Navigating new horizons: Prospects of NET-targeted radiopharmaceuticals in precision medicine.

In the evolving landscape of precision medicine, NET-targeted radiopharmaceuticals are emerging as pivotal tools for the diagnosis and treatment of a range of conditions, from heart failure and neurodegenerative disorders to neuroendocrine cancers. This review evaluates the advancements offered by 18F-labeled PET tracers and 211At alpha-particle therapy, juxtaposed with current 123I-MIBG SPECT and 131I-MIBG therapies. The enhanced spatial resolution and capability for quantitative analysis render 18F-labeled PET tracers potential candidates for improved detection and management of diseases. Alpha-particle therapy with 211At may offer increased specificity and tumoricidal efficacy, pointing towards a shift in therapeutic protocols. While preliminary data is promising, these innovative approaches require thorough validation against current modalities. Ongoing clinical trials are pivotal to confirm the expected clinical benefits and to address safety concerns. This review underscores the need for rigorous research to verify the clinical utility of NET-targeted radiopharmaceuticals, which may redefine precision medicine paradigms and significantly impact patient care.

CRISPR Advancements for Human Health.

CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) has emerged as a powerful gene editing technology that is revolutionizing biomedical research and clinical medicine. The CRISPR system allows scientists to rewrite the genetic code in virtually any organism. This review provides a comprehensive overview of CRISPR and its clinical applications. We first introduce the CRISPR system and explain how it works as a gene editing tool. We then highlight current and potential clinical uses of CRISPR in areas such as genetic disorders, infectious diseases, cancer, and regenerative medicine. Challenges that need to be addressed for the successful translation of CRISPR to the clinic are also discussed. Overall, CRISPR holds great promise to advance precision medicine, but ongoing research is still required to optimize delivery, efficacy, and safety.

Noninvasive brain stimulation in autism: review and outlook for personalized interventions in adult patients.

Noninvasive brain stimulation (NIBS) has been increasingly investigated during the last decade as a treatment option for persons with autism spectrum disorder (ASD). Yet, previous studies did not reach a consensus on a superior treatment protocol or stimulation target. Persons with ASD often suffer from social isolation and high rates of unemployment, arising from difficulties in social interaction. ASD involves multiple neural systems involved in perception, language, and cognition, and the underlying brain networks of these functional domains have been well documented. Aiming to provide an overview of NIBS effects when targeting these neural systems in late adolescent and adult ASD, we conducted a systematic search of the literature starting at 631 non-duplicate publications, leading to six studies corresponding with inclusion and exclusion criteria. We discuss these studies regarding their treatment rationale and the accordingly chosen methodological setup. The results of these studies vary, while methodological advances may allow to explain some of the variability. Based on these insights, we discuss strategies for future clinical trials to personalize the selection of brain stimulation targets taking into account intersubject variability of brain anatomy as well as function.

Towards personalized medicine: a scoping review of immunotherapy in sepsis.

Despite significant progress in our understanding of the pathophysiology of sepsis and extensive clinical research, there are few proven therapies addressing the underlying immune dysregulation of this life-threatening condition. The aim of this scoping review is to describe the literature evaluating immunotherapy in adult patients with sepsis, emphasizing on methods providing a "personalized immunotherapy" approach, which was defined as the classification of patients into a distinct subgroup or subphenotype, in which a patient's immune profile is used to guide treatment. Subgroups are subsets of sepsis patients, based on any cut-off in a variable. Subphenotypes are subgroups that can be reliably discriminated from other subgroup based on data-driven assessments. Included studies were randomized controlled trials and cohort studies investigating immunomodulatory therapies in adults with sepsis. Studies were identified by searching PubMed, Embase, Cochrane CENTRAL and ClinicalTrials.gov, from the first paper available until January 29th, 2024. The search resulted in 15,853 studies. Title and abstract screening resulted in 1409 studies (9%), assessed for eligibility; 771 studies were included, of which 282 (37%) were observational and 489 (63%) interventional. Treatment groups included were treatments targeting the innate immune response, the complement system, coagulation and endothelial dysfunction, non-pharmalogical treatment, pleiotropic drugs, immunonutrition, concomitant treatments, Traditional Chinese Medicine, immunostimulatory cytokines and growth factors, intravenous immunoglobulins, mesenchymal stem cells and immune-checkpoint inhibitors. A personalized approach was incorporated in 70 studies (9%). Enrichment was applied using cut-offs in temperature, laboratory, biomarker or genetic variables. Trials often showed conflicting results, possibly due to the lack of patient stratification or the potential influence of severity and timing on immunomodulatory therapy results. When a personalized approach was applied, trends of clinical benefit for several interventions emerged, which hold promise for future clinical trials using personalized immunotherapy.

Envisioning the Future of Personalized Medicine: Role and Realities of Digital Twins.

Digital twins have emerged as a groundbreaking concept in personalized medicine, offering immense potential to transform health care delivery and improve patient outcomes. It is important to highlight the impact of digital twins on personalized medicine across the understanding of patient health, risk assessment, clinical trials and drug development, and patient monitoring. By mirroring individual health profiles, digital twins offer unparalleled insights into patient-specific conditions, enabling more accurate risk assessments and tailored interventions. However, their application extends beyond clinical benefits, prompting significant ethical debates over data privacy, consent, and potential biases in health care. The rapid evolution of this technology necessitates a careful balancing act between innovation and ethical responsibility. As the field of personalized medicine continues to evolve, digital twins hold tremendous promise in transforming health care delivery and revolutionizing patient care. While challenges exist, the continued development and integration of digital twins hold the potential to revolutionize personalized medicine, ushering in an era of tailored treatments and improved patient well-being. Digital twins can assist in recognizing trends and indicators that might signal the presence of diseases or forecast the likelihood of developing specific medical conditions, along with the progression of such diseases. Nevertheless, the use of human digital twins gives rise to ethical dilemmas related to informed consent, data ownership, and the potential for discrimination based on health profiles. There is a critical need for robust guidelines and regulations to navigate these challenges, ensuring that the pursuit of advanced health care solutions does not compromise patient rights and well-being. This viewpoint aims to ignite a comprehensive dialogue on the responsible integration of digital twins in medicine, advocating for a future where technology serves as a cornerstone for personalized, ethical, and effective patient care.

[The revolution of AI in drug development]. / L'intelligence artificielle, une révolution dans le développement des médicaments.

Artificial intelligence and machine learning enable the construction of predictive models, which are currently used to assist in decision-making throughout the process of drug discovery and development. These computational models can be used to represent the heterogeneity of a disease, identify therapeutic targets, design and optimize drug candidates, and evaluate the efficacy of these drugs on virtual patients or digital twins. By combining detailed patient characteristics with the prediction of potential drug-candidate properties, artificial intelligence promotes the emergence of a "computational" precision medicine, allowing for more personalized treatments, better tailored to patient specificities with the aid of such predictive models. Based on such new capabilities, a mixed reality approach to the development of new drugs is being adopted by the pharmaceutical industry, which integrates the outputs of predictive virtual models with real-world empirical studies.

Title: L'intelligence artificielle, une révolution dans le développement des médicaments. Abstract: L'intelligence artificielle (IA) et l'apprentissage automatique produisent des modèles prédictifs qui aident à la prise de décisions dans le processus de découverte de nouveaux médicaments. Cette modélisation par ordinateur permet de représenter l'hétérogénéité d'une maladie, d'identifier des cibles thérapeutiques, de concevoir et optimiser des candidats-médicaments et d'évaluer ces médicaments sur des patients virtuels, ou des jumeaux numériques. En facilitant à la fois une connaissance détaillée des caractéristiques des patients et en prédisant les propriétés de multiples médicaments possibles, l'IA permet l'émergence d'une médecine de précision « computationnelle ¼ offrant des traitements parfaitement adaptés aux spécificités des patients.

Innovations in Medicine: Exploring ChatGPT's Impact on Rare Disorder Management.

Artificial intelligence (AI) is rapidly transforming the field of medicine, announcing a new era of innovation and efficiency. Among AI programs designed for general use, ChatGPT holds a prominent position, using an innovative language model developed by OpenAI. Thanks to the use of deep learning techniques, ChatGPT stands out as an exceptionally viable tool, renowned for generating human-like responses to queries. Various medical specialties, including rheumatology, oncology, psychiatry, internal medicine, and ophthalmology, have been explored for ChatGPT integration, with pilot studies and trials revealing each field's potential benefits and challenges. However, the field of genetics and genetic counseling, as well as that of rare disorders, represents an area suitable for exploration, with its complex datasets and the need for personalized patient care. In this review, we synthesize the wide range of potential applications for ChatGPT in the medical field, highlighting its benefits and limitations. We pay special attention to rare and genetic disorders, aiming to shed light on the future roles of AI-driven chatbots in healthcare. Our goal is to pave the way for a healthcare system that is more knowledgeable, efficient, and centered around patient needs.

[Personalized medicine in oncology]. / Personalisierte Medizin in der Onkologie.

Due to the considerable technological progress in molecular and genetic diagnostics as well as increasing insights into the molecular pathogenesis of diseases, there has been a fundamental paradigm shift in the past two decades from a "one-size-fits-all approach" to personalized, molecularly informed treatment strategies. Personalized medicine or precision medicine focuses on the genetic, physiological, molecular, and biochemical differences between individuals and considers their effects on the development, prevention, and treatment of diseases. As a pioneer of personalized medicine, the field of oncology is particularly noteworthy, where personalized diagnostics and treatment have led to lasting change in the treatment of cancer patients in recent years. In this article, the significant change towards personalized treatment concepts, especially in the field of personalized oncology, will be discussed and examined in more detail.

Digital twins for cardiac electrophysiology: state of the art and future challenges.

Cardiac arrhythmias remain a major cause of death and disability. Current antiarrhythmic therapies are effective to only a limited extent, likely in large part due to their mechanism-independent approach. Precision cardiology aims to deliver targeted therapy for an individual patient to maximize efficacy and minimize adverse effects. In-silico digital twins have emerged as a promising strategy to realize the vision of precision cardiology. While there is no uniform definition of a digital twin, it typically employs digital tools, including simulations of mechanistic computer models, based on patient-specific clinical data to understand arrhythmia mechanisms and/or make clinically relevant predictions. Digital twins have become part of routine clinical practice in the setting of interventional cardiology, where commercially available services use digital twins to non-invasively determine the severity of stenosis (computed tomography-based fractional flow reserve). Although routine clinical application has not been achieved for cardiac arrhythmia management, significant progress towards digital twins for cardiac electrophysiology has been made in recent years. At the same time, significant technical and clinical challenges remain. This article provides a short overview of the history of digital twins for cardiac electrophysiology, including recent applications for the prediction of sudden cardiac death risk and the tailoring of rhythm control in atrial fibrillation. The authors highlight the current challenges for routine clinical application and discuss how overcoming these challenges may allow digital twins to enable a significant precision medicine-based advancement in cardiac arrhythmia management.

Theranostics and artificial intelligence: new frontiers in personalized medicine.

The field of theranostics is rapidly advancing, driven by the goals of enhancing patient care. Recent breakthroughs in artificial intelligence (AI) and its innovative theranostic applications have marked a critical step forward in nuclear medicine, leading to a significant paradigm shift in precision oncology. For instance, AI-assisted tumor characterization, including automated image interpretation, tumor segmentation, feature identification, and prediction of high-risk lesions, improves diagnostic processes, offering a precise and detailed evaluation. With a comprehensive assessment tailored to an individual's unique clinical profile, AI algorithms promise to enhance patient risk classification, thereby benefiting the alignment of patient needs with the most appropriate treatment plans. By uncovering potential factors unseeable to the human eye, such as intrinsic variations in tumor radiosensitivity or molecular profile, AI software has the potential to revolutionize the prediction of response heterogeneity. For accurate and efficient dosimetry calculations, AI technology offers significant advantages by providing customized phantoms and streamlining complex mathematical algorithms, making personalized dosimetry feasible and accessible in busy clinical settings. AI tools have the potential to be leveraged to predict and mitigate treatment-related adverse events, allowing early interventions. Additionally, generative AI can be utilized to find new targets for developing novel radiopharmaceuticals and facilitate drug discovery. However, while there is immense potential and notable interest in the role of AI in theranostics, these technologies do not lack limitations and challenges. There remains still much to be explored and understood. In this study, we investigate the current applications of AI in theranostics and seek to broaden the horizons for future research and innovation.

Revolutionizing bone tumor management: cutting-edge breakthroughs in limb-saving treatments.

Limb salvage surgery has revolutionized the approach to bone tumors in orthopedic oncology, steering away from historical amputations toward preserving limb function and enhancing patient quality of life. This transformative shift underscores the delicate balance between tumor eradication and optimal postoperative function. Primary and metastatic bone tumors present challenges in early detection, differentiation between benign and malignant tumors, preservation of function, and the risk of local recurrence. Conventional methods, including surgery, radiation therapy, chemotherapy, and targeted therapies, have evolved with a heightened focus on personalized medicine. A groundbreaking development in limb salvage surgery is the advent of 3D-printed patient-specific implants, which significantly enhance anatomical precision, stability, and fixation. These implants reduce soft tissue disruption and the associated risks, fostering improved osseointegration and correction of deformities for a more natural and functional postoperative outcome. Biological and molecular research has reshaped the understanding of bone tumors, guiding surgical interventions with advancements such as genomic profiling, targeted intraoperative imaging, precision targeting of molecular pathways, and immunotherapy tailored to individual tumor characteristics. In the realm of imaging technologies, MRI, CT scans, and intraoperative navigation systems have redefined preoperative planning, minimizing collateral damage and optimizing outcomes through accurate resections. Postoperative rehabilitation plays a crucial role in restoring function and improving the quality of life. Emphasizing early mobilization, effective pain management, and a multidisciplinary approach, rehabilitation addresses the physical, psychological, and social aspects of recovery. Looking ahead, future developments may encompass advanced biomaterials, smart implants, AI algorithms, robotics, and regenerative medicine. Challenges lie in standardization, cost-effectiveness, accessibility, long-term outcome assessment, mental health support, and fostering global collaboration. As research progresses, limb salvage surgery emerges not just as a preservation tool but as a transformative approach, restoring functionality, resilience, and hope in the recovery journey. This review summarizes the recent advances in limb salvage therapy for bone tumors over the past decade.

Phenomics and Robust Multiomics Data for Cardiovascular Disease Subtyping.

The complex landscape of cardiovascular diseases encompasses a wide range of related pathologies arising from diverse molecular mechanisms and exhibiting heterogeneous phenotypes. This variety of manifestations poses significant challenges in the development of treatment strategies. The increasing availability of precise phenotypic and multiomics data of cardiovascular disease patient populations has spurred the development of a variety of computational disease subtyping techniques to identify distinct subgroups with unique underlying pathogeneses. In this review, we outline the essential components of computational approaches to select, integrate, and cluster omics and clinical data in the context of cardiovascular disease research. We delve into the challenges faced during different stages of the analysis, including feature selection and extraction, data integration, and clustering algorithms. Next, we highlight representative applications of subtyping pipelines in heart failure and coronary artery disease. Finally, we discuss the current challenges and future directions in the development of robust subtyping approaches that can be implemented in clinical workflows, ultimately contributing to the ongoing evolution of precision medicine in health care.

Converging on a Cure: The Roads to Predictive Immunotherapy.

SUMMARY: Convergence science teams integrating clinical, biological, engineering, and computational expertise are inventing new forecast systems to monitor and predict evolutionary changes in tumor and immune interactions during early cancer progression and therapeutic response. The resulting methods should inform a new predictive medicine paradigm to select adaptive immunotherapeutic regimens personalized to patients' tumors at a given time during their cancer progression for durable patient response.

Emerging Personalized Opportunities for Enhancing Translational Readthrough in Rare Genetic Diseases and Beyond.

Nonsense mutations trigger premature translation termination and often give rise to prevalent and rare genetic diseases. Consequently, the pharmacological suppression of an unscheduled stop codon represents an attractive treatment option and is of high clinical relevance. At the molecular level, the ability of the ribosome to continue translation past a stop codon is designated stop codon readthrough (SCR). SCR of disease-causing premature termination codons (PTCs) is minimal but small molecule interventions, such as treatment with aminoglycoside antibiotics, can enhance its frequency. In this review, we summarize the current understanding of translation termination (both at PTCs and at cognate stop codons) and highlight recently discovered pathways that influence its fidelity. We describe the mechanisms involved in the recognition and readthrough of PTCs and report on SCR-inducing compounds currently explored in preclinical research and clinical trials. We conclude by reviewing the ongoing attempts of personalized nonsense suppression therapy in different disease contexts, including the genetic skin condition epidermolysis bullosa.