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The healthcare landscape has undergone a profound transformation due to digitalisation in recent decades, shifting from manual practices to automated processes. Digital biomarkers have become increasingly important in healthcare across multiple domains for regular monitoring of health parameters, aiding in early disease detection and prevention. The present narrative review aims to explore the utility and advantages of digital biomarkers in healthcare. Notably, the literature emphasises portable biomarkers capable of actively recording user data. These advanced biomarkers efficiently identify physiological, biochemical and psychological parameters, offering predictive capabilities for health conditions and treatment outcomes. In this dynamic healthcare landscape, digital science has brought transformative changes. Investments in digital health show promising outcomes for disease management and patient care. Overall, the integration of digital biomarkers into healthcare represents a significant leap forward in improving healthcare delivery, diagnosis and treatment. It leverages technology to enhance the precision and efficiency of healthcare, ultimately benefiting both healthcare professionals and patients. While the scope of digital biomarkers continues to expand and is expected to grow in the future, addressing potential challenges is essential for sustained innovation in this field.

Abstract The KidGen Collaborative's Policy Implementation Workshop 2023 celebrated the 10th anniversary of Australia's first kidney genetics clinic in Brisbane. This event marked the establishment of a national network now comprising 19 kidney genetics clinics across Australia, all dedicated to providing equitable access to genomic testing for families affected by genetic kidney diseases. The workshop reflected on past progress and outlined future objectives for kidney genetics in Australia, recognising the collaborative efforts of clinical teams, researchers, and patients. Key insights from the workshop are documented in the proceedings.

INTRODUCTION. Small interfering RNAs (siRNAs) are among the most promising types of therapeutic nucleic acids aiming at the inhibition of pathogenetically relevant gene expression through the RNA interference mechanism. However, the limited bioavailability and immunogenicity of siRNAs and imperfect delivery systems hinder the clinical potential and applicability of siRNA medicinal products.AIM. This study aimed to summarise recent advances in the development of siRNA medicinal products and the corresponding delivery systems, review clinical trial results, and outline future development prospects for these medicinal products.DISCUSSION. This article covers the molecular mechanisms underlying RNA interference, the considerations for siRNA development, and the techniques for effective siRNA delivery. The article dwells upon various systems for nucleic acid delivery to targeted cells. The most promising delivery systems are non-viral systems, including liposomes, exosomes, nanoparticles, polymers, cell-penetrating peptides, and GalNAc ligands. Their main advantages include their ease of complexation with nucleic acids, modification and functionalization potential, favourable safety profile, ability to cross biological barriers, and tropism to target tissues. The article summarises the information that has accumulated over the past few years in clinical trials of siRNA medicinal products for a range of conditions, including metabolic disorders, infections, and cancers, as well as hereditary, ophthalmic, renal, and hepatic diseases. Special attention is paid to siRNA medicinal products undergoing clinical trials (over 10 products) and approved for clinical use (6 products, including MIR 19, the first authorised Russian siRNA medicinal product).CONCLUSION. Ultimately, siRNA medicinal products are a promising tool for personalised medicine, exhibiting therapeutic potential for a wide range of pathological conditions. Further studies of siRNA medicinal products should aim at improving siRNA production technology to increase their bioavailability and half-life period. In addition, these studies should aim at enhancing delivery systems for these products to mitigate toxicity risks and maximise efficacy.

Summary: The human transcriptome predominantly consists of noncoding RNAs (ncRNAs), transcripts that do not encode proteins. The noncoding transcriptome governs a multitude of pathophysiological processes, offering a rich source of next-generation biomarkers. Toward achieving a holistic view of disease, the integration of these transcripts with clinical records and additional data from omic technologies (“multiomic” strategies) has motivated the adoption of artificial intelligence (AI) approaches. Given their intricate biological complexity, machine learning (ML) techniques are becoming a key component of ncRNA-based research. This article presents an overview of the potential and challenges associated with employing AI/ML-driven approaches to identify clinically relevant ncRNA biomarkers and to decipher ncRNA-associated pathogenetic mechanisms. Methodological and conceptual constraints are discussed, along with an exploration of ethical considerations inherent to AI applications for healthcare and research. The ultimate goal is to provide a comprehensive examination of the multifaceted landscape of this innovative field and its clinical implications.

Metabolomics involves the identification and quantitative analysis of all small metabolites present in cells, tissues, and bodily fluids that are formed as a result of biochemical reactions within the cell. These metabolites form a large pool of substrates and can be modified by serving as a substrate for enzymes involved in other metabolic pathways. Therefore, the metabolome in an organism is so dynamic that there is variation in their quantity and chemical composition over time. Nuclear Magnetic Resonance (NMR), Gas Chromatography Mass Spectrometry (GC-MS), and Liquid Chromatography Mass Spectrometry (LC-MS) are the most commonly used technologies in metabolomics. The metabolites are first isolated based on their polarity, chemical composition, and structural resemblances, after which they undergo specialised processes and are then analysed. Metabolomics, coupled with MS, has advanced rapidly and found widespread use in periodontal research. The presence of distinct metabolic and microbiological profiles in different types of periodontitis, as well as their link to clinical indicators of periodontal inflammation, has demonstrated the usefulness of metabolomics in screening, preventing, and monitoring prognosis. Conventional diagnostics fail to detect periodontitis in its early stages, cannot discriminate between past and present disease activity, and are incapable of analysing the entire repertoire of biomarkers in the biological system. Therefore, metabolomics, in conjunction with other omics technologies, can provide tailored periodontal disease therapy. The present review aimed to explore metabolomics, its applications in periodontics, and the potential for personalised treatment.

Abstract Background There is evidence of pre-established vulnerability in individuals that increases the risk of their progression to severe disease or death, although the mechanisms causing this are still not fully understood. Previous research has demonstrated that a urinary peptide classifier (COV50) predicts disease progression and death from SARS-CoV-2 at an early stage, indicating that the outcome prediction may be partly due to vulnerabilities that are already present. The aim of this study is to examine the ability of COV50 to predict future non-COVID-19-related mortality, and evaluate whether the pre-established vulnerability can be generic and explained on a molecular level by urinary peptides. Methods Urinary proteomic data from 9193 patients (1719 patients sampled at intensive care unit (ICU) admission and 7474 patients with other diseases (non-ICU)) were extracted from the Human Urinary Proteome Database. The previously developed COV50 classifier, a urinary proteomics biomarker panel consisting of 50 peptides, was applied to all datasets. The association of COV50 scoring with mortality was evaluated. Results In the ICU group, an increase in the COV50 score of one unit resulted in a 20% higher relative risk of death [adjusted HR 1.2 (95% CI 1.17–1.24)]. The same increase in COV50 in non-ICU patients resulted in a higher relative risk of 61% [adjusted HR 1.61 (95% CI 1.47–1.76)], consistent with adjusted meta-analytic HR estimate of 1.55 [95% CI 1.39–1.73]. The most notable and significant changes associated with future fatal events were reductions of specific collagen fragments, most of collagen alpha I (I). Conclusion The COV50 classifier is predictive of death in the absence of SARS-CoV-2 infection, suggesting that it detects pre-existing vulnerability. This prediction is mainly based on collagen fragments, possibly reflecting disturbances in the integrity of the extracellular matrix. These data may serve as a basis for proteomics-guided intervention aiming towards manipulating/ improving collagen turnover, thereby reducing the risk of death.

Abstract Background Personalised medicine (PM) has been fostered by technological and medical advances, but all stakeholders, including healthcare professionals, citizens and policy makers, should achieve adequate health literacy to promote PM implementation. The “Integrating China in the International Consortium for Personalised Medicine” (IC2PerMed) project, funded by the International Consortium for Personalised Medicine, focuses on this issue by highlighting the need to educate healthcare professionals and empower citizens. Within the aforementioned project, building on a mapping of European and Chinese policies in PM, experts in the field of PM participated in an online workshop and a following two-round Delphi survey, in order to identify the priority areas of intervention for healthcare professionals’ education and curricula, engagement and empowerment of citizens and patients. Results Nine experts completed the survey and reached a consensus on seventeen priorities: seven were related to health professionals’ education and curricula, whereas ten on citizen and patients’ awareness and empowerment. Conclusion These priorities emphasized the importance of education and health literacy, multidisciplinary and international collaboration, public trust, and consideration of ethical, legal, and social issues. The present experience highlights the relevance of the involvement of stakeholders in informing decision-makers, developing appropriate national plans, strategies, and policies, and ensuring the adequate implementation of PM in health systems.

Abstract Background Personalised medicine is a medical model that aims to provide tailor-made prevention and treatment strategies for defined groups of individuals. The concept brings new challenges to the translational step, both in clinical relevance and validity of models. We have developed a set of recommendations aimed at improving the robustness of preclinical methods in translational research for personalised medicine. Methods These recommendations have been developed following four main steps: (1) a scoping review of the literature with a gap analysis, (2) working sessions with a wide range of experts in the field, (3) a consensus workshop, and (4) preparation of the final set of recommendations. Results Despite the progress in developing innovative and complex preclinical model systems, to date there are fundamental deficits in translational methods that prevent the further development of personalised medicine. The literature review highlighted five main gaps, relating to the relevance of experimental models, quality assessment practices, reporting, regulation, and a gap between preclinical and clinical research. We identified five points of focus for the recommendations, based on the consensus reached during the consultation meetings: (1) clinically relevant translational research, (2) robust model development, (3) transparency and education, (4) revised regulation, and (5) interaction with clinical research and patient engagement. Here, we present a set of 15 recommendations aimed at improving the robustness of preclinical methods in translational research for personalised medicine. Conclusions Appropriate preclinical models should be an integral contributor to interventional clinical trial success rates, and predictive translational models are a fundamental requirement to realise the dream of personalised medicine. The implementation of these guidelines is ambitious, and it is only through the active involvement of all relevant stakeholders in this field that we will be able to make an impact and effectuate a change which will facilitate improved translation of personalised medicine in the future.

Abstract Background Triple-negative breast cancer (TNBC) is a very heterogeneous disease. Several gene expression and mutation profiling approaches were used to classify it, and all converged to the identification of distinct molecular subtypes, with some overlapping across different approaches. However, a standardised tool to routinely classify TNBC in the clinics and guide personalised treatment is lacking. We aimed at defining a specific gene signature for each of the six TNBC subtypes proposed by Lehman et al. in 2011 (basal-like 1 (BL1); basal-like 2 (BL2); mesenchymal (M); immunomodulatory (IM); mesenchymal stem-like (MSL); and luminal androgen receptor (LAR)), to be able to accurately predict them. Methods Lehman’s TNBCtype subtyping tool was applied to RNA-sequencing data from 482 TNBC (GSE164458), and a minimal subtype-specific gene signature was defined by combining two class comparison techniques with seven attribute selection methods. Several machine learning algorithms for subtype prediction were used, and the best classifier was applied on microarray data from 72 Italian TNBC and on the TNBC subset of the BRCA-TCGA data set. Results We identified two signatures with the 120 and 81 top up- and downregulated genes that define the six TNBC subtypes, with prediction accuracy ranging from 88.6 to 89.4%, and even improving after removal of the least important genes. Network analysis was used to identify highly interconnected genes within each subgroup. Two druggable matrix metalloproteinases were found in the BL1 and BL2 subsets, and several druggable targets were complementary to androgen receptor or aromatase in the LAR subset. Several secondary drug–target interactions were found among the upregulated genes in the M, IM and MSL subsets. Conclusions Our study took full advantage of available TNBC data sets to stratify samples and genes into distinct subtypes, according to gene expression profiles. The development of a data mining approach to acquire a large amount of information from several data sets has allowed us to identify a well-determined minimal number of genes that may help in the recognition of TNBC subtypes. These genes, most of which have been previously found to be associated with breast cancer, have the potential to become novel diagnostic markers and/or therapeutic targets for specific TNBC subsets.

Abstract Background Trigeminal neuralgia is an episodic severe neuralgic pain and can be managed both medically and surgically. If possible, this should be directed by a Multidisciplinary Team (MDT) of specialised surgeons, physicians, dentists, psychologists and specialist nurses with access to all treatment modalities, which enables patients to make an informed decision about their future management. Objective The aim of this study was to review the outcomes of patients managed by an MDT clinic, in a single institute over an eleven-year period. Methods A prospective database was used to identify patients with trigeminal neuralgia or its variants who had attended a joint MDT clinic. The electronic notes were examined for demographics, onset and duration of trigeminal neuralgia, medications history, pain scores and details of surgical procedures if any by two independent assessors. Results Three hundred thirty-four patients attended the MDT between 2008–2019. Forty-nine of them had surgery before being referred to the service and were included but analysed as a subgroup. Of the remaining patients, 54% opted to have surgery following the MDT either immediately or at a later date. At the last reported visit 55% of patients who opted to have surgery were pain free and off medications, compared to 15.5% of medically managed patients. Surgical complications were mostly attributable to numbness and in the majority of cases this was temporary. All patients who were not pain free, had complications after surgery or opted to remain on medical therapy were followed up in a facial pain clinic which has access to pain physicians, clinical nurse specialists and a tailored pain management program. Regular patient related outcome measures are collected to evaluate outcomes. Conclusion An MDT clinic offers an opportunity for shared decision making with patients deciding on their personal care pathway which is valued by patients. Not all patients opt for surgery, and some continue to attend a multidisciplinary follow up program. Providing a full range of services including psychological support, improves outcomes.

The article provides an overview of important topics in contemporary medical ethics. Methodologically, it is a literature review. The article addresses only a limited selection of the problematic areas, which are, however, related to each other: digitisation of medicine, genome editing, personalised medicine as well as ethical problems and dilemmas of allocation in healthcare. The global COVID-19 pandemic has emerged as a focus and trigger. Reflections on human rights and justice in medicine are fundamental not only on the individual and social level but also on a global scale. The fundamental question is how society as a whole can be involved in the complex biopolitical and bioethical debate. The social and cultural consequences of life increasingly being understood as a technical product rather than a gift are serious. Contribution: The article also reflects on the specific contribution that Christian theology, and in particular the reformed heritage, can make to bioethical debates in modern society. The distinction between instrumental knowledge [ Verfügungswissen ] and orientational knowledge [ Orientierungswissen ] is helpful for its better understanding. A crucial result of this article is that medical treatment is repeatedly faced with ethical dilemmas. Moreover, medical progress not only creates new and better solutions to medical problems, it also raises new ethical questions that did not exist before. The purpose of medical ethics lies in identifying such dilemmas and developing ethical decision-making processes that help us to deal with such dilemmas to some extent. [ABSTRACT FROM AUTHOR]

Background: Tailoring interventions to patient subgroups can improve intervention outcomes for various conditions. However, it is unclear how much of this improvement is due to the pharmacological personalisation versus the non-specific effects of the contextual factors involved in the tailoring process, such as the therapeutic interaction. Here, we tested whether presenting a (placebo) analgesia machine as personalised would improve its effectiveness. Methods: We recruited 102 adults in two samples (N1=17, N2=85) to receive painful heat stimulations on their forearm. During half of the stimulations, a machine purportedly delivered an electric current to reduce their pain. The participants were either told that the machine was personalised to their genetics and physiology, or that it was effective in reducing pain generally. Results: Participants told that the machine was personalised reported more relief in pain intensity than the control group in both the feasibility study (standardised β=−0.50 [–1.08, 0.08]) and the pre-registered double-blind confirmatory study (β=−0.20 [–0.36, –0.04]). We found similar effects on pain unpleasantness, and several personality traits moderated the results. Conclusions: We present some of the first evidence that framing a sham treatment as personalised increases its effectiveness. Our findings could potentially improve the methodology of precision medicine research and inform practice. Funding: This study was funded by the Social Science and Humanities Research Council (93188) and Genome Québec (95747).

Introduction: One-third of colorectal cancer (CRC) patients present with advanced disease, and establishing control remains a challenge. Identifying novel biomarkers to facilitate earlier diagnosis is imperative in enhancing oncological outcomes. We aimed to create miRNA oncogenic signature to aid CRC diagnosis. Methods: Tumour and tumour-associated normal (TAN) were extracted from 74 patients during surgery for CRC. RNA was isolated and target miRNAs were quantified using real-time reverse transcriptase polymerase chain reaction. Regression analyses were performed in order to identify miRNA targets capable of differentiating CRC from TAN and compared with two endogenous controls (miR-16 and miR-345) in each sample. Areas under the curve (AUCs) in Receiver Operating Characteristic (ROC) analyses were determined. Results: MiR-21 (β-coefficient:3.661, SE:1.720, p = 0.033), miR-31 (β-coefficient:2.783, SE:0.918, p = 0.002), and miR-150 (β-coefficient:−4.404, SE:0.526, p = 0.004) expression profiles differentiated CRC from TAN. In multivariable analyses, increased miR-31 (β-coefficient:2.431, SE:0.715, p < 0.001) and reduced miR-150 (β-coefficient:−4.620, SE:1.319, p < 0.001) independently differentiated CRC from TAN. The highest AUC generated for miR-21, miR-31, and miR-150 in an oncogenic expression assay was 83.0% (95%CI: 61.7–100.0, p < 0.001). In the circulation of 34 independent CRC patients and 5 controls, the mean expression of miR-21 (p = 0.001), miR-31 (p = 0.001), and miR-150 (p < 0.001) differentiated CRC from controls; however, the median expression of miR-21 (p = 0.476), miR-31 (p = 0.933), and miR-150 (p = 0.148) failed to differentiate these groups. Conclusion: This study identified a five-miRNA signature capable of distinguishing CRC from normal tissues with a high diagnostic test accuracy. Further experimentation with this signature is required to elucidate its diagnostic relevance in the circulation of CRC patients.

DNA repair deficiencies in cancers may result in characteristic mutational patterns, as exemplified by deficiency of BRCA1/2 and efficacy prediction for PARP inhibitors. We trained and evaluated predictive models for loss-of-function (LOF) of 145 individual DNA damage response genes based on genome-wide mutational patterns, including structural variants, indels, and base-substitution signatures. We identified 24 genes whose deficiency could be predicted with good accuracy, including expected mutational patterns for BRCA1/2, MSH3/6, TP53, and CDK12 LOF variants. CDK12 is associated with tandem duplications, and we here demonstrate that this association can accurately predict gene deficiency in prostate cancers (area under the receiver operator characteristic curve = 0.97). Our novel associations include mono- or biallelic LOF variants of ATRX, IDH1, HERC2, CDKN2A, PTEN, and SMARCA4, and our systematic approach yielded a catalogue of predictive models, which may provide targets for further research and development of treatment, and potentially help guide therapy.

Abstract This commentary presents the vision of the International Consortium for Personalised Medicine (ICPerMed) on how personalised medicine (PM) will lead to the next generation of healthcare by 2030. This vision focuses on five perspectives: individual and public engagement, involvement of health professionals, implementation within healthcare systems, health-related data, and the development of sustainable economic models that allow improved therapy, diagnostic and preventive approaches as new healthcare concepts for the benefit of the public. We further identify four pillars representing transversal issues that are crucial for the successful implementation of PM in all perspectives. The implementation of PM will result in more efficient and equitable healthcare, access to modern healthcare methods, and improved control by individuals of their own health data, as well as economic development in the health sector.

The alarming increase in global diabetes, especially Type 2 diabetes mellitus (T2DM), is affecting diverse populations and leading to consequent burdens of morbidities, mortalities, and healthcare costs. Physical activity and nutritional approaches form the cornerstones of lifestyle T2DM prevention. Advances in understanding an individual's behaviour and biological responses to different exercise conditions are concurrent with new personalised exercise and nutritional and behavioural tools effective for preventing T2DM and associated chronic diseases. Targeted exercise and nutrition interventions can be personalised across diverse population groups and different settings. Such approaches can benefit from evolving technologies embedding genomics, metabolomics, proteomics, and transcriptomics, together with behavioural reduction strategies such as addressing sedentary behaviour. This narrative review focusses on describing personalised lifestyle prevention approaches, which address different population needs and environmental settings. These methods can be better directed towards translating T2DM interventions and laboratory trials into sustainable, healthy behaviours, and help form personalised lifestyle T2DM prevention guidelines.

Prostate cancer is the second most occurring cancer in men worldwide. To better understand the mechanisms of tumorigenesis and possible treatment responses, we developed a mathematical model of prostate cancer which considers the major signalling pathways known to be deregulated. We personalised this Boolean model to molecular data to reflect the heterogeneity and specific response to perturbations of cancer patients. A total of 488 prostate samples were used to build patient-specific models and compared to available clinical data. Additionally, eight prostate cell line-specific models were built to validate our approach with dose-response data of several drugs. The effects of single and combined drugs were tested in these models under different growth conditions. We identified 15 actionable points of interventions in one cell line-specific model whose inactivation hinders tumorigenesis. To validate these results, we tested nine small molecule inhibitors of five of those putative targets and found a dose-dependent effect on four of them, notably those targeting HSP90 and PI3K. These results highlight the predictive power of our personalised Boolean models and illustrate how they can be used for precision oncology.

In the modern ages of rapid technological and computational advances, development of numerous novel diagnostic imaging techniques have enabled not only visual detection of disease processes, but also provided the tools to achieve quantified functional, structural, dynamic, metabolic, and molecular information. A pathway toward the quantitative imaging by use of quantitative imaging biomarkers analysis has been widely opened, enabling further development of radiomics, radiogenomics, and radioproteinomics analysis in addition, and initiating the dawn of theranostics, eventually bringing the wishful personalised medicine concept closer to reality.

Cancer is the second leading cause of mortality in EU countries, and the needs to tackle cancer are obvious. New scientific understanding, techniques and methodologies are opening up horizons for significant improvements in diagnosis and care. However, take-up is uneven, research needs and potential outstrip currently available resources, manifestly beneficial practices—such as population-level screening for lung cancer—are still not generalised, and the quality of life of patients and survivors is only beginning to be given attention it merits. This paper, mainly based on a series of multistakeholder expert workshops organised by the European Alliance for Personalised Medicine (EAPM), looks at some of those specifics in the interest of planning a way forward. Part of this exercise also involves taking account of the specific nature of Europe and its constituent countries, where the complexities of planning a way forward are redoubled by the wide variations in national and regional approaches to cancer, local epidemiology and the wide disparities in health systems. Despite all the differences between cancers and national and regional resources and approaches to cancer care, there is a common objective in pursuing broader and more equal access to the best available care for all European citizens.

Thyroid cancer (TC) is the most common malignancy of the endocrine system that affects the thyroid gland. It is usually treatable and, in most cases, curable. The central issues are how to improve knowledge on TC, to accurately identify cases at an early stage that can benefit from effective intervention, optimise therapy, and reduce the risk of overdiagnosis and unnecessary treatment. Questions remain about management, about treating all patients in referral centres, and about which treatment should be proposed to any individual patient and how this can be optimised. The European Alliance for Personalised Medicine (EAPM) hosted an expert panel discussion to elucidate some of the challenges, and to identify possible steps towards effective responses at the EU and member state level, particularly in the context of the opportunities in the European Union’s evolving initiatives—notably its Beating Cancer Plan, its Cancer Mission, and its research funding programmes. Recommendations emerging from the panel focus on improved infrastructure and funding, and on promoting multi-stakeholder collaboration between national and European initiatives to complement, support, and mutually reinforce efforts to improve patient care.

As Europe and the world continue to battle against COVID, the customary complacency of society over future threats is clearly on display. Just 30 months ago, such a massive disruption to global lives, livelihoods and quality of life seemed unimaginable. Some remedial European Union action is now emerging, and more is proposed, including in relation to tackling “unmet medical need” (UMN). This initiative—directing attention to the future of treating disease and contemplating incentives to stimulate research and development—is welcome in principle. But the current approach being considered by EU officials merits further discussion, because it may prove counter-productive, impeding rather than promoting innovation. This paper aims to feed into these ongoing policy discussions, and rather than presenting research in the classical sense, it discusses the key elements from a multistakeholder perspective. Its central concern is over the risk that the envisaged support will fail to generate valuable new treatments if the legislation is phrased in a rigidly linear manner that does not reflect the serpentine realities of the innovation process, or if the definition placed on unmet medical need is too restrictive. It cautions that such an approach presumes that “unmet need” can be precisely and comprehensively defined in advance on the basis of the past. It cautions that such an approach can reinforce the comfortable delusion that the future is totally predictable—the delusion that left the world as easy prey to COVID. Instead, the paper urges reflection on how the legislation that will shortly enter the pipeline can be phrased so as to allow for the flourishing of a culture capable of rapid adaptation to the unexpected.

Introduction: Oncologists have traditionally administered the maximum tolerated doses of drugs in chemotherapy. However, these toxicity-guided doses may lead to suboptimal efficacy. CURATE.AI is an indication-agnostic, mechanism-independent and efficacy-driven personalised dosing platform that may offer a more optimal solution. While CURATE.AI has already been applied in a variety of clinical settings, there are no prior randomised controlled trials (RCTs) on CURATE.AI-guided chemotherapy dosing for solid tumours. Therefore, we aim to assess the technical and logistical feasibility of a future RCT for CURATE.AI-guided solid tumour chemotherapy dosing. We will also collect exploratory data on efficacy and toxicity, which will inform RCT power calculations.Methods and analysis: This is an open-label, single-arm, two-centre, prospective pilot clinical trial, recruiting adults with metastatic solid tumours and raised baseline tumour marker levels who are planned for palliative-intent, capecitabine-based chemotherapy. As CURATE.AI is a small data platform, it will guide drug dosing for each participant based only on their own tumour marker levels and drug doses as input data. The primary outcome is the proportion of participants in whom CURATE.AI is successfully applied to provide efficacy-driven personalised dosing, as judged based on predefined considerations. Secondary outcomes include the timeliness of dose recommendations, participant and physician adherence to CURATE.AI-recommended doses, and the proportion of clinically significant dose changes. We aim to initially enrol 10 participants from two hospitals in Singapore, perform an interim analysis, and consider either cohort expansion or an RCT. Recruitment began in August 2020. This pilot clinical trial will provide key data for a future RCT of CURATE.AI-guided personalised dosing for precision oncology.Ethics and dissemination: The National Healthcare Group (NHG) Domain Specific Review Board has granted ethical approval for this study (DSRB 2020/00334). We will distribute our findings at scientific conferences and publish them in peer-reviewed journals.Trial registration number: NCT04522284

Multiple myeloma is increasingly being recognised as more than one disease, characterised by marked cytogenetic, molecular, and proliferative heterogeneity. The prognosis is widely varied, ranging from low to very high-risk, based on cytogenetic and molecular studies. Although novel agents, such as proteasome inhibitors and immunomodulators, have been developed, which have improved treatment responses and disease prognosis, multiple myeloma remains an incurable disease. Based on highly sensitive detection tools, such as gene expression profiling and next generation sequence analysis, and the understanding of the pathogenesis of multiple myeloma, many potential agents, including monoclonal antibodies, drug-conjugated antibodies, drugs targeted to molecular abnormalities, microRNA inhibitors or mimics, and immune therapies, such as chimeric antigen receptors T cells and anti-PD1 agents, can be considered personalised therapies. In this paper, multiple myeloma pathogenesis and potential molecular and immunotherapies are reviewed.

Abstract Background Establishing the cancer type and site of origin is important in determining the most appropriate course of treatment for cancer patients. Patients with cancer of unknown primary, where the site of origin cannot be established from an examination of the metastatic cancer cells, typically have poor survival. Here, we evaluate the potential and limitations of utilising gene alteration data from tumour DNA to identify cancer types. Methods Using sequenced tumour DNA downloaded via the cBioPortal for Cancer Genomics, we collected the presence or absence of calls for gene alterations for 6640 tumour samples spanning 28 cancer types, as predictive features. We employed three machine-learning techniques, namely linear support vector machines with recursive feature selection, L 1-regularised logistic regression and random forest, to select a small subset of gene alterations that are most informative for cancer-type prediction. We then evaluated the predictive performance of the models in a comparative manner. Results We found the linear support vector machine to be the most predictive model of cancer type from gene alterations. Using only 100 somatic point-mutated genes for prediction, we achieved an overall accuracy of 49.4±0.4 % (95 % confidence interval). We observed a marked increase in the accuracy when copy number alterations are included as predictors. With a combination of somatic point mutations and copy number alterations, a mere 50 genes are enough to yield an overall accuracy of 77.7±0.3 %. Conclusions A general cancer diagnostic tool that utilises either only somatic point mutations or only copy number alterations is not sufficient for distinguishing a broad range of cancer types. The combination of both gene alteration types can dramatically improve the performance.

Abstract There is increasing international policy and clinical interest in developing learning health systems and delivering precision medicine, which it is hoped will help reduce variation in the quality and safety of care, improve efficiency, and lead to increasing the personalisation of healthcare. Although reliant on similar policies, informatics tools, and data science and implementation research capabilities, these two major initiatives have thus far largely progressed in parallel. In this opinion piece, we argue that they should be considered as complementary, synergistic initiatives whereby the creation of learning health systems infrastructure can support and catalyse the delivery of precision medicine that maximises the benefits and minimises the risks associated with treatments for individual patients. We illustrate this synergy by considering the example of treatments for asthma, which is now recognised as an umbrella term for a heterogeneous group of related conditions.

Not only in the technological world (“Industry 4.0”), but also in medicine, a paradigmatic change is taking place: We are already on the threshold of “Medicine 4.0”. Molecular biology has long played a leading role in life sciences. Scientists now realise that, with increasing miniaturisation, microelectronic systems downsized to the dimensions of cellular systems will facilitate new therapeutic approaches. But conventional telecommunications systems can also be equipped with sensors and transformed into intelligent medical monitoring devices that can help patients become part of the diagnostic and therapeutic process. This article illustrates development trends that will lead to modern, electronically supported healthcare concepts.

Abstract Background Personalised treatment that is uniquely tailored to an individual’s phenotype has become a key goal of clinical and pharmaceutical development across many, particularly chronic, diseases. For type 2 diabetes, the importance of the underlying clinical heterogeneity of the condition is emphasised and a range of treatments are now available, with personalised approaches being developed. While a close connection between risk factors for type 2 diabetes and gestational diabetes has long been acknowledged, stratification of screening, treatment and obstetric intervention remains in its infancy. Conclusions Although there have been major advances in our understanding of glucose tolerance in pregnancy and of the benefits of treatment of gestational diabetes, we argue that far more vigorous approaches are needed to enable development of companion diagnostics, and to ensure the efficacious and safe use of novel therapeutic agents and strategies to improve outcomes in this common condition.

Abstract Over the last decade, there have been major advances in the understanding of the genetic basis of hyperuricaemia and gout as well as of the pharmacogenetics of urate-lowering therapy. Key findings include the reporting of 28 urate-associated loci, the discovery that ABCG2 plays a central role on extra-renal uric acid excretion, the identification of genes associated with development of gout in the context of hyperuricaemia, recognition that ABCG2 variants influence allopurinol response, and the impact of HLA-B*5801 testing in reducing the prevalence of allopurinol hypersensitivity in high-risk populations. These advances, together with the reducing cost of whole genome sequencing, mean that integrated personalised medicine approaches may soon be possible in clinical practice. Genetic data may inform assessment of disease prognosis in individuals with hyperuricaemia or established gout, personalised lifestyle advice, selection and dosing of urate-lowering therapy, and prevention of serious medication adverse effects. In this article, we summarise the discoveries from genome-wide association studies and discuss the potential for translation of these findings into clinical practice.

Abstract Background Precision medicine is a new and important topic in psychiatry. Psychiatry has not yet benefited from the advanced diagnostic and therapeutic technologies that form an integral part of other clinical specialties. Thus, the vision of precision medicine as applied to psychiatry – ‘precision psychiatry’ – promises to be even more transformative than in other fields of medicine, which have already lessened the translational gap. Discussion Herein, we describe ‘precision psychiatry’ and how its several implications promise to transform the psychiatric landscape. We pay particular attention to biomarkers and to how the development of new technologies now makes their discovery possible and timely. The adoption of the term ‘precision psychiatry’ will help propel the field, since the current term ‘precision medicine’, as applied to psychiatry, is impractical and does not appropriately distinguish the field. Naming the field ‘precision psychiatry’ will help establish a stronger, unique identity to what promises to be the most important area in psychiatry in years to come. Conclusion In summary, we provide a wide-angle lens overview of what this new field is, suggest how to propel the field forward, and provide a vision of the near future, with ‘precision psychiatry’ representing a paradigm shift that promises to change the landscape of how psychiatry is currently conceived.

Every individual has a different genetic make-up; even the genetic alterations are different in the same type of cancer. This demands specific and personalised treatment for the individual. Thus, studying the patient’s genetic and clinical profile is the foundation for precision medicine. With the developing technologies and newer molecular tools, it has become easy to select a target-specific treatment based on the genetic makeup of an individual. More individualised approach through precision medicines will not only help in treating cancer but shall negate the scope of early detection with the advanced screening techniques and might also aid in preventing them. In this review, author discuss different aspects of precision medicine in cancer therapy.

Highlights • Treatment outcomes in MDR-TB are poor and cause long term functional disability. • Adjunct host-directed therapies (HDTs) can improve MDR-TB treatment outcomes. • Cancer immunotherapy provides a template for personalised cellular HDTs for MDR-TB. • Resource-limited, TB-endemic settings call for HDT clinical trials infrastructure. Abstract Multidrug-resistant TB (MDR-TB) is a major threat to global health security. In 2017, only 50% of patients with MDR-TB who received WHO-recommended treatment were cured. Most MDR-TB patients who recover continue to suffer from functional disability due to long-term lung damage. Whilst new MDR-TB treatment regimens are becoming available, conventional drug therapies need to be complemented with host-directed therapies (HDTs) to reduce tissue damage and improve functional treatment outcomes. This viewpoint highlights recent data on biomarkers, immune cells, circulating effector molecules and genetics which could be utilised for developing personalised HDTs. Novel technologies currently used for cancer therapy which could facilitate in-depth understanding of host genetics and the microbiome in patients with MDR-TB are discussed. Against this background, personalised cell-based HDTs for adjunct MDR-TB treatment to improve clinical outcomes are proposed as a possibility for complementing standard therapy and other HDT agents. Insights into the molecular biology of the mechanisms of action of cellular HDTs may also aid to devise non-cell-based therapies targeting defined inflammatory pathway(s) in Mtb -driven immunopathology. [ABSTRACT FROM AUTHOR]

Patient-derived xenograft (PDX) models are increasingly being used in oncology drug development because they offer greater predictive value than traditional cell line models. Using novel tools to critique model validity and reliability we performed a systematic review to identify all original publications describing the derivation of PDX models of colon, prostate, breast and lung cancer. Validity was defined as the ability to recapitulate the disease of interest. The study protocol was registered with the Collaborative Approach to Meta-Analysis and Review of Animal Data from Experimental Studies (CAMARADES). Searches were performed in Embase, MEDLINE and Pubmed up to July 2017. A narrative data synthesis was performed. We identified 105 studies of model validations; 29 for breast, 29 for colon, 25 for lung, 23 for prostate and 4 for multiple tissues. 133 studies were excluded because they did not perform any validation experiments despite deriving a PDX. Only one study reported following the ARRIVE guidelines; developed to improve the standard of reporting for animal experimentation. Remarkably, half of all breast (52%) and prostate (50%) studies were judged to have high concern, in contrast to 16% of colon and 28% of lung studies. The validation criteria that most commonly failed (evidence to the contrary) were: tissue of origin not proven and histology of the xenograft not comparable to the parental tumour. Overall, most studies were categorized as unclear because one or more validation conditions were not reported, or researchers failed to provide data for a proportion of their models. For example, failure to demonstrate tissue of origin, response to standard of care agents and to exclude development of lymphoma. Validation tools have the potential to improve reproducibility, reduce waste in research and increase the success of translational studies.

Background: Understanding the heterogeneous genotypes and phenotypes of prostate cancer is fundamental to improving the way we treat this disease. As yet, there are no validated descriptions of prostate cancer subgroups derived from integrated genomics linked with clinical outcome. Methods: In a study of 482 tumour, benign and germline samples from 259 men with primary prostate cancer, we used integrative analysis of copy number alterations (CNA) and array transcriptomics to identify genomic loci that affect expression levels of mRNA in an expression quantitative trait loci (eQTL) approach, to stratify patients into subgroups that we then associated with future clinical behaviour, and compared with either CNA or transcriptomics alone. Findings: We identified five separate patient subgroups with distinct genomic alterations and expression profiles based on 100 discriminating genes in our separate discovery and validation sets of 125 and 103 men. These subgroups were able to consistently predict biochemical relapse (p = 0.0017 and p = 0.016 respectively) and were further validated in a third cohort with long-term follow-up (p = 0.027). We show the relative contributions of gene expression and copy number data on phenotype, and demonstrate the improved power gained from integrative analyses. We confirm alterations in six genes previously associated with prostate cancer (MAP3K7, MELK, RCBTB2, ELAC2, TPD52, ZBTB4), and also identify 94 genes not previously linked to prostate cancer progression that would not have been detected using either transcript or copy number data alone. We confirm a number of previously published molecular changes associated with high risk disease, including MYC amplification, and NKX3-1, RB1 and PTEN deletions, as well as over-expression of PCA3 and AMACR, and loss of MSMB in tumour tissue. A subset of the 100 genes outperforms established clinical predictors of poor prognosis (PSA, Gleason score), as well as previously published gene signatures (p = 0.0001). We further show how our molecular profiles can be used for the early detection of aggressive cases in a clinical setting, and inform treatment decisions. Interpretation: For the first time in prostate cancer this study demonstrates the importance of integrated genomic analyses incorporating both benign and tumour tissue data in identifying molecular alterations leading to the generation of robust gene sets that are predictive of clinical outcome in independent patient cohorts.

Systems medicine is a promising new paradigm for discovering associations, causal relationships and mechanisms in medicine. But it faces some tough challenges that arise from the use of big data: in particular, the problem of how to integrate evidence and the problem of how to structure the development of models. I argue that objective Bayesian models offer one way of tackling the evidence integration problem. I also offer a general methodology for structuring the development of models, within which the objective Bayesian approach fits rather naturally. [ABSTRACT FROM AUTHOR]

Rather than seek to distinguish hype from legitimate promise, it may be more helpful to think about personalised medicine as embodying a promissory economy which serves both to mobilize resources for research and — partly at least — to determine the ends to which that research is directed. Personalised medicine is a development of the larger promissory economy of medical biotechnology. As such, it systematically conflates public benefit with the pursuit of commercial and especially pharmaceutical interests. Consequently, research and development in personalised medicine tends to favour the production of expensive new treatments over unprofitable forms of prevention or more effective use of older therapies. A rebalancing of research priorities is needed to favour the pursuit of public benefit, even when it does not deliver private profits. This will in turn require sustained reflection, self-criticism and often self-denial on the part of public research funders and the scientists they support. [ABSTRACT FROM AUTHOR]

In engaging critically with personalised medicine and mapping pitfalls which mark its progress this project aims to stimulate conversations which deal intelligently with controversies for the sake of consensus. We aim to ask the ethical questions which will lead to the improvement of healthcare and we take an open-minded approach to finding answers to them over time. What is or should be meant by ‘personalised medicine’ is a major theme of this issue. It is a debate bound up with question of both values in the sense of ethical reflection and value in the sense of economic return. This editorial discusses and interrelates the articles of the issue under four headings: the promise and the hype of personalised medicine; the human person and the communication of risk; data sharing and participation; value, equity and power. A key intention throughout is to provoke discourse and debate, to identify aspirations which are more grounded in myth or hype than reality and to challenge them; and to identify focussed, practical questions which need further examination. [ABSTRACT FROM AUTHOR]

Ideas about ownership are sometimes used in discussions of data sharing in personalised medicine. Personal health data are thought by many to be ‘theirs’. Paradoxically, personalised medicine (at least in the context of genomics) relies on the aggregation of private data into a dataset that is held as a form of knowledge commons. When the notions of private and common property that lie behind this discourse are made explicit we can use thinking about the justifications and jurisprudence of property both to clarify the persuasiveness and limits of such claims, and also how they differ from other principles that are at stake in the interplay between individual and collective goods in the delivery of personalised medicine. This shows that ownership might more plausibly lie with health professionals than patients. In a socialised medicine system, such as the NHS, such professionals are agents of the state and ownership would lie with the commons rather than any individual. Common rather than private ownership of genomic information may be more appropriate. [ABSTRACT FROM AUTHOR]

Precision medicine carries huge potential in the treatment of many diseases, particularly those with high-penetrance monogenic underpinnings. However, precision medicine through genomic technologies also has ethical implications. We will define allocative, personal, and technical value (‘triple value’) in healthcare and how this relates to equity. Equity is here taken to be implicit in the concept of triple value in countries that have publicly funded healthcare systems. It will be argued that precision medicine risks concentrating resources to those that already experience greater access to healthcare and power in society, nationally as well as globally. Healthcare payers, clinicians, and patients must all be involved in optimising the potential of precision medicine, without reducing equity. Throughout, the discussion will refer to the NHS RightCare Programme, which is a national initiative aiming to improve value and equity in the context of NHS England. [ABSTRACT FROM AUTHOR]

Technological advances enabling us to personalise medical interventions at the biological level must be matched by parallel advances in how we support the informed choices essential to patient and public participation. We cannot take participation for granted. To be truly personalised, medicine must take account of the perceptions and capabilities that shape participation. To do this, we need a better understanding of how people perceive personalised medicine and how they judge its value and risks. To realise the promise of 4P medicine we need to personalise at the psychosocial as well as biological dimension, putting the person into personalised medicine. [ABSTRACT FROM AUTHOR]

One of the promised benefits of ‘personalised’ medicine is that it will have a positive impact on disease screening and prevention, by enabling more person-specific estimates of risk, and hence more personalised strategies for screening and risk reduction. This article summarises some of the areas in which these approaches are already being implemented, but also outlines some of the potential difficulties and limitations — and challenges the assumption that ‘personalised’ approaches are necessarily superior for prevention and screening. [ABSTRACT FROM AUTHOR]