Through a glass darkly: economics and personalised medicine

The characteristics and role of digital literacy in an effective health protection

The evolution of modern healthcare underscores the crucial question of societal priorities in development. When distributing finite healthcare resources, decision-makers must weigh numerous factors and navigate a range of sometimes conflicting criteria, including equity, access, fairness, and effectiveness. This chapter aims to survey the social implications of digital literacy as a pivotal element for the optimal advancement of P5 medicine, with the objective of enhancing healthcare services. The untapped potential of telemedicine and other emerging P5 technologies remains substantial in terms of their integration into routine medical practice. Adequate digital literacy among both healthcare providers and users stands as a vital prerequisite for the successful adoption and further evolution of these innovations. Coupled with improved health literacy, these factors collectively should contribute to the provision of superior healthcare services and consequently to the overall well-being of the populace.

Pharmacogenomics in clinical trials: an overview

With the trend towards promoting personalised medicine (PM), the application of pharmacogenetics and pharmacogenomics (PGx) is of growing importance. For the purposes of clinical trials, the inclusion of PGx is an additional tool that should be considered for improving our knowledge about the effectiveness and safety of new drugs. A search of available clinical trials containing pharmacogenetic and PGx information was conducted on ClinicalTrials.gov. The results show there has been an increase in the number of trials containing PGx information since the 2000 s, with particular relevance in the areas of Oncology (28.43%) and Mental Health (10.66%). Most of the clinical trials focus on treatment as their primary purpose. In those clinical trials entries where the specific genes considered for study are detailed, the most frequently explored genes are CYP2D6 (especially in Mental Health and Pain), CYP2C9 (in Hematology), CYP2C19 (in Cardiology and Mental Health) and ABCB1 and CYP3A5 (particularly prominent in Transplantation and Cardiology), among others. Researchers and clinicans should be trained in pharmacogenetics and PGx in order to be able to make a proper interpretation of this data, contributing to better prescribing decisions and an improvement in patients' care, which would lead to the performance of PM.

Economic evaluations of predictive genetic testing: A scoping review

Predictive genetic testing can provide information about whether or not someone will develop or is likely to develop a specific condition at a later stage in life. Economic evaluation can assess the value of money for such testing. Studies on the economic evaluation of predictive genetic testing have been carried out in a variety of settings, and this research aims to conduct a scoping review of findings from these studies. We searched the PubMed, Web of Science, Embase, and Cochrane databases with combined search terms, from 2019 to 2022. Relevant studies from 2013 to 2019 in a previous systematic review were also included. The study followed the recommended stages for undertaking a scoping review. A total of 53 studies were included, including 33 studies from the previous review and 20 studies from the search of databases. A significant number of studies focused on the US, UK, and Australia (34%, 23%, and 11%). The most frequently included health conditions were cancer and cardiovascular diseases (68% and 19%). Over half of the studies compared predictive genetic testing with no genetic testing, and the majority of them concluded that at least some type of genetic testing was cost-effective compared to no testing (94%). Some studies stated that predictive genetic testing is becoming more cost-effective with the trend of lowering genetic testing costs. Studies on predictive genetic testing covered various health conditions, particularly cancer and cardiovascular diseases. Most studies indicated that predictive genetic testing is cost-effective compared to no testing.

Physiologically Based Pharmacokinetic Modeling to Describe the CYP2D6 Activity Score-Dependent Metabolism of Paroxetine, Atomoxetine and Risperidone

The cytochrome P450 2D6 (CYP2D6) genotype is the single most important determinant of CYP2D6 activity as well as interindividual and interpopulation variability in CYP2D6 activity. Here, the CYP2D6 activity score provides an established tool to categorize the large number of CYP2D6 alleles by activity and facilitates the process of genotype-to-phenotype translation. Compared to the broad traditional phenotype categories, the CYP2D6 activity score additionally serves as a superior scale of CYP2D6 activity due to its finer graduation. Physiologically based pharmacokinetic (PBPK) models have been successfully used to describe and predict the activity score-dependent metabolism of CYP2D6 substrates. This study aimed to describe CYP2D6 drug–gene interactions (DGIs) of important CYP2D6 substrates paroxetine, atomoxetine and risperidone by developing a substrate-independent approach to model their activity score-dependent metabolism. The models were developed in PK-Sim^®, using a total of 57 plasma concentration–time profiles, and showed good performance, especially in DGI scenarios where 10/12, 5/5 and 7/7 of DGI AUC_last ratios and 9/12, 5/5 and 7/7 of DGI C_max ratios were within the prediction success limits. Finally, the models were used to predict their compound’s exposure for different CYP2D6 activity scores during steady state. Here, predicted DGI AUC_ss ratios were 3.4, 13.6 and 2.0 (poor metabolizers; activity score = 0) and 0.2, 0.5 and 0.95 (ultrarapid metabolizers; activity score = 3) for paroxetine, atomoxetine and risperidone active moiety (risperidone + 9-hydroxyrisperidone), respectively.

Real Impact of Novel Immunotherapy Drugs in Cancer. The Experience of 10 Last Years

Intense research on immunotherapy has been conducted during recent years. As advances in the field have started changing the landscape of cancer therapy, it is necessary to assess the impact of immunotherapeutic modalities in the treatment of various cancers. Ten years ago, in 2011, ipilimumab was the first of the newest immunotherapeutic drugs against cancer to be approved by the FDA. Then several drugs followed and formed a therapeutic arsenal to fight cancer. Initial studies were performed on metastatic patients, but there are currently several studies in patients with potentially curable cancers. All these developments have created a new environment for oncology which we will present in this article. This review examines the current evidence related to the impact of immunotherapy on various cancers and discusses its potential clinical and research implications, including its effectiveness in comparison to other treatment modalities (chemotherapy, radiotherapy), its toxicity and prospective research opportunities. While constant updates and further research is critical to understand the impact of immunotherapy in cancer therapy, not only does it seem to be important to assess the current state of knowledge highlighting the success but also to determine the challenging aspects of cancer immunotherapy.

Clinical pharmacology applications in clinical drug development and clinical care: A focus on Saudi Arabia

Drug development, from preclinical to clinical studies, is a lengthy and complex process. There is an increased interest in the Kingdom of Saudi Arabia (KSA) to promote innovation, research and local content including clinical trials (Phase I-IV). Currently, there are over 650 registered clinical trials in Saudi Arabia, and this number is expected to increase. An important part of drug development and clinical trials is to assure the safe and effective use of drugs. Clinical pharmacology plays a vital role in informed decision making during the drug development stage as it focuses on the effects of drugs in humans. Disciplines such as pharmacokinetics, pharmacodynamics and pharmacogenomics are components of clinical pharmacology. It is a growing discipline with a range of applications in all phases of drug development, including selecting optimal doses for Phase I, II and III studies, evaluating bioequivalence and biosimilar studies and designing clinical studies. Incorporating clinical pharmacology in research as well as in the requirements of regulatory agencies will improve the drug development process and accelerate the pipeline. Clinical pharmacology is also applied in direct patient care with the goal of personalizing treatment. Tools such as therapeutic drug monitoring, pharmacogenomics and model informed precision dosing are used to optimize dosing for patients at an individual level. In KSA, the science of clinical pharmacology is underutilized and we believe it is important to raise awareness and educate the scientific community and healthcare professionals in terms of its applications and potential. In this review paper, we provide an overview on the use and applications of clinical pharmacology in both drug development and clinical care.

Gene Therapy for Angelman Syndrome: Contemporary Approaches and Future Endeavors

Background:

Angelman Syndrome (AS) is a congenital non inherited neurodevelopmental disorder. The contemporary AS management is symptomatic and it has been accepted that gene therapy may play a key role in the treatment of AS.

Objective:

The purpose of this study is to summarize existing and suggested gene therapy approaches to Angelman syndrome.

Methods:

This is a literature review. Pubmed and Scopus databases were researched with keywords (gene therapy, Angelman’s syndrome, neurological disorders, neonates). Peer-reviewed studies that were closely related to gene therapies in Angelman syndrome and available in English, Greek, Ukrainian or Indonesian were included. Studies that were published before 2000 were excluded and did not align with the aforementioned criteria.

Results:

UBE3A serves multiple roles in signaling and degradation procedures. Although the restoration of UBE3A expression rather than targeting known activities of the molecule would be the optimal therapeutic goal, it is not possible so far. Reinstatement of paternal UBE3A appears as an adequate alternative. This can be achieved by administering topoisomerase-I inhibitors or reducing UBE3A antisense transcript (UBE3A-ATS), a molecule which silences paternal UBE3A.

Conclusion:

Understanding UBE3A imprinting unravels the path to an etiologic treatment of AS. Gene therapy models tested on mice appeared less effective than anticipated pointing out that activation of paternal UBE3A cannot counteract the existing CNS defects. On the other hand, targeting abnormal downstream cell signaling pathways has provided promising rescue effects. Perhaps, combined reinstatement of paternal UBE3A expression with abnormal signaling pathways-oriented treatment is expected to provide better therapeutic effects. However, AS gene therapy remains debatable in pharmacoeconomics and ethics context.

Pharmacogenetics in the clinical analysis laboratory: clinical practice, research, and drug development pipeline

Introduction: Over the last decade, the spread of next-generation sequencing technology along with the rising cost in health management in national health systems has led to widespread use/abuse of pharmacogenetic tests (PGx) in the practice of many clinical disciplines. However, given their clinical significance, it is important to standardize these tests for having an interaction with the clinical analysis laboratory (CAL), in which a PGx service can meet these requirements. Areas covered: A diagnostic test must meet the criteria of reproducibility and validity for its utility in the clinical routine. This present review mainly describes the utility of introducing PGx tests in the CAL routine to produce correct results useful for setting up personalized drug treatments. Expert opinion: With a PGx service, CALs can provide the right tool to help clinicians to make better choices about different categories of drugs and their dosage and to manage the economic impact both in hospital-based settings and in National Health Services, throughout electronic health records. Advances in PGx also allow a new approach for pharmaceutical companies in order to improve drug development and clinical trials. As a result, CALs can achieve a powerful source of epidemiological, clinical, and research findings from PGx tests.

Pharmacogenomics in the Nigerian population: the past, the present and the future

The Nigerian population exhibits huge ethnic and genetic diversity, typical of African populations, which can be harnessed for improved drug-response and disease management. Existing data on genes relevant to drug response, so far generated for the population, indeed confirm the prevalence of some clinically significant pharmacogenes. These reports detail prevailing genetic alleles and metabolic phenotypes of vital drug metabolizing monooxygenases, transferases and drug transporters. While the utilization of existing pharmacogenomic data for healthcare delivery remains unpopular, several past and on-going studies suggest that a future shift toward genotype-stratified dosing of drugs and disease management in the population is imminent. This review discusses the present state of pharmacogenomics in Nigeria and the potential benefits of sustained research in this field for the population.

Generic Cost-Effectiveness Models: A Proof of Concept of a Tool for Informed Decision-Making for Public Health Precision Medicine

BACKGROUND/AIMS

Economic evaluation is integral to informed public health decision-making in the rapidly growing field of precision and personalized medicine (PM); however, this research requires specialized expertise and significant resources. Generic models are a novel innovation to efficiently address a critical PM evidence shortage and implementation barrier by enabling use of population-specific input values. This is a generic PM economic evaluation model proof-of-concept study for a pharmacogenomic use case.

METHODS

An 8-step generic economic model development process was applied to the use case of human leukocyte antigen (HLA)-B*15:02genotyping for prediction of carbamazepine-induced cutaneous reactions, with a user-friendly decision-making tool relying on user-provided input values. This generic model was transparently documented and validated, including cross-validation comparing cost-effectiveness results with 3 country-specific models.

RESULTS

A generic pharmacogenomic use case cost-effectiveness model with decision-making tool was successfully developed and cross-validated using input values for 6 populations which produced consistent results for HLA-B*15:02 screening at country-specific cost-effectiveness threshold values. Differences between the generic and country-specific model results were largely due to differences in model structure and assumptions.

CONCLUSION

This proof on concept demonstrates the feasibility of generic models to provide useful PM economic evidence, supporting their use as a pragmatic and timely approach to address a growing need.

Cost-effectiveness of pharmacogenetic-guided treatment: are we there yet?

Pharmacogenetics (PGx) has the potential to personalize pharmaceutical treatments. Many relevant gene-drug associations have been discovered, but PGx-guided treatment needs to be cost-effective as well as clinically beneficial to be incorporated into standard health-care. We reviewed economic evaluations for PGx associations listed in the US Food and Drug Administration (FDA) Table of Pharmacogenomic Biomarkers in Drug Labeling. We determined the proportion of evaluations that found PGx-guided treatment to be cost-effective or dominant over the alternative strategies, and estimated the impact on this proportion of removing the cost of genetic testing. Of the 137 PGx associations in the FDA table, 44 economic evaluations, relating to 10 drugs, were identified. Of these evaluations, 57% drew conclusions in favour of PGx testing, of which 30% were cost-effective and 27% were dominant (cost-saving). If genetic information was freely available, 75% of economic evaluations would support PGx-guided treatment, of which 25% would be cost-effective and 50% would be dominant. Thus, PGx-guided treatment can be a cost-effective and even a cost-saving strategy. Having genetic information readily available in the clinical health record is a realistic future prospect, and would make more genetic tests economically worthwhile.

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.

Evidence used in model-based economic evaluations for evaluating pharmacogenetic and pharmacogenomic tests: a systematic review protocol

INTRODUCTION

Decision models can be used to conduct economic evaluations of new pharmacogenetic and pharmacogenomic tests to ensure they offer value for money to healthcare systems. These models require a great deal of evidence, yet research suggests the evidence used is diverse and of uncertain quality. By conducting a systematic review, we aim to investigate the test-related evidence used to inform decision models developed for the economic evaluation of genetic tests.

METHODS AND ANALYSIS

We will search electronic databases including MEDLINE, EMBASE and NHS EEDs to identify model-based economic evaluations of pharmacogenetic and pharmacogenomic tests. The search will not be limited by language or date. Title and abstract screening will be conducted independently by 2 reviewers, with screening of full texts and data extraction conducted by 1 reviewer, and checked by another. Characteristics of the decision problem, the decision model and the test evidence used to inform the model will be extracted. Specifically, we will identify the reported evidence sources for the test-related evidence used, describe the study design and how the evidence was identified. A checklist developed specifically for decision analytic models will be used to critically appraise the models described in these studies. Variations in the test evidence used in the decision models will be explored across the included studies, and we will identify gaps in the evidence in terms of both quantity and quality.

DISSEMINATION

The findings of this work will be disseminated via a peer-reviewed journal publication and at national and international conferences.

Combining Health Data Uses to Ignite Health System Learning

OBJECTIVES

In this paper we aim to characterise the critical mass of linked data, methods and expertise required for health systems to adapt to the needs of the populations they serve - more recently known as learning health systems. The objectives are to: 1) identify opportunities to combine separate uses of common data sources in order to reduce duplication of data processing and improve information quality; 2) identify challenges in scaling-up the reuse of health data sufficiently to support health system learning.

METHODS

The challenges and opportunities were identified through a series of e-health stakeholder consultations and workshops in Northern England from 2011 to 2014. From 2013 the concepts presented here have been refined through feedback to collaborators, including patient/citizen representatives, in a regional health informatics research network (www.herc.ac.uk).

RESULTS

Health systems typically have separate information pipelines for: 1) commissioning services; 2) auditing service performance; 3) managing finances; 4) monitoring public health; and 5) research. These pipelines share common data sources but usually duplicate data extraction, aggregation, cleaning/preparation and analytics. Suboptimal analyses may be performed due to a lack of expertise, which may exist elsewhere in the health system but is fully committed to a different pipeline. Contextual knowledge that is essential for proper data analysis and interpretation may be needed in one pipeline but accessible only in another. The lack of capable health and care intelligence systems for populations can be attributed to a legacy of three flawed assumptions: 1) universality: the generalizability of evidence across populations; 2) time-invariance: the stability of evidence over time; and 3) reducibility: the reduction of evidence into specialised sub-systems that may be recombined.

CONCLUSIONS

We conceptualize a population health and care intelligence system capable of supporting health system learning and we put forward a set of maturity tests of progress toward such a system. A factor common to each test is data-action latency; a mature system spawns timely actions proportionate to the information that can be derived from the data, and in doing so creates meaningful measurement about system learning. We illustrate, using future scenarios, some major opportunities to improve health systems by exchanging conventional intelligence pipelines for networked critical masses of data, methods and expertise that minimise data-action latency and ignite system-learning.

Personalized medicine and economic evaluation in oncology: all theory and no practice?

The clinical definition of personalized medicine (PM) is closely related to that of pharmacogenomics. Ideally, PM could lead the pharmaceutical industry to differentiate products by subgroups of patients with the same pathology and find new gene targets for drug discovery. Here, we focus on the potential impact of PM on the design of clinical trials and economic evaluations limited to oncology (its first and main field of application). Then, we assess the European economic evaluations focused on trastuzumab and cetuximab, the two drugs usually mentioned as emblematic examples of targeted therapies. Clinical results of PM in oncology have not been as encouraging as hoped so far. Of course, economic evaluations on targeted therapies cannot help overcome the lack of clinical evidence for most of them. The two paradigmatic examples of cetuximab and trastuzumab indicate that the methodological implications on economic evaluations debated in the literature are more theoretical than practical.

Self-monitoring and psychoeducation in bipolar patients with a smart-phone application (SIMPLe) project: design, development and studies protocols

BACKGROUND

New technologies have recently been used for monitoring signs and symptoms of mental health illnesses and particularly have been tested to improve the outcomes in bipolar disorders. Web-based psychoeducational programs for bipolar disorders have also been implemented, yet to our knowledge, none of them have integrated both approaches in one single intervention. The aim of this project is to develop and validate a smartphone application to monitor symptoms and signs and empower the self-management of bipolar disorder, offering customized embedded psychoeducation contents, in order to identify early symptoms and prevent relapses and hospitalizations.

METHODS/DESIGN

The project will be carried out in three complementary phases, which will include a feasibility study (first phase), a qualitative study (second phase) and a randomized controlled trial (third phase) comparing the smartphone application (SIMPLe) on top of treatment as usual with treatment as usual alone. During the first phase, feasibility and satisfaction will be assessed with the application usage log data and with an electronic survey. Focus groups will be conducted and technical improvements will be incorporated at the second phase. Finally, at the third phase, survival analysis with multivariate data analysis will be performed and relationships between socio-demographic, clinical variables and assessments scores with relapses in each group will be explored.

DISCUSSION

This project could result in a highly available, user-friendly and not costly monitoring and psychoeducational intervention that could improve the outcome of people suffering from bipolar disorders in a practical and secure way.

TRIAL REGISTRATION

Clinical Trials.gov: NCT02258711 (October 2014).