Enhancing breast cancer treatment through pharmacogenomics: A narrative review.

Pharmacogenomics has become integral to personalised medicine in breast cancer, utilising genetic insights to customize treatment strategies and enhance patient outcomes. Understanding how genetic variations influence drug metabolism, response, and toxicity is crucial for guiding treatment selection and dosing regimens. Genetic polymorphisms in drug-metabolizing enzymes and transporters significantly impact pharmacokinetic variability, influencing the efficacy and safety of chemotherapy agents and targeted therapies. Biomarkers associated with the hormone receptor status of breast cancer and mutations serve as key determinants of treatment response, aiding in the selection of therapies. Despite substantial progress in understanding the pharmacogenomic landscape of breast cancer, efforts to identify novel genetic markers and refine treatment optimisation strategies are required. Genome-wide association studies and advanced sequencing technologies hold promise for uncovering genetic determinants of drug response variability and elucidating complex pharmacogenomic interactions. The future of pharmacogenomics in breast cancer lies in real-time treatment monitoring, the discovery of additional predictive markers, and the seamless integration of pharmacogenomic data into clinical decision-making processes. However, translating pharmacogenomic discoveries into routine clinical practice requires collaborative efforts among stakeholders to address implementation challenges and ensure equitable access to genetic testing. By embracing pharmacogenomics, clinicians can tailor treatment approaches to individual patients, maximizing therapeutic benefits while minimizing adverse effects. This review discusses the integration of pharmacogenomics in breast cancer treatment, highlighting the significance of understanding genetic influences on treatment response and toxicity, and the potential of advanced technologies in refining treatment strategies.

Unveiling G-protein coupled receptors as potential targets for ovarian cancer nanomedicines: from RNA sequencing data analysis to in vitro validation.

Genetic heterogeneity in ovarian cancer indicates the need for personalised treatment approaches. Currently, very few G-protein coupled receptors (GPCRs) have been investigated for active targeting with nanomedicines such as antibody-conjugated drugs and drug-loaded nanoparticles, highlighting a neglected potential to develop personalised treatment. To address the genetic heterogeneity of ovarian cancer, a future personalised approach could include the identification of unique GPCRs expressed in cancer biopsies, matched with personalised GPCR-targeted nanomedicines, for the delivery of lethal drugs to tumour tissue before, during and after surgery. Here we report on the systematic analysis of public ribonucleic acid-sequencing (RNA-seq) gene expression data, which led to prioritisation of 13 GPCRs as candidates with frequent overexpression in ovarian cancer tissues. Subsequently, primary ovarian cancer cells derived from ascites and ovarian cancer cell lines were used to confirm frequent gene expression for the selected GPCRs. However, the expression levels showed high variability within our selection of samples, therefore, supporting and emphasising the need for the future development of case-to-case personalised targeting approaches.

Potential New Inflammatory Markers in Bronchiectasis: A Literature Review.

Specific molecular and inflammatory endotypes have been identified for chronic respiratory disorders, including asthma and COPD (chronic obstructive pulmonary disease). These endotypes correspond with clinical aspects of disease, enabling targeted medicines to address certain pathophysiologic pathways, often referred to as "precision medicine". With respect to bronchiectasis, many comorbidities and underlying causes have been identified. Inflammatory endotypes have also been widely studied and reported. Additionally, several genes have been shown to affect disease progression. However, the lack of a clear classification has also hampered our understanding of the disease's natural course. The aim of this review is, thus, to summarize the current knowledge on biomarkers and actionable targets of this complex pathologic condition and to point out unmet needs, which are required in the design of effective diagnostic and therapeutic trials.

How does sutures pattern influence stomach motility after endoscopic sleeve gastroplasty? A computational study.

The relatively recent adoption of Endoscopic Sleeve Gastroplasty (ESG) amongst obese patients has gained approval within the surgical community due to its notable benefits, including significant weight loss, safety, feasibility, repeatability, and potential reversibility. However, despite its promising clinical outcomes and reduced invasiveness, there is still a lack of standardised procedures for performing ESG. Multiple suture patterns and stitching methods have been proposed over time, yet rational tools to quantify and compare their effects on gastric tissues are absent. To address this gap, this study proposed a computational approach. The research involved a case study analyzing three distinct suture patterns (C-shaped, U-shaped and Z-shaped) using a patient-specific computational stomach model generated from magnetic resonance imaging. Simulations mimicked food intake by placing wire features in the intragastric cavity to replicate sutures, followed by applying a linearly increasing internal pressure up to 15 mmHg. The outcomes facilitated comparisons between suture configurations based on pressure-volume behaviours and the distribution of maximum stress on biological tissues, revealing the U-shaped as the more effective in terms of volume reduction, even if with reduced elongation strains and increased tissues stresses, whereas the Z-shaped is responsible of the greatest stomach shortness after ESG. In summary, computational biomechanics methods serve as potent tools in clinical and surgical settings, offering insights into aspects that are challenging to explore in vivo, such as tissue elongation and stress. These methods allow for mechanical comparisons between different configurations, although they might not encompass crucial clinical outcomes.

The Transformative Power of Virtual Hospitals for Revolutionising Healthcare Delivery.

Objectives: The objective of this narrative review is to explore the advantages and limitations of VHs in delivering healthcare, including access to specialized professionals, streamlined communication, efficient scheduling, integration of electronic health records, ongoing monitoring, and support, transcending geographical boundaries, and resource optimization. Methods: Review of literature. Results: The national healthcare systems are facing an alarming rise in pressure due to global shifts. Virtual hospitals (VH) offer a practical solution to numerous systemic challenges, including rising costs and increased workloads for healthcare providers. VH also facilitate the delivery of personalized services and enable the monitoring of patients beyond the conventional confines of healthcare settings, reducing the reliance on waiting medicine carried out in doctors' offices or hospitals. Conclusion: VH can mirror the conventional healthcare referral system.

The development of a set of key points to aid clinicians and researchers in designing and conducting n-of-1 trials.

INTRODUCTION: n-of-1 trials are undertaken to optimise the evaluation of health technologies in individual patients. They involve a single patient receiving treatments, both interventional and control, consecutively over set periods of time, the order of which is decided at random. Although n-of-1 trials are undertaken in medical research it could be argued they have the utility to be undertaken more frequently. We undertook the National Institute for Health Research (NIHR) commissioned DIAMOND (Development of generalisable methodology for n-of-1 trials delivery for very low volume treatments) project to develop key points to assist clinicians and researchers in designing and conducting n-of-1 trials. METHODS: The key points were developed by undertaking a stakeholder workshop, followed by a discussion within the study team and then a stakeholder dissemination and feedback event. The stakeholder workshop sought to gain the perspectives of a variety of stakeholders (including clinicians, researchers and patient representatives) on the design and use of n-of-1 trials. A discussion between the study team was held to reflect on the workshop and draft the key points. Lastly, the stakeholders from the workshop were invited to a dissemination and feedback session where the proposed key points were presented and their feedback gained. RESULTS: A set of 22 key points were developed based on the insights from the workshop and subsequent discussions. They provide guidance on when an n-of-1 trial might be a viable or appropriate study design and discuss key decisions involved in the design of n-of-1 trials, including determining an appropriate number of treatment periods and cycles, the choice of comparator, recommended approaches to randomisation and blinding, the use of washout periods and approaches to analysis. CONCLUSIONS: The key points developed in the project will support clinical researchers to understand key considerations when designing n-of-1 trials. It is hoped they will support the wider implementation of the study design.

Machine learning for catalysing the integration of noncoding RNA in research and clinical practice.

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.

GEN-RWD Sandbox: bridging the gap between hospital data privacy and external research insights with distributed analytics.

BACKGROUND: Artificial intelligence (AI) has become a pivotal tool in advancing contemporary personalised medicine, with the goal of tailoring treatments to individual patient conditions. This has heightened the demand for access to diverse data from clinical practice and daily life for research, posing challenges due to the sensitive nature of medical information, including genetics and health conditions. Regulations like the Health Insurance Portability and Accountability Act (HIPAA) in the U.S. and the General Data Protection Regulation (GDPR) in Europe aim to strike a balance between data security, privacy, and the imperative for access. RESULTS: We present the Gemelli Generator - Real World Data (GEN-RWD) Sandbox, a modular multi-agent platform designed for distributed analytics in healthcare. Its primary objective is to empower external researchers to leverage hospital data while upholding privacy and ownership, obviating the need for direct data sharing. Docker compatibility adds an extra layer of flexibility, and scalability is assured through modular design, facilitating combinations of Proxy and Processor modules with various graphical interfaces. Security and reliability are reinforced through components like Identity and Access Management (IAM) agent, and a Blockchain-based notarisation module. Certification processes verify the identities of information senders and receivers. CONCLUSIONS: The GEN-RWD Sandbox architecture achieves a good level of usability while ensuring a blend of flexibility, scalability, and security. Featuring a user-friendly graphical interface catering to diverse technical expertise, its external accessibility enables personnel outside the hospital to use the platform. Overall, the GEN-RWD Sandbox emerges as a comprehensive solution for healthcare distributed analytics, maintaining a delicate equilibrium between accessibility, scalability, and security.

Artificial intelligence for cardiovascular disease risk assessment in personalised framework: a scoping review.

Background: The field of precision medicine endeavors to transform the healthcare industry by advancing individualised strategies for diagnosis, treatment modalities, and predictive assessments. This is achieved by utilizing extensive multidimensional biological datasets encompassing diverse components, such as an individual's genetic makeup, functional attributes, and environmental influences. Artificial intelligence (AI) systems, namely machine learning (ML) and deep learning (DL), have exhibited remarkable efficacy in predicting the potential occurrence of specific cancers and cardiovascular diseases (CVD). Methods: We conducted a comprehensive scoping review guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework. Our search strategy involved combining key terms related to CVD and AI using the Boolean operator AND. In August 2023, we conducted an extensive search across reputable scholarly databases including Google Scholar, PubMed, IEEE Xplore, ScienceDirect, Web of Science, and arXiv to gather relevant academic literature on personalised medicine for CVD. Subsequently, in January 2024, we extended our search to include internet search engines such as Google and various CVD websites. These searches were further updated in March 2024. Additionally, we reviewed the reference lists of the final selected research articles to identify any additional relevant literature. Findings: A total of 2307 records were identified during the process of conducting the study, consisting of 564 entries from external sites like arXiv and 1743 records found through database searching. After 430 duplicate articles were eliminated, 1877 items that remained were screened for relevancy. In this stage, 1241 articles remained for additional review after 158 irrelevant articles and 478 articles with insufficient data were removed. 355 articles were eliminated for being inaccessible, 726 for being written in a language other than English, and 281 for not having undergone peer review. Consequently, 121 studies were deemed suitable for inclusion in the qualitative synthesis. At the intersection of CVD, AI, and precision medicine, we found important scientific findings in our scoping review. Intricate pattern extraction from large, complicated genetic datasets is a skill that AI algorithms excel at, allowing for accurate disease diagnosis and CVD risk prediction. Furthermore, these investigations have uncovered unique genetic biomarkers linked to CVD, providing insight into the workings of the disease and possible treatment avenues. The construction of more precise predictive models and personalised treatment plans based on the genetic profiles of individual patients has been made possible by the revolutionary advancement of CVD risk assessment through the integration of AI and genomics. Interpretation: The systematic methodology employed ensured the thorough examination of available literature and the inclusion of relevant studies, contributing to the robustness and reliability of the study's findings. Our analysis stresses a crucial point in terms of the adaptability and versatility of AI solutions. AI algorithms designed in non-CVD domains such as in oncology, often include ideas and tactics that might be modified to address cardiovascular problems. Funding: No funding received.

Drug repurposing for personalized medicine.

Personalized medicine has emerged as a revolutionary approach to healthcare in the 21st century. By understanding a patient's unique genetic and biological characteristics, it aims to tailor treatments specifically to the individual. This approach takes into account factors such as an individual's lifestyle, genetic makeup, and environmental factors to provide targeted therapies that have the potential to be more effective and lower the risk of side reactions or ineffective treatments. It is a paradigm shift from the traditional "one size fits all" approach in medicine, where patients with similar symptoms or diagnoses receive the same standard treatments regardless of their differences. It leads to improved clinical outcomes and more efficient use of healthcare resources. Drug repurposing is a strategy that uses existing drugs for new indications and aims to take advantage of the known safety profiles, pharmacokinetics, and mechanisms of action of these drugs to accelerate the development process. Precision medicine may undergo a revolutionary change as a result, enabling the rapid development of novel treatment plans utilizing drugs that traditional methods would not otherwise link to. In this chapter, we have focused on a few strategies wherein drug repurposing has shown great success for precision medicine. The approach is particularly useful in oncology as there are many variations induced in the genetic material of cancer patients, so tailored treatment approaches go a long way. We have discussed the cases of breast cancer, glioblastoma and hepatocellular carcinoma. Other than that, we have also looked at drug repurposing approaches in anxiety disorders and COVID-19.

Rhodiola rosea: a review in the context of PPPM approach.

A natural "medicine and food" plant, Rhodiola rosea (RR) is primarily made up of organic acids, phenolic compounds, sterols, glycosides, vitamins, lipids, proteins, amino acids, trace elements, and other physiologically active substances. In vitro, non-clinical and clinical studies confirmed that it exerts anti-inflammatory, antioxidant, and immune regulatory effects, balances the gut microbiota, and alleviates vascular circulatory disorders. RR can prolong life and has great application potential in preventing and treating suboptimal health, non-communicable diseases, and COVID-19. This narrative review discusses the effects of RR in preventing organ damage (such as the liver, lung, heart, brain, kidneys, intestines, and blood vessels) in non-communicable diseases from the perspective of predictive, preventive, and personalised medicine (PPPM/3PM). In conclusion, as an adaptogen, RR can provide personalised health strategies to improve the quality of life and overall health status.

How do we reach the goal of personalized medicine for neuroprotection in neonatal hypoxic-ischemic encephalopathy?

Therapeutic hypothermia is now standard of care for neonates with hypoxic-ischemic encephalopathy (HIE) in high income countries (HIC). Conversely, compelling trial evidence suggests that hypothermia is ineffective, and may be deleterious, in low- and middle-income countries (LMIC), likely reflecting the lower proportion of infants who had sentinel events at birth, suggesting that injury had advanced to a stage when hypothermia is no longer effective. Although hypothermia significantly reduced the risk of death and disability in HICs, many infants survived with disability and in principle may benefit from targeted add-on neuroprotective or neurorestorative therapies. The present review will assess biomarkers that could be used to personalize treatment for babies with HIE - to determine first whether an individual infant is likely to respond to hypothermia, and second, whether additional treatments may be beneficial.

The European psychiatric association (EPA) - early career psychiatrists committee survey on trainees' and early-career psychiatrists' attitudes towards therapeutic drug monitoring (TDM) use and utility during antipsychotic treatment.

OBJECTIVES: This survey assessed psychiatry residents'/early-career psychiatrists' attitudes towards the utility of therapeutic drug monitoring (TDM) of antipsychotics. METHODS: A previously developed questionnaire on attitudes on TDM utility during antipsychotic treatment was cross-sectionally disseminated by national coordinators between 01/01/2022-31/12/2023. The frequency of using TDM for antipsychotics other than clozapine was the main outcome in a linear regression analysis, including sex, clinical setting, caseload, and factors generated by an exploratory factor analysis. Comparisons between residents and early-career psychiatrists, respondents working in in- and outpatient settings, and low-/middle- and high-income countries were performed. RESULTS: Altogether, 1,237 respondents completed the survey, with 37.9% having never used TDM for antipsychotics. Seven factors explained 41% of response variance; six of them were associated with frequency of TDM use (p < 0.05). Items with highest loadings for factors included clinical benefits of TDM (factors A and E: 0.7), negative expectations for beliefs of patients towards TDM (factor B: 0.6-0.7), weak TDM scientific evidence (factor C: 0.8), and TDM availability (factor D: -0.8). Respondents from low-/middle-income countries were less likely to frequently/almost always use TDM compared to high-income countries (9.4% vs. 21.5%, p < 0.001). DISCUSSION: TDM use for antipsychotics was poor and associated with limited knowledge and insufficient availability.

Patient perspectives on personalised medicine for obesity: An IMI2 SOPHIA Study.

OBJECTIVE: Personalised medicine is seen as an exciting opportunity to improve the health outcomes of people with obesity. As research on phenotyping and personalised treatment for obesity rapidly advances, this study sought to understand patient preferences and perspectives on personalised medicine for obesity. METHODS: A participatory world café methodology was used to garner the perspectives of people living with obesity on the potential opportunities and limitations associated with a personalised approach to obesity risk identification and treatment. Data were recorded by participants on tablemats and analysed thematically using thematic analysis. RESULTS: Patients expressed the hope that personalised medicine for obesity would reduce stigma, support understanding of obesity as a disease, and improve treatment outcomes and acceptance. They also expressed concern about the accuracy of personalised medicine for obesity, its implications for insurance and that further advances in individual, personalised medicine, would detract attention from social, environmental, economic and psychological drivers of obesity. CONCLUSIONS: This study highlights how patients are generally very optimistic about the potential for personalised obesity medicine but also raise a number of legitimate concerns that will be of interest to clinicians, industry, and policy makers.

Can we effectively manage chronic kidney disease with a precision-based pharmacotherapy plan? Where are we?

INTRODUCTION: In recent years, thanks to significant advances in basic science and biotechnologies, nephrology has witnessed a deeper understanding of the mechanisms leading to various conditions associated with or causing kidney disease, opening new perspectives for developing specific treatments. These new possibilities have brought increased challenges to physicians, who face with a new complexity in disease characterization and selection the right treatment for individual patients. AREAS COVERED: We chose four therapeutic situations: anaemia in chronic kidney disease (CKD), heart failure in CKD, IgA nephropathy (IgAN) and membranous nephropathy (MN). The literature search was made through PubMed. EXPERT OPINION: Anaemia management remains challenging in CKD; a personalized therapeutic approach is often needed. Identifying patients who could benefit from a specific therapy is also an important goal for patients with CKD and heart failure with reduced ejection fraction. Several new treatments are under clinical development for IgAN; interestingly, they target specifically the pathogenetic mechanisms of the disease. The understanding of MN pathogenesis as an autoimmune disease and the discovery of several autoantibodies allows a better characterization of patients. High-sensible techniques for lymphocyte counting open the possibility of more personalized use of anti CD20 therapies.

Process mining and customer journey mapping in healthcare: Enhancing patient-centred care in stroke rehabilitation.

Background: Patient-centred care and enhancing patient experience is a priority across Australia. Stroke rehabilitation has multiple consumer touchpoints that would benefit from a better understanding of customer journeys, subsequently impacting better patient-centred care, and contributing to process improvements and better patient outcomes. Customer journey mapping through process mining extracts process data from event logs in existing information systems discovering patient journeys, which can be utilized to monitor guideline compliance and uncover nonconformance. Methodology: Utilizing process mining and variant analysis, customer journey maps were developed for 130 stroke rehabilitation patients from referral to discharge. In total, 168 cases from the Australasian Rehabilitation Outcomes Centre dataset were matched with 6291 cases from inpatient stroke data. Variants were explored for age, gender, outcome measures, length of stay and functional independence measure (FIM) change. Results: The study illustrated the process, process variants and patient journey map in stroke rehabilitation. Process characteristics of stroke rehabilitation patients were extracted and represented utilizing process mining and results highlighted process variation, attributes, touchpoints and timestamps across stroke rehabilitation patient journeys categorized by patient demographics and outcome variables. Patients demonstrated a mean and median duration of 49.5 days and 44 days, respectively, across the patient journeys. Nine variants were discovered, with 78.46% (n = 102) of patients following the expected sequence of activities in their stroke rehabilitation patient journey. Relationships involving age, gender, length of stay and FIM change along the patient journeys were evident, with four cases experiencing stroke rehabilitation journeys of more than 100 days, warranting further investigation. Conclusion: Process mining can be utilized to visualize and analyse patient journeys and identify gaps in service quality, thus contributing to better patient-centred care and improved patient outcomes and experiences in stroke rehabilitation.

Evidence for Therapeutic Drug Monitoring of Atypical Antipsychotics.

Second-generation antipsychotics (SGAs), also known as atypical antipsychotics, are a newer class of antipsychotic drugs used to treat schizophrenia, bipolar disorder, and related psychiatric conditions. The plasma concentration of antipsychotic drugs is a valid measure of the drug at its primary target structure in the brain, and therefore determines the efficacy and safety of these drugs. However, despite the well-known high variability in pharmacokinetics of these substances, psychiatric medication is usually administered in uniform dosage schedules. Therapeutic drug monitoring (TDM), as the specific method that can help personalised medicine in dose adjustment according to the characteristics of the individual patient, minimizing the risk of toxicity, monitoring adherence, and increasing cost-effectiveness in the treatment, thus seems to be an elegant tool to solve this problem. Non-response to therapeutic doses, uncertain adherence to medication, suboptimal tolerability, or pharmacokinetic drug-drug interactions are typical indications for TDM of SGAs. This review aims to summarize an overview of the current knowledge and evidence of the possibilities to tailor the dosage of selected SGAs using TDM, including the necessary pharmacokinetic parameters for personalised pharmacotherapy.

Image-based predictive modelling frameworks for personalised drug delivery in cancer therapy.

Personalised drug delivery enables a tailored treatment plan for each patient compared to conventional drug delivery, where a generic strategy is commonly employed. It can not only achieve precise treatment to improve effectiveness but also reduce the risk of adverse effects to improve patients' quality of life. Drug delivery involves multiple interconnected physiological and physicochemical processes, which span a wide range of time and length scales. How to consider the impact of individual differences on these processes becomes critical. Multiphysics models are an open system that allows well-controlled studies on the individual and combined effects of influencing factors on drug delivery outcomes while accommodating the patient-specific in vivo environment, which is not economically feasible through experimental means. Extensive modelling frameworks have been developed to reveal the underlying mechanisms of drug delivery and optimise effective delivery plans. This review provides an overview of currently available models, their integration with advanced medical imaging modalities, and code packages for personalised drug delivery. The potential to incorporate new technologies (i.e., machine learning) in this field is also addressed for development.