The economic case for precision medicine

Ultrasound-guided synovial tissue biopsy for people with rheumatoid arthritis: a micro-costing study

Objective

Identify and quantify the resource use and cost per patient of performing a US-guided synovial tissue biopsy for people with RA within a routine healthcare setting.

Method

A micro-costing study was performed from a healthcare system perspective (National Health Service England). A service pathway conceptual model described how the procedure will be embedded within routine care to inform management decisions. Consumables were estimated from existing standard operating procedures for synovial biopsies. Staff time was estimated by expert input (clinical rheumatologist and patient). The time for tissue sample acquisition was obtained from data within the biopsy-driven R4RA trial. Unit costs were sourced from historic purchase prices, published salary scales and public-facing list prices. One-way sensitivity analysis identified key drivers of total cost per patient. Scenario analyses explored the cost impact if less senior healthcare staff performed the biopsy or if an additional outpatient appointment was required to identify joint suitability.

Results

The total cost of US-guided synovial tissue biopsy was £356.24/patient (best-case estimate: £185.30; worst-case estimate: £812.46). The key driver of total cost was the time taken to perform tissue sample acquisition. The total cost was lower if a registrar performed the biopsy (£294.24) and higher if an additional outpatient appointment was required for joint selection (£438.98).

Conclusion

Interventions requiring synovial tissue samples to inform treatment selection or prognosis are emerging. The findings can inform cost parameters for future cost-effectiveness analyses. These results will help resource allocation and clinical decisions to improve the value of treatments for RA.

A national framework for transition to precision medicine

Precision medicine (PM) is transforming healthcare by offering tailored interventions that address individual variability, transforming patient care and outcomes. PM is based on providing health-oriented services according to genetic characteristics, individual and family medical history, lifestyle, place of residence, and other personalized characteristics. This study aims to establish an appropriate framework for implementing PM in Iran. First, the global transition framework to PM was drawn by a systematic review, and then a framework for transition to PM in Iran was drawn by a case study through semi-structured interviews, an expert panel, and an analytic hierarchy process (AHP) questionnaire. The statistical sample of the study comprised PM specialists, researchers, and patients whose PM plays a significant role in their diagnosis and treatment. The sampling method was non-random with a combination of purposive and snowball techniques. The results from the systematic review show that for the transition to PM, we must first move from common medicine to stratified medicine and then PM. Moving toward PM requires strong economic, social, political, institutional, industrial, and, most importantly, technological infrastructures. These infrastructures will vary from country to country. In general, coexistence between the health system and PM technologies did not exist in the beginning, but it will emerge with its development. The resistance of the health system to accepting PM will gradually decrease. Furthermore, the government plays a key role in the early phases, while market and PM demand become more prominent during the development. New health actors will also develop PM, and out-of-date actors will be deleted or replaced. But moving toward PM is slightly different in Iran, particularly in the middle phases of transition.

Current Role and Potential of Triple Quadrupole Mass Spectrometry in Biomedical Research and Clinical Applications

Mass-spectrometry-based assays nowadays play an essential role in biomedical research and clinical applications. There are different types of commercial mass spectrometers on the market today, and triple quadrupole (QqQ) is one of the time-honored systems. Here, we overview the main areas of QqQ applications in biomedicine and assess the current level, evolution, and trends in the use of QqQ in these areas. Relevant data were extracted from the Scopus database using the specified terms and Boolean operators defined for each field of the QqQ application. We also discuss the recent advances in QqQ and QqQ-based analytical platforms, which promote the clinical application of these systems, and explain the indicated substantial increase in triple quadrupole use in biomedicine. The number of biomedical studies utilizing QqQ increased 2-3 times this decade. Triple quadrupole is most intensively used in the field of endocrine research and testing. On the contrary, the relative rate of immunoassay utilization-a major competitor of chromatography-mass spectrometry-decreased in this area as well as its use within Therapeutic drug monitoring (TDM) and forensic toxicology. Nowadays, the applications of high-resolution accurate mass (HRAM) mass spectrometers in the investigated areas represent only a small fraction of the total amount of research using mass spectrometry; however, their application substantially increased during the last decade in the untargeted search for new biomarkers.

Clinicopathological analysis of thyroid carcinomas with the RET and NTRK fusion genes: characterization for genetic analysis

Thyroid carcinomas exhibit various genetic alterations, including the RET and NTRK fusion genes that are targets for molecular therapies. Thus, detecting fusion genes is crucial for devising effective treatment plans. This study characterized the pathological findings associated with these genes to identify the specimens suitable for genetic analysis. Thyroid carcinoma cases positive for the fusion genes were analyzed using the Oncomine Dx Target Test. Clinicopathological data were collected and assessed. Among the 74 patients tested, 8 had RET and 1 had NTRK3 fusion gene. Specifically, of the RET fusion gene cases, 6 exhibited "BRAF-like" atypia and 2 showed "RAS-like" atypia, while the single case with an NTRK3 fusion gene presented "RAS-like" atypia. Apart from one poorly differentiated thyroid carcinoma, most cases involved papillary thyroid carcinomas (PTCs). Primary tumors showed varied structural patterns and exhibited a high proportion of non-papillary structures. Dysmorphic clear cells were frequently observed. BRAF V600E immunoreactivity was negative in all cases. Interestingly, some cases exhibited similarities to diffuse sclerosing variant of PTC characteristics. While calcification in lymph node metastases was mild, primary tumors typically required hydrochloric acid-based decalcification for tissue preparation. This study highlights the benefits of combining morphological and immunohistochemical analyses for gene detection and posits that lymph node metastases are more suitable for genetic analysis owing to their mild calcification. Our results emphasize the importance of accurate sample processing in diagnosing and treating thyroid carcinomas.

Accuracy of machine learning in predicting outcomes post-percutaneous coronary intervention: a systematic review

Background

Recent studies have shown potential in introducing machine learning (ML) algorithms to predict outcomes post-percutaneous coronary intervention (PCI).

Aims

We aimed to critically appraise current ML models' effectiveness as clinical tools to predict outcomes post-PCI.

Methods

Searches of four databases were conducted for articles published from the database inception date to 29 May 2021. Studies using ML to predict outcomes post-PCI were included. For individual post-PCI outcomes, measures of diagnostic accuracy were extracted. An adapted checklist comprising existing frameworks for new risk markers, diagnostic accuracy, prognostic tools and ML was used to critically appraise the included studies along the stages of the translational pathway: development, validation, and impact. Quality of training data and methods of dealing with missing data were evaluated.

Results

Twelve cohorts from 11 studies were included with a total of 4,943,425 patients. ML models performed with high diagnostic accuracy. However, there are concerns over the development of the ML models. Methods of dealing with missing data were problematic. Four studies did not discuss how missing data were handled. One study removed patients if any of the predictor variable data points were missing. Moreover, at the validation stage, only three studies externally validated the models presented. There could be concerns over the applicability of these models. None of the studies discussed the cost-effectiveness of implementing the models.

Conclusions

ML models show promise as a useful clinical adjunct to traditional risk stratification scores in predicting outcomes post-PCI. However, significant challenges need to be addressed before ML can be integrated into clinical practice.

Development of a biomarker prediction model for post-trauma multiple organ failure/dysfunction syndrome based on the blood transcriptome

BACKGROUND

Multiple organ failure/dysfunction syndrome (MOF/MODS) is a major cause of mortality and morbidity among severe trauma patients. Current clinical practices entail monitoring physiological measurements and applying clinical score systems to diagnose its onset. Instead, we aimed to develop an early prediction model for MOF outcome evaluated soon after traumatic injury by performing machine learning analysis of genome-wide transcriptome data from blood samples drawn within 24 h of traumatic injury. We then compared its performance to baseline injury severity scores and detection of infections.

METHODS

Buffy coat transcriptome and linked clinical datasets from blunt trauma patients from the Inflammation and the Host Response to Injury Study ("Glue Grant") multi-center cohort were used. According to the inclusion/exclusion criteria, 141 adult (age ≥ 16 years old) blunt trauma patients (excluding penetrating) with early buffy coat (≤ 24 h since trauma injury) samples were analyzed, with 58 MOF-cases and 83 non-cases. We applied the Least Absolute Shrinkage and Selection Operator (LASSO) and eXtreme Gradient Boosting (XGBoost) algorithms to select features and develop models for MOF early outcome prediction.

RESULTS

The LASSO model included 18 transcripts (AUROC [95% CI]: 0.938 [0.890-0.987] (training) and 0.833 [0.699-0.967] (test)), and the XGBoost model included 41 transcripts (0.999 [0.997-1.000] (training) and 0.907 [0.816-0.998] (test)). There were 16 overlapping transcripts comparing the two panels (0.935 [0.884-0.985] (training) and 0.836 [0.703-0.968] (test)). The biomarker models notably outperformed models based on injury severity scores and sex, which we found to be significantly associated with MOF (APACHEII + sex-0.649 [0.537-0.762] (training) and 0.493 [0.301-0.685] (test); ISS + sex-0.630 [0.516-0.744] (training) and 0.482 [0.293-0.670] (test); NISS + sex-0.651 [0.540-0.763] (training) and 0.525 [0.335-0.714] (test)).

CONCLUSIONS

The accurate assessment of MOF from blood samples immediately after trauma is expected to aid in improving clinical decision-making and may contribute to reduced morbidity, mortality and healthcare costs. Moreover, understanding the molecular mechanisms involving the transcripts identified as important for MOF prediction may eventually aid in developing novel interventions.

Toward Realizing the Promise of AI in Precision Health Across the Spectrum of Care

Significant progress has been made in augmenting clinical decision-making using artificial intelligence (AI) in the context of secondary and tertiary care at large academic medical centers. For such innovations to have an impact across the spectrum of care, additional challenges must be addressed, including inconsistent use of preventative care and gaps in chronic care management. The integration of additional data, including genomics and data from wearables, could prove critical in addressing these gaps, but technical, legal, and ethical challenges arise. On the technical side, approaches for integrating complex and messy data are needed. Data and design imperfections like selection bias, missing data, and confounding must be addressed. In terms of legal and ethical challenges, while AI has the potential to aid in leveraging patient data to make clinical care decisions, we also risk exacerbating existing disparities. Organizations implementing AI solutions must carefully consider how they can improve care for all and reduce inequities.

Evaluating Information Technology-enabled Precision Prevention Initiatives in Health and Care

Information technology-enabled precision prevention is a relatively new approach designed to improve population health. It forms an organic development linking principles of optimizing added value from health-related information technology and data systems with clinical aspirations to add longer-term problem prevention to immediate illness treatment. It includes drawing on information technology to identify persons at risk for developing certain conditions and then developing targeted behavioral and psychosocial approaches to modifying the behaviors of individuals or specific groups. We here discuss evaluation challenges associated with information technology-enabled precision prevention approaches to facilitate the development of an empirical evidence base. Challenges associated with measuring the impact of information technology-enabled precision prevention initiatives include considerations surrounding the relevance and fit of external data sources, the accuracy of prediction models, establishing added benefits of preventative activities, measuring pre-post outcomes at individual and population levels, and considerations surrounding cost-benefit analysis. Challenges associated with assessing processes of information technology-enabled precision prevention initiatives include the quality of data used to create underlying data models, exploring processes not necessarily related to each other, evolving social and environmental determinants of health and individual circumstances, the evolving nature of needs and interventions over time, and ethical considerations. If these challenges are attended to in evaluation activities, this will help to ensure that information technology-enabled approaches to precision prevention will have a positive impact on individual and population health.

Advances in Addressing Patient Heterogeneity in Economic Evaluation: A Review of the Methods Literature

Cost-effectiveness analyses commonly use population or sample averages, which can mask key differences across subgroups and may lead to suboptimal resource allocation. Despite there being several new methods developed over the last decade, there is no recent summary of what methods are available to researchers. This review sought to identify advances in methods for addressing patient heterogeneity in economic evaluations and to provide an overview of these methods. A literature search was conducted using the Econlit, Embase and MEDLINE databases to identify studies published after 2011 (date of a previous review on this topic). Eligible studies needed to have an explicit methodological focus, related to how patient heterogeneity can be accounted for within a full economic evaluation. Sixteen studies were included in the review. Methodologies were varied and included regression techniques, model design and value of information analysis. Recent publications have applied methodologies more commonly used in other fields, such as machine learning and causal forests. Commonly noted challenges associated with considering patient heterogeneity included data availability (e.g., sample size), statistical issues (e.g., risk of false positives) and practical factors (e.g., computation time). A range of methods are available to address patient heterogeneity in economic evaluation, with relevant methods differing according to research question, scope of the economic evaluation and data availability. Researchers need to be aware of the challenges associated with addressing patient heterogeneity (e.g., data availability) to ensure findings are meaningful and robust. Future research is needed to assess whether and how methods are being applied in practice.

Overcoming provider barriers to therapeutic drug monitoring of tumour necrosis factor inhibitors for rheumatoid arthritis: a qualitative analysis

Objective

Therapeutic drug monitoring (TDM) of tumour necrosis factor-α inhibitors (TNFi), by measuring drug levels and/or anti-drug antibodies, is being considered by various international bodies to improve patient health outcomes and the value of care for people with rheumatoid arthritis. Rheumatology care providers may perceive barriers to adopting TNFi TDM within their own clinical practice, limiting the potential for patients and health care systems to benefit. This study aimed to explore the barriers perceived by rheumatologists that may reduce their uptake of TNFi TDM for rheumatoid arthritis.

Method

Semi-structured one-to-one telephone interviews were performed with a convenience sample of senior rheumatologists with experience of managing people with rheumatoid arthritis. The interviews explored the rheumatologists' understanding of TDM and their beliefs about how it can be integrated into their own routine practice. Interviews were audio recorded, transcribed verbatim and anonymized. Transcripts were coded inductively and barriers to using TNFi TDM were identified by thematic framework analysis.

Result

A sample of eleven senior rheumatologists were interviewed. The rheumatologists described five barriers to adopting TNFi TDM in routine practice: (i) observing clinical need; (ii) understanding how testing can improve practice; (iii) insufficient clinical evidence; (iv) insufficient resources to pay for testing; and (v) insufficient capability to deliver testing.

Conclusion

Barriers to adopting TNFi TDM in routine care settings will restrict the ability for patients to benefit from effective monitoring strategies as they begin to emerge. Strategies to overcome these barriers are suggested which will require a coordinated response from stakeholders across health care systems.

Prescription Precision: A Comprehensive Review of Intelligent Prescription Systems

Intelligent Prescription Systems (IPS) represent a promising frontier in healthcare, offering the potential to optimize medication selection, dosing, and monitoring tailored to individual patient needs. This comprehensive review explores the current landscape of IPS, encompassing various technological approaches, applications, benefits, and challenges. IPS leverages advanced computational algorithms, machine learning techniques, and big data analytics to analyze patient-specific factors, such as medical history, genetic makeup, biomarkers, and lifestyle variables. By integrating this information with evidence-based guidelines, clinical decision support systems, and real-time patient data, IPS generates personalized treatment recommendations that enhance therapeutic outcomes while minimizing adverse effects and drug interactions. Key components of IPS include predictive modeling, drug-drug interaction detection, adverse event prediction, dose optimization, and medication adherence monitoring. These systems offer clinicians invaluable decision-support tools to navigate the complexities of medication management, particularly in the context of polypharmacy and chronic disease management. While IPS holds immense promise for improving patient care and reducing healthcare costs, several challenges must be addressed. These include data privacy and security concerns, interoperability issues, integration with existing electronic health record systems, and clinician adoption barriers. Additionally, the regulatory landscape surrounding IPS requires clarification to ensure compliance with evolving healthcare regulations. Despite these challenges, the rapid advancements in artificial intelligence, data analytics, and digital health technologies are driving the continued evolution and adoption of IPS. As precision medicine gains momentum, IPS is poised to play a central role in revolutionizing medication management, ultimately leading to more effective, personalized, and patient-centric healthcare delivery.

Can knowledgeable experts assess costs and outcomes as if they were ignorant? An experiment within precision medicine evaluation

OBJECTIVES

The purpose of this study is to evaluate the validity of the standard approach in expert judgment for evaluating precision medicines, in which experts are required to estimate outcomes as if they did not have access to diagnostic information, whereas in fact, they do.

METHODS

Fourteen clinicians participated in an expert judgment task to estimate the cost and medical outcomes of the use of exome sequencing in pediatric patients with intractable epilepsy in Thailand. Experts were randomly assigned to either an "unblind" or "blind" group; the former was provided with the exome sequencing results for each patient case prior to the judgment task, whereas the latter was not provided with the exome sequencing results. Both groups were asked to estimate the outcomes for the counterfactual scenario, in which patients had not been tested by exome sequencing.

RESULTS

Our study did not show significant results, possibly due to the small sample size of both participants and case studies.

CONCLUSIONS

A comparison of the unblind and blind approach did not show conclusive evidence that there is a difference in outcomes. However, until further evidence suggests otherwise, we recommend the blind approach as preferable when using expert judgment to evaluate precision medicines because this approach is more representative of the counterfactual scenario than the unblind approach.

Precision medicine in nasopharyngeal carcinoma: comprehensive review of past, present, and future prospect

Nasopharyngeal carcinoma (NPC) is an aggressive malignancy with high propensity for lymphatic spread and distant metastasis. It is prominent as an endemic malignancy in Southern China and Southeast Asia regions. Studies on NPC pathogenesis mechanism in the past decades such as through Epstein Barr Virus (EBV) infection and oncogenic molecular aberrations have explored several potential targets for therapy and diagnosis. The EBV infection introduces oncoviral proteins that consequently hyperactivate many promitotic pathways and block cell-death inducers. EBV infection is so prevalent in NPC patients such that EBV serological tests were used to diagnose and screen NPC patients. On the other hand, as the downstream effectors of oncogenic mechanisms, the promitotic pathways can potentially be exploited therapeutically. With the apparent heterogeneity and distinct molecular aberrations of NPC tumor, the focus has turned into a more personalized treatment in NPC. Herein in this comprehensive review, we depict the current status of screening, diagnosis, treatment, and prevention in NPC. Subsequently, based on the limitations on those aspects, we look at their potential improvements in moving towards the path of precision medicine. The importance of recent advances on the key molecular aberration involved in pathogenesis of NPC for precision medicine progression has also been reported in the present review. Besides, the challenge and future outlook of NPC management will also be highlighted.

Precision medicine for cardiometabolic disease: a framework for clinical translation

Cardiometabolic disease is a major threat to global health. Precision medicine has great potential to help to reduce the burden of this common and complex disease cluster, and to enhance contemporary evidence-based medicine. Its key pillars are diagnostics; prediction (of the primary disease); prevention (of the primary disease); prognosis (prediction of complications of the primary disease); treatment (of the primary disease or its complications); and monitoring (of risk exposure, treatment response, and disease progression or remission). To contextualise precision medicine in both research and clinical settings, and to encourage the successful translation of discovery science into clinical practice, in this Series paper we outline a model (the EPPOS model) that builds on contemporary evidence-based approaches; includes precision medicine that improves disease-related predictions by stratifying a cohort into subgroups of similar characteristics, or using participants' characteristics to model treatment outcomes directly; includes personalised medicine with the use of a person's data to objectively gauge the efficacy, safety, and tolerability of therapeutics; and subjectively tailors medical decisions to the individual's preferences, circumstances, and capabilities. Precision medicine requires a well functioning system comprised of multiple stakeholders, including health-care recipients, health-care providers, scientists, health economists, funders, innovators of medicines and technologies, regulators, and policy makers. Powerful computing infrastructures supporting appropriate analysis of large-scale, well curated, and accessible health databases that contain high-quality, multidimensional, time-series data will be required; so too will prospective cohort studies in diverse populations designed to generate novel hypotheses, and clinical trials designed to test them. Here, we carefully consider these topics and describe a framework for the integration of precision medicine in cardiometabolic disease.

Evaluating the Value for Money of Precision Medicine from Early Cycle to Market Access: A Comprehensive Review of Approaches and Challenges

OBJECTIVES

This study aimed to perform a comprehensive review of modeling approaches and methodological and policy challenges in the economic evaluation (EE) of precision medicine (PM) across clinical stages.

METHODS

First, a systematic review was performed to assess the approaches of EEs in the past 10 years. Next, a targeted review of methodological articles was conducted for methodological and policy challenges in performing EEs of PM. All findings were synthesized into a structured framework that focused on patient population, Intervention, Comparator, Outcome, Time, Equity and ethics, Adaptability and Modeling aspects, named the "PICOTEAM" framework. Finally, a stakeholder consultation was conducted to understand the major determinants of decision making in PM investment.

RESULTS

In 39 methodological articles, we identified major challenges to the EE of PM. These challenges include that PM applications involve complex and evolving clinical decision space, that clinical evidence is sparse because of small subgroups and complex pathways in PM settings, a one-time PM application may have lifetime or intergenerational impacts but long-term evidence is often unavailable, and that equity and ethics concerns are exceptional. In 275 EEs of PM, current approaches did not sufficiently capture the value of PM compared with targeted therapies, nor did they differentiate Early EEs from Conventional EEs. Finally, policy makers perceived the budget impact, cost savings, and cost-effectiveness of PM as the most important determinants in decision making.

CONCLUSIONS

There is an urgent need to modify existing guidelines or develop a new reference case that fits into the new healthcare paradigm of PM to guide decision making in research and development and market access.

DeepInsight-3D architecture for anti-cancer drug response prediction with deep-learning on multi-omics

Modern oncology offers a wide range of treatments and therefore choosing the best option for particular patient is very important for optimal outcome. Multi-omics profiling in combination with AI-based predictive models have great potential for streamlining these treatment decisions. However, these encouraging developments continue to be hampered by very high dimensionality of the datasets in combination with insufficiently large numbers of annotated samples. Here we proposed a novel deep learning-based method to predict patient-specific anticancer drug response from three types of multi-omics data. The proposed DeepInsight-3D approach relies on structured data-to-image conversion that then allows use of convolutional neural networks, which are particularly robust to high dimensionality of the inputs while retaining capabilities to model highly complex relationships between variables. Of particular note, we demonstrate that in this formalism additional channels of an image can be effectively used to accommodate data from different omics layers while implicitly encoding the connection between them. DeepInsight-3D was able to outperform other state-of-the-art methods applied to this task. The proposed improvements can facilitate the development of better personalized treatment strategies for different cancers in the future.

Economics and Precision Medicine

Introducing precision medicine strategies into routine practice will require robust economic evidence. Decision-makers need to understand the value of a precision medicine strategy compared with alternative ways to treat patients. This chapter describes health economic analysis techniques that are needed to generate this evidence. The value of any precision medicine strategy can be demonstrated early to inform evidence generation and improve the likelihood of translation into routine practice. Advances in health economic analysis techniques are also explained and their relevance to precision medicine is highlighted. Ensuring that constraints on delivery are resolved to increase uptake and implementation will improve the value of a new precision medicine strategy. Empirical methods to quantify stakeholders' preferences can be effective to inform the design of a precision medicine intervention or service delivery model. A range of techniques to generate relevant economic evidence are now available to support the development and translation of precision medicine into routine practice. This economic evidence is essential to inform resource allocation decisions and will enable patients to benefit from cost-effective precision medicine strategies in the future.

Central resources of variant discovery and annotation and its role in precision medicine

Rapid technological advancement in high-throughput genomics, microarray, and deep sequencing technologies has accelerated the possibility of more complex precision medicine research using large amounts of heterogeneous health-related data from patients, including genomic variants. Genomic variants can be identified and annotated based on the reference human genome either within the sequence as a whole or in a putative functional genomic element. The American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) mutually created standards and guidelines for the appraisal of proof to expand consistency and straightforwardness in clinical variation interpretations. Various efforts toward precision medicine have been facilitated by many national and international public databases that classify and annotate genomic variation. In the present study, several resources are highlighted with recognition and data spreading of clinically important genetic variations.

Tackling the translational challenges of multi-omics research in the realm of European personalised medicine: A workshop report

Personalised medicine (PM) presents a great opportunity to improve the future of individualised healthcare. Recent advances in -omics technologies have led to unprecedented efforts characterising the biology and molecular mechanisms that underlie the development and progression of a wide array of complex human diseases, supporting further development of PM. This article reflects the outcome of the 2021 EATRIS-Plus Multi-omics Stakeholder Group workshop organised to 1) outline a global overview of common promises and challenges that key European stakeholders are facing in the field of multi-omics research, 2) assess the potential of new technologies, such as artificial intelligence (AI), and 3) establish an initial dialogue between key initiatives in this space. Our focus is on the alignment of agendas of European initiatives in multi-omics research and the centrality of patients in designing solutions that have the potential to advance PM in long-term healthcare strategies.

A scoping review on patient heterogeneity in economic evaluations of precision medicine based on basket trials

INTRODUCTION

Considerable challenges in the economic evaluation of precision medicines have been mentioned in previous studies. However, they have not addressed how an economic assessment would be conducted based on basket trials (novel studies for evaluation of precision medicine effects) in which the included populations have specific biomarkers and various cancers. Since basket trial populations have remarkable heterogeneity, this study aims to investigate the concept of heterogeneity and specific method(s) for considering it in economic evaluations through guidelines and studies that could be applicable in economic evaluation based on basket trials.

AREA COVERED

We searched PubMed, Web of Science, Scopus, Google Scholar, and Google to find studies and pharmacoeconomics guidelines. The inclusion criteria included subjects of patient heterogeneity and suggested explicit method(s). Thirty-nine guidelines and 43 studies were included and evaluated. None of these materials mentioned disease types in a target population as a factor causing heterogeneity. Moreover, in economic evaluations, patient heterogeneity has been considered with four general approaches subgroup analysis, individual-based models, sensitivity analysis, and regression models.

EXPERT OPINION

Type of disease is not considered a contributing factor in population heterogeneity, and the probable appropriate method for this issue could be individual-based models.

The role of precision medicine in interstitial lung disease

The management of interstitial lung disease (ILD) may benefit from a conceptual shift. Increased understanding of this complex and heterogeneous group of disorders over the past 20 years has highlighted the need for individualised treatment strategies that encompass diagnostic classification and disease behaviour. Biomarker-based approaches to precision medicine hold the greatest promise. Robust, large-scale biomarker-based technologies supporting ILD diagnosis have been developed, and future applications relating to staging, prognosis and assessment of treatment response are emerging. Artificial intelligence may redefine our ability to base prognostic evaluation on both diagnosis and underlying disease processes, sharpening individualised treatment algorithms to a level not previously achieved. Compared with therapeutic areas such as oncology, precision medicine in ILD is still in its infancy. However, the heterogeneous nature of ILD suggests that many relevant molecular, environmental and behavioural targets may serve as useful biomarkers if we are willing to invest in their identification and validation.

Polygenic Risk Scores for Cardiovascular Disease: A Scientific Statement From the American Heart Association

Cardiovascular disease is the leading contributor to years lost due to disability or premature death among adults. Current efforts focus on risk prediction and risk factor mitigation' which have been recognized for the past half-century. However, despite advances, risk prediction remains imprecise with persistently high rates of incident cardiovascular disease. Genetic characterization has been proposed as an approach to enable earlier and potentially tailored prevention. Rare mendelian pathogenic variants predisposing to cardiometabolic conditions have long been known to contribute to disease risk in some families. However, twin and familial aggregation studies imply that diverse cardiovascular conditions are heritable in the general population. Significant technological and methodological advances since the Human Genome Project are facilitating population-based comprehensive genetic profiling at decreasing costs. Genome-wide association studies from such endeavors continue to elucidate causal mechanisms for cardiovascular diseases. Systematic cataloging for cardiovascular risk alleles also enabled the development of polygenic risk scores. Genetic profiling is becoming widespread in large-scale research, including in health care-associated biobanks, randomized controlled trials, and direct-to-consumer profiling in tens of millions of people. Thus, individuals and their physicians are increasingly presented with polygenic risk scores for cardiovascular conditions in clinical encounters. In this scientific statement, we review the contemporary science, clinical considerations, and future challenges for polygenic risk scores for cardiovascular diseases. We selected 5 cardiometabolic diseases (coronary artery disease, hypercholesterolemia, type 2 diabetes, atrial fibrillation, and venous thromboembolic disease) and response to drug therapy and offer provisional guidance to health care professionals, researchers, policymakers, and patients.

Using ncRNAs as Tools in Cancer Diagnosis and Treatment-The Way towards Personalized Medicine to Improve Patients' Health

Although the first discovery of a non-coding RNA (ncRNA) dates back to 1958, only in recent years has the complexity of the transcriptome started to be elucidated. However, its components are still under investigation and their identification is one of the challenges that scientists are presently facing. In addition, their function is still far from being fully understood. The non-coding portion of the genome is indeed the largest, both quantitatively and qualitatively. A large fraction of these ncRNAs have a regulatory role either in coding mRNAs or in other ncRNAs, creating an intracellular network of crossed interactions (competing endogenous RNA networks, or ceRNET) that fine-tune the gene expression in both health and disease. The alteration of the equilibrium among such interactions can be enough to cause a transition from health to disease, but the opposite is equally true, leading to the possibility of intervening based on these mechanisms to cure human conditions. In this review, we summarize the present knowledge on these mechanisms, illustrating how they can be used for disease treatment, the current challenges and pitfalls, and the roles of environmental and lifestyle-related contributing factors, in addition to the ethical, legal, and social issues arising from their (improper) use.

Health economic evidence for the use of molecular biomarker tests in hematological malignancies: A systematic review

Objectives

Molecular biomarker tests can inform the clinical management of genomic heterogeneous hematological malignancies, yet their availability in routine care largely depends on the supporting health economic evidence. This study aims to systematically review the economic evidence for recent molecular biomarker tests in hematological malignancies.

Methods

We conducted a systematic search in five electronic databases for studies published between January 2010 and October 2020. Publications were independently screened by two reviewers. Clinical study characteristics, economic methodology, and results were extracted, and reporting quality was assessed.

Results

Fourteen studies were identified, of which half (n = 7; 50%) were full economic evaluations examining both health and economic outcomes. Studies were predominantly conducted in a first‐line treatment setting (n = 7; 50%) and adopted a non‐lifetime time horizon to measure health outcomes and costs (n = 7; 50%). Five studies reported that companion diagnostics for associated therapies were likely cost‐effective for acute myeloid leukemia, chronic myeloid leukemia, diffuse large B‐cell lymphoma, and multiple myeloma. Four studies suggested molecular biomarker tests for treatment monitoring in chronic myeloid leukemia were likely cost‐saving.

Conclusions

Although there is initial confirmation of the promising health economic results, the present research for molecular biomarker tests in hematological malignancies is sparse with many applications of technological advances yet to be evaluated.

Quantifying the Impact of Capacity Constraints in Economic Evaluations: An Application in Precision Medicine

BACKGROUND

Examples of precision medicine are complex interventions featuring both testing and treatment components. Because of this complexity, there are often barriers to the introduction of such interventions. Few economic evaluations attempt to determine the impact of these barriers on the cost-effectiveness of the intervention. This study presents a case study economic evaluation that illustrates how the value of implementation methods may be used to quantify the impact of capacity constraints in a decision-analytic model.

METHODS

A baseline decision-analytic model-based economic evaluation of ALK mutation testing was reproduced from a published technology appraisal. Three constraints (commissioning awareness, localization of testing, and pathology laboratory capacity) were identified using qualitative interviews, parameterized, and incorporated into the model. Value of implementation methods were used alongside incremental cost-effectiveness ratios (ICERs) to quantify the impact on the cost-effectiveness and net monetary benefit (NMB) of each capacity constraint and from the 3 constraints combined.

RESULTS

Each of the 3 capacity constraints resulted in a loss of NMB ranging from £7773 (0.1% of the total) per year for localized testing to £4,907,893 (77%) for a lack of awareness about commissioning ALK testing. When combined, the constraints resulted in a loss of NMB of £5,289,414 (83%). The localization and limited pathology capacity constraints slightly increased the ICER, but the lack of commissioning awareness constraint did not change the ICER.

CONCLUSIONS

Capacity constraints may have a significant impact on the NMB produced by examples of precision medicine. Value of implementation methods can be used to quantify the impact of such constraints by combining the impact of the constraints on the cost-effectiveness of the intervention with the impact on the number of patients receiving the intervention.

HIGHLIGHTS

While capacity constraints may prevent the use of precision medicine in clinical practice, economic evaluations rarely account for the impact of such barriers.This study demonstrates how constraints can be identified using qualitative methods and subsequently incorporated into decision-analytic models using quantitative value of implementation methods.In addition, this article demonstrates how value of implementation methods can be used to account for the impact of capacity constraints on the costs and benefits of an intervention as well as the number of patients receiving the intervention.In the case study presented herein, a capacity constraint reducing patient access to an example of precision medicine caused the biggest loss of net monetary benefit.Health economists should consider moving beyond incremental cost-effectiveness ratios to measures of total net monetary benefit to fully capture the impact of implementing precision medicine.

Gut microbiota-drug interactions in cancer pharmacotherapies: implications for efficacy and adverse effects

INTRODUCTION

The gut microbiota is involved in host physiology and health. Reciprocal microbiota-drug interactions are increasingly recognized as underlying some individual differences in therapy response and adverse events. Cancer pharmacotherapies are characterized by a high degree of interpatient variability in efficacy and side effect profile and recently, the microbiota has emerged as a factor that may underlie these differences.

AREAS COVERED

The effects of cancer pharmacotherapy on microbiota composition and function are reviewed with consideration of the relationship between baseline microbiota composition, microbiota modification, antibiotics exposure and cancer therapy efficacy. We assess the evidence implicating the microbiota in cancer therapy-related adverse events including impaired gut function, cognition and pain perception. Finally, potential mechanisms underlying microbiota-cancer drug interactions are described, including direct microbial metabolism, and microbial modulation of liver metabolism and immune function. This review focused on preclinical and clinical studies conducted in the last 5 years.

EXPERT OPINION

Preclinical and clinical research supports a role for baseline microbiota in cancer therapy efficacy, with emerging evidence that the microbiota modification may assist in side effect management. Future efforts should focus on exploiting this knowledge towards the development of microbiota-targeted therapies. Finally, a focus on specific drug-microbiota-cancer interactions is warranted.

Economic evaluation of genomic/genetic tests: a review and future directions

It has been suggested that health economists need to improve their methods in order to meet the challenges of evaluating genomic/genetic tests. In this article, we set out twelve challenges identified from a rapid review of the literature and suggest solutions to the challenges identified. Two challenges were common to all economic evaluations: choice of perspective and time-horizon. Five challenges were relevant for all diagnostic technologies: complexity of analysis; range of costs; under-developed evidence base; behavioral aspects; and choice of outcome metrics. The final five challenges were pertinent for genomic tests and only these may require methodological development: heterogeneity of tests and platforms, increasing stratification, capturing personal utility; incidental findings; and spillover effects. Current methods of economic evaluation are generally able to cope with genomic/genetic tests, although a renewed focus on specific decision-makers’ needs and a willingness to move away from cost-utility analysis may be required. Certain analysts may be constrained by reference cases developed primarily for the assessment of pharmaceuticals. The combined impact of multiple challenges may require analysts to be particularly careful in setting the scope of their analysis in order to ensure that feasibility is balanced with usefulness to the decision maker. A key issue is the under-developed evidence-base and it may be necessary to rethink translation processes to ensure sufficient, relevant evidence is available to support economic evaluation and adoption of genomic/genetic tests.

A sociology of precision-in-practice: The affective and temporal complexities of everyday clinical care

The idea of 'precision medicine', which has gained increasing traction since the early 2000s, is now ubiquitous in health and medicine. Though varied in its implementation across fields, precision medicine has raised hopes of revolutionary treatments and has spurred the proliferation of novel therapeutics, the alteration of professional trajectories and various reconfigurations of health/care. Nowhere is the promise of precision medicine more apparent, nor further institutionalised, than in the field of oncology. While the transformative potential of precision medicine is widely taken for granted, there remains scant attention to how it is being experienced at the coalface of care. Here, drawing on the perspectives of 54 cancer care professionals gleaned through eight focus group discussions in two hospitals in Australia, we explore clinicians' experiences of the day-to-day dynamics of precision-in-practice. We illustrate some of the affective and temporal complexities, analysed here under the rubrics of enchantment, acceleration and distraction that are emerging alongside the uptake of precision medicine in the field of oncology. We argue that these complexities, and their dis/continuities with earlier iterations of cancer care, demonstrate the need for sociological analyses of precision medicine as it is being implemented in practice and its varied effects on 'routine' care.

Recommendations for streamlining precision medicine in breast cancer care in Latin America

Background

The incidence of breast cancer (BC) in LMICs has increased by more than 20% within the last decade. In areas such as Latin America (LA), addressing BC at national levels evoke discussions surrounding fragmented care, limited resources, and regulatory barriers. Precision Medicine (PM), specifically companion diagnostics (CDx), links disease diagnosis and treatment for better patient outcomes. Thus, its application may aid in overcoming these barriers.

Recent findings

A panel of LA experts in fields related to BC and PM were provided with a series of relevant questions to address prior to a multi‐day conference. Within this conference, each narrative was edited by the entire group, through numerous rounds of discussion until a consensus was achieved. The panel proposes specific, realistic recommendations for implementing CDx in BC in LA and other LMIC regions. In these recommendations, the authors strived to address all barriers to the widespread use and access mentioned previously within this manuscript.

Conclusion

This manuscript provides a review of the current state of CDx for BC in LA. Of most importance, the panel proposes practical and actionable recommendations for the implementation of CDx throughout the Region in order to identify the right patient at the right time for the right treatment.

Lost in application: Measuring hypoxia for radiotherapy optimisation

The history of radiotherapy is intertwined with research on hypoxia. There is level 1a evidence that giving hypoxia-targeting treatments with radiotherapy improves locoregional control and survival without compromising late side-effects. Despite coming in and out of vogue over decades, there is now an established role for hypoxia in driving molecular alterations promoting tumour progression and metastases. While tumour genomic complexity and immune profiling offer promise, there is a stronger evidence base for personalising radiotherapy based on hypoxia status. Despite this, there is only one phase III trial targeting hypoxia modification with full transcriptomic data available. There are no biomarkers in routine use for patients undergoing radiotherapy to aid management decisions, and a roadmap is needed to ensure consistency and provide a benchmark for progression to application. Gene expression signatures address past limitations of hypoxia biomarkers and could progress biologically optimised radiotherapy. Here, we review recent developments in generating hypoxia gene expression signatures and highlight progress addressing the challenges that must be overcome to pave the way for their clinical application.

Precision Oncology, Signaling and Anticancer Agents in Cancer Therapeutics

BACKGROUND

The global alliance for genomics and healthcare facilities provides innovational solutions to expedite research and clinical practices for complex and incurable health conditions. Precision oncology is an emerging field explicitly tailored to facilitate cancer diagnosis, prevention and treatment based on patients' genetic profile. Advancements in "omics" techniques, next-generation sequencing, artificial intelligence and clinical trial designs provide a platform for assessing the efficacy and safety of combination therapies and diagnostic procedures.

METHOD

Data were collected from Pubmed and Google scholar using keywords: "Precision medicine", "precision medicine and cancer", "anticancer agents in precision medicine" and reviewed comprehensively.

RESULTS

Personalized therapeutics including immunotherapy, cancer vaccines, serve as a groundbreaking solution for cancer treatment. Herein, we take a measurable view of precision therapies and novel diagnostic approaches targeting cancer treatment. The contemporary applications of precision medicine have also been described along with various hurdles identified in the successful establishment of precision therapeutics.

CONCLUSION

This review highlights the key breakthroughs related to immunotherapies, targeted anticancer agents, and target interventions related to cancer signaling mechanisms. The success story of this field in context to drug resistance, safety, patient survival and in improving quality of life is yet to be elucidated. We conclude that, in the near future, the field of individualized treatments may truly revolutionize the nature of cancer patient care.

Pharmacogenomics Implementation and Hurdles to Overcome; In the Context of a Developing Country

Having multiple dimensions, uncertainties and several stakeholders, the costly pharmacogenomics (PGx) is associated with dynamic implementation complexities. Identification of these challenges is critical to harness its full potential, especially in developing countries with fragile healthcare systems and scarce resources. This is the first study aimed to identify most salient challenges related to PGx implementation, with respect to the experiences of early-adopters and local experts' prospects, in the context of a developing country in the Middle East. To perform a comprehensive reconnaissance on PGx adoption challenges a scoping literature review was conducted based on national drug policy components: efficacy/safety, access, affordability and rational use of medicine (RUM). Strategic option development and analysis workshop method with cognitive mapping as the technique was used to evaluate challenges in the context of Iran. The cognitive maps were face-validated and analyzed via Decision Explorer XML. The findings indicated a complex network of issues relative to PGx adoption, categorized in national drug policy indicators. In the rational use of medicine category, ethics, education, bench -to- bedside strategies, guidelines, compliance, and health system issues were found. Clinical trial issues, test's utility, and biomarker validation were identified in the efficacy group. Affordability included pricing, reimbursement, and value assessment issues. Finally, access category included regulation, availability, and stakeholder management challenges. The current study identified the most significant challenges ahead of clinical implementation of PGx in a developing country. This could be the basis of a policy-note development in future work, which may consolidate vital communication among stakeholders and accelerate the efficient implementation in developing new-comer countries.

THE FUTURE OF MEDICINE, healthcare innovation through precision medicine: policy case study of Qatar

In 2016, the World Innovation Summit for Health (WISH) published its Forum Report on precision medicine "PRECISION MEDICINE - A GLOBAL ACTION PLAN FOR IMPACT". Healthcare is undergoing a transformation, and it is imperative to leverage new technologies to generate new data and support the advent of precision medicine (PM). Recent scientific breakthroughs and technological advancements have improved our disease knowledge and altered diagnosis and treatment approaches resulting in a more precise, predictive, preventative and personalized health care that is customized for the individual patient. Consequently, the big data revolution has provided an opportunity to apply artificial intelligence and machine learning algorithms to mine such a vast data set. Additionally, personalized medicine promises to revolutionize healthcare, with its key goal of providing the right treatment to the right patient at the right time and dose, and thus the potential of improving quality of life and helping to bring down healthcare costs.This policy briefing will look in detail at the issues surrounding continued development, sustained investment, risk factors, testing and approval of innovations for better strategy and faster process. The paper will serve as a policy bridge that is required to enhance a conscious decision among the powers-that-be in Qatar in order to find a way to harmonize multiple strands of activity and responsibility in the health arena. The end goal will be for Qatar to enhance public awareness and engagement and to integrate effectively the incredible advances in research into healthcare systems, for the benefit of all patients.The PM policy briefing provides concrete recommendations on moving forward with PM initiatives in Qatar and internationally. Equally important, integration of PM within a primary care setting, building a coalition of community champions through awareness and advocacy, finally, communicating PM value, patient engagement/empowerment and education/continued professional development programs of the healthcare workforce.Key recommendations for implementation of precision medicine inside and outside Qatar: 1. Create Community Awareness and PM Education Programs 2. Engage and Empower Patients 3. Communicate PM Value 4. Develop appropriate Infrastructure and Information Management Systems 5. Integrate PM into standard Healthcare System and Ensure Access to Care PM is no longer futuristic. It is here. Implementing PM in routine clinical care does require some investment and infrastructure development. Invariably, cost and lack of expertise are cited as barriers to PM implementation. Equally consequential, are the curriculum and professional development of medical care experts.Policymakers need to lead and coordinate effort among stakeholders and consider cultural and faith perspectives to ensure success. It is essential that policymakers integrate PM approaches into national strategies to improve health and health care for all, and to drive towards the future of medicine precision health.

Model-Informed Precision Dosing: Background, Requirements, Validation, Implementation, and Forward Trajectory of Individualizing Drug Therapy

Model-informed precision dosing (MIPD) has become synonymous with modern approaches for individualizing drug therapy, in which the characteristics of each patient are considered as opposed to applying a one-size-fits-all alternative. This review provides a brief account of the current knowledge, practices, and opinions on MIPD while defining an achievable vision for MIPD in clinical care based on available evidence. We begin with a historical perspective on variability in dose requirements and then discuss technical aspects of MIPD, including the need for clinical decision support tools, practical validation, and implementation of MIPD in health care. We also discuss novel ways to characterize patient variability beyond the common perceptions of genetic control. Finally, we address current debates on MIPD from the perspectives of the new drug development, health economics, and drug regulations.

Incorporating Pharmacometrics into Pharmacoeconomic Models: Applications from Drug Development

Pharmacometrics is the science of quantifying the relationship between the pharmacokinetics and pharmacodynamics of drugs in combination with disease models and trial information to aid in drug development and dosing optimization for clinical practice. Considering the variability in the dose-concentration-effect relationship of drugs, an opportunity exists in linking pharmacokinetic and pharmacodynamic model-based estimates with pharmacoeconomic models. This link may provide early estimates of the cost effectiveness of drug therapies, thus informing late-stage drug development, pricing, and reimbursement decisions. Published case studies have demonstrated how integrated pharmacokinetic-pharmacodynamic-pharmacoeconomic models can complement traditional pharmacoeconomic analyses by identifying the impact of specific patient sub-groups, dose, dosing schedules, and adherence on the cost effectiveness of drugs, thus providing a mechanistic basis to predict the economic value of new drugs. Greater collaboration between the pharmacoeconomics and pharmacometrics community can enable methodological improvements in pharmacokinetic-pharmacodynamic-pharmacoeconomic models to support drug development.

Community Perspectives on Communicating About Precision Medicine in an Alaska Native Tribal Health Care System

BACKGROUND

Precision medicine seeks to better tailor medical care to the needs of individual patients, but there are challenges involved in communicating to patients, health care providers, and health system leaders about this novel and complex approach to research and clinical care. These challenges may be exacerbated for Alaska Native and American Indian (ANAI) people, whose experiences of unethical research practices have left some ANAI communities hesitant to engage in research that involves extensive data-sharing and diminished control over the terms of data management and who may have distinct, culturally-informed communication needs and preferences. There is need for communication research to support Tribal health organizations and ANAI people as they consider implementation of and participation in precision medicine. To address that need, this study characterizes the informational needs and communication preferences of patients, providers, and leaders at an Alaska Native Tribal health organization.

METHODS

We conducted 46 individual, semi-structured interviews to explore perspectives on precision medicine and related communication needs among patients, providers, and leaders of a Tribal health organization. Analysis involved team-based coding to identify a priori and emergent themes, followed by identification and recoding of content relevant to precision medicine informational needs and communication preferences.

RESULTS

Patients, providers, and leaders were described as both sources and recipients of information about precision medicine. Information deemed essential for making decisions about whether to participate in or implement a precision medicine program included information about the clinical and research applications of precision medicine, benefits and risks, health system costs and impacts, and data management practices. Preferred communication channels included digital and non-digital informational materials, as well as in-person learning opportunities for individuals and groups. Participants also describe contextual factors and barriers that influenced the acceptability and effectiveness of approaches to health communication.

CONCLUSION

Results can inform approaches to communicating information about precision medicine to stakeholders within Tribal and other health care systems considering implementation of precision medicine in clinical or research contexts.

Generating evidence to inform health technology assessment of treatments for SLE: a systematic review of decision-analytic model-based economic evaluations

This study aimed to understand and appraise the approaches taken to handle the complexities of a multisystem disease in published decision-analytic model-based economic evaluations of treatments for SLE. A systematic review was conducted to identify all published model-based economic evaluations of treatments for SLE. Treatments that were considered for inclusion comprised antimalarial agents, immunosuppressive therapies, and biologics including rituximab and belimumab. Medline and Embase were searched electronically from inception until September 2018. Titles and abstracts were screened against the inclusion criteria by two reviewers; agreement between reviewers was calculated according to Cohen’s κ. Predefined data extraction tables were used to extract the key features, structural assumptions and data sources of input parameters from each economic evaluation. The completeness of reporting for the methods of each economic evaluation was appraised according to the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) statement. Six decision-analytic model-based economic evaluations were identified. The studies included azathioprine (n=4), mycophenolate mofetil (n=3), cyclophosphamide (n=2) and belimumab (n=1) as relevant comparator treatments; no economic evaluation estimated the relative cost-effectiveness of rituximab. Six items of the CHEERS statement were reported incompletely across the sample: target population, choice of comparators, measurement and valuation of preference-based outcomes, estimation of resource use and costs, choice of model, and the characterisation of heterogeneity. Complexity in the diagnosis, management and progression of disease can make decision-analytic model-based economic evaluations of treatments for SLE a challenge to undertake. The findings from this study can be used to improve the relevance of model-based economic evaluations in SLE and as an agenda for research to inform future health technology assessment and decision-making.

Precision and Personalized Medicine: How Genomic Approach Improves the Management of Cardiovascular and Neurodegenerative Disease

Life expectancy has gradually grown over the last century. This has deeply affected healthcare costs, since the growth of an aging population is correlated to the increasing burden of chronic diseases. This represents the interesting challenge of how to manage patients with chronic diseases in order to improve health care budgets. Effective primary prevention could represent a promising route. To this end, precision, together with personalized medicine, are useful instruments in order to investigate pathological processes before the appearance of clinical symptoms and to guide physicians to choose a targeted therapy to manage the patient. Cardiovascular and neurodegenerative diseases represent suitable models for taking full advantage of precision medicine technologies applied to all stages of disease development. The availability of high technology incorporating artificial intelligence and advancement progress made in the field of biomedical research have been substantial to understand how genes, epigenetic modifications, aging, nutrition, drugs, microbiome and other environmental factors can impact health and chronic disorders. The aim of the present review is to address how precision and personalized medicine can bring greater clarity to the clinical and biological complexity of these types of disorders associated with high mortality, involving tremendous health care costs, by describing in detail the methods that can be applied. This might offer precious tools for preventive strategies and possible clues on the evolution of the disease and could help in predicting morbidity, mortality and detecting chronic disease indicators much earlier in the disease course. This, of course, will have a major effect on both improving the quality of care and quality of life of the patients and reducing time efforts and healthcare costs.

Clinical application of next-generation sequencing to the practice of neurology

Next-generation sequencing technologies allow for rapid and inexpensive large-scale genomic analysis, creating unprecedented opportunities to integrate genomic data into the clinical diagnosis and management of neurological disorders. However, the scale and complexity of these data make them difficult to interpret and require the use of sophisticated bioinformatics applied to extensive datasets, including whole exome and genome sequences. Detailed analysis of genetic data has shown that accurate phenotype information is essential for correct interpretation of genetic variants and might necessitate re-evaluation of the patient in some cases. A multidisciplinary approach that incorporates bioinformatics, clinical evaluation, and human genetics can help to address these challenges. However, despite numerous studies that show the efficacy of next-generation sequencing in establishing molecular diagnoses, pathogenic mutations are generally identified in fewer than half of all patients with genetic neurological disorders, exposing considerable gaps in the understanding of the human genome and providing opportunities to focus research on improving the usefulness of genomics in clinical practice. Looking forward, the emergence of precision health in neurological care will increasingly apply genomic data analysis to pharmacogenetics, preventive medicine, and patient-targeted therapies.

Engaging in building the educational support needed to deliver precision health in Canada

Canadian healthcare facilities are poised to benefit from the tools of precision health. To do so effectively, however, it is crucial that we align changes in how healthcare professionals are educated in order to ensure they have the skills, knowledge, and training to fully engage with its tools. Here, we propose that the design, development, and delivery of novel educational programs focused on precision health should be prioritized for healthcare professionals, and we suggest some essential curricular considerations to that end. Additionally, it is crucial to see engagement on the part of health leaders, who will ultimately be most responsible for managing the changing needs of frontline staff as the tools of precision health become increasingly available. By engaging directly with educators in establishing new programs for precision health, we can ensure that its promise is fully for healthcare facilities, practitioners, and patients.

Light-Sensitive Membrane Proteins as Tools to Generate Precision Treatments

INTRODUCTION BY ANA-NICOLETA BONDAR, BIOPHYSICS SECTION HEAD EDITOR: This issue of the Journal of Membrane Biology inaugurates Up-and-Coming Scientist, in which investigators at early career stages are invited to present recent research in the broad context of their discipline. We inaugurate Up-and-Coming Scientist with the essay by Dr. Elena Lesca of the ETH Zürich and the Paul Scherrer Institut, Switzerland. Dr. Lesca has completed her doctoral degree at the Technical University München, Germany, in 2014, and pursued postdoctoral research at the ETH Zürich and Paul Scherrer Institut, where she is Senior Assistant since 2019. Two recent papers by Dr. Lesca et al. (references 33 and 39) have used X-ray crystallography and experimental biophysics approaches to shed light on the mechanism of action of a membrane receptor from the G Protein-Coupled Receptor (GPCR) family, Jumping Spider Rhodopsin-1 (JSR-1). JSR-1 is a visual rhodopsin activated upon absorption of light by its covalently bound retinal chromophore. Unlike the better-understood bovine rhodopsin GPCR, which is monostable, JSR-1 is bistable (i.e., in JSR-1 the Schiff base that binds retinal to the protein stays protonated throughout the reaction cycle), and absorption of a second photon resets the retinal ligand to the resting state configuration. In her essay, Dr. Lesca discusses the implications of her work on JSR-1 and, more broadly, GPCR research, for state-of-the-art applications in optogenetics and drug design.

Breathomics and treatable traits for chronic airway diseases

PURPOSE OF REVIEW

The long-term management goals of the inflammatory airway diseases asthma and chronic obstructive pulmonary disease (COPD) are similar and focus on symptom control and reduction of exacerbation frequency and severity. Treatable traits have recently been postulated as a management concept which complements the traditional diagnostic labels 'asthma' and 'COPD', thereby focusing on therapy targeted to a patients' individual disease-associated characteristics. Exhaled volatile organic compounds (VOCs) may be utilized as noninvasive biomarker for disease activity or manifestation in asthma and COPD. In this review, we provide an overview of the current achievements concerning exhaled breath analysis in the field of uncontrolled chronic airways diseases.

RECENT FINDINGS

Monitoring of (airway) inflammation and identification of (molecular) phenotypic characteristics in asthma and COPD through exhaled VOC analysis by either mass spectrometry (MS) based or sensor-driven electronic nose technology (eNose) seems to be feasible, however pending confirmation could hamper the valorization of breathomics into clinical tests.

SUMMARY

Exhaled VOC analysis and the management of asthma and COPD through the concept of pulmonary treatable traits are an interesting match. To develop exhaled breath analysis into an added value for pulmonary treatable traits, multicentre studies are required following international standards for study populations, sampling methods and analytical strategies enabling external validation.