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Competing Interests: LG has no financial relationships with any organization that might have an interest in the submitted work and no other relationships or activities that could appear to have influenced the submitted work. IC is an investigator in relacorilant studies (Corcept Therapeutics) in patients with hypercortisolism and received consulting fees from Corcept Therapeutics and HRA Pharma.

In 1911, more than a century ago, Peyton Rous described a curious observation, later explained by a virus named for him that led to the discovery of oncogenes, the modern era of cancer research, and the emergent field of precision medicine (1911. J. Exp. Med. https://doi.org/10.1084/jem.13.4.397)., (© 2021 Elemento.)

Precision/personalized medicine is a hot topic in health care. Often presented with the motto "the right drug, for the right patient, at the right dose, and the right time," precision medicine is a theory for rational therapeutics as well as practice to individualize health interventions (e.g., drugs, food, vaccines, medical devices, and exercise programs) using biomarkers. Yet, an alien visitor to planet Earth reading the contemporary textbooks on diagnostics might think precision medicine requires only two biomolecules omnipresent in the literature: nucleic acids (e.g., DNA) and proteins, known as the first and second alphabet of biology, respectively. However, the precision/personalized medicine community has tended to underappreciate the third alphabet of life, the "sugar code" (i.e., the information stored in glycans, glycoproteins, and glycolipids). This article brings together experts in precision/personalized medicine science, pharmacoglycomics, emerging technology governance, cultural studies, contemporary art, and responsible innovation to critically comment on the sociomateriality of the three alphabets of life together. First, the current transformation of targeted therapies with personalized glycomedicine and glycan biomarkers is examined. Next, we discuss the reasons as to why unraveling of the sugar code might have lagged behind the DNA and protein codes. While social scientists have historically noted the importance of constructivism (e.g., how people interpret technology and build their values, hopes, and expectations into emerging technologies), life scientists relied on the material properties of technologies in explaining why some innovations emerge rapidly and are more popular than others. The concept of sociomateriality integrates these two explanations by highlighting the inherent entanglement of the social and the material contributions to knowledge and what is presented to us as reality from everyday laboratory life. Hence, we present a hypothesis based on a sociomaterial conceptual lens: because materiality and synthesis of glycans are not directly driven by a template, and thus more complex and open ended than sequencing of a finite length genome, social construction of expectations from unraveling of the sugar code versus the DNA code might have evolved differently, as being future-uncertain versus future-proof, respectively, thus potentially explaining the "sugar lag" in precision/personalized medicine diagnostics over the past decades. We conclude by introducing systems scientists, physicians, and biotechnology industry to the concept, practice, and value of responsible innovation, while glycomedicine and other emerging biomarker technologies (e.g., metagenomics and pharmacomicrobiomics) transition to applications in health care, ecology, pharmaceutical/diagnostic industries, agriculture, food, and bioengineering, among others.

The emergence of multidrug-resistant pathogens and their rapid adaptation against new antibiotics is a major challenge for scientists and medical professionals. Different approaches have been taken to combat this problem, which includes rationally designed potent antimicrobial peptides (AMPs) and several nanoparticles and quantum dots. AMPs are considered as a new generation of super antibiotics that hold enormous potential to fight against bacterial resistance by the rapidly killing planktonic as well as their biofilm form while keeping low toxicity profile against eukaryotic cells. Various nanoparticles and quantum dots have proved their effectiveness against a vast array of infections and diseases. Conjugation and functionalization of nanoparticles with potentially active antimicrobial peptides have added advantages that widen their applications in the field of drug discovery as well as delivery system including imaging and diagnostics. This article reviews the current progress and implementation of different nanoparticles and quantum dots conjugated antimicrobial peptides in terms of bio-stability, drug delivery, and therapeutic applications., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Progress toward the implementation of a molecular characterization paradigm in cancer drug development over the past 20 years has markedly enhanced our capability to select patients who are more likely to benefit from cancer therapy. Improvements in genomic and related diagnostic testing platforms have permitted evaluation of the efficacy of treatment assignment based on predefined biologic features of a patient's tumor or germline using master protocols that may include many malignancies and their molecularly characterized subsets. With this approach, a wide range of new targeted and immunologic treatment approaches have been defined for patients who, heretofore, lacked effective therapeutic options., (Published by Elsevier Inc.)

In evidence-based mental health practice, decisions must often be made for which there is little or no empirical basis. A common example of this is when there are multiple empirically supported interventions for a person with a given diagnosis, where the aim is to recommend the treatment most likely to be effective for that person. Data obtained from randomized clinical trials allow for the identification of patient characteristics that could be used to match patients to treatments. Historically, researchers have focused on individual moderators, single variables that interact statistically with treatment type, but these have rarely proved powerful enough to inform treatment decisions. Recently, researchers have begun to explore ways in which the use of multivariable algorithms might improve clinical decision-making. Common pitfalls have been identified, including the use of methods that provide overoptimistic estimates of the gains that can be expected from the applications of an algorithm in a clinical setting. It is too early to tell if these efforts will pay off and, if so, how much their use can increase the efficiency and effectiveness of mental health systems. It behooves the field to continue to learn and develop the most powerful methods that can produce generalizable knowledge that will advance the aims of precision mental health., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

In the last third of the 20th century, etiological epidemiology within academia in high-income countries shifted its primary concern from attempting to tackle the apparent epidemic of noncommunicable diseases to an increasing focus on developing statistical and causal inference methodologies. This move was mutually constitutive with the failure of applied epidemiology to make major progress, with many of the advances in understanding the causes of noncommunicable diseases coming from outside the discipline, while ironically revealing the infectious origins of several major conditions. Conversely, there were many examples of epidemiologic studies promoting ineffective interventions and little evident attempt to account for such failure. Major advances in concrete understanding of disease etiology have been driven by a willingness to learn about and incorporate into epidemiology developments in biology and cognate data science disciplines. If fundamental epidemiologic principles regarding the rooting of disease risk within populations are retained, recent methodological developments combined with increased biological understanding and data sciences capability should herald a fruitful post-Modern Epidemiology world., (© The Author(s) 2019. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health.)

Oncologic phase II trials that evaluate the activity of new therapeutic agents have evolved dramatically over the past 50 years. The standard approach beginning in the late 1960s focused on individual studies that evaluated new anticancer agents against a wide range of both solid and hematopoietic malignancies often in a single "broad phase II trial" that included hundreds of patients; such studies efficiently established the landscape for subsequent development of a specific drug with respect to likely disease focus, toxicity, dose, and schedule. In the 1980s and 1990s, emphasis on histological context drove an explosion in the number of individual phase II trials conducted; despite this increase in trial activity, investigations based on histology per se failed to improve the success rate of new agents brought to the clinic. Over the past 20 years, evolution toward a molecular drug development paradigm has demonstrably improved our ability to select patients more likely to benefit from systemic treatment; simultaneously, technological advances have permitted initial attempts at the rapid assignment of therapy based on predefined molecular characteristics of tumor or germline in broad-based master protocols that are inclusive of many diseases and molecularly characterized disease subsets, akin to but much more sophisticated scientifically than the broad phase II platforms of the past.

The finalization of the Human Genome Project in 2003 paved the way for a deeper understanding of cancer, favouring a faster progression towards "personalized" medicine. Research in oncology has progressively focused on the sequencing of cancer genomes, to better understand the genetic basis of tumorigenesis and identify actionable alterations to guide cancer therapy. Thanks to the development of next-generation-sequencing (NGS) techniques, sequencing of tumoral DNA is today technically easier, faster and cheaper. Commercially available NGS panels enable the detection of single or global genomic alterations, namely gene mutation and mutagenic burden, both on germline and somatic DNA, potentially predicting the response or resistance to cancer treatments. Profiling of tumor DNA is nowadays a standard in cancer research and treatment. In this review we discuss the history, techniques and applications of NGS in cancer care, under a "personalized tailored therapy" perspective., (Copyright © 2018 Elsevier B.V. All rights reserved.)

In this review, we summarize current knowledge on the genetics of coronary artery disease, based on 10 years of genome-wide association studies. The discoveries began with individual studies using 200K single nucleotide polymorphism arrays and progressed to large-scale collaborative efforts, involving more than a 100 000 people and up to 40 Mio genetic variants. We discuss the challenges ahead, including those involved in identifying causal genes and deciphering the links between risk variants and disease pathology. We also describe novel insights into disease biology based on the findings of genome-wide association studies. Moreover, we discuss the potential for discovery of novel treatment targets through the integration of different layers of 'omics' data and the application of systems genetics approaches. Finally, we provide a brief outlook on the potential for precision medicine to be enhanced by genome-wide association study findings in the cardiovascular field.

Precision medicine (PM) is currently placed at the center of global attention following decades of research towards the improvement of medical practice. The subject of this study was to examine whether this trend had emerged earlier, in fact if the fundamentals of PM can be traced back to the ancient Greek era. For this reason, we studied the collection of all the Hippocratic texts, called the Corpus Hippocraticum, using original translations, and attempted an interpretation of the ancient authors in the context of the modern concept of PM. The most important points located in the ancient passages were: (1) medicine in not 'absolute', thus its directions cannot be generalized to everybody, (2) each human body/organism is different and responds differently to therapy; therefore, the same treatment cannot be suitable for everybody and (3) the physician should choose the appropriate treatment, depending on the patients' individual characteristics, such as different health status and life style (activities, diet, etc.). Although the ancient 'precision medicine' is different from its modern description, the latter derived from well-established experimental conclusions, it becomes apparent that there is a common conception, aiming to achieve more effective healing by focusing on the individual.

Since the completion of the first human DNA sequence, genomic approaches have penetrated into cancer research and therapy: first through expression profiling for diagnostic, prognostic and predictive purposes, then by sequencing of tumour DNA in order to define and apply targeted therapies. These overlapping changes occurred quite rapidly and are now overshadowed by immuno-oncology approaches that show much promise. There is however still much left to understand to make this more widely applicable, and the extreme cost of these therapies is a serious concern., (© 2017 médecine/sciences – Inserm.)

Artificial Intelligence (AI) is a general term that implies the use of a computer to model intelligent behavior with minimal human intervention. AI is generally accepted as having started with the invention of robots. The term derives from the Czech word robota, meaning biosynthetic machines used as forced labor. In this field, Leonardo Da Vinci's lasting heritage is today's burgeoning use of robotic-assisted surgery, named after him, for complex urologic and gynecologic procedures. Da Vinci's sketchbooks of robots helped set the stage for this innovation. AI, described as the science and engineering of making intelligent machines, was officially born in 1956. The term is applicable to a broad range of items in medicine such as robotics, medical diagnosis, medical statistics, and human biology-up to and including today's "omics". AI in medicine, which is the focus of this review, has two main branches: virtual and physical. The virtual branch includes informatics approaches from deep learning information management to control of health management systems, including electronic health records, and active guidance of physicians in their treatment decisions. The physical branch is best represented by robots used to assist the elderly patient or the attending surgeon. Also embodied in this branch are targeted nanorobots, a unique new drug delivery system. The societal and ethical complexities of these applications require further reflection, proof of their medical utility, economic value, and development of interdisciplinary strategies for their wider application., (Copyright © 2017. Published by Elsevier Inc.)

Radiogenomics designates a scientific field that addresses possible associations between genetic germline alterations and normal tissue toxicity after radiotherapy. The ultimate aim of this research is to establish a gene-based predictive test for normal tissue radiosensitivity. During the last 5 years, substantial progress has been achieved in this field. Several compelling associations for SNPs have been demonstrated in large candidate gene studies as well as genome wide association studies. These findings shed new light on radiobiology and expand our understanding of the processes that lead to side effects after radiotherapy. Despite this, certain fundamental challenges still relate to genomic approaches. Based on the latest insights into complex trait genetics and molecular genetics, we provide an analysis of these challenges and propose putative strategies to further advance the field. These strategies include 'big data approaches' and collaborative research within international consortia. Furthermore, research that combines the study of radiation-induced gene expression and genome-wide SNP genotype may discover genetic alterations that regulate the biological response to ionizing radiation. Thus, such integrative approaches may lead to genetic alterations that affect risk of normal tissue toxicity., (Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.)

Background: The nineteenth century saw the rise of what historians of medicine have termed the 'medical gaze'. Physicians used instrumentation and trained senses to locate the site of disease within the patient's body. This change in practice went alongside changes in the physician's power and how diseases were understood. In the twenty-first century, the rise of high-throughput biomedical experiments, especially in genomics, is leading to equally dramatic shifts in medicine. Increasingly, clinical decisions may be made on the basis of data and statistical associations rather than the particularities of the case at hand., Objective: The aim of this commentary iso re-evaluate the status of precision and evidence-based medicine in light of the social, political and economic shifts they entail., Discussion: Increasingly, the statistical view of diseases and people threatens to take judgment and expertise out of medical decision making. It threatens the centrality of the physician in the relationship between patient and disease.

The 21st century has seen rapid, positive changes in the management of chronic lymphocytic leukaemia from the patient's perspective. New prognostic and predictive markers have ushered in the start of more precise and individualized therapy. For the first time, combined therapy [fludarabine, cyclophosphamide and rituximab] has been shown to prolong life significantly. Clinical trials have become more adaptive, faster and more patient friendly. Perhaps the greatest change of all is the development of novel oral agents (ibrutinib and idelalisib) and powerful monoclonal antibodies that offer robust and durable disease control. Finally, access to and understanding of these changes through an empowered and educated patient population has grown through live education forums and the Internet., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Epidemiology is concerned with determining the distribution and causes of disease. Throughout its history, epidemiology has drawn upon statistical ideas and methods to achieve its aims. Because of the exponential growth in our capacity to measure and analyze data on the underlying processes that define each person's state of health, there is an emerging opportunity for population-based epidemiologic studies to influence health decisions made by individuals in ways that take into account the individuals' characteristics, circumstances, and preferences. We refer to this endeavor as "individualized health." The present article comprises 2 sections. In the first, we describe how graphical, longitudinal, and hierarchical models can inform the project of individualized health. We propose a simple graphical model for informing individual health decisions using population-based data. In the second, we review selected topics in causal inference that we believe to be particularly useful for individualized health. Epidemiology and biostatistics were 2 of the 4 founding departments in the world's first graduate school of public health at Johns Hopkins University, the centennial of which we honor. This survey of a small part of the literature is intended to demonstrate that the 2 fields remain just as inextricably linked today as they were 100 years ago., (© The Author 2016. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

This is an essay dealing with the 1785 cohort study by William Withering (the "account"), in which he reported the results of the treatment with foxglove (Digitalis purpurea) in 163 patients suffering from various forms of hydropsy (water retention). Withering reported the results of all patients, and classified them into responders and non-responders. He identified the responders as suffering from heart failure. In the 18th century, medical treatments were judged as successful if they complied with the criteria a priori of the theory of the four humors, and not on the patient's response to the treatment. Withering was the first not only to compare the patient's conditions before and after treatment, but also to identify the individual clinical characteristics of the patients who responded. In modern medicine, drugs are released on the market and approved for use after what is known as "population-derived clinical research", principally randomized controlled trials, and guidelines. More than 200 years ago, Withering anticipated the current and growing trend towards individual responses to treatment, and personalized medicine.

With the increasing use of genomics, computational analytics, emerging technologies, and personalized medicine, the possibility of a new research model is emerging. Using the clues from thousands of species living on our planet, scientists from many disciplines (medicine, veterinary medicine, wildlife) must collaborate, prioritize, and strategize on how to address causes of health and disease. Such clues should guide disease prevention, as well as the development of innovative, efficacious, and gentler therapies. Geographic and language barriers must be broken down, and scientists--even within a single academic, corporate, or government research site--must be vigilant in seeking the help of nonmedical disciplines of colleagues from whence answers might come. The public will become more interested in and demanding of such a model, desiring that all family members (humans and animals) have an opportunity for a long and healthy life. Above all, such activities will be humanely conducted with outcomes having the greatest chance for success.

Human individuals differ from one another in almost all of their genes due to single nucleotide polymorphisms (SNPs). When the maternal and the paternal genomes become combined in a F1 individual, the two alleles of each gene represent arbitrary combinations. In consequence, individuals show high variability in protein expression. Furthermore, within a proteome, the proteins form networks of protein-protein interactions. These networks differ between individuals in robustness against genetic or/and environmental perturbation due to polymorphisms, which differ in type and composition between individuals, and modify the arrangement of proteins in the proteomic network. As a general conclusion, the robustness of a human individual against diseases may depend on the structure and expression of the protein-protein interaction network that varies in its functional efficiency between individuals due to "network-polymorphisms". This article is part of a Special Issue entitled: 20years of Proteomics in memory of Viatliano Pallini. Guest Editors: Luca Bini, Juan J. Calvete, Natacha Turck, Denis Hochstrasser and Jean-Charles Sanchez., (Copyright © 2014 Elsevier B.V. All rights reserved.)

This article announces the recipient of the 2014 inaugural Werner Kalow Responsible Innovation Prize in Global Omics and Personalized Medicine by the Pacific Rim Association for Clinical Pharmacogenetics (PRACP): Bernard Lerer, professor of psychiatry and director of the Biological Psychiatry Laboratory, Hadassah-Hebrew University Medical Center, Jerusalem, Israel. The Werner Kalow Responsible Innovation Prize is given to an exceptional interdisciplinary scholar who has made highly innovative and enduring contributions to global omics science and personalized medicine, with both vertical and horizontal (transdisciplinary) impacts. The prize is established in memory of a beloved colleague, mentor, and friend, the late Professor Werner Kalow, who cultivated the idea and practice of pharmacogenetics in modern therapeutics commencing in the 1950s. PRACP, the prize's sponsor, is one of the longest standing learned societies in the Asia-Pacific region, and was founded by Kalow and colleagues more than two decades ago in the then-emerging field of pharmacogenetics. In announcing this inaugural prize and its winner, we seek to highlight the works of prize winner, Professor Lerer. Additionally, we contextualize the significance of the prize by recalling the life and works of Professor Kalow and providing a brief socio-technical history of the rise of pharmacogenetics and personalized medicine as a veritable form of 21(st) century scientific practice. The article also fills a void in previous social science analyses of pharmacogenetics, by bringing to the fore the works of Kalow from 1995 to 2008, when he presciently noted the rise of yet another field of postgenomics inquiry--pharmacoepigenetics--that railed against genetic determinism and underscored the temporal and spatial plasticity of genetic components of drug response, with invention of the repeated drug administration (RDA) method that estimates the dynamic heritabilities of drug response. The prize goes a long way to cultivate transgenerational capacity and broader cognizance of the concept and practice of responsible innovation as an important criterion of 21(st) century omics science and personalized medicine. A new call is presently in place for the 2016 PRACP Werner Kalow prize. Nominations can be made in support of an exceptional individual interdisciplinary scholar, or alternatively, an entire research team, from any region in the world with a record of highly innovative contributions to global omics science and/or personalized medicine, in the spirit of responsible innovation. The application process is straightforward, requiring a signed, 1500-word nomination letter (by the applicant or sponsor) submitted not later than May 31, 2015.

Physicians have long puzzled over a well-known phenomenon: different patients respond differently to the same treatment. Although many explanations exist, pharmacogenetics has now captured the medical imagination. While this might seem part of the broader interest in all things genetic, the early history of pharmacogenetics reveals the specific factors that contributed to the emergence of genetics within pharmacology. This paper examines the work of one pioneering pharmacologist, Werner Kalow, to trace the evolving intellectual formations of pharmacogenetics and, in particular, the focus on race. Working in the 1950s and 1960s, Kalow made three arguments to demonstrate the relevance of genetics to pharmacology, based on laboratory techniques, analogies to differences between other animal species, and appeals to the logic of natural selection. After contributing to the emergence of the field, Kalow maintained his advocacy for pharmacogenetics for four decades, collecting more evidence for its relevance, navigating controversies about race and science, and balancing genetics against other possible explanations of patient variability. Kalow's work demonstrates the deep roots of the genetic and racial preoccupations in pharmacology. Understanding this history can restore attention to other explanations of individuality in medical practice, something of increasing importance given the current interest in personalized medicine.

The present work has the main objective of summarizing the history of pharmacovigilance and the associated methods and legislation and of showing how it could/should be reformulated in terms of a transition from a drug-centered to a patient/population-centered approach. The recurrent emergencies associated with new drug molecules raise many questions about the efficacy and efficiency of methodological tools as well as the role of regulatory systems. Drugs cannot be considered as an independent variable: the evaluation of all their effects must take into account the real contexts in which they are used and which affect not only their efficacy but also their tolerability and safety. Specific emphasis is given to recent and promising developments focused on the participation of patients and populations as key actors in producing knowledge that could technically integrate what has been produced so far and allow the evolution of surveillance from a role of controlling severe adverse reactions attributable to individual molecules to one of promoting a comprehensive assessment of the benefit/risk profile of drugs as they are utilized in society.

The active practical introduction of the achievements of genomics, proteomics, metabolomics, and bioinformatics brought about a fundamental change in views on the role and place of medicine in the structure of healthcare at the turn of the 1980s-1990s, by giving impetus to the development of the radically new health care area--preventive, predictive, and personalized medicine (PPPM).