with simple testing  Prevent Disease by identifying risks, early interventions  Diagnose Conditions less invasively, more accurately  Select Drugs that maximize benefits & minimize risks  Calibrate Treatments to heighten proven efficacy & recovery  Stable Genomics (Inherited Genes) – BRCA 1 & 2 predictor of breast and ovarian cancer risks – LDLR and APOB predictor of developing early coronary artery disease – MODY 1-6 predictor of MODY diabetes; subtypes affect treatment choice – CYP2D6/C19 Main cytochrome P450 genes that affects drug metabolism  dosing – CYP2C9/VKORC1 variants in these cP450 genes affect warfarin metabolism – TPMT guides adjustment of Purinethol dosing in Acute Leukemia patients Treat/Cure Disease using our own genes Dynamic Genomics (Gene Expression, Biomarkers…) – Estrogen Receptor predicts response to Tamoxifen in breast cancer – HER-2 Receptor predicts response to Herceptin in breast cancer – PSA predicts risk of prostate cancer – Cholesterol predicts risk of heart disease and strokes – HIV Genotyping to guide selection of therapy – PET Scans to diagnose and help manage treatment options for various cancers 13 Practical Use of Genomic Medicine
  • Challenges of Personalized Medicine Application of data integration concepts & approaches to genomic medicine  Data warehouse approaches  Database federation approaches  Peer data management system approaches  Ontologies  Semi-structured data Gaps remaining in data integration both clinical & research to facilitate genomic medicine  Data availability  Privacy issues (HIPAA)  Data issues  Scalability  Standards adoption Bridging disciplines: collaborations vs convergence TACTICALLY  Storage - large and diverse data sets.  Intelligent Hierarchical Archive Management  DICOMization of Raw Data  Integration of HL7 data; MPI, CDR & History  National Library of Medicine (NLM) = Genetic Data Management ―Research‖  HIPAA – Genetic Data Encryption STRATEGICALLY  Ontology – MeSH, UMLS, Ontofusion, SnoMeds  Patent Workflow, Management, Parsing, Aggregation & Mining  XDS  Limited tools for appropriate standardization of findings  CRO – Clinical Research Organization  Genome based CDS  Provide genomic content for CDS 14
  • Challenges of Personalized Medicine 15
  • Commoditization  The cost of sequencing and analysis of an entire human genome will drop to about $100 by 2012 (from Dr. George Church, Broad Institute, MIT/Harvard).  This will effectively require a new storage & data management scheme to handle Genomic data that has been sequenced.  Sequenced genomic data & analysis of this data to convey via a ―patentable‖ ontology in existing EHRs (the ability to communicate with disparate systems is a ―must‖)  Although Clinical Decision Support / Clinical Content is paramount & the ultimate goal for evidence based medicine, outcomes based medicine and to mitigate medication risk, the short term opportunity is in intelligent storage 16
  • Historical trends in storage prices vs DNA sequencing costs 17
  • Pharmacogenomics Opportunities will increase the value of the drugs being developed using Genomic / Genetic Medicine:  Obtain greater understanding of disease  Predict disease severity, onset, progression  Identify genetic subtypes of disease  Lower ADRs in Clinical setting  Lower unknown contraindications  Aid in discovery of new drug targets  Distinguish subgroups of patients who respond differently to drug treatment  Aid interpretation of clinical study results  Evolution of Pharmacogenetics – advancing requirements for research & clinical trial environments 18 Average Cost to Develop a Medication in the US $800 Million USD
  • Evaluate Order Obtain medication related history Sequenced Genomic Data; Managed, Archived, Parsed, Aggregated & available @ the Point of Care CPOE Documentati on medication history & updates Intervene as indicated for ADR errors Select Medication Prepare Medication Dispense / Dist. IV & Medication CIS/CDS /CDR Assess & document patient response to medication according to defined parameters Closed Loop Medication Workflow w/ Genomic CDS Educate patient regarding medication Educate staff regarding medications 19
  • Sequencing to DICOM MAPPING Identify set of clones that span region of genome to be sequenced LIBRARY CREATION Purify DNA from smaller clones Setup & perform sequencing chemistries Make sets of smaller clones from mapped clones TEMPLATE PREPERATION Determine sequences from smaller clones Specialty techniques to produce high quality sequences DATA EDITING / ANNOTATION GENOME SEQUENCING PRE-FINISHING & FINISHING Quality assurance Verification Biological annotation Submission to public DB 21
  • Clinical Decision Support  Today, at best …  Population based-evidence combined with individual clinician experience  Calculators, nomograms and criteria to risk stratify individuals Clinical Decision Support - The act of providing clinicians, patients and other health care stakeholders with pertinent knowledge and/or person specific information, intelligently filtered or presented at appropriate times  CDS systems with 4 critical features significantly improved clinical practice in 94% of RCTs  Tomorrow, decision making will be …  Phenotype, environmental, clinical, and genomic factors in a ―black box‖ risk model  Patient and physician will need to interpret meaning of risk  Personalized medicine complements evidence-based medicine (Kawamoto et al., 2005): 1) automatic provision of CDS as part of clinician workflow 2) provision of recommendations 3) provision of CDS at time & location of decision making 4) computer-based generation of the CDS 23
  • Key Interfaces 24
  • Workflow Agility Software - Input 1. Lab Test / Lab 2. DNA Sequence (SNPs & Genome) 3. DICOM Transport & Query Retrieve 4. Encryption & Audit Initiated 5. Genomic Research - Genotype 6. Screening – Genotype 7. Molecular / Medical Imaging – Genotype, Phenotype & Environment Agility Future Software & Services 10. Integrated – Genotype & Phenotype assessment for inherited & common conditions 11. EHR Portal enabled 12. Genome-based Clinical Decision Support (clinical content or ―full‖ logic for direct CPOE communiqués) 13. Provider Portal enabled 14. Public health / syndromic Portal enabled 15. PHR Portal enabled Agility Software & Services - Output 8. Archive 9. Research Database – parsing, aggregation & mining 10. Interface (current chasm) – LIS, PACS / RIS, CVIS, HIS, EHR & EDMS (Electronic Doc Mgt. System) 25
  • Building Blocks PERSONALIZED Biologic Sciences Informatics & HCIT Molecular Basis of Disease Novel Therapies Disease Subtypes MEDICINE EHR Adoption BioStat Modeling Molecular Diagnostics Clinical Database ―Data warehouse‖ Genomics Epigenetics Proteomics 26
  • Marketing Considerations Marketing Strategy  Competitive Positioning  Brand Strategy  Pricing  Distribution Channels  Sales Process  Marketing Campaigns  Marketing Plans Managing Sales & Marketing  Business Development  Telemarketing  Sales Management  Direct Mail  Email Marketing  Search Engine Marketing  Trade Shows  Publicity  Customer Retention  Traditional Media  Online Advertising  Social Media Tools & Processes  Naming  Messaging  Corporate Identity  Sales Literature (Collateral)  Sales Content  Website - Blog  CRM 27
  • Horizontal Styled SWOT Strength Weakness Incredible growth, and investment Tactically we can leverage Existing Client Base • Highlight storage, scalability & encryption • Flexibility of HSM • Cloud Computing / Archiving • Huge Data Sets Integration chasm & output requires investment & development of software, resources & related services. Requires a move to a new clinical & bioinformatics vertical. Opportunity Evolution of Image archiving means going beyond the walls of Radiology & Cardiology – diversification of current product suite offers advantages as does growing market & new discipline. HHS will require genomic data embedded in to the EHR 2012 enforcement 2014 Threat Market vertical is growing & commoditization will require FDA to regulate & CMS to make procedure viable ―reimbursement challenge‖ ―genomic information exchange‖ very academic presently – the move from Bio to HIT has challenges 28
  • Reference Material        The Human Genome Project: www.genome.gov The NUGene Project: www.NUGene.org Clinical Proteomics Project: http://proteomics.cancer.gov The FDA and Genomics: www.fda.gov/cder/genomics The CDC and Genomics: www.cdc.gov/genomics/default.htm NIH & Pharmacogenetics: www.nigms.nih.gov/Initiatives/PGRN Non-Profit Organizations  http://bioitalliance.org  http://www.personalizedmedicinecoalition.org  News and Updates  http://www.ageofpersonalizedmedicine.org  http://www.genomenewsnetwork.org/ 29
  • Image Relation Imaging Visual Reference 30
  • Imaging + Genomics Data 31
  • Gene regulation: TIGR MeV + CAVEman Gene expression datasets are initially analysed using the MultiExperiment Viewer. Patterns of interest are visualized as animated color maps directly on the chosen organs. 32
  • Pharmacokinetics 500mg Aspirin (acetylsalicylic acid) example Processes: Absorption > distribution > metabolism > excretion (ADME) Systems: alimentary > cardiovascular > urinary 33
  • 4D ADME Visualization (a) (Absorption, Distribution, Metabolism, Elimination) ABSORPTION The original compound (e.g. acetylsalicylic acid or acetaminophen) is shown in red and is first absorbed through the digestive organs. 34
  • 4D ADME Visualization (b) DISTRIBUTION, METABOLISM Simultaneously with its absorption into the blood stream, it is being metabolized into derivative compounds, shown in green (e.g. salicylic acid, acetaminophen sulfate, acetaminophen glucuronide, etc.), which are distributed throughout the body. 35
  • 4D ADME Visualization (c) ELIMINATION Eventually all compounds are eliminated from the blood and are excreted through the urinary system 36
  • Drug modeling: Aspirin, Tylenol, Prozac, Lipitor 37
  • Exploring complex 4D data along any axis (with Dr. Benedikt Hallgrímsson, U of C) Sagittal Frontal Right: Integration, quantification and visualization of data in 3D / 4D Averaged Above: 3D morphometric (micro-CT) and cell proliferation (histochemistry) data in mouse embryos Embryo 2 Embryo 1 External 38
  • Data integration mechanism Gene Expression Omnibus Terminologia Anatomica Cytomer GSM88529 : Cardiac A12.1.00.001 Heart Heart GSM88484 : Coronary artery A12.2.03.101 Right coronary artery Right coronary artery A12.2.03.201 Left coronary artery Left coronary artery A06.5.01.002 Right lung Left inferior pulmonary lobe A06.5.01.003 Left lung Middle lobe of right lung GSM88522 : Lung Left superior pulmonary lobe Right inferior pulmonary lobe Right superior pulmonary lobe Terminological discrepancies between ontologies make integration difficult. 39
  • Ontology support: All data sources are connected Domains ontologies are semantically related. Data sources are indexed via ontologies. Visualization device may be either a virtual reality (e.g., CAVE) or a common desktop. 40
  • Future research directions Model more molecular & anatomical processes Develop more variety of interactions Utilize more human body models – Female, child Genetic Variation Pathways Cellular Processes Phenotypes 41