European healthcare systems readiness to shift from ‘one-size fits all’ to personalized medicine

Integrating biomarkers for hemostatic disorders into computational models of blood clot formation: A systematic review

In the pursuit of personalized medicine, there is a growing demand for computational models with parameters that are easily obtainable to accelerate the development of potential solutions. Blood tests, owing to their affordability, accessibility, and routine use in healthcare, offer valuable biomarkers for assessing hemostatic balance in thrombotic and bleeding disorders. Incorporating these biomarkers into computational models of blood coagulation is crucial for creating patient-specific models, which allow for the analysis of the influence of these biomarkers on clot formation. This systematic review aims to examine how clinically relevant biomarkers are integrated into computational models of blood clot formation, thereby advancing discussions on integration methodologies, identifying current gaps, and recommending future research directions. A systematic review was conducted following the PRISMA protocol, focusing on ten clinically significant biomarkers associated with hemostatic disorders: D-dimer, fibrinogen, Von Willebrand factor, factor Ⅷ, P-selectin, prothrombin time (PT), activated partial thromboplastin time (APTT), antithrombin Ⅲ, protein C, and protein S. By utilizing this set of biomarkers, this review underscores their integration into computational models and emphasizes their integration in the context of venous thromboembolism and hemophilia. Eligibility criteria included mathematical models of thrombin generation, blood clotting, or fibrin formation under flow, incorporating at least one of these biomarkers. A total of 53 articles were included in this review. Results indicate that commonly used biomarkers such as D-dimer, PT, and APTT are rarely and superficially integrated into computational blood coagulation models. Additionally, the kinetic parameters governing the dynamics of blood clot formation demonstrated significant variability across studies, with discrepancies of up to 1, 000-fold. This review highlights a critical gap in the availability of computational models based on phenomenological or first-principles approaches that effectively incorporate affordable and routinely used clinical test results for predicting blood coagulation. This hinders the development of practical tools for clinical application, as current mathematical models often fail to consider precise, patient-specific values. This limitation is especially pronounced in patients with conditions such as hemophilia, protein C and S deficiencies, or antithrombin deficiency. Addressing these challenges by developing patient-specific models that account for kinetic variability is crucial for advancing personalized medicine in the field of hemostasis.

CPMKG: a condition-based knowledge graph for precision medicine

Personalized medicine tailors treatments and dosages based on a patient's unique characteristics, particularly its genetic profile. Over the decades, stratified research and clinical trials have uncovered crucial drug-related information-such as dosage, effectiveness, and side effects-affecting specific individuals with particular genetic backgrounds. This genetic-specific knowledge, characterized by complex multirelationships and conditions, cannot be adequately represented or stored in conventional knowledge systems. To address these challenges, we developed CPMKG, a condition-based platform that enables comprehensive knowledge representation. Through information extraction and meticulous curation, we compiled 307 614 knowledge entries, encompassing thousands of drugs, diseases, phenotypes (complications/side effects), genes, and genomic variations across four key categories: drug side effects, drug sensitivity, drug mechanisms, and drug indications. CPMKG facilitates drug-centric exploration and enables condition-based multiknowledge inference, accelerating knowledge discovery through three pivotal applications. To enhance user experience, we seamlessly integrated a sophisticated large language model that provides textual interpretations for each subgraph, bridging the gap between structured graphs and language expressions. With its comprehensive knowledge graph and user-centric applications, CPMKG serves as a valuable resource for clinical research, offering drug information tailored to personalized genetic profiles, syndromes, and phenotypes. Database URL: https://www.biosino.org/cpmkg/.

Financial incentives to promote personalized medicine in Europe: an overview and guidance for implementation

The implementation of adequate financing and reimbursement of personalized medicine (PM) in Europe is still turbulent. The views and experience of stakeholders about barriers in financing and reimbursing PM and potential solutions were elicited and supplemented with literature findings to draft a set of recommendations. Key recommendations to overcome the barriers for adequately financing and reimbursing PM in different healthcare systems in Europe included the provision of legal foundations and establishment of large pan-European databases, use of financial-based agreements and regulation of transparency of prices and reimbursement, and creating a business-friendly environment and attractive market for innovation. The recommendations could be used by health authorities for designing a sequence of policy steps to ensure the timely access to beneficial PM.

Barriers and Facilitators to the Implementation of Personalised Medicine across Europe

(1) Background: Personalised medicine (PM) is an innovative way to produce better patient outcomes by using an individualised or stratified approach to disease and treatment rather than a collective approach to treating patients. PM is a major challenge for all European healthcare systems. This article aims to identify the needs of citizens in terms of PM adaptation, as well as to provide insights into the barriers and facilitators categorised in relation to key stakeholders of their implementation. (2) Methods: This article presents data obtained from the survey "Barriers and facilitators of Personalised Medicine implementation-qualitative study under Regions4PerMed (H2020) project". Semi-structured questions were included in the above-mentioned survey. The questions included both structured and unstructured segments in an online questionnaire (Google Forms). Data were compiled into a data base. The results of the research were presented in the study. The number of people who participated in the survey can be considered an insufficient sample size for statistical measurement. In order to avoid collecting unreliable data, the questionnaires were sent to various stakeholders of the Regions4PerMed project, which includes members of the Advisory Board of the Regions4PerMed Project, but also speakers of conferences and workshops, and participants in these events. The professional profiles of the respondents are also diverse. (3) Results: The insights on what would help in the adaptation of Personal Medicine to citizen needs have been categorised into 7 areas of need: education; finances; dissemination; data protection/IT/data sharing; system changes/governmental level; cooperation/collaboration; public/citizens. Barriers and facilitators have been categorised into ten key stakeholders of the implementation barriers: government and government agencies; medical doctors/practitioners; healthcare system; healthcare providers; patients and patient organisations; medical sector, scientific community, researchers, stakeholders; industry; technology developers; financial institutions; media. (4) Conclusions: Barriers to the implementation of Personalised Medicine are observed across Europe. The barriers and facilitators mentioned in the article need to be effectively managed in healthcare systems across Europe. There is an urgent need to remove as many barriers as possible and create as many facilitators as possible to implement personalized medicine in the European system.

Promise of Real-World Evidence for Patient Centricity in Gulf Cooperation Council Countries: Call to Action

Presently, Gulf Cooperation Council countries are lagging in the generation of real-world data and use of real-world evidence for patient-centered care compared with the global average. In a collaborative effort, experts from multiple domains of the healthcare environment from the Gulf Cooperation Council countries came together to present their views and recommended key action points for the generation of robust real-world data and leveraging real-world evidence in the countries. The opinions of the experts are presented, along with existing barriers to the effective generation of real-world evidence in the countries. The Gulf Cooperation Council countries are undergoing transformative changes paving the way for improved healthcare measures; however, the challenges in generating reliable, robust, accessible, and secure real-world evidence are persistent. Hence, ongoing public-private engagements, as well as collaborations between regulators, policymakers, healthcare professionals, insurance and pharmaceutical companies, and patients, are warranted. A few notable examples of real-world evidence studies highlighting the benefits of real-world evidence for gaining valuable insights into patient-centric decision making are also discussed. The actionable steps identified for successful real-world evidence generation would provide long-term, real-world evidence-based patient-centric benefits for the countries.

Expanding the scope, reach, and impact of evidence-based psychological treatments

The development and evaluation of evidence-based treatments (EBTs) for mental disorders represent an enormous advance with continued progress designed to understand the techniques and increase their use in clinical practice. This article suggests ways of expanding research along several fronts including the extension of the types of randomized controlled trials that are conducted, the use of more diverse samples to encompass different cultures and countries, the expansion of assessments to better reflect client functioning in everyday life, consideration of the impact of treatments for mental disorders on physical health, the careful evaluation of exceptional responders, the use of mixed-methods research, and the development of versions of EBTs that can be scaled. EBTs have been studied in well-controlled settings and extended to clinical settings, albeit less often. The least attention has been accorded their evaluation on a large scale to reach a greater portion of people in need of services but who do not receive any treatment.

Financing and Reimbursement Models for Personalised Medicine: A Systematic Review to Identify Current Models and Future Options

BACKGROUND

The number of healthcare interventions described as 'personalised medicine' (PM) is increasing rapidly. As healthcare systems struggle to decide whether to fund PM innovations, it is unclear what models for financing and reimbursement are appropriate to apply in this context.

OBJECTIVE

To review financing and reimbursement models for PM, summarise their key characteristics, and describe whether they can influence the development and uptake of PM.

METHODS

A literature review was conducted in Medline, Embase, Web of Science, and Econlit to identify studies published in English between 2009 and 2021, and reviews published before 2009. Grey literature was identified through Google Scholar, Google and subject-specific webpages. Articles that described financing and reimbursement of PM, and financing of non-PM were included. Data were extracted and synthesised narratively to report on the models, as well as facilitators, incentives, barriers and disincentives that could influence PM development and uptake.

RESULTS

One hundred and fifty-three papers were included. Research and development of PM was financed through both public and private sources and reimbursed largely through traditional models such as single fees, Diagnosis-Related Groups, and bundled payments. Financial-based reimbursement, including rebates and price-volume agreements, was mainly applied to targeted therapies. Performance-based reimbursement was identified mainly for gene and targeted therapies, and some companion diagnostics. Gene therapy manufacturers offered outcome-based rebates for treatment failure for interventions including Luxturna®, Kymriah®, Yescarta®, Zynteglo®, Zolgensma® and Strimvelis®, and coverage with evidence development for Kymriah® and Yescarta®. Targeted testing with OncotypeDX® was granted value-based reimbursement through initial coverage with evidence development. The main barriers and disincentives to PM financing and reimbursement were the lack of strong links between stakeholders and the lack of demonstrable benefit and value of PM.

CONCLUSIONS

Public-private financing agreements and performance-based reimbursement models could help facilitate the development and uptake of PM interventions with proven clinical benefit.

The Creation and Management of Innovations in Healthcare and ICT: The European and African Experience

The purpose of the study was to gain new insights into innovation systems by comparing state-of-the-art of existing approaches of innovation creation and innovation management in healthcare and ICT. It is unique, in that it compares countries in Africa with countries in Europe in order to identify similarities and differences regarding the creation and management of innovations. The main similarity is that early dialogue between different stakeholders was underrepresented during the whole innovation process in all countries. Our results also indicated that the various stakeholders often work in silos. The main difference was that the countries face problems at different stages of the innovation process. Whereas European countries face more problems in the innovation creation process, African countries experience difficulty sustaining and managing innovation. To overcome barriers, we suggest the application of systematic early dialogue between all key stakeholders.

EARLY DIALOGUE IN EUROPE: PERSPECTIVES ON VALUE, CHALLENGES, AND CONTINUING EVOLUTION

OBJECTIVES

This study aims to assess participants' views on previous experiences, the current situation and future perspectives for early dialogue between the pharmaceutical industry, a regulatory agency and health technology assessment bodies (HTABs) in Europe.

METHODS

Eleven semi-structured interviews were arranged purposively with experienced people from the pharmaceutical industry, the European Medicines Agency, and an expert in Health Economics. The interview questions focused on the value of early dialogue, the challenges faced during the process of early dialogue, the best time to start an early dialogue, the kind of products most suitable for early dialogue, the current situation, and future perspectives for the early dialogue process. The interviews were recorded and then transcribed for open and axial coding to summarize the findings.

RESULTS

All interviewees agreed that early dialogue is a valuable process that helps to inform the development program and accordingly provide patients with faster access to new medicines. However, at this stage, the pharmaceutical industry acknowledged certain challenges: (i) Finding resources within pharmaceutical companies and HTABs to support early dialogues (ii) Requirements between regulators and HTABs in different countries diverge.

CONCLUSIONS

This study revealed that people from the pharmaceutical industry perceive early dialogue as a valuable tool that can bring medicines to patients faster by streamlining development. However, the challenges mentioned above need to be mitigated to build a sustainable mechanism for early dialogue.

Potentials and Challenges of the Health Data Cooperative Model

INTRODUCTION

Currently, abundances of highly relevant health data are locked up in data silos due to decentralized storage and data protection laws. The health data cooperative (HDC) model is established to make this valuable data available for societal purposes. The aim of this study is to analyse the HDC model and its potentials and challenges.

RESULTS

An HDC is a health data bank. The HDC model has as core principles a cooperative approach, citizen-centredness, not-for-profit structure, data enquiry procedure, worldwide accessibility, cloud computing data storage, open source, and transparency about governance policy. HDC members have access to the HDC platform, which consists of the "core," the "app store," and the "big data." This, respectively, enables the users to collect, store, manage, and share health information, to analyse personal health data, and to conduct big data analytics. Identified potentials of the HDC model are digitization of healthcare information, citizen empowerment, knowledge benefit, patient empowerment, cloud computing data storage, and reduction in healthcare expenses. Nevertheless, there are also challenges linked with this approach, including privacy and data security, citizens' restraint, disclosure of clinical results, big data, and commercial interest. Limitations and Outlook: The results of this article are not generalizable because multiple studies with a limited number of study participants are included. Therefore, it is recommended to undertake further elaborate research on these topics among larger and various groups of individuals. Additionally, more pilots on the HDC model are required before it can be fully implemented. Moreover, when the HDC model becomes operational, further research on its performances should be undertaken.

Attitudes towards Personal Genomics and Sharing of Genetic Data among Older Swiss Adults: A Qualitative Study

OBJECTIVE

To assess the willingness of older Swiss adults to share genetic data for research purposes and to investigate factors that might impact their willingness to share data.

METHODS

Semi-structured interviews were conducted among 40 participants (19 male and 21 female) aged between 67 and 92 years, between December 2013 and April 2014 attending the Seniorenuniversität Zürich, Switzerland. All interviews were audio-recorded, transcribed verbatim, and anonymized. For the analysis of the interviews, an initial coding scheme was developed, refined over time, and applied afterwards to all interviews.

RESULTS

The majority of participants were in favor of placing genetic data to research's disposal. Participant's motivations to share data were mainly driven by altruistic reasons and by contributing to the greater good. Furthermore, several factors which might impact the willingness to share data such as sharing data with private companies, generational differences, differences between sharing genetic data or health data, and sharing due to financial incentives were highlighted. Last, some participants indicated concerns regarding data sharing such as misuse of data, the fear of becoming a transparent citizen, and data safety. However, 20% of the participants express confidence in data protection. Even participants who were skeptical in the beginning of the interviews admitted the benefits of data sharing.

DISCUSSION

Overall, this study suggests older citizens are willing to share their data for research purposes. However, most of them will only contribute if their data is appropriately protected and if they trust the research institution to use the shared data responsibly. More transparency and detailed information regarding the data usage are urgently needed. There is a great need to increase the engagement of older adults in research since they present a large segment of our society - one which is often underexamined in research.

CONCLUSION

Increased focus on general public engagement, especially of older adults, in scientific research activities known as "citizen science" is needed to further strengthen the uptake of personalized medicine.