The faces of personalized medicine: a framework for understanding its meaning and scope

Inflammatory and metabolic disturbances are associated with more severe trajectories of late-life depression

Late-life depression is a highly prevalent mental health condition with devastating consequences even from its earliest stages. Alterations in physiological functions, such as inflammatory and metabolic, have been described in patients with depression. However, little is known on the association between depression symptom course and metabolic and inflammation dysregulation. This study aimed to depict the course of depression symptoms while ageing, taking into consideration inter-individual heterogeneity. Moreover, it intended to study the associations between inflammatory and metabolic risk profiles and symptom trajectories. To do so, data from 13,203 adults aged 50-90 years (52.75% women; mean age at baseline = 65.07, SD = 10.00) were used. Blood sample and blood pressure measures were taken from 1536 participants (56.58% women; mean age at baseline = 61.73 years, sd = 7.64). Depression symptoms were assessed every two years across a 10-year follow-up. Trajectories were identified by means of latent class mixed modelling. Inflammation and metabolic risk profile scores were obtained from plasma and diagnostic-based indicators in the follow-up, using a robust latent-factor approach. Multigroup modelling was used to study the associations between the profiles and symptom trajectories. As a result, three heterogeneous trajectories of symptoms were identified (low-symptom, moderate-symptom and high-symptom trajectory). Participants depicting a high-symptom trajectory showed the greatest inflammation profile score and high metabolic risk. Moderate-symptom trajectory was also related to high inflammation and metabolic risk. To sum up, at-risk trajectories of symptoms were associated with high inflammation and risk of metabolic diseases. This study provides valuable evidence to advance personalised medicine and mental health precision, considering person-specific profiles and physiological concomitants.

Pre-approval incentives to promote adoption of personalized medicine: a theoretical approach

BACKGROUND

Currently, personalised medicine is becoming more frequently used and many drug companies are including this strategy to gain market access for very specialized therapies. In this article, in order to understand the relationships between the health authority and the drug company when deciding upon the implementation of personalized medicines, we take a theoretical perspective to model it when the price and reimbursement policy follows a pay-for-performance scheme. During the development of a new drug, the firm must decide whether to generate additional knowledge by investing in additional resources to stratify the target population based on a biomarker or directly apply for marketing authorization for the new treatment without information on the characteristics of patients who could respond to it. In this context, we assume that the pricing policy is set by the health authority, and then we characterize the pricing and investment decisions contingent on the rate of response to the treatment.

RESULTS

We find that the price when the firm carries out R&D leading to the personalized treatments is not necessarily higher than the price if the firm does not carry out the R&D investment. When the rate of response to the treatment is too low, then the new drug is not marketed. If the rate of response is too high, personalized medicine is not implemented. For intermediate values of the rate of response, the adoption of personalized medicine may occur if the investment costs are sufficiently low; otherwise, the treatment is given to all patients without additional information on their characteristics. The higher the quality of the genetic test (in terms of its sensitivity and specificity), the wider the interval for the values of the proportional responders for which personalized medicine may be implemented.

CONCLUSIONS

Our findings show that pre-approval incentives (prices) to promote the personalized treatments depend on the specific characteristics of the disease and the efficacy of the treatment. The model gives an intuitive idea about what to expect in terms of price incentives when the possibility of personalizing treatments becomes a strategic decision for the stakeholders.

Artificial intelligence in cardiovascular imaging: state of the art and implications for the imaging cardiologist

Healthcare, conceivably more than any other area of human endeavour, has the greatest potential to be affected by artificial intelligence (AI). This potential has been shown by several reports that demonstrate equal or superhuman performance in medical tasks that aim to improve efficiency, diagnosis and prognosis. This review focuses on the state of the art of AI applications in cardiovascular imaging. It provides an overview of the current applications and studies performed, including the potential value, implications, limitations and future directions of AI in cardiovascular imaging.It is envisioned that AI will dramatically change the way doctors practise medicine. In the short term, it will assist physicians with easy tasks, such as automating measurements, making predictions based on big data, and putting clinical findings into an evidence-based context. In the long term, AI will not only assist doctors, it has the potential to significantly improve access to health and well-being data for patients and their caretakers. This empowers patients. From a physician's perspective, reliable AI assistance will be available to support clinical decision-making. Although cardiovascular studies implementing AI are increasing in number, the applications have only just started to penetrate contemporary clinical care.

A paradigm shift in medicine: A comprehensive review of network-based approaches

Network medicine is a rapidly evolving new field of medical research, which combines principles and approaches of systems biology and network science, holding the promise to uncovering the causes and to revolutionize the diagnosis and treatments of human diseases. This new paradigm reflects the fact that human diseases are not caused by single molecular defects, but driven by complex interactions among a variety of molecular mediators. The complexity of these interactions embraces different types of information: from the cellular-molecular level of protein-protein interactions to correlational studies of gene expression and regulation, to metabolic and disease pathways up to drug-disease relationships. The analysis of these complex networks can reveal new disease genes and/or disease pathways and identify possible targets for new drug development, as well as new uses for existing drugs. In this review, we offer a comprehensive overview of network types and algorithms used in the framework of network medicine. This article is part of a Special Issue entitled: Transcriptional Profiles and Regulatory Gene Networks edited by Dr. Dr. Federico Manuel Giorgi and Dr. Shaun Mahony.

Ways of knowing in precision health

Precision health can provide an avenue to bridge and integrate ways of knowing for research and practice. Nurse scientists have a long-standing interest in using multiple sources of information to address research questions of significance to the profession and discipline of nursing, which can lead to much needed contributions to precision health care. In this paper, nursing scientists discuss emerging research methods including omics, electronic sensors, and geospatial data, and mixed methods that further develop nursing science and contribute to precision health initiatives. The authors provide exemplars of the types of knowledge and ways of knowing that, using these and other advanced data and analytic strategies, may advance precision health within the context of nursing science.

Application of NanoString technologies in companion diagnostic development

Introduction: NanoString nCounter technology, a novel molecular assay, is gaining prevalent use in clinical settings as it can overcome some common constraints that are associated with the use of polymerase chain reaction (PCR). Compared to PCR, NanoString technology does not involve any amplification steps, which significantly minimizes the chance of contamination. NanoString measures the number of mRNA transcripts directly by 'molecular counting', as up to 800 colored probes can be run simultaneously in a single reaction. Areas covered: This manuscript reviews the principle of NanoString and covers the main applications of NanoString in companion diagnostics with a focus on cancer immunotherapy and disease prognosis estimation. This review has also taken a step in the direction of personalized medicine, with the application of NanoString on the realm of companion diagnostics. Expert opinion: NanoString is going to take a vital role in companion diagnostics and personalized medicine, owing to its simple and easy to use characteristics. Yet, the use of NanoString requires normalization of expression level, which is represented by the copy number of respective mRNA, with a reference gene. Furthermore, difficulty in probe design, which demands prior knowledge of known sequence, has also been a limitation of NanoString.

The Relationship Between rs3212986C>A Polymorphism and Tumor Stage in Lung Cancer Patients

Background

The nucleotide excision repair (NER) system is one of the most important deoxyribonucleic acid (DNA) repair mechanisms and is critical for chemotherapy resistance. We conducted the present study to investigate the association between two polymorphisms of excision of repair cross-complementing group 1 (ERCC1), the key component of the NER pathway, and the clinicopathological features of patients with non-small cell lung cancer (NSCLC).

Methods

A total of 38 patients with confirmed NSCLC were included in our study. DNA was extracted from peripheral blood. ERCC1 rs3212986 (8092) and rs11615 (118) were genotyped using molecular assays including polymerase chain reaction (PCR) with restriction fragment length polymorphism (by MboII and HpyCH4 enzymes) and sequencing.

Results

The PCR results indicated the correct performance of the genomics extraction and molecular protocols. The distribution of C/C, C/A and A/A genotypes at position 8092 was 42.10%, 47.36%, and 10.52% respectively (P=0.03). Multivariate regression analysis showed that there was a significant correlation between C8092A (rs3212986) polymorphism and metastasis, grade of the tumor, and response to treatment. Individuals carrying the rs3212986 CA genotype and A allele had a significantly worse response to the treatment. Also, the correlation between alteration at this genomics location and patients with NSCLC who used to smoke cigarettes was positive. However, no significant association was detected between rs11615 C118>T polymorphism and demographic characteristics of patients with NSCLC.

Conclusion

We concluded that in lung cancer patients there is a relationship between tumor stage and rs3212986C>A polymorphism.

Advanced therapy medicinal products and health technology assessment principles and practices for value-based and sustainable healthcare

BACKGROUND

Advanced therapy medicinal products (ATMPs) are beginning to reach European markets, and questions are being asked about their value for patients and how healthcare systems should pay for them.

OBJECTIVES

To identify and discuss potential challenges of ATMPs in view of current health technology assessment (HTA) methodology-specifically economic evaluation methods-in Europe as it relates to ATMPs, and to suggest potential solutions to these challenges.

METHODS

An Expert Panel reviewed current HTA principles and practices in relation to the specific characteristics of ATMPs.

RESULTS

Three key topics were identified and prioritised for discussion-uncertainty, discounting, and health outcomes and value. The panel discussed that evidence challenges linked to increased uncertainty may be mitigated by collection of follow-on data, use of value of information analysis, and/or outcomes-based contracts. For discount rates, an international, multi-disciplinary forum should be established to consider the economic, social and ethical implications of the choice of rate. Finally, consideration of the feasibility of assessing the value of ATMPs beyond health gain may also be key for decision-making.

CONCLUSIONS

ATMPs face a challenge in demonstrating their value within current HTA frameworks. Consideration of current HTA principles and practices with regards to the specific characteristics of ATMPs and continued dialogue will be key to ensuring appropriate market access.

CLASSIFICATION CODE

I.

Machine Learning SNP Based Prediction for Precision Medicine

In the past decade, precision genomics based medicine has emerged to provide tailored and effective healthcare for patients depending upon their genetic features. Genome Wide Association Studies have also identified population based risk genetic variants for common and complex diseases. In order to meet the full promise of precision medicine, research is attempting to leverage our increasing genomic understanding and further develop personalized medical healthcare through ever more accurate disease risk prediction models. Polygenic risk scoring and machine learning are two primary approaches for disease risk prediction. Despite recent improvements, the results of polygenic risk scoring remain limited due to the approaches that are currently used. By contrast, machine learning algorithms have increased predictive abilities for complex disease risk. This increase in predictive abilities results from the ability of machine learning algorithms to handle multi-dimensional data. Here, we provide an overview of polygenic risk scoring and machine learning in complex disease risk prediction. We highlight recent machine learning application developments and describe how machine learning approaches can lead to improved complex disease prediction, which will help to incorporate genetic features into future personalized healthcare. Finally, we discuss how the future application of machine learning prediction models might help manage complex disease by providing tissue-specific targets for customized, preventive interventions.

Association Between 6-Week Postdischarge Risk Classification and 12-Month Outcomes After Orthopedic Trauma

Importance

Numerous studies have demonstrated that long-term outcomes after orthopedic trauma are associated with psychosocial and behavioral health factors evident early in the patient's recovery. Little is known about how to identify clinically actionable subgroups within this population.

Objectives

To examine whether risk and protective factors measured at 6 weeks after injury could classify individuals into risk clusters and evaluate whether these clusters explain variations in 12-month outcomes.

Design, Setting, and Participants

A prospective observational study was conducted between July 16, 2013, and January 15, 2016, among 352 patients with severe orthopedic injuries at 6 US level I trauma centers. Statistical analysis was conducted from October 9, 2017, to July 13, 2018.

Main Outcomes and Measures

At 6 weeks after discharge, patients completed standardized measures for 5 risk factors (pain intensity, depression, posttraumatic stress disorder, alcohol abuse, and tobacco use) and 4 protective factors (resilience, social support, self-efficacy for return to usual activity, and self-efficacy for managing the financial demands of recovery). Latent class analysis was used to classify participants into clusters, which were evaluated against measures of function, depression, posttraumatic stress disorder, and self-rated health collected at 12 months.

Results

Among the 352 patients (121 women and 231 men; mean [SD] age, 37.6 [12.5] years), latent class analysis identified 6 distinct patient clusters as the optimal solution. For clinical use, these clusters can be collapsed into 4 groups, sorted from low risk and high protection (best) to high risk and low protection (worst). All outcomes worsened across the 4 clinical groupings. Bayesian analysis shows that the mean Short Musculoskeletal Function Assessment dysfunction scores at 12 months differed by 7.8 points (95% CI, 3.0-12.6) between the best and second groups, by 10.3 points (95% CI, 1.6-20.2) between the second and third groups, and by 18.4 points (95% CI, 7.7-28.0) between the third and worst groups.

Conclusions and Relevance

This study demonstrates that during early recovery, patients with orthopedic trauma can be classified into risk and protective clusters that account for a substantial amount of the variance in 12-month functional and health outcomes. Early screening and classification may allow a personalized approach to postsurgical care that conserves resources and targets appropriate levels of care to more patients.

Translating Pharmacogenetics and Pharmacogenomics to the Clinic: Progress in Human and Veterinary Medicine

As targeted personalized therapy becomes more widely used in human medicine, clients will expect the veterinary clinician to be able to implement an evidence-based strategy regarding both the prescribing of medicines and also recognition of the potential for adverse drug reactions (ADR) for their pet, at breed and individual level. This review aims to provide an overview of current developments and challenges in pharmacogenetics in medicine for a veterinary audience and to map these to developments in veterinary pharmacogenetics. Pharmacogenetics has been in development over the past 100 years but has been revolutionized following the publication of the human, and then veterinary species genomes. Genetic biomarkers called pharmacogenes have been identified as specific genetic loci on chromosomes which are associated with either positive or adverse drug responses. Pharmacogene variation may be classified according to the associated drug response, such as a change in (1) the pharmacokinetics; (2) the pharmacodynamics; (3) genes in the downstream pathway of the drug or (4) the effect of “off-target” genes resulting in a response that is unrelated to the intended target. There are many barriers to translation of pharmacogenetic information to the clinic, however, in human medicine, international initiatives are promising real change in the delivery of personalized medicine by 2025. We argue that for effective translation into the veterinary clinic, clinicians, international experts, and stakeholders must collaborate to ensure quality assurance and genetic test validation so that animals may also benefit from this genomics revolution.

Proteomics and Metabolomics for AKI Diagnosis

Acute kidney injury (AKI) is a severe and frequent condition in hospitalized patients. Currently, no efficient therapy of AKI is available. Therefore, efforts focus on early prevention and potentially early initiation of renal replacement therapy to improve the outcome in AKI. The detection of AKI in hospitalized patients implies the need for early, accurate, robust, and easily accessible biomarkers of AKI evolution and outcome prediction because only a narrow window exists to implement the earlier-described measures. Even more challenging is the multifactorial origin of AKI and the fact that the changes of molecular expression induced by AKI are difficult to distinguish from those of the diseases associated or causing AKI as shock or sepsis. During the past decade, a considerable number of protein biomarkers for AKI have been described and we expect from recent advances in the field of omics technologies that this number will increase further in the future and be extended to other sorts of biomolecules, such as RNAs, lipids, and metabolites. However, most of these biomarkers are poorly defined by their AKI-associated molecular context. In this review, we describe the state-of-the-art tissue and biofluid proteomic and metabolomic technologies and new bioinformatics approaches for proteomic and metabolomic pathway and molecular interaction analysis. In the second part of the review, we focus on AKI-associated proteomic and metabolomic biomarkers and briefly outline their pathophysiological context in AKI.

Pharmacogenomics and Psychiatric Nursing

The treatment of mental illness is often done on a trial-and-error basis and achieving therapeutic benefits from a medication is not always guaranteed. Pharmacogenomics explores the role of gene-gene interactions and interindividual responses to a drug and may be promising in the guidance of pharmacotherapeutic options. In the present study, the impact of pharmacogenomic testing in management of mental health medication was investigated. Participants were identified at a local outpatient mental health facility through convenience sampling. Retrospective chart review included medication history, adverse drug reactions, pharmacogenomic history, and demographic data including insurance coverage. Chart review focused on six months pre- and post-pharmacogenomic for a comparison with the patient serving as their own control. Results indicate a high incidence of alterations in two specific cytochrome enzymes, CYP2D6 and CYP2C19. In total, 82% of the sample had variations with CYP2D6, while 64% of individuals had variations with CYP2C19. In total, 91% of patients tested received Medicaid or Medicare. Post-pharmacogenomic testing, all patient drug regimens were modified, and all reported less adverse side effects. Moreover, advanced practice nurse providers educated patients about the availability of genetic testing, initiated testing and provided care based on findings. These results demonstrate the utility of genetic testing in the realm of mental health. Future directions involve further exploring the benefits of pharmacogenomic testing in this vulnerable population.

Prostate cancer and inflammation: A new molecular imaging challenge in the era of personalized medicine

The relationship between cancer and inflammation is one of the most important fields for both clinical and translational research. Despite numerous studies reported interesting and solid data about the prognostic value of the presence of inflammatory infiltrate in cancers, the biological role of inflammation in prostate cancer development is not yet fully clarified. The characterization of molecular pathways that connect altered inflammatory response and prostate cancer progression can provide the scientific rationale for the identification of new prognostic and predictive biomarkers. Specifically, the detection of infiltrating immune cells or related-cytokines by histology and/or by molecular imaging techniques could profoundly change the management of prostate cancer patients. In this context, the anatomic pathology and imaging diagnostic teamwork can provide a valuable support for the validation of new targets for diagnosis and therapy of prostate cancer lesions associated to the inflammatory infiltrate. The aim of this review is to summarize the current literature about the role of molecular imaging technique and anatomic pathology in the study of the mutual interaction occurring between prostate cancer and inflammation. Specifically, we reported the more recent advances in molecular imaging and histological methods for the early detection of prostate lesions associated to the inflammatory infiltrate.

Multidisciplinary approaches for targeting the secretase protein family as a therapeutic route for Alzheimer's disease

The continual increase of the aging population worldwide renders Alzheimer's disease (AD) a global prime concern. Several attempts have been focused on understanding the intricate complexity of the disease's development along with the on- andgoing search for novel therapeutic strategies. Incapability of existing AD drugs to effectively modulate the pathogenesis or to delay the progression of the disease leads to a shift in the paradigm of AD drug discovery. Efforts aimed at identifying AD drugs have mostly focused on the development of disease-modifying agents in which effects are believed to be long lasting. Of particular note, the secretase enzymes, a group of proteases responsible for the metabolism of the β-amyloid precursor protein (βAPP) and β-amyloid (Aβ) peptides production, have been underlined for their promising therapeutic potential. This review article attempts to comprehensively cover aspects related to the identification and use of drugs targeting the secretase enzymes. Particularly, the roles of secretases in the pathogenesis of AD and their therapeutic modulation are provided herein. Moreover, an overview of the drug development process and the contribution of computational (in silico) approaches for facilitating successful drug discovery are also highlighted along with examples of relevant computational works. Promising chemical scaffolds, inhibitors, and modulators against each class of secretases are also summarized herein. Additionally, multitarget secretase modulators are also taken into consideration in light of the current growing interest in the polypharmacology of complex diseases. Finally, challenging issues and future outlook relevant to the discovery of drugs targeting secretases are also discussed.

The Impact of Pharmacogenomics in Personalized Medicine

Recent advances in Pharmacogenomics have made it possible to understand the reasons behind the different response of a drug. Discovery of genetic variants and its association with the varying response of drug provide the basis for recommending a drug and its dose to an individual patient. Genetic makeup-based prescription, design, and implementation of therapy not only improve the outcome of treatments but also reduce the risk of toxicity and other adverse effects. A better understanding of individual variations and their effect on drug response, metabolism excretion, and toxicity will replace the trial-and-error approach of treatment. Evidence of the clinical utility of pharmacogenetics testing is only available for a few medications, and FDA labels only require pharmacogenetics testing for a small number of drugs. Although there is a great promise, there are not many examples where Pharmacogenomics impacts clinical utility. Some genetic variants related to different diseases have been reported, and many have not been studied yet. The information related to the outcome of treatment with a particular drug and a genetic variant can be used to release a warning/label for the use of that drug. There are many limitations in the way of implementing the goal of personalized medicine. Future advances in the field of genomics, diagnosis approaches, data analysis, clinical decision-making, and sustainable business model for personalization of therapy can speed up the individualization of therapy based on genetic makeup.

COPD patient education and support - Achieving patient-centredness

BACKGROUND

The art of medicine is undergoing a dramatic shift in focus, evolving to focus on patient involvement as partners in care, transforming the traditional, prescriptive, reactive practice of healthcare into a proactive discipline. The personal and societal burden of chronic diseases is burgeoning and unsustainable in current systems, novel approaches are required to address this.

DISCUSSION

Although considerable progress has been made in the development of diagnostics, therapeutics and care guidelines for patients with chronic obstructive pulmonary disease (COPD), questions remain surrounding the implementation of best practice education and support. Current educational programmes, personal limitations and preferences and patient-clinician communication in modification of coping styles and behaviour are discussed. A novel holistic model, the Kaleidoscope Model of Care is proposed to address the barriers to optimal self-care behaviours.

CONCLUSION AND PRACTICE IMPLICATIONS

Holistic approaches are essential for optimal self-management and improved outcomes. Guidance on personalised goals for patients to help meeting their therapy priorities is needed to aid healthcare professionals (HCPs) and funders to minimise healthcare burden and costs. The novel KALMOD approach may optimise patient empowerment, exploring whole-life factors that impact COPD care and improve interactions between patients and HCPs for optimised outcomes.

Economic Evaluations of Next-Generation Precision Oncology: A Critical Review

Purpose

Precision oncology has the potential to improve patient health and reduce treatment costs. Yet the up-front cost of genomic testing with next-generation sequencing (NGS) technologies can be prohibitive. Our study is a structured review of economic evaluations of precision oncology informed by NGS. The aim is to characterize the availability and scope of economic evidence.

Materials and Methods

We searched Medline (PubMed), Embase (Ovid), and Web of Science databases for English-language full-text peer-reviewed articles published between 2000 and 2016. We focused our search on articles that estimated the benefit of precision oncology in relation to its costs. We excluded studies that did not undertake full economic evaluations or did not focus on NGS technologies. We reviewed all included studies and summarized key methodological and empirical study characteristics.

Results

Fifty-five economic evaluations met our inclusion criteria. The number of published studies increased steadily, from three studies between 2005 and 2007 to 26 between 2014 and 2016. Most studies evaluated multiplex panels (86%). We found testing was frequently used to predict prognosis (67%), to diagnose patients (24%), or to identify targeted therapeutic options (7%). Methods and cost effectiveness differed according to NGS technology, test strategy, and cancer type. Deterministic and probabilistic analyses were typically used to characterize parameter and decision uncertainty (91% and 75%).

Conclusion

Although the availability of economic evidence examining precision oncology increased over time, methods used often did not align with current guidelines. Future evaluations should undertake extensive sensitivity analysis to address all sources of uncertainty associated with rapidly changing NGS technologies. Furthermore, additional research is needed evaluating the cost effectiveness of more comprehensive next-generation technologies before implementing these on a wider scale.

An unsupervised machine learning method for discovering patient clusters based on genetic signatures

INTRODUCTION

Many chronic disorders have genomic etiology, disease progression, clinical presentation, and response to treatment that vary on a patient-to-patient basis. Such variability creates a need to identify characteristics within patient populations that have clinically relevant predictive value in order to advance personalized medicine. Unsupervised machine learning methods are suitable to address this type of problem, in which no a priori class label information is available to guide this search. However, it is challenging for existing methods to identify cluster memberships that are not just a result of natural sampling variation. Moreover, most of the current methods require researchers to provide specific input parameters a priori.

METHOD

This work presents an unsupervised machine learning method to cluster patients based on their genomic makeup without providing input parameters a priori. The method implements internal validity metrics to algorithmically identify the number of clusters, as well as statistical analyses to test for the significance of the results. Furthermore, the method takes advantage of the high degree of linkage disequilibrium between single nucleotide polymorphisms. Finally, a gene pathway analysis is performed to identify potential relationships between the clusters in the context of known biological knowledge.

DATASETS AND RESULTS

The method is tested with a cluster validation and a genomic dataset previously used in the literature. Benchmark results indicate that the proposed method provides the greatest performance out of the methods tested. Furthermore, the method is implemented on a sample genome-wide study dataset of 191 multiple sclerosis patients. The results indicate that the method was able to identify genetically distinct patient clusters without the need to select parameters a priori. Additionally, variants identified as significantly different between clusters are shown to be enriched for protein-protein interactions, especially in immune processes and cell adhesion pathways, via Gene Ontology term analysis.

CONCLUSION

Once links are drawn between clusters and clinically relevant outcomes, Immunochip data can be used to classify high-risk and newly diagnosed chronic disease patients into known clusters for predictive value. Further investigation can extend beyond pathway analysis to evaluate these clusters for clinical significance of genetically related characteristics such as age of onset, disease course, heritability, and response to treatment.

Personalized Medicine and Pay for Performance: Should Pharmaceutical Firms be Fully Penalized when Treatment Fails?

In this article, we model the behavior of a pharmaceutical firm that has marketing authorization for a new therapy believed to be a candidate for personalized use in a subset of patients, but that lacks information as to why a response is seen only in some patients. We characterize the optimal outcome-based reimbursement policy a health authority should follow to encourage the pharmaceutical firm to undertake research and development activities to generate the information needed to effectively stratify patients. Consistent with the literature, we find that for a pharmaceutical firm that does not undertake research and development activities, when the treatment fails, the total price of the drug must be returned to the healthcare system (full penalization). By contrast, if the firm undertakes research and development activities that make the implementation of personalized medicine possible, treatment failure should not be fully penalized. Surprisingly, in some cases, particularly for high-efficacy drugs and small target populations, the optimal policy may not require any penalty for treatment failure. To illustrate the main results of the analysis, we provide a numerical simulation and a graphical analysis.

The economic case for precision medicine

Introduction: The advancement of precision medicine into routine clinical practice has been highlighted as an agenda for national and international health care policy. A principle barrier to this advancement is in meeting requirements of the payer or reimbursement agency for health care. This special report aims to explain the economic case for precision medicine, by accounting for the explicit objectives defined by decision-makers responsible for the allocation of limited health care resources. Areas covered: The framework of cost-effectiveness analysis, a method of economic evaluation, is used to describe how precision medicine can, in theory, exploit identifiable patient-level heterogeneity to improve population health outcomes and the relative cost-effectiveness of health care. Four case studies are used to illustrate potential challenges when demonstrating the economic case for a precision medicine in practice. Expert commentary: The economic case for a precision medicine should be considered at an early stage during its research and development phase. Clinical and economic evidence can be generated iteratively and should be in alignment with the objectives and requirements of decision-makers. Programmes of further research, to demonstrate the economic case of a precision medicine, can be prioritized by the extent that they reduce the uncertainty expressed by decision-makers.

Understanding patient outcomes after acute respiratory distress syndrome: identifying subtypes of physical, cognitive and mental health outcomes

PURPOSE

With improving short-term mortality in acute respiratory distress syndrome (ARDS), understanding survivors' posthospitalisation outcomes is increasingly important. However, little is known regarding associations among physical, cognitive and mental health outcomes. Identification of outcome subtypes may advance understanding of post-ARDS morbidities.

METHODS

We analysed baseline variables and 6-month health status for participants in the ARDS Network Long-Term Outcomes Study. After division into derivation and validation datasets, we used weighted network analysis to identify subtypes from predictors and outcomes in the derivation dataset. We then used recursive partitioning to develop a subtype classification rule and assessed adequacy of the classification rule using a kappa statistic with the validation dataset.

RESULTS

Among 645 ARDS survivors, 430 were in the derivation and 215 in the validation datasets. Physical and mental health status, but not cognitive status, were closely associated. Four distinct subtypes were apparent (percentages in the derivation cohort): (1) mildly impaired physical and mental health (22% of patients), (2) moderately impaired physical and mental health (39%), (3) severely impaired physical health with moderately impaired mental health (15%) and (4) severely impaired physical and mental health (24%). The classification rule had high agreement (kappa=0.89 in validation dataset). Female Latino smokers had the poorest status, while male, non-Latino non-smokers had the best status.

CONCLUSIONS

We identified four post-ARDS outcome subtypes that were predicted by sex, ethnicity, pre-ARDS smoking status and other baseline factors. These subtypes may help develop tailored rehabilitation strategies, including investigation of combined physical and mental health interventions, and distinct interventions to improve cognitive outcomes.

Critical Care and Personalized or Precision Medicine: Who needs whom?

The current paradigm of modern healthcare is a reactive response to patient symptoms, subsequent diagnosis and corresponding treatment of the specific disease(s). This approach is predicated on methodologies first espoused by the Cnidean School of Medicine approximately 2500years ago. More recently escalating healthcare costs and relatively poor disease treatment outcomes have fermented a rethink in how we carry out medical practices. This has led to the emergence of "P-Medicine" in the form of Personalized and Precision Medicine. The terms are used interchangeably, but in fact there are significant differences in the way they are implemented. The former relies on an "N-of-1" model whereas the latter uses a "1-in-N" model. Personalized Medicine is still in a fledgling and evolutionary phase and there has been much debate over its current status and future prospects. A confounding factor has been the sudden development of Precision Medicine, which has currently captured the imagination of policymakers responsible for modern healthcare systems. There is some confusion over the terms Personalized versus Precision Medicine. Here we attempt to define the key differences and working definitions of each P-Medicine approach, as well as a taxonomic relationship tree. Finally, we discuss the impact of Personalized and Precision Medicine on the practice of Critical Care Medicine (CCM). Practitioners of CCM have been participating in Personalized Medicine unknowingly as it takes the protocols of sepsis, mechanical ventilation, and daily awakening trials and applies it to each individual patient. However, the immediate next step for CCM should be an active development of Precision Medicine. This developmental process should break down the silos of modern medicine and create a multidisciplinary approach between clinicians and basic/translational scientists.

Liquid dynamic medicine and N-of-1 clinical trials: a change of perspective in oncology research

The increasing use of genomics to define the pattern of actionable mutations and to test and validate new therapies for individual cancer patients, and the growing application of liquid biopsy to dynamically track tumor evolution and to adapt molecularly targeted therapy according to the emergence of tumor clonal variants is shaping modern medical oncology., In order to better describe this new therapeutic paradigm we propose the term "Liquid dynamic medicine" in the place of "Personalized or Precision medicine". Clinical validation of the "Liquid dynamic medicine" approach is best captured by N-of-1 trials where each patient acts as tester and control of truly personalized therapies.

Subgrouping Patients With Nonspecific Low Back Pain: Hope or Hype?

Clinicians and clinical researchers share a common goal of achieving better outcomes for patients with low back pain (LBP). For that, randomized controlled trials and systematic reviews are the most reliable study designs to determine the effects of interventions. Subgroup analyses in these research designs have been used to examine treatment-effect modification across subgroups defined by patient characteristics. In this Viewpoint, the authors present supporting and opposing arguments for the subgrouping approach in nonspecific LBP, considering the progress made so far in the LBP field and the relevant literature in adjacent fields. J Orthop Sports Phys Ther 2017;47(2):44-48. doi:10.2519/jospt.2017.0602.

Implementing personalized medicine with asymmetric information on prevalence rates

Although personalized medicine is becoming the new paradigm to manage some diseases, the economics of personalized medicine have only focused on assessing the efficiency of specific treatments, lacking a theoretical framework analyzing the interactions between pharmaceutical firms and healthcare systems leading to the implementation of personalized treatments. We model the interaction between the hospitals and the manufacturer of a new treatment as an adverse selection problem where the firm does not have perfect information on the prevalence across hospitals of the genetic characteristics of the patients making them eligible to receive a new treatment. As a result of the model, hospitals with high prevalence rates benefit from the information asymmetry only when the standard treatment is inefficient when applied to the patients eligible to receive the new treatment. Otherwise, information asymmetry has no value. Personalized medicine may be fully or partially implemented depending on the proportion of high prevalence hospitals.

Physiologically-based pharmacokinetic models: approaches for enabling personalized medicine

Personalized medicine strives to deliver the 'right drug at the right dose' by considering inter-person variability, one of the causes for therapeutic failure in specialized populations of patients. Physiologically-based pharmacokinetic (PBPK) modeling is a key tool in the advancement of personalized medicine to evaluate complex clinical scenarios, making use of physiological information as well as physicochemical data to simulate various physiological states to predict the distribution of pharmacokinetic responses. The increased dependency on PBPK models to address regulatory questions is aligned with the ability of PBPK models to minimize ethical and technical difficulties associated with pharmacokinetic and toxicology experiments for special patient populations. Subpopulation modeling can be achieved through an iterative and integrative approach using an adopt, adapt, develop, assess, amend, and deliver methodology. PBPK modeling has two valuable applications in personalized medicine: (1) determining the importance of certain subpopulations within a distribution of pharmacokinetic responses for a given drug formulation and (2) establishing the formulation design space needed to attain a targeted drug plasma concentration profile. This review article focuses on model development for physiological differences associated with sex (male vs. female), age (pediatric vs. young adults vs. elderly), disease state (healthy vs. unhealthy), and temporal variation (influence of biological rhythms), connecting them to drug product formulation development within the quality by design framework. Although PBPK modeling has come a long way, there is still a lengthy road before it can be fully accepted by pharmacologists, clinicians, and the broader industry.

Patient Heterogeneity in Health Economic Decision Models for Chronic Obstructive Pulmonary Disease: Are Current Models Suitable to Evaluate Personalized Medicine?

OBJECTIVES

To assess how suitable current chronic obstructive pulmonary disease (COPD) cost-effectiveness models are to evaluate personalized treatment options for COPD by exploring the type of heterogeneity included in current models and by validating outcomes for subgroups of patients.

METHODS

A consortium of COPD modeling groups completed three tasks. First, they reported all patient characteristics included in the model and provided the level of detail in which the input parameters were specified. Second, groups simulated disease progression, mortality, quality-adjusted life-years (QALYs), and costs for hypothetical subgroups of patients that differed in terms of sex, age, smoking status, and lung function (forced expiratory volume in 1 second [FEV₁] % predicted). Finally, model outcomes for exacerbations and mortality for subgroups of patients were validated against published subgroup results of two large COPD trials.

RESULTS

Nine COPD modeling groups participated. Most models included sex (seven), age (nine), smoking status (six), and FEV₁% predicted (nine), mainly to specify disease progression and mortality. Trial results showed higher exacerbation rates for women (found in one model), higher mortality rates for men (two models), lower mortality for younger patients (four models), and higher exacerbation and mortality rates in patients with severe COPD (four models).

CONCLUSIONS

Most currently available COPD cost-effectiveness models are able to evaluate the cost-effectiveness of personalized treatment on the basis of sex, age, smoking, and FEV₁% predicted. Treatment in COPD is, however, more likely to be personalized on the basis of clinical parameters. Two models include several clinical patient characteristics and are therefore most suitable to evaluate personalized treatment, although some important clinical parameters are still missing.

Systematic overview of cost-effectiveness thresholds in ten countries across four continents

AIM

To provide an overview of thresholds for incremental cost-effectiveness ratios (ICERs) representing willingness-to-pay (WTP) across multiple countries and insights into exemptions pertaining to the ICER (e.g., cancer). To compare ICER thresholds to individual country's estimated ability-to-pay.

MATERIALS & METHODS

We included AHRQ/USA, BIQG-GOEG/Austria, CADTH/Canada, DAHTA@DIMDI/Germany, DECIT-CGATS/Brazil, HAS/France, HITAP/Thailand, IQWiG/Germany, LBI-HTA/Austria, MSAC/Australia, NICE/England/Wales and SBU/Sweden. ICER thresholds were derived from systematic literature/website search/expert surveys. WTP was compared with ATP using Spearman's rank correlation.

RESULTS

Two general and explicitly acknowledged thresholds (England/Wales, Thailand), implicit thresholds in six countries and different ICER thresholds/decision-making rules in oncology were identified. Correlation between WTP and ability-to-pay was moderate.

DISCUSSION

Our overview supports country-specific discussions on WTP and on how to define value(s) within societies.

Conceptual and terminological confusion around personalised medicine: a coping strategy

BACKGROUND

The idea of personalised medicine (PM) has gathered momentum recently, attracting funding and generating hopes as well as scepticism. As PM gives rise to differing interpretations, there have been several attempts to clarify the concept. In an influential paper published in this journal, Schleidgen and colleagues have proposed a precise and narrow definition of PM on the basis of a systematic literature review. Given that their conclusion is at odds with those of other recent attempts to understand PM, we consider whether their systematic review gives them an edge over competing interpretations.

DISCUSSION

We have found some methodological weaknesses and questionable assumptions in Schleidgen and colleagues' attempt to provide a more specific definition of PM. Our perplexities concern the lack of criteria for assessing the epistemic strength of the definitions that they consider, as well as the logical principles used to extract a more precise definition, the narrowness of the pool from which they have drawn their empirical data, and finally their overlooking the fact that definitions depend on the context of use. We are also worried that their ethical assumption that only patients' interests are legitimate is too simplistic and drives all other stakeholders' interests-including those that are justifiable-underground, thus compromising any hope of a transparent and fair negotiation among a plurality of actors and interests.

CONCLUSION

As an alternative to the shortcomings of attempting a semantic disciplining of the concept we propose a pragmatic approach. Rather than considering PM to be a scientific concept in need of precise demarcation, we look at it as an open and negotiable concept used in a variety of contexts including at the level of orienting research goals and policy objectives. We believe that since PM is still more an ideal than an achieved reality, a plurality of visions is to be expected and we need to pay attention to the people, reasons and interests behind these alternative conceptions. In other words, the logic and politics of PM cannot be disentangled and disagreements need to be tackled addressing the normative and strategic conflicts behind them.

Other side of the coin for personalised medicine and healthcare: content analysis of 'personalised' practices in the literature

OBJECTIVES

Various terms and definitions are used to describe personalised approaches to medicine and healthcare, but in ambiguous and inconsistent ways. They mostly have been defined in a top-down manner. However, actual practices might take different paths. Here, we aimed to provide a 'practice-based' perspective on the debate by analysing the content of 'personalised' practices published in the literature.

METHODS

The search in PubMed and EMBASE (April 2014) using the terms frequently used for personalised approaches resulted in 5333 records. 2 independent researchers used different strategies for screening, resulting in 157 articles describing 88 'personalised' practices that were implemented/presented on at least 1 individual/patient case. The content analysis was grounded on these data and did not have a priori analytical frameworks.

RESULTS

'Personalised medicine/healthcare' can be a commodity in the healthcare market, a way how health services are provided, or a keyword for emerging applications. It can help individuals/patients to gain control of their health, health professionals to provide better services, healthcare organisations to increase effectiveness and efficiency, or national health systems to increase performance. Country examples indicated that for integration of practices into health services, attitude towards innovations and health system and policy context is important. Categorisation based on the terms or the technologies used, if any, was not possible.

CONCLUSIONS

This study is the first to provide a comprehensive content analysis of the 'personalised' practices in the literature. Unlike the top-down definitions, our findings highlighted not the technologies but real-life issues faced by the practices. 'Personalised medicine' and 'personalised healthcare' can be differentiated by using the former for specific tools available and the latter for health services with a holistic approach, implemented in certain contexts. To realise integration of 'personalised medicine/healthcare' into real life, science, technology, health policy and practice, and society domains must work together.

Precision or Personalized Medicine for Cancer Chemotherapy: Is there a Role for Herbal Medicine

Although over 100 chemotherapeutic agents are currently available for the treatment of cancer patients, the overall long term clinical benefit is disappointing due to the lack of effectiveness or severe side effects from these agents. In order to improve the therapeutic outcome, a new approach called precision medicine or personalized medicine has been proposed and initiated by the U.S. National Institutes of Health. However, the limited availability of effective medications and the high cost are still the major barriers for many cancer patients. Thus alternative approaches such as herbal medicines could be a feasible and less costly option. Unfortunately, scientific evidence for the efficacy of a majority of herbal medicines is still lacking and their development to meet FDA approval or other regulatory agencies is a big challenge. However, herbal medicines may be able to play an important role in precision medicine or personalized medicine. This review will focus on the existing and future technologies that could speed the development of herbal products for treatment of resistant cancer in individual patients. Specifically, it will concentrate on reviewing the phenotypic (activity based) rather than genotypic (mechanism based) approach to develop herbal medicine useful for personalized cancer chemotherapy.

Toward precision medicine of breast cancer

In this review, we report on breast cancer's molecular features and on how high throughput technologies are helping in understanding the dynamics of tumorigenesis and cancer progression with the aim of developing precision medicine methods. We first address the current state of the art in breast cancer therapies and challenges in order to progress towards its cure. Then, we show how the interaction of high-throughput technologies with in silico modeling has led to set up useful inferences for promising strategies of target-specific therapies with low secondary effect incidence for patients. Finally, we discuss the challenge of pharmacogenetics in the clinical practice of cancer therapy. All these issues are explored within the context of precision medicine.

Some economics on personalized and predictive medicine

OBJECTIVE

To contribute to the theoretical literature on personalized medicine, analyzing and integrating in an economic model, the decision a health authority faces when it must decide on the implementation of personalized medicine in a context of uncertainty.

METHODS

We carry out a stylized model to analyze the decision health authorities face when they do not have perfect information about the best treatment for a population of patients with a given disease. The health authorities decide whether to use a test to match patients with treatments (personalized medicine) to maximize health outcomes. Our model characterizes the situations under which personalized medicine dominates the alternative option of business-as-usual (treatment without previous test). We apply the model to the KRAS test for colorectal cancer, the PCA3 test for prostate cancer and the PCR test for the X-fragile syndrome, to illustrate how the parameters and variables of the model interact.

RESULTS

Implementation of personalized medicine requires, as a necessary condition, having some tests with high discriminatory power. This is not a sufficient condition and expected health outcomes must be taken into account to make a decision. When the specificity and the sensitivity of the test are low, the health authority prefers to apply a treatment to all patients without using the test. When both characteristic of the test are high, the health authorities prefer to personalize the treatments when expected health outcomes are better than those under the standard treatment. When we applied the model to the three aforementioned tests, the results illustrate how decisions are adopted in real world.

CONCLUSIONS

Although promising, the use of personalized medicine is still under scrutiny as there are important issues demanding a response. Personalized medicine may have an impact in the drug development processes, and contribute to the efficiency and effectiveness of health care delivery. Nevertheless, more accurate statistical and economic information related to tests results and treatment costs as well as additional medical information on the efficacy of the treatments are needed to adopt decisions that incorporate economic rationality.

The 3-I framework: a framework for developing public policies regarding pharmacogenomics (PGx) testing in Canada

The 3-I framework of analyzing the ideas, interests, and institutions around a topic has been used by political scientists to guide public policy development. In Canada, there is a lack of policy governing pharmacogenomics (PGx) testing compared to other developed nations. The goal of this study was to use the 3-I framework, a policy development tool, and apply it to PGx testing to identify and analyze areas where current policy is limited and challenges exist in bringing PGx testing into wide-spread clinical practice in Canada. A scoping review of the literature was conducted to determine the extent and challenges of PGx policy implementation at federal and provincial levels. Based on the 3-I analysis, contentious ideas related to PGx are (i) genetic discrimination, (ii) informed consent, (iii) the lack of knowledge about PGx in health care, (iv) the value of PGx testing, (v) the roles of health care workers in the coordination of PGx services, and (vi) confidentiality and privacy. The 3-I framework is a useful tool for policy makers, and applying it to PGx policy development is a new approach in Canadian genomics. Policy makers at every organizational level can use this analysis to help develop targeted PGx policies.

Metabolomics and Personalized Medicine

Current clinical practice strongly relies on the prognosis, diagnosis, and treatment of diseases using methods determined and averaged for the specific diseased cohort/population. Although this approach complies positively with most patients, misdiagnosis, treatment failure, relapse, and adverse drug effects are common occurrences in many individuals, which subsequently hamper the control and eradication of a number of diseases. These incidences can be explained by individual variation in the genome, transcriptome, proteome, and metabolome of a patient. Various "omics" approaches have investigated the influence of these factors on a molecular level, with the intention of developing personalized approaches to disease diagnosis and treatment. Metabolomics, the newest addition to the "omics" domain and the closest to the observed phenotype, reflects changes occurring at all molecular levels, as well as influences resulting from other internal and external factors. By comparing the metabolite profiles of two or more disease phenotypes, metabolomics can be applied to identify biomarkers related to the perturbation being investigated. These biomarkers can, in turn, be used to develop personalized prognostic, diagnostic, and treatment approaches, and can also be applied to the monitoring of disease progression, treatment efficacy, predisposition to drug-related side effects, and potential relapse. In this review, we discuss the contributions that metabolomics has made, and can potentially still make, towards the field of personalized medicine.

Economics of stratified medicine in rheumatoid arthritis

Clinically relevant examples of stratified medicine are available for patients with rheumatoid arthritis (RA). The aim of this study was to understand the current economic evidence for stratified medicine in RA. Two systematic reviews were conducted to identify: (1) all economic evaluations of stratified treatments for rheumatoid arthritis, or those which have used a subgroup analysis, and (2) all stated preference studies of treatments for rheumatoid arthritis. Ten economic evaluations of stratified treatments for RA, 38 economic evaluations including with a subgroup analysis and eight stated preference studies were identified. There was some evidence to support that stratified approaches to treating a patient with RA may be cost-effective. However, there remain key gaps in the economic evidence base needed to support the introduction of stratified medicine in RA into healthcare systems and considerable uncertainty about how proposed stratified approaches will impact future patient preferences, outcomes and costs when used in routine practice.

Economic evaluations of personalized medicine: existing challenges and current developments

Personalized medicine, with the aim of safely, effectively, and cost-effectively targeting treatment to a prespecified patient population, has always been a long-time goal within health care. It is often argued that personalizing treatment will inevitably improve clinical outcomes for patients and help achieve more effective use of health care resources. Demand is increasing for demonstrable evidence of clinical and cost-effectiveness to support the use of personalized medicine in health care. This paper begins with an overview of the existing challenges in conducting economic evaluations of genetics- and genomics-targeted technologies, as an example of personalized medicine. Our paper illustrates the complexity of the challenges faced by these technologies by highlighting the variations in the issues faced by diagnostic tests for somatic variations, generally referring to genetic variation in a tumor, and germline variations, generally referring to inherited genetic variation in enzymes involved in drug metabolic pathways. These tests are typically aimed at stratifying patient populations into subgroups on the basis of clinical effectiveness (response) or safety (avoidance of adverse events). The paper summarizes the data requirements for economic evaluations of genetics and genomics-based technologies while outlining that the main challenges relating to data requirements revolve around the availability and quality of existing data. We conclude by discussing current developments aimed to address the challenges of assessing the cost-effectiveness of genetics and genomics-based technologies, which revolve around two central issues that are interlinked: the need to adapt available evaluation methods and identifying who is responsible for generating evidence for these technologies.