Special Issue "Untold Stories about Personalized Medicine: If You Thought Personalized Medicine Is for Others to Worry about, Think Again"
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A special issue of Journal of Personalized Medicine (ISSN 2075-4426).
Deadline for manuscript submissions: closed (31 August 2012)
Special Issue Editor
Guest Editor
Dr. Felix W. Frueh
Institute for Pharmacogenomics and Individualized Therapy, University of North Carolina, Chapel Hill, USA
E-Mail: felix@frueh.us
Phone: +1 240 839 8374
Interests: biomarkers; clinical trials; clinical practice; translational research; diagnostics; comparative effectiveness
Special Issue Information
Dear Colleagues,
Technologies advances, new scientific insights, and improved routes of access are propelling personalized medicine into the clinic. Patients are being treated more precisely with medicines that allow an a priori evaluation whether it may work, at what level of risk a patient may get exposed to them, which dose might be the most appropriate - in other words, these new medical products are developed with the idea in mind that the evidence of benefit must be demonstrated at the level of an individual patient, and in a predictable manner. However, the opportunities this field of medicine offers are further reaching than simply treating (an individual) patient: a great deal of ethical and societal, regulatory and reimbursement, business and even political aspects are debated and will, for good or bad, impact the future of personalized medicine. What are these factors exactly and how do or might they influence personalized medicine? Why does personalized medicine not stop at the patient - doctor interaction, but may affect us in many more ways? Does the ongoing debate over health care reform impact personalized medicine and if so how? This special issue of the Journal of Personalized Medicine looks into the future and asks several stakeholders, some obvious and some less obvious how they view personalized medicine influencing, or being influenced by, their area of interest.
Dr. Felix W. Frueh
Guest Editor
Submission
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are refereed through a peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Personalized Medicine is an international peer-reviewed Open Access quarterly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. For the first couple of issues the Article Processing Charge (APC) will be waived for well-prepared manuscripts.
English correction and/or formatting fees of 250 CHF (Swiss Francs) will be charged in certain cases for those articles accepted for publication that require extensive additional formatting and/or English corrections.
Keywords
- personalized medicine
- translation
- clinical application
- stakeholders
- ethics
- society
- business
- politics
- regulation
- reimbursement
- health care
- reform
- evidence
- future
Published Papers (10 papers)
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Received: 2 August 2012; in revised form: 9 September 2012 / Accepted: 13 September 2012 / Published: 27 September 2012
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Abstract: We hypothesize that local customized modeling will provide more accurate mortality prediction than the current standard approach using existing scoring systems. Mortality prediction models were developed for two subsets of patients in Multi-parameter Intelligent Monitoring for Intensive Care (MIMIC), a public de-identified ICU database, and for the subset of patients >80 years old in a cardiac surgical patient registry. Logistic regression (LR), Bayesian network (BN) and artificial neural network (ANN) were employed. The best-fitted models were tested on the remaining unseen data and compared to either the Simplified Acute Physiology Score (SAPS) for the ICU patients, or the EuroSCORE for the cardiac surgery patients. Local customized mortality prediction models performed better as compared to the corresponding current standard severity scoring system for all three subsets of patients: patients with acute kidney injury (AUC = 0.875 for ANN, vs. SAPS, AUC = 0.642), patients with subarachnoid hemorrhage (AUC = 0.958 for BN, vs. SAPS, AUC = 0.84), and elderly patients undergoing open heart surgery (AUC = 0.94 for ANN, vs. EuroSCORE, AUC = 0.648). Rather than developing models with good external validity by including a heterogeneous patient population, an alternative approach would be to build models for specific patient subsets using one’s local database.
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Received: 24 August 2012; in revised form: 21 September 2012 / Accepted: 21 September 2012 / Published: 1 October 2012
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Abstract: Thousands of samples for pharmacogenetic tests have been analysed in our laboratory since its establishment. In this article we describe some of the most interesting cases of CYP poor metabolisers associated with adverse reactions to psychotropic drugs. Prevention of disease/illness, including Adverse Drug Reaction (ADR), is an aim of modern medicine. Scientific data supports the fact that evaluation of drug toxicology includes several factors, one of which is genetic variations in pharmacodynamics and pharmacokinetics of drug pathways. These variations are only a part of toxicity evaluation, however, even if it would help to prevent only a small percentage of patients from suffering adverse drug reactions, especially life threatening ADRs, pharmacogenetic testing should play a significant role in any modern psychopharmacologic practice. Medical practitioners should also consider the use of other medications or alternative dosing strategies for drugs in patients identified as altered metabolisers. This will promise not only better and safer treatments for patients, but also potentially lowering overall healthcare costs.
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Received: 28 August 2012; in revised form: 1 October 2012 / Accepted: 10 October 2012 / Published: 17 October 2012
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Abstract: HMG-CoA reductase inhibitors, commonly known as statins, are some of the most widely prescribed medications worldwide and have been shown to be effective at lowering cholesterol in numerous long-term prospective trials, yet there are significant limitations to their use. First, patients receiving statin therapy have relatively low levels of medication adherence compared with other drug classes. Next, numerous statin formulations are available, each with its own unique safety and efficacy profile, and it may be unclear to prescribers which treatment is optimal for their patients. Finally, statins have class-wide side effects of myopathy and rhabdomyolysis that have resulted in a product recall and dosage limitations. Recent evidence suggests that two genomic markers, KIF6 and SLCO1B1, may inform the therapy choice of patients initiating statins. Given the prevalence of statin usage, their potential health advantages and their overall cost to the healthcare system, there could be significant clinical benefit from creating personalized treatment regimens. Ultimately, if this approach is effective it may encourage higher adoption of generic statins when appropriate, promote adherence, lower rates of myopathy, and overall achieve higher value cardiovascular care. This paper will review the evidence for personalized prescribing of statins via KIF6 and SLCO1B1 and consider some of the implications for testing these markers as part of routine clinical care.
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Received: 20 September 2012 / Accepted: 17 October 2012 / Published: 29 October 2012
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Abstract: CYP3A ranks among the most abundant cytochrome P450 enzymes in the liver, playing a dominant role in metabolic elimination of clinically used drugs. A main member in CYP3A family, CYP3A4 expression and activity vary considerably among individuals, attributable to genetic and non-genetic factors, affecting drug dosage and efficacy. However, the extent of genetic influence has remained unclear. This review assesses current knowledge on the genetic factors influencing CYP3A4 activity. Coding region CYP3A4 polymorphisms are rare and account for only a small portion of inter-person variability in CYP3A metabolism. Except for the promoter allele CYP3A4*1B with ambiguous effect on expression, common CYP3A4 regulatory polymorphisms were thought to be lacking. Recent studies have identified a relatively common regulatory polymorphism, designated CYP3A4*22 with robust effects on hepatic CYP3A4 expression. Combining CYP3A4*22 with CYP3A5 alleles *1, *3 and *7 has promise as a biomarker predicting overall CYP3A activity. Also contributing to variable expression, the role of polymorphisms in transcription factors and microRNAs is discussed.
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Received: 3 September 2012; in revised form: 22 October 2012 / Accepted: 25 October 2012 / Published: 30 October 2012
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Abstract: Direct to consumer (DTC) genomic services facilitate the personalized and participatory aspects of “P4” medicine, but raise questions regarding use of genomic data in providing predictive and preventive healthcare. We illustrate the issues involved by describing a pregnancy management case in which a treatment plan was modified based on a DTC result. A woman whose personal and family history were otherwise unremarkable for thromboembolism learned through DTC testing about the presence of a prothrombin (factor 2) gene mutation (rs1799963). Twice daily injections of enoxaparin were recommended throughout pregnancy for this patient who, without prior knowledge of this mutation, would not have been offered such therapy. Moreover, genetically based medical guidelines are a moving target, and treatment of thrombophilic conditions in asymptomatic patients is controversial. We address the state of the art in actionable personalized medicine with respect to clotting disorders in pregnancy, as well as other factors at play— economics, patient preference, and clinical decision support. We also discuss what steps are needed to increase the utility of genomic data in personalized medicine by collecting information and converting it into actionable knowledge.
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Received: 31 August 2012; in revised form: 16 October 2012 / Accepted: 17 October 2012 / Published: 30 October 2012
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Abstract: Adoption of personalized medicine in practice has been slow, in part due to the lack of evidence of clinical benefit provided by these technologies. Coverage by insurers is a critical step in achieving widespread adoption of personalized medicine. Insurers consider a variety of factors when formulating medical coverage policies for personalized medicine, including the overall strength of evidence for a test, availability of clinical guidelines and health technology assessments by independent organizations. In this study, we reviewed coverage policies of the largest U.S. insurers for genomic (disease-related) and pharmacogenetic (PGx) tests to determine the extent that these tests were covered and the evidence basis for the coverage decisions. We identified 41 coverage policies for 49 unique testing: 22 tests for disease diagnosis, prognosis and risk and 27 PGx tests. Fifty percent (or less) of the tests reviewed were covered by insurers. Lack of evidence of clinical utility appears to be a major factor in decisions of non-coverage. The inclusion of PGx information in drug package inserts appears to be a common theme of PGx tests that are covered. This analysis highlights the variability of coverage determinations and factors considered, suggesting that the adoption of personal medicine will affected by numerous factors, but will continue to be slowed due to lack of demonstrated clinical benefit.
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Received: 18 September 2012; in revised form: 30 October 2012 / Accepted: 1 November 2012 / Published: 13 November 2012
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Abstract: While the full promise of genomic medicine may be many years in the future, personalized health care (PHC) can begin solving important health care needs now and provide a framework for the adoption of genomic technologies as they are validated. PHC is a strategic approach to medicine that is individualized, predictive, preventive, and involves intense patient engagement. There is great need for more effective models of care as nearly half of Medicare patients age 65 and older have three or more preventable chronic conditions and account for 89% of Medicare’s growing expenditures. With its focus on reactive care, the current health care system is not designed to effectively prevent disease nor manage patients with multiple chronic conditions. PHC may be a solution for improving care for this population and therefore has been adopted as the delivery platform along with a new personalized health plan tool for 230 multi-morbid, homebound Medicare recipients in Durham, North Carolina who have been high utilizers of health care resources. PHC integrates available personalized health technologies, standards of care, and personalized health planning to serve as a model for rational health care delivery. Importantly, the PHC model of care will serve as a market for emerging predictive and personalized technologies to foster genomic medicine.
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Received: 5 September 2012; in revised form: 13 November 2012 / Accepted: 15 November 2012 / Published: 20 November 2012
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Abstract: Findings from pharmacogenomics (PGx) studies have the potential to be applied to individualize drug therapy to improve efficacy and reduce adverse drug events. Researchers have identified factors influencing uptake of genomics in medicine, but little is known about the specific technical barriers to incorporating PGx into existing clinical frameworks. We present the design and development of a prototype PGx clinical decision support (CDS) system that builds on existing clinical infrastructure and incorporates semi-active and active CDS. Informing this work, we updated previous evaluations of PGx knowledge characteristics, and of how the CDS capabilities of three local clinical systems align with data and functional requirements for PGx CDS. We summarize characteristics of PGx knowledge and technical needs for implementing PGx CDS within existing clinical frameworks. PGx decision support rules derived from FDA drug labels primarily involve drug metabolizing genes, vary in maturity, and the majority support the post-analytic phase of genetic testing. Computerized provider order entry capabilities are key functional requirements for PGx CDS and were best supported by one of the three systems we evaluated. We identified two technical needs when building on this system, the need for (1) new or existing standards for data exchange to connect clinical data to PGx knowledge, and (2) a method for implementing semi-active CDS. Our analyses enhance our understanding of principles for designing and implementing CDS for drug therapy individualization and our current understanding of PGx characteristics in a clinical context. Characteristics of PGx knowledge and capabilities of current clinical systems can help govern decisions about CDS implementation, and can help guide decisions made by groups that develop and maintain knowledge resources such that delivery of content for clinical care is supported.
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Received: 1 October 2012; in revised form: 9 November 2012 / Accepted: 12 November 2012 / Published: 26 November 2012
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Abstract: The twin forces of payors seeking fair pricing and the rising costs of developing new medicines has driven a closer relationship between pharmaceutical companies and diagnostics companies, because stratified medicines, guided by companion diagnostics, offer better commercial, as well as clinical, outcomes. Stratified medicines have created clinical success and provided rapid product approvals, particularly in oncology, and indeed have changed the dynamic between drug and diagnostic developers. The commercial payback for such partnerships offered by stratified medicines has been less well articulated, but this has shifted as the benefits in risk management, pricing and value creation for all stakeholders become clearer. In this larger healthcare setting, stratified medicine provides both physicians and patients with greater insight on the disease and provides rationale for providers to understand cost-effectiveness of treatment. This article considers how the economic value of stratified medicine relationships can be recognized and translated into better outcomes for all healthcare stakeholders.
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Received: 22 November 2012; in revised form: 19 December 2012 / Accepted: 19 December 2012 / Published: 27 December 2012
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Abstract: Cancer care is often inconsistently delivered with inadequate incorporation of patient values and objective evidence into decision-making. Utilization of time limited trials of care with predefined decision points that are based on iteratively updated best evidence, tools that inform providers about a patient’s experience and values, and known information about a patient’s disease will allow superior matched care to be delivered. Personalized medicine does not merely refer to the incorporation of genetic information into clinical care, it involves utilization of the wide array of data points relevant to care, many of which are readily available at the bedside today. By pushing uptake of personalized matching available today, clinicians can better address the triple aim of improved health, lowers costs, and enhanced patient experience, and we can prepare the health care landscape for the iterative inclusion of progressively more sophisticated information as newer tests and information become available to support the personalized medicine paradigm.
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Last update: 19 April 2013
