Sex-tailored pharmacology and COVID-19: Next steps towards appropriateness and health equity.

Making gender bias visible allows to fill the gaps in knowledge and understand health records and risks of women and men. The coronavirus disease 2019 (COVID-19) pandemic has shown a clear gender difference in health outcomes. The more severe symptoms and higher mortality in men as compared to women are likely due to sex and age differences in immune responses. Age-associated decline in sex steroid hormone levels may mediate proinflammatory reactions in older adults, thereby increasing their risk of adverse outcomes, whereas sex hormones and/or sex hormone receptor modulators may attenuate the inflammatory response and provide benefit to COVID-19 patients. While multiple pharmacological options including anticoagulants, glucocorticoids, antivirals, anti-inflammatory agents and traditional Chinese medicine preparations have been tested to treat COVID-19 patients with varied levels of evidence in terms of efficacy and safety, information on sex-targeted treatment strategies is currently limited. Women may have more benefit from COVID-19 vaccines than men, despite the occurrence of more frequent adverse effects, and long-term safety data with newly developed vectors are eagerly awaited. The prevalent inclusion of men in randomized clinical trials (RCTs) with subsequent extrapolation of results to women needs to be addressed, as reinforcing sex-neutral claims into COVID-19 research may insidiously lead to increased inequities in health care. The huge worldwide effort with over 3000 ongoing RCTs of pharmacological agents should focus on improving knowledge on sex, gender and age as pillars of individual variation in drug responses and enforce appropriateness.

Current Microsatellite Instability Testing in Management of Colorectal Cancer.

Colorectal cancer (CRC) is the third most common cancer worldwide. In the past decade, mismatch repair deficiency (dMMR), manifested as microsatellite instability-high (MSI-H), has been recognized as a distinct mechanism promoting tumorigenesis in 15% of CRCs including 3% Lynch syndrome and 12% sporadic CRCs. As the molecular classifications of CRCs are continuously evolving, MSI-H CRCs appear to be the most homogeneous CRCs with distinct molecular, morphologic, and clinical features. MSI-H CRCs have dMMR causing MSI-H and genetic hypermutation but with diploid chromosomes. Morphologically, MSI-H CRCs appear as poorly differentiated or mucinous adenocarcinoma with characteristic lymphocytic infiltration. Most importantly, MSI-H CRCs have better stage-adjusted survival, do not respond well to standard 5-fluorouracil-based adjuvant chemotherapy, but do respond to immunotherapy. The United States Food and Drug Administration granted accelerated approval to immune checkpoint inhibitors, anti-programmed cell death protein-1 antibodies pembrolizumab and nivolumab, and the combination of nivolumab with anti-CTLA4 antibody ipilimumab for the second-line treatment of patients with stage IV MSI-H CRCs in 2017. There are still ongoing phase III clinical trials evaluating pembrolizumab and anti-programmed death-ligand 1 antibody atezolizumab as the first-line treatment in stage IV MSI-H CRCs and a phase I study on the combination of nivolumab and ipilimumab in patients with early stage CRC. These ongoing clinical studies on immunotherapy may lead to practice-changing results in the management of MSI-H CRCs. The National Comprehensive Cancer Network 2018 guidelines recommended MSI to be tested in all newly diagnosed CRCs. The MSI test will become increasingly vital in guiding adjuvant chemotherapy and immunotherapy in the management of CRCs.

Machine learning and network medicine: a novel approach for precision medicine and personalized therapy in cardiomyopathies.

The early identification of pathogenic mechanisms is essential to predict the incidence and progression of cardiomyopathies and to plan appropriate preventive interventions. Noninvasive cardiac imaging such as cardiac computed tomography, cardiac magnetic resonance, and nuclear imaging plays an important role in diagnosis and management of cardiomyopathies and provides useful prognostic information. Most molecular factors exert their functions by interacting with other cellular components, thus many diseases reflect perturbations of intracellular networks. Indeed, complex diseases and traits such as cardiomyopathies are caused by perturbations of biological networks. The network medicine approach, by integrating systems biology, aims to identify pathological interacting genes and proteins, revolutionizing the way to know cardiomyopathies and shifting the understanding of their pathogenic phenomena from a reductionist to a holistic approach. In addition, artificial intelligence tools, applied to morphological and functional imaging, could allow imaging scans to be automatically analyzed to extract new parameters and features for cardiomyopathy evaluation. The aim of this review is to discuss the tools of network medicine in cardiomyopathies that could reveal new candidate genes and artificial intelligence imaging-based features with the aim to translate into clinical practice as diagnostic, prognostic, and predictive biomarkers and shed new light on the clinical setting of cardiomyopathies. The integration and elaboration of clinical habits, molecular big data, and imaging into machine learning models could provide better disease phenotyping, outcome prediction, and novel drug targets, thus opening a new scenario for the implementation of precision medicine for cardiomyopathies.

Prognostic Parameters in Myeloid Malignancies in a Historical Context - From Microscopy to Individualized Medicine.

With this article, we would like to take the reader on a journey into the world of molecular medicine as it has evolved over the past decades, enabled by advances in genomics. These findings advanced both the development of prognostic parameters and the evolvement of therapy strategies. In this manuscript, we will present haematopoietic diseases as a prime example of this progress because they are relevant not only for their frequency but also for the evident diagnostic and therapeutic progress. The growing understanding of the underlying pathophysiology originates from the cellular pathology as it was described by, e.g., Rudolf Virchow (1821-1902). The identification of specific genomic changes in haematological malignancies and solid tumour diseases provided us with very sensitive tools for diagnostics and prediction of prognosis. Thus, it paved the way for individualized or personalized therapy. In particular, the rapid development of sequencing techniques for the human genome using Next Generation Sequencing (NGS) has contributed to this progress. Recently, artificial intelligence provided us with the tools to analyze the complex interactions of genomic alterations, course of the disease, and further factors of as yet unknown significance. With all these indisputable improvements, we should not neglect the holistic treatment mandate of personalized therapy, i.e., therapy appropriate to the individual. In this context, the treating physician should address relevant co-morbidities, the psychosocial embedding of the patient and his desire for treatment.

Practicing precision medicine with intelligently integrative clinical and multi-omics data analysis.

Precision medicine aims to empower clinicians to predict the most appropriate course of action for patients with complex diseases like cancer, diabetes, cardiomyopathy, and COVID-19. With a progressive interpretation of the clinical, molecular, and genomic factors at play in diseases, more effective and personalized medical treatments are anticipated for many disorders. Understanding patient's metabolomics and genetic make-up in conjunction with clinical data will significantly lead to determining predisposition, diagnostic, prognostic, and predictive biomarkers and paths ultimately providing optimal and personalized care for diverse, and targeted chronic and acute diseases. In clinical settings, we need to timely model clinical and multi-omics data to find statistical patterns across millions of features to identify underlying biologic pathways, modifiable risk factors, and actionable information that support early detection and prevention of complex disorders, and development of new therapies for better patient care. It is important to calculate quantitative phenotype measurements, evaluate variants in unique genes and interpret using ACMG guidelines, find frequency of pathogenic and likely pathogenic variants without disease indicators, and observe autosomal recessive carriers with a phenotype manifestation in metabolome. Next, ensuring security to reconcile noise, we need to build and train machine-learning prognostic models to meaningfully process multisource heterogeneous data to identify high-risk rare variants and make medically relevant predictions. The goal, today, is to facilitate implementation of mainstream precision medicine to improve the traditional symptom-driven practice of medicine, and allow earlier interventions using predictive diagnostics and tailoring better-personalized treatments. We strongly recommend automated implementation of cutting-edge technologies, utilizing machine learning (ML) and artificial intelligence (AI) approaches for the multimodal data aggregation, multifactor examination, development of knowledgebase of clinical predictors for decision support, and best strategies for dealing with relevant ethical issues.

Individualized Therapy for Hypothyroidism: Is T4 Enough for Everyone?

CONTEXT: It is well recognized that some hypothyroid patients on levothyroxine (LT4) remain symptomatic, but why patients are susceptible to this condition, why symptoms persist, and what is the role of combination therapy with LT4 and liothyronine (LT3), are questions that remain unclear. Here we explore evidence of abnormal thyroid hormone (TH) metabolism in LT4-treated patients, and offer a rationale for why some patients perceive LT4 therapy as a failure. EVIDENCE ACQUISITION: This review is based on a collection of primary and review literature gathered from a PubMed search of "hypothyroidism," "levothyroxine," "liothyronine," and "desiccated thyroid extract," among other keywords. PubMed searches were supplemented by Google Scholar and the authors' prior knowledge of the subject. EVIDENCE SYNTHESIS: In most LT4-treated patients, normalization of serum thyrotropin levels results in decreased serum T3/T4 ratio, with relatively lower serum T3 levels; in at least 15% of the cases, serum T3 levels are below normal. These changes can lead to a reduction in TH action, which would explain the slower rate of metabolism and elevated serum cholesterol levels. A small percentage of patients might also experience persistent symptoms of hypothyroidism, with impaired cognition and tiredness. We propose that such patients carry a key clinical factor, for example, specific genetic and/or immunologic makeup, that is well compensated while the thyroid function is normal but might become apparent when compounded with relatively lower serum T3 levels. CONCLUSIONS: After excluding other explanations, physicians should openly discuss and consider therapy with LT4 and LT3 with those hypothyroid patients who have persistent symptoms or metabolic abnormalities despite normalization of serum thyrotropin level. New clinical trials focused on symptomatic patients, genetic makeup, and comorbidities, with the statistical power to identify differences between monotherapy and combination therapy, are needed.

Targeted biologic therapy for asthma.

BACKGROUND: Asthma is a common and potentially serious condition affecting 300 million people worldwide. For many years, we have relied on a one-size-fits-all approach to its management, using corticosteroids and bronchodilators for all symptomatic patients. However, with more recent advances, it has become clear that asthma is a heterogeneous condition with multiple different underlying pathways. Understanding the different subtypes will be a key to giving us the ability to intervene in a targeted way to personalize care for patients with asthma. SOURCES OF DATA: Key published literature, guidelines and trials from clinicaltrials.gov. AREAS OF AGREEMENT: The most widely studied of these subtypes is T2 high eosinophilic asthma, for which there are an increasing number of biologic therapies available. T2 high asthma is associated with the cytokines interleukin (IL)-4, IL-5 and IL-13, for each of which biologics have been developed. AREAS OF CONTROVERSY: It is currently unclear which of the available biologics provides superior efficacy. It is also unclear how to select which biologic for which patient. GROWING POINTS: Head-to-head trials of the available T2 biologics will be important to determine superiority, and a suggested order for trialling biologics. Going further than this, we would like to see further analyses of available biologics to allow us to predict responders from non-responders in advance of administering therapy. AREAS TIMELY FOR DEVELOPING RESEARCH: Non-eosinophilic T2 low asthma is an area that is under-researched and for which there are few treatments available. It is likely that there are different subtypes in this category of asthma and unravelling what these are will be crucial to developing effective treatments.

'Without data, you're just another person with an opinion'.

Introduction: Given the recent impressive digital transformation worldwide, the importance of data has reached a new dimension. It is, therefore, provocative to ask whether data can save healthcare systems from bankruptcy.Areas covered: We reviewed published examples in the search for the evidence on how the growing amount of data could change the way we used to assess the value of healthcare technologies, ensuring a more holistic approach in the decision-making process while reducing the waste in the healthcare.Expert opinion: The growing amount of data will continue to provide a multitude of valuable insights that can save healthcare systems from bankruptcy. Electronic medical records, IoT, wearables, and mobile applications generate constant data streams that can be utilized endlessly thanks to methodological advancements such as SNA, unsupervised and supervised machine learning, and natural language programming. However, interoperability across these multiple data sources still pose a challenge for the future development of data-driven healthcare. Already today however, decision makers can utilize Big Data to develop conditional coverage schemes for very expensive and complicated health technologies suitable for personalized healthcare. More advanced payers may utilize even data analytics even further and develop AI-based pricing schemes.

[Italian nephrology and the progress of dialysis from its dawn to the present day].

In Italy, over the last 50 years, dialysis has been the driving force of research in nephrology. The work of many Italian nephrologists has fueled progress in dialytic techniques worldwide, improving dramatically the quality of dialytic therapy. Our foreign colleagues unanimously agree that we have been the first to look into the complexities of dialysis, into the many differences between dialytic patients and how to best address this diversity. This has allowed us to adopt a holistic approach, deeply connected to technological innovation, with the aim of putting the patient center stage and creating a "precision dialysis".

Managing Paediatric Growth Disorders: Integrating Technology Into a Personalised Approach

Long-term growth management can be challenging for patients, families and healthcare professionals (HCP). Personalised optimal responses to growth hormone (GH) therapy depend on the creation of a good working relationship between the patient and family and the HCPs responsible for care. Current unmet needs in growth management will be discussed, focusing on the likelihood of a poor growth response and its identification and management with emphasis on the importance of good adherence to GH therapy. Digital tools are now available to record injections and communicate accurate adherence data to the HCP and patient. Psychological barriers to good adherence will be covered, with techniques identified to change behaviour and improve outcome. Motivational interviewing is a valuable skill in this respect and should be taught to both medical and nursing HCPs to enhance the quality of the relationship with the patient and family. Key messages are, firstly, the importance of personalised care with the HCP using acquired psychological skills to prevent and manage poor adherence. Secondly, a human-eHealth partnership is necessary to maximise the benefit of new digital tools to aid in successful growth management.

Bifidobacterium spp: the promising Trojan Horse in the era of precision oncology.

Selective delivery of therapeutic agents into solid tumors has been a major challenge impeding the achievement of long-term disease remission and cure. The need to develop alternative drug delivery routes to achieve higher drug concentration in tumor tissue, reduce unwanted off-target side effects and thus achieve greater therapeutic efficacy, has resulted in an explosive body of research. Bifidobacterium spp. are anaerobic, nonpathogenic, Gram-positive bacteria, commensal to the human gut that are a possible anticancer drug-delivery vehicle. In this review, we describe Bifidobacterium's microbiology, current clinical applications, overview of the preclinical work investigating Bifidobacterium's potential to deliver anticancer therapy, and review the different strategies used up to date. Finally, we discuss both current challenges and future prospects.

The hubris and humility of cancer pharmacology in the post immuno-oncology era.

Cancer is a dreaded word, which has stimulated monumental efforts to discover and deliver effective cancer treatments for more than half a century. During the past two decades, our understanding of the molecular pathogenesis of cancer has increased remarkably. This has fostered an explosion in the number of experimental agents and clinical trials coupled with a dramatic rise in the regulatory approval of therapies for human cancers. Unfortunately, our preclinical models perform poorly as predictive platforms for the ultimate success of clinical candidates, reflecting the complexity of cancer. Moreover the common combination of cancer drugs prescribes the need for a better understanding of the fundamental pharmacology of each agent. Here I briefly outline some of the fundamental changes that have and have not occurred in cancer pharmacology during the past two decades and prognosticate on possible future directions.

Cancer drug development: The missing links.

IMPACT STATEMENT: The success rate for cancer drugs which enter into phase 1 clinical trials is utterly less. Why the vast majority of drugs fail is not understood but suggests that pre-clinical studies are not adequate for human diseases. In 1975, as per the Tufts Center for the Study of Drug Development, pharmaceutical industries expended 100 million dollars for research and development of the average FDA approved drug. By 2005, this figure had more than quadrupled, to $1.3 billion. In order to recover their high and risky investment cost, pharmaceutical companies charge more for their products. However, there exists no correlation between drug development cost and actual sale of the drug. This high drug development cost could be due to the reason that all patients might not respond to the drug. Hence, a given drug has to be tested in large number of patients to show drug benefits and obtain significant results.

Histo-genomics: digital pathology at the forefront of precision medicine.

The toughest challenge OMICs face is that they provide extremely high molecular resolution but poor spatial information. Understanding the cellular/histological context of the overwhelming genetic data is critical for a full understanding of the clinical behavior of a malignant tumor. Digital pathology can add an extra layer of information to help visualize in a spatial and microenvironmental context the molecular information of cancer. Thus, histo-genomics provide a unique chance for data integration. In the era of a precision medicine, a four-dimensional (4D) (temporal/spatial) analysis of cancer aided by digital pathology can be a critical step to understand the evolution/progression of different cancers and consequently tailor individual treatment plans. For instance, the integration of molecular biomarkers expression into a three-dimensional (3D) image of a digitally scanned tumor can offer a better understanding of its subtype, behavior, host immune response and prognosis. Using advanced digital image analysis, a larger spectrum of parameters can be analyzed as potential predictors of clinical behavior. Correlation between morphological features and host immune response can be also performed with therapeutic implications. Radio-histomics, or the interface of radiological images and histology is another emerging exciting field which encompasses the integration of radiological imaging with digital pathological images, genomics, and clinical data to portray a more holistic approach to understating and treating disease. These advances in digital slide scanning are not without technical challenges, which will be addressed carefully in this review with quick peek at its future.

Evidence for personalised medicine: mechanisms, correlation, and new kinds of black box.

Personalised medicine (PM) has been discussed as a medical paradigm shift that will improve health while reducing inefficiency and waste. At the same time, it raises new practical, regulatory, and ethical challenges. In this paper, we examine PM strategies epistemologically in order to develop capacities to address these challenges, focusing on a recently proposed strategy for developing patient-specific models from induced pluripotent stem cells (iPSCs) so as to make individualised treatment predictions. We compare this strategy to two main PM strategies-stratified medicine and computational models. Drawing on epistemological work in the philosophy of medicine, we explain why these two methods, while powerful, are neither truly personalised nor, epistemologically speaking, novel strategies. Both are forms of correlational black box. We then argue that the iPSC models would count as a new kind of black box. They would not rely entirely on mechanistic knowledge, and they would utilise correlational evidence in a different way from other strategies-a way that would enable personalised predictions. In arguing that the iPSC models would present a novel method of gaining evidence for clinical practice, we provide an epistemic analysis that can help to inform the practical, regulatory, and ethical challenges of developing an iPSC system.

User Models for Personalized Physical Activity Interventions: Scoping Review.

BACKGROUND: Fitness devices have spurred the development of apps that aim to motivate users, through interventions, to increase their physical activity (PA). Personalization in the interventions is essential as the target users are diverse with respect to their activity levels, requirements, preferences, and behavior. OBJECTIVE: This review aimed to (1) identify different kinds of personalization in interventions for promoting PA among any type of user group, (2) identify user models used for providing personalization, and (3) identify gaps in the current literature and suggest future research directions. METHODS: A scoping review was undertaken by searching the databases PsycINFO, PubMed, Scopus, and Web of Science. The main inclusion criteria were (1) studies that aimed to promote PA; (2) studies that had personalization, with the intention of promoting PA through technology-based interventions; and (3) studies that described user models for personalization. RESULTS: The literature search resulted in 49 eligible studies. Of these, 67% (33/49) studies focused solely on increasing PA, whereas the remaining studies had other objectives, such as maintaining healthy lifestyle (8 studies), weight loss management (6 studies), and rehabilitation (2 studies). The reviewed studies provide personalization in 6 categories: goal recommendation, activity recommendation, fitness partner recommendation, educational content, motivational content, and intervention timing. With respect to the mode of generation, interventions were found to be semiautomated or automatic. Of these, the automatic interventions were either knowledge-based or data-driven or both. User models in the studies were constructed with parameters from 5 categories: PA profile, demographics, medical data, behavior change technique (BCT) parameters, and contextual information. Only 27 of the eligible studies evaluated the interventions for improvement in PA, and 16 of these concluded that the interventions to increase PA are more effective when they are personalized. CONCLUSIONS: This review investigates personalization in the form of recommendations or feedback for increasing PA. On the basis of the review and gaps identified, research directions for improving the efficacy of personalized interventions are proposed. First, data-driven prediction techniques can facilitate effective personalization. Second, use of BCTs in automated interventions, and in combination with PA guidelines, are yet to be explored, and preliminary studies in this direction are promising. Third, systems with automated interventions also need to be suitably adapted to serve specific needs of patients with clinical conditions. Fourth, previous user models focus on single metric evaluations of PA instead of a potentially more effective, holistic, and multidimensional view. Fifth, with the widespread adoption of activity monitoring devices and mobile phones, personalized and dynamic user models can be created using available user data, including users' social profile. Finally, the long-term effects of such interventions as well as the technology medium used for the interventions need to be evaluated rigorously.