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Aim: The ICPerMed, international initiative promoting personalized medicine, has realized a survey among a group of experts, to define a common vision for the deployment of personalized medicine across healthcare systems until 2030. Materials & methods: ICPerMed defined five perspectives (p.4) and addressed an online questionnaire to 97 internation...
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... 2 'Informed, empowered and responsible health providers' benefited as well as Perspective 1 from a large approval by the experts: 91% agreed or strongly agreed with the suggested definition ( Figure 4). Fewer respondents decided to comment this perspective and their remarks were related to the reinforcement of the education of health professionals but also their engagement in PM approaches. ...
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... 2 'Informed, empowered and responsible health providers' benefited as well as Perspective 1 from a large approval by the experts: 91% agreed or strongly agreed with the suggested definition ( Figure 4). Fewer respondents decided to comment this perspective and their remarks were related to the reinforcement of the education of health professionals but also their engagement in PM approaches. ...
Citations
... For example, the International Consortium for Personalized Medicine (ICPerMed) is composed of experts who aim to foster initiatives focused on healthcare system improvement, medication market access, and patient empowerment (Venne et al., 2020). Since its launch in 2016, the consortium has created and developed numerous action plans, frameworks, and funding roadmaps designed to support both local and international research, education, and innovative solutions. ...
Personalized medicine (PM) promises to transform healthcare by providing treatments tailored to individual genetic, environmental, and lifestyle factors. However, its high costs and infrastructure demands raise concerns about exacerbating health disparities, especially between high-income countries (HICs) and low- and middle-income countries (LMICs). While HICs benefit from advanced PM applications through AI and genomics, LMICs often lack the resources necessary to adopt these innovations, leading to a widening healthcare divide. This paper explores the financial and ethical challenges of PM implementation, with a focus on ensuring equitable access. It proposes strategies for global collaboration, infrastructure development, and ethical frameworks to support LMICs in adopting PM, aiming to prevent further disparities in healthcare accessibility and outcomes.
... The use of big data and new technologies, 2 of 8 including the use of artificial intelligence, requires many aspects of ethics and equity to be considered [9]. The International Consortium for Personalised Medicine (ICPerMed), through a survey [10] and an active discussion with European and International experts in fields of medical sciences, defined in 2019 a vision for implementing the use of personalized medicine (PM) in 2030, in order to increase effectiveness, economic value, and equitable access for all citizens [11,12]. The discovery of new molecular characteristics and the development of new diagnostic tests, which allow a more detailed redefinition of pathologies as well as the identification of subgroups of patients who respond well to a therapy, opened important scenarios for the medicine of the future. ...
Citation: Cinti, C.; Trivella, M.G.; Joulie, M.; Ayoub, H.; Frenzel, M., on behalf of the International Consortium for Personalised Medicine and Working Group 'Personalised Medicine in Healthcare' (WG2). The Roadmap toward Personalized Medicine: Challenges and Opportunities. Abstract: In 2019, the International Consortium for Personalised Medicine (ICPerMed) developed a vision on how the use of personalized medicine (PM) approaches will promote "next-generation" medicine in 2030 more firmly centered on the individual's personal characteristics, leading to improved health outcomes within sustainable healthcare systems through research, development, innovation, and implementation for the benefit of patients, citizens, and society. Nevertheless, there are significant hurdles that healthcare professionals, researchers, policy makers, and patients must overcome to implement PM. The ICPerMed aims to provide recommendations to increase stakehold-ers' awareness on actionable measures to be implemented for the realization of PM. Starting with best practice examples of PM together with consultation of experts and stakeholders, a careful analysis that underlined hurdles, opportunities, recommendations, and information, aiming at developing knowledge on the requirements for PM implementation in healthcare practices, has been provided. A pragmatic roadmap has been defined for PM integration into healthcare systems, suggesting actions to overcome existing barriers and harness the potential of PM for improved health outcomes. In fact, to facilitate the adoption of PM by diverse stakeholders, it is mandatory to have a comprehensive set of resources tailored to stakeholder needs in critical areas of PM. These include engagement strategies, collaboration frameworks, infrastructure development, education and training programs, ethical considerations, resource allocation guidelines, regulatory compliance, and data management and privacy.
... The full adoption of precision medicine within the healthcare system hinges not only on the e cient development of precision treatments by industry, but also on the judicious utilization of these treatments by physicians and the establishment of a supportive healthcare system that can effectively deliver the bene ts of PM. This supportive healthcare system necessitates an innovation ecosystem that encompasses a range of precision medicine readiness principles, including patient empowerment, infrastructure and information management, education and awareness, ensuring access to care, and recognition of value [1][2][3][4][5]. ...
INTRODUCTION: The successful adoption of precision medicine relies on the development of effective treatments and judicious utilization within a supportive healthcare system. Embracing a learning healthcare capability will be crucial to navigating the disruptions arising from rapid scientific and technological innovation.
AIM: to build a Precision Medicine (PM) competency framework that can be used across the Medical Technology and Pharmaceutical (MTP) industries to build a confident and capable workforce, support cross-disciplinary work and collaboration, and instil a continuous learning mindset.
METHODOLOGY: A desktop research review of current literature, curriculum, and healthcare trends identified a core set of domains and subdomains related to precision medicine competencies. A survey was distributed to the Industry Genomics Network Alliance (InGENA) members in 2021 to confirm the relevance and applicability of the domains and subdomains to their current work practice and their expected work practice in 5 years’ time.
RESULTS: Four domains were identified: medical science and technology; translational and clinical application; governance and regulation and professional practice. Each domain has a series of subdomains and patient needs were integrated across all four of the domains. Survey results confirmed the applicability of these domains to the MTP industry.
CONCLUSION: The Framework was well accepted by industry, with a strong interest from related disciplines including allied health professionals. Given the pace of change this framework will need regular review and updating. The principles can be used to define frameworks for other technologies such as Cell and Gene Technologies (CGT) and Regenerative Medicines (RM).
... In this paper, we will attempt to highlight the importance of patients' data management and present essential functionalities of ASCAPE Architecture achieved within first and third subsystems. Also, we will briefly present crucial functionalities of Cloud/Edge approach which is adopted in our architecture for personalised medical decisions Tyler et al., 2020;Venne et al., 2020). ...
In contemporary society people constantly are facing situations that influence appearance of serious diseases. For the development of intelligent decision support systems and services in medical and health domains, it is necessary to collect huge amount of patients’ complex data. Patient’s multimodal data must be properly prepared for intelligent processing and obtained results should be presented in a friendly way to the physicians/caregivers to recommend tailored actions that will improve patients’ quality of life. Advanced artificial intelligence approaches like machine/deep learning, federated learning, explainable artificial intelligence open new paths for more quality use of medical and health data in future. In this paper, we will focus on presentation of a part of a novel Open AI Architecture for cancer patients that is devoted to intelligent medical data management. Essential activities are data collection, proper design and preparation of data to be used for training machine learning predictive models. Another key aspect is oriented towards intelligent interpretation and visualisation of results about patient’s quality of life obtained from machine learning models. The Architecture has been developed as a part of complex project in which 15 institutions from 8 European countries have been participated.
... In personalized medicine, clinical tests, such as cancer genome sequencing and selection of optimal drugs and dosing, and genetic tests for pharmacogenetic polymorphisms, play an important role in considering inter-individual differences in therapeutic effects and adverse drug reactions. [1][2][3][4][5][6] Cancer genomic medicine has been developed and clinically implemented in recent decades, and therapeutic drug monitoring is performed for dose adjustment. 7,8) However, severe adverse drug responses, owing to inappropriate dosing, can lead to the termination of drug therapy; therefore, it is necessary to perform appropriate dosing before drug therapy. ...
... Personalized medicine for improving treatment efficiency and preventing adverse drug reactions is required worldwide. 1,2) Inter-individual differences in CYP3A4 activity represent a critical factor associated with treatment efficiency and adverse drug reaction because CYP3A4 contributes to the metabolism of more than 30% of clinically used drugs, including several anti-cancer drugs, such as cabozantinib, docetaxel, gefitinib, and imatinib. 11,12,34) Previously, severe toxicity Data represent RE and CV values for samples prepared independently in triplicate. ...
CYP3A4, which contributes to the metabolism of more than 30% of clinically used drugs, exhibits high variation in its activity; therefore, predicting CYP3A4 activity before drug treatment is vital for determining the optimal dosage for each patient. We aimed to develop and validate an LC-tandem mass spectrometry (LC-MS/MS) method that simultaneously measures the levels of CYP3A4 activity-related predictive biomarkers (6β-hydroxycortisol (6β-OHC), cortisol (C), 1β-hydroxydeoxycholic acid (1β-OHDCA), and deoxycholic acid (DCA)). Chromatographic separation was achieved using a YMC-Triart C18 column and a gradient flow of the mobile phase comprising deionized water/25% ammonia solution (100 : 0.1, v/v) and methanol/acetonitrile/25% ammonia solution (50 : 50 : 0.1, v/v/v). Selective reaction monitoring in the negative-ion mode was used for MS/MS, and run times of 33 min were used. All analytes showed high linearity in the range of 3–3000 ng/mL. Additionally, their concentrations in urine samples derived from volunteers were analyzed via treatment with deconjugation enzymes, ignoring inter-individual differences in the variation of other enzymatic activities. Our method satisfied the analytical validation criteria under clinical conditions. Moreover, the concentrations of each analyte were quantified within the range of calibration curves for all urine samples. The conjugated forms of each analyte were hydrolyzed to accurately examine CYP3A4 activity. Non-invasive urine sampling employed herein is an effective alternative to invasive plasma sampling. The analytically validated simultaneous quantification method developed in this study can be used to predict CYP3A4 activity in precision medicine and investigate the potential clinical applications of CYP3A4 biomarkers (6β-OHC/C and 1β-OHDCA/DCA ratios).
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... The partnership will aim to define priorities for research and education in personalized medicine; support research projects on cancer prevention, diagnosis, and treatment; and outline a set of recommendations for the establishment of personalized medicine approaches in clinical practice and medical research. Those goals have also been pursued by the International Consortium for Personalized Medicine (ICPerMed), launched in November 2016 [341,342]. The ICPerMed has outlined a vision for what personalized medicine will come to represent: the ultimate expression of medical evolution in the era of biotechnology and big data. ...
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.
... Personalized medicine (also called precision medicine) which is concerned with tailoring healthcare to individual patients has received increased global attention over the years. For example, the International Consortium for Personalized Medicine (ICPerMed) predicted that personalized medicine will be deployed across healthcare systems by 2030 (Venne et al., 2020;Vicente et al., 2020). This vision is increasingly becoming a reality with recent advances and widespread applications of artificial intelligence. ...
Traditional health systems mostly rely on rules created by experts to offer adaptive interventions to patients. However, with recent advances in artificial intelligence (AI) and machine learning (ML) techniques, health-related systems are becoming more sophisticated with higher accuracy in providing more personalized interventions or treatments to individual patients. In this paper, we present an extensive literature review to explore the current trends in ML-based adaptive systems for health and well-being. We conduct a systematic search for articles published between January 2011 and April 2022 and selected 87 articles that met our inclusion criteria for review. The selected articles target 18 health and wellness domains including disease management, assistive healthcare, medical diagnosis, mental health, physical activity, dietary management, health monitoring , substance use, smoking cessation, homeopathy remedy finding, patient privacy, mobile health (mHealth) apps finder, clinician knowledge representation for neonatal emergency care, dental and oral health, medication management, disease surveillance, medical specialty recommendation , and health awareness. Our review focuses on five key areas across the target domains: data collection strategies, model development process, ML techniques utilized, model evaluation techniques, as well as adaptive or personalization strategies for health and wellness interventions. We also identified various technical and methodological challenges including data volume constraints , data quality issues, data diversity or variability issues, infrastructure-related issues, and suitability of interventions which offer directions for future work in this area. Finally, we offer recommendations for tackling these challenges, leveraging on technological advances such as multi-modality, Cloud technology, online learning, edge computing, automatic re-calibration, Bluetooth auto-reconnection, feedback pipeline, federated learning, explainable AI, and co-creation of health and wellness interventions.
... Modern, emergent approaches in collecting, processing, and analyzing patient's data support more appropriate interventions, and usually more tailored and personalized treatments [4], [10]. In this paper we will present the current state-of the-art in developing medical and clinical platforms and frameworks and discuss crucial aspects, functionalities, and characteristic examples in the area of application of a range of artificial intelligence approaches in (personalized) medical decisions [23], [24]. ...
In modern dynamic constantly developing society, more and more people suffer from chronic and serious diseases and doctors and patients need special and sophisticated medical and health support. Accordingly, prominent health stakeholders have recognized the importance of development of such services to make patients life easier. Such support requires the collection of huge amount of patients complex data like clinical, environmental, nutritional, daily activities, variety of data from smart wearable devices, data from clothing equipped with sensors etc. Holistic patients data must be properly aggregated, processed, analyzed, and presented to the doctors and caregivers to recommend adequate treatment and actions to improve patients health related parameters and general wellbeing. Advanced artificial intelligence techniques offer the opportunity to analyze such big data, consume them, and derive new knowledge to support personalized medical decisions. New approaches like those based on advanced machine learning, federated learning, transfer learning, explainable artificial intelligence open new paths for more quality use of health and medical data in future. In this paper, we will present some crucial aspects and characteristic examples in the area of application of a range of artificial intelligence approaches in personalized medical decisions.
... The growing flood of data published in recent years on personalized medicine (PM) clearly indicates its superior capacity both in the diagnosis and treatment outcome of a wide range of pathologies, for example, in cancer, cardiovascular diseases, fertility issues and many other fields [1,2]. The term precision medicine is also frequently used (number of publications updated in March 2022, 79.37 versus 110.69 papers for personalized medicine). ...
Allergic diseases are particularly suitable for personalized medicine, because they meet
the needs for therapeutic success, which include a known molecular mechanism of the disease, a diagnostic tool for that disease and a treatment that blocks this mechanism. A range of tools is available for personalized allergy diagnosis, including molecular diagnostics, treatable traits and omics (i.e., proteomics, epigenomics, metabolomics, transcriptomics and breathomics), to predict patient response to therapies, detect biomarkers and mediators and assess disease control status. Such tools enhance allergen immunotherapy. Higher diagnostic accuracy results in a significant increase (based on a greater performance achieved with personalized treatment) in efficacy, further increasing the known and unique characteristics of a treatment designed to work on allergy causes.
... Besides PM programs initiated by individual countries, some international PM programs have also been established, such as the International HundredK+ Cohorts Consortium (IHCC) (21), the International Consortium for Personalized Medicine (ICPerMed) (22,23), 1+ Million Genomes Initiative (1+MG) (24), and the Global Alliance for Genomics and Health (GA4GH) (25). These international programs are designed to make up for possible shortcomings of individual programs, bringing together the strengths of different individuals to work together for a common goal. ...
In the past one or two decades, countries across the world have successively implemented different precision medicine (PM) programs, and also cooperated to implement international PM programs. We are now in the era of PM. Singapore's National Precision Medicine (NPM) program, initiated in 2017, is now entering its second phase to generate a large genomic database for Asians. The National University of Singapore (NUS) also launched its own PM translational research program (TRP) in 2021, aimed at consolidating multidisciplinary expertise within the Yong Loo Lin School of Medicine to develop collaborative projects that can help to identify and validate novel therapeutic targets for the realization of PM. To achieve this, appropriate data collection, data processing, and results interpretation must be taken into consideration. There may be some difficulties during these processes, but with the improvement of relevant rules and the continuous development of omics-based technologies, we will be able to solve these problems, eventually achieving precise prediction, diagnosis, treatment, or even prevention of diseases.
