Systems biology: personalized medicine for the future?

Analysis of systemic effects of dioxin on human health through template-and-anchor modeling

Dioxins are persistent environmental pollutants known for their multiple health effects, from skin rashes to liver dysfunction, reproductive toxicity and cancer. While the hazards of dioxins have been well documented, the challenge of developing a comprehensive understanding of the overall health impact of dioxins remains. We propose to address this challenge with a new approach methodology (NAM) consisting of a novel adaptation of the Template-and-Anchor (T&A) modeling paradigm. Generically, the template model is defined as a high-level coarse-grained model capturing the main physiological processes of the system. The variables of this template model are anchor models, which represent component sub-systems in greater detail at lower biological levels. For the case of dioxin, we design the template to capture the systemic effects of dioxin on the body's handling of cholesterol. Two new anchor models within this template elucidate the effects of dioxin on cholesterol transport in the bloodstream and on sex hormone steroidogenesis and the menstrual cycle. A third anchor model, representing dioxin-mediated effects on cholesterol biosynthesis via the mevalonate pathway, had been developed previously. The T&A modeling paradigm enables a holistic evaluation of the impact of toxicants, which in the future may be translated into a powerful tool for comprehensive computational health risk assessments, personalized medicine, and the development of virtual clinical trials.

Clinical significance of PCT, CRP, IL-6, NLR, and TyG Index in early diagnosis and severity assessment of acute pancreatitis: A retrospective analysis

To evaluate the clinical utility of PCT, CRP, IL-6, NLR, and TyG index in improving the early diagnosis and severity assessment of acute pancreatitis (AP). This retrospective study included 137 AP patients and 30 healthy controls from Hunan Provincial People's Hospital (January 2021-September 2023). Univariate and multivariate logistic regression analyses assessed the associations between biomarkers and severe acute pancreatitis (SAP). Receiver operating characteristic (ROC) curves, DeLong test, and Bonferroni correction were used to evaluate predictive performance. Model robustness was validated via 5-fold cross-validation. PCT, CRP, IL-6, NLR, and TyG index levels were significantly elevated in AP patients compared to controls (P < 0.001) and correlated with disease severity (P < 0.05). CRP and NLR levels differed significantly among mild, moderate, and severe AP (P < 0.01). Alcohol consumption and hyperlipidemia were significantly linked to AP severity (P for trend < 0.0001). Multivariate analysis identified hyperlipidemia (OR = 3.030, P = 0.040), CRP (OR = 1.011, P < 0.001), and NLR (OR = 1.078, P = 0.020) as independent SAP predictors. The combined model of CRP + NLR + TyG achieved the highest AUC (0.882, sensitivity = 77.2%, specificity = 88.5%), though it was not significantly better than CRP + NLR or CRP + TyG models (P > 0.05). 5-fold cross-validation confirmed consistent performance (mean AUC = 0.817 ± 0.118). PCT, CRP, IL-6, NLR, and TyG index are valuable in diagnosing and assessing AP prognosis. Hyperlipidemia, CRP, and NLR are reliable independent predictors of SAP. Combining multiple biomarkers enhances diagnostic precision and provides guidance for personalized treatment strategies in AP.

Integrating system biology and intratumor gene therapy by trans-complementing the appropriate co-stimulatory molecule as payload in oncolytic herpes virus

Systems biology has been applied at the multi-scale level within the cancer field, improving cancer prevention, diagnosis and enabling precision medicine approaches. While systems biology can expand the knowledge and skills for oncological treatment, it also represents a challenging expedition due to cancer complexity, heterogeneity and diversity not only between different cancer indications, but also in its evolution process through space and time. Here, by characterizing the transcriptional perturbations of the tumor microenvironment induced by oncolytic, we aimed to rationally design a novel armed oncolytic herpes virus. We found that intratumor oncovirotherapy with HSV-1 induces T-cell activation signatures and transcriptionally activates several costimulatory molecules. We identified differentially expressed costimulatory receptors and binding partners, where inducible co-stimulators (ICOS) resulted in the potentially most beneficial targeted therapy. Through an ex-vivo transcriptomic analysis, we explored the potential of arming an oncolytic virus as a combination therapy strategy; in particular, we engineered a targeted herpes virus encoding ICOSL (THV_ICOSL), which resulted in a significant improvement in tumor size control compared to unarmed parental virus. Also, combination with a PD-1 inhibitor enhanced antitumor efficacy as predictable by upregulation of PD-1 and ligands pair (PD-L1/PD-L2) upon oncolytic virus injection. Generation of the human version of this virus encoding hICOSL orthologue effectively and specifically activated human T cells by triggering the ICOS pathway. Our data support the data-driven generation of armed oncolytic viruses as combination immunotherapeutic with checkpoint inhibitors.

Gene Dosage of F5 c.3481C>T Stop-Codon (p.R1161Ter) Switches the Clinical Phenotype from Severe Thrombosis to Recurrent Haemorrhage: Novel Hypotheses for Readthrough Strategy

Inherited defects in the genes of blood coagulation essentially express the severity of the clinical phenotype that is directly correlated to the number of mutated alleles of the candidate leader gene (e.g., heterozygote vs. homozygote) and of possible additional coinherited traits. The F5 gene, which codes for coagulation factor V (FV), plays a two-faced role in the coagulation cascade, exhibiting both procoagulant and anticoagulant functions. Thus, defects in this gene can be predisposed to either bleeding or thrombosis. A Sanger sequence analysis detected a premature stop-codon in exon 13 of the F5 gene (c.3481C>T; p.R1161Ter) in several members of a family characterised by low circulating FV levels and contrasting clinical phenotypes. The propositus, a 29 y.o. male affected by recurrent haemorrhages, was homozygous for the F5 stop-codon and for the F5 c.1691G>A (p.R506Q; FV-Leiden) inherited from the heterozygous parents, which is suggestive of combined cis-segregation. The homozygous condition of the stop-codon completely abolished the F5 gene expression in the propositus (FV:Ag < 1%; FV:C < 1%; assessed by ELISA and PT-based one-stage clotting assay respectively), removing, in turn, any chance for FV-Leiden to act as a prothrombotic molecule. His father (57 y.o.), characterised by severe recurrent venous thromboses, underwent a complete molecular thrombophilic screening, revealing a heterozygous F2 G20210A defect, while his mother (56 y.o.), who was negative for further common coagulation defects, reported fully asymptomatic anamnesis. To dissect these conflicting phenotypes, we performed the ProC®Global (Siemens Helthineers) coagulation test aimed at assessing the global pro- and anticoagulant balance of each family member, investigating the responses to the activated protein C (APC) by means of an APC-sensitivity ratio (APC-sr). The propositus had an unexpectedly poor response to APC (APC-sr: 1.09; n.v. > 2.25), and his father and mother had an APC-sr of 1.5 and 2.0, respectively. Although ProC®Global prevalently detects the anticoagulant side of FV, the exceptionally low APC-sr of the propositus and his discordant severe-moderate haemorrhagic phenotype could suggest a residual expression of mutated FV p.506QQ through a natural readthrough or possible alternative splicing mechanisms. The coagulation pathway may be physiologically rebalanced through natural and induced strategies, and the described insights might be able to track the design of novel treatment approaches and rebalancing molecules.

Ayurgenomics-based frameworks in precision and integrative medicine: Translational opportunities

In today's globalized and flat world, a patient can access and seek multiple health and disease management options. A digitally enabled participatory framework that allows an evidence-based informed choice is likely to assume an immense importance in the future. In India, traditional knowledge systems, like Ayurveda, coexist with modern medicine. However, due to limited crosstalk between the clinicians of both disciplines, a patient attempts integrative medicine by seeking both options independently with limited understanding and evidence. There is a need for an integrative medicine platform with a formalized approach, which allows practitioners from the two diverse systems to crosstalk, coexist, and coevolve for an informed cross-referral that benefits the patients. To be successful, this needs frameworks that enable the bridging of disciplines through a common interface with shared ontologies. Ayurgenomics is an emerging discipline that explores the principles and practices of Ayurveda combined with genomics approaches for mainstream integration. The present review highlights how in conjunction with different disciplines and technologies this has provided frameworks for (1) the discovery of molecular correlates to build ontological links between the two systems, (2) the discovery of biomarkers and targets for early actionable interventions, (3) understanding molecular mechanisms of drug action from its usage perspective in Ayurveda with applications in repurposing, (4) understanding the network and P4 medicine perspective of Ayurveda through a common organizing principle, (5) non-invasive stratification of healthy and diseased individuals using a compendium of system-level phenotypes, and (6) developing evidence-based solutions for practice in integrative medicine settings. The concordance between the two contrasting streams has been built through extensive explorations and iterations of the concepts of Ayurveda and genomic observations using state-of-the-art technologies, computational approaches, and model system studies. These highlight the enormous potential of a trans-disciplinary approach in evolving solutions for personalized interventions in integrative medicine settings.

Responsive Nanostructure for Targeted Drug Delivery

Currently, intelligent, responsive biomaterials have been widely explored, considering the fact that responsive biomaterials provide controlled and predictable results in various biomedical systems. Responsive nanostructures undergo reversible or irreversible changes in the presence of a stimulus, and that stimuli can be temperature, a magnetic field, ultrasound, pH, humidity, pressure, light, electric field, etc. Different types of stimuli being used in drug delivery shall be explained here. Recent research progress in the design, development and applications of biomaterials comprising responsive nanostructures is also described here. More emphasis will be given on the various nanostructures explored for the smart stimuli responsive drug delivery at the target site such as wound healing, cancer therapy, inflammation, and pain management in order to achieve the improved efficacy and sustainability with the lowest side effects. However, it is still a big challenge to develop well-defined responsive nanostructures with ordered output; thus, challenges faced during the design and development of these nanostructures shall also be included in this article. Clinical perspectives and applicability of the responsive nanostructures in the targeted drug delivery shall be discussed here.

Influence of Oxidative Stress and Inflammation on Nutritional Status and Neural Plasticity: New Perspectives on Post-Stroke Neurorehabilitative Outcome

Beyond brain deficits caused by strokes, the effectiveness of neurorehabilitation is strongly influenced by the baseline clinical features of stroke patients, including a patient's current nutritional status. Malnutrition, either as a pre-stroke existing condition or occurring because of ischemic injury, predisposes patients to poor rehabilitation outcomes. On the other hand, a proper nutritional status compliant with the specific needs required by the process of brain recovery plays a key role in post-stroke rehabilitative outcome favoring neuroplasticity mechanisms. Oxidative stress and inflammation play a role in stroke-associated malnutrition, as well as in the cascade of ischemic events in the brain area, where ischemic damage leads to neuronal death and brain infarction, and, via cell-to-cell signaling, the alteration of neuroplasticity processes underlying functional recovery induced by multidisciplinary rehabilitative treatment. Nutrition strategies based on food components with oxidative and anti-inflammatory properties may help to reverse or stop malnutrition and may be a prerequisite for supporting the ability of neuronal plasticity to result in satisfactory rehabilitative outcome in stroke patients. To expand nutritional recommendations for functional rehabilitation recovery, studies considering the evolution of nutritional status changes in post-stroke patients over time are required. The assessment of nutritional status must be included as a routine tool in rehabilitation settings for the integrated care of stroke-patients.

Predictive genomic tools in disease stratification and targeted prevention: a recent update in personalized therapy advancements

In the current era of medical revolution, genomic testing has guided the healthcare fraternity to develop predictive, preventive, and personalized medicine. Predictive screening involves sequencing a whole genome to comprehensively deliver patient care via enhanced diagnostic sensitivity and specific therapeutic targeting. The best example is the application of whole-exome sequencing when identifying aberrant fetuses with healthy karyotypes and chromosomal microarray analysis in complicated pregnancies. To fit into today's clinical practice needs, experimental system biology like genomic technologies, and system biology viz., the use of artificial intelligence and machine learning is required to be attuned to the development of preventive and personalized medicine. As diagnostic techniques are advancing, the selection of medical intervention can gradually be influenced by a person's genetic composition or the cellular profiling of the affected tissue. Clinical genetic practitioners can learn a lot about several conditions from their distinct facial traits. Current research indicates that in terms of diagnosing syndromes, facial analysis techniques are on par with those of qualified therapists. Employing deep learning and computer vision techniques, the face image assessment software DeepGestalt measures resemblances to numerous of disorders. Biomarkers are essential for diagnostic, prognostic, and selection systems for developing personalized medicine viz. DNA from chromosome 21 is counted in prenatal blood as part of the Down's syndrome biomarker screening. This review is based on a detailed analysis of the scientific literature via a vigilant approach to highlight the applicability of predictive diagnostics for the development of preventive, targeted, personalized medicine for clinical application in the framework of predictive, preventive, and personalized medicine (PPPM/3 PM). Additionally, targeted prevention has also been elaborated in terms of gene-environment interactions and next-generation DNA sequencing. The application of 3 PM has been highlighted by an in-depth analysis of cancer and cardiovascular diseases. The real-time challenges of genome sequencing and personalized medicine have also been discussed.

Systems Biology Approach to Investigate Biomarkers, Boron-10 Carriers, and Mechanisms Useful for Improving Boron Neutron Capture Therapy

Systems biology approach, carried out with high-throughput omics technologies, has become a fundamental aspect of the study of complex diseases like cancer. It can molecularly characterize subjects, physiopathological conditions, and interactions, allowing a precise description, to reach personalized medicine. In particular, proteomics, typically performed with liquid chromatography coupled to mass spectrometry, is a powerful tool for systems biology, giving the possibility to perform diagnosis, patient stratification, and prediction of therapy effects. Boron Neutron Capture Therapy (BNCT) is a selective antitumoral radiotherapy based on a nuclear reaction that occurs when ¹⁰B atoms are irradiated by low-energy thermal neutrons, leading to cell death, thanks to the production of high-energy α particles. Since BNCT is recently becoming an important therapy for the treatment of different types of solid tumors such as gliomas, head and neck cancers, and others, it can take advantage of molecular investigation to improve the understanding of effects and mechanisms and so help its clinical applications. In this context, proteomics can provide a better understanding of mechanisms related to BNCT effect, identify potential biomarkers, and individuate differential responses by specific patients, stratifying responders and nonresponders. Another key aspect of BNCT is the study of new potential Boron-10 carriers to improve the selectivity of Boron delivery to tumors and proteomics can be important in this application, studying the effectiveness of new boron delivery agents, including protein-based carriers, also using computational studies that can investigate new molecules, such as boronated monoclonal antibodies, for improving BNCT.

Microbiome systems biology advancements for natural well-being

Throughout the years all data from epidemiological, physiological and omics have suggested that the microbial communities play a considerable role in modulating human health. The population of microorganisms residing in the human intestine collectively known as microbiota presents a genetic repertoire that is higher in magnitude than the human genome. They play an essential role in host immunity and neuronal signaling. Rapid enhancement of sequence based screening and development of humanized gnotobiotic model has sparked a great deal of interest among scientists to probe the dynamic interactions of the commensal bacteria. This review focuses on systemic analysis of the gut microbiome to decipher the complexity of the host-microbe intercommunication and gives a special emphasis on the evolution of targeted precision medicine through microbiome engineering. In addition, we have also provided a comprehensive description of how interconnection between metabolism and biochemical reactions in a specific organism can be obtained from a metabolic network or a flux balance analysis and combining multiple datasets helps in the identification of a particular metabolite. The review highlights how genetic modification of the critical components and programming the resident microflora can be employed for targeted precision medicine. Inspite of the ongoing debate on the utility of gut microbiome we have explored on the probable new therapeutic avenues like FMT (Fecal microbiota transplant) can be utilized. This review also recapitulates integrating human-relevant 3D cellular models coupled with computational models and the metadata obtained from interventional and epidemiological studies may decipher the complex interactome of diet-microbiota-disease pathophysiology. In addition, it will also open new avenues for the development of therapeutics derived from microbiome or implementation of personalized nutrition. In addition, the identification of biomarkers can also help towards the development of new diagnostic tools and eventually will lead to strategic management of the disease. Inspite of the ongoing debate on the utility of the gut microbiome we have explored how probable new therapeutic avenues like FMT (Fecal microbiota transplant) can be utilized. This review also summarises integrating human-relevant 3D cellular models coupled with computational models and the metadata obtained from interventional and epidemiological studies may decipher the complex interactome of diet- microbiota-disease pathophysiology. In addition, it will also open new avenues for the development of therapeutics derived from the microbiome or implementation of personalized nutrition. In addition, the identification of biomarkers can also help towards the development of new diagnostic tools and eventually will lead to strategic management of disease.

Osteoarthritis, Corticosteroids and Role of CYP Genes in COVID-19 Patients: A Mini Review

Objectives of this review is to evaluate the role of cytochrome P450 gene polymorphisms in COVID-19 infected patients with pre-existing OA on corticosteroids. The purpose of this review is to analyze whether polymorphisms of Cytochrome p450 isoforms (CYP2C9 and CYP3A4) affect the dosage of steroids in OA patients in COVID-19 infected patients. This review may provide more therapeutic options; suggest a few guidelines which may be useful in managing COVID-19 patients with pre-existing osteoarthritis. The important role of corticosteroids in treating patients infected with COVID-19 with preexisting osteoarthritis, its influence on incidence of mortality or morbidity may be highlighted. The influence of CYP enzymes and their polymorphisms suggest safety of treatments as well as the possible need for the dosage adjustment or their discontinuation.

The Supportive Role of International Government Funds on the Progress of Sepsis Research During the Past Decade (2010-2019): A Narrative Review

This narrative review aimed to clarify the characteristics of international government support for sepsis research, trends in published literature on sepsis, and potential contributions of government-source grants to progress in sepsis research between fiscal years 2010 and 2019. The data in this study were collected from the National Institutes of Health (NIH, https://projectreporter.nih.gov/reporter.cfm/) of the United States of America (USA), National Natural Science Foundation of China (NSFC, https://isisn.nsfc.gov.cn/egrantweb/), and Japan Society for the Promotion of Science (JSPS, https://kaken.nii.ac.jp/). All sepsis-related projects approved by the NIH, NSFC, and JSPS were retrieved by searching the project titles, abstracts, and key words for “sepsis,” “septic shock,” or “sepsis inflammatory response syndrome” between 2010 and 2019. Representative sepsis-related studies published between Jan 2010 and Aug 2020 by the first/corresponding authors from these countries were obtained by searching the PubMed database using Medical Subject Heading terms for “sepsis” in representative journals, including Nature, Cell, Science, The Lancet, New England Journal of medicine (New Engl J Med), The Journal of American Medical Association (JAMA), Critical Care Medicine (CCM), Intensive Care Medicine (ICM), Chest, Annals of Emergency Medicine (Ann Emerg Med), and American Thoracic Society journals (ATS). The total/annual institutional budgets, major funding mechanisms and schemes, superior institutions and individual principal investigators, and published original research articles in the field of sepsis in the USA, China, and Japan during the past decade were investigated. The national supporting schemes of the NIH, NSFC, and JSPS were similar. Support from these institutions is quite important for the development of the field of “sepsis” which was acknowledged in 57–64% of original research articles published in CCM. For the future development of precision medicine in sepsis, more government funding support is necessary.

Results of the Seventh Scientific Workshop of ECCO: Precision Medicine in IBD-What, Why, and How

Many diseases that affect modern humans fall in the category of complex diseases, thus called because they result from a combination of multiple aetiological and pathogenic factors. Regardless of the organ or system affected, complex diseases present major challenges in diagnosis, classification, and management. Current forms of therapy are usually applied in an indiscriminate fashion based on clinical information, but even the most advanced drugs only benefit a limited number of patients and to a variable and unpredictable degree. This 'one measure does not fit all' situation has spurred the notion that therapy for complex disease should be tailored to individual patients or groups of patients, giving rise to the notion of 'precision medicine' [PM]. Inflammatory bowel disease [IBD] is a prototypical complex disease where the need for PM has become increasingly clear. This prompted the European Crohn's and Colitis Organisation to focus the Seventh Scientific Workshop on this emerging theme. The articles in this special issue of the Journal address the various complementary aspects of PM in IBD, including what PM is; why it is needed and how it can be used; how PM can contribute to prediction and prevention of IBD; how IBD PM can aid in prognosis and improve response to therapy; and the challenges and future directions of PM in IBD. This first article of this series is structured on three simple concepts [what, why, and how] and addresses the definition of PM, discusses the rationale for the need of PM in IBD, and outlines the methodology required to implement PM in IBD in a correct and clinically meaningful way.

Metabolomics Profiling of Cystic Renal Disease towards Biomarker Discovery

Simple Summary

Cystic renal disease (CRD) is a group of diseases characterized by abnormal sacs, or cysts, in the kidneys. CRD can be detected using certain imaging modalities (i.e., ultrasound). Patients with CRD might be symptoms-free, while others can show symptoms long after cysts development. Although these cysts represent structural changes, we hypothesized that they have an underlying biochemical alteration. If so, this would open the floor for potential biomarker discovery, which would aid in CRD diagnosis and, possibly, treatment. On that basis, this study focuses on identifying biomarkers for CRD. To achieve that, we employed a metabolomics-based approach to identify intermediate molecules inside the cells that are byproducts of biochemical reactions. We used dry blood spots and serum samples of CRD patients and healthy controls to study the differences in their metabolomic profile. Our results suggest that certain metabolites, including uridine diphosphate, cystine-5-diphosphate, and morpholine, are potential biomarkers for CRD. The affected biochemical pathways in CRD include aminoacyl-tRNA biosynthesis, purine, pyrimidine, glutathione, TCA cycle, and some amino acid metabolism. These preliminary results could be the starting point for possible diagnostic and therapeutic approaches for CRD in the future.

Abstract

Cystic renal disease (CRD) comprises a heterogeneous group of genetic and acquired disorders. The cystic lesions are detected through imaging, either incidentally or after symptoms develop, due to an underlying disease process. In this study, we aim to study the metabolomic profiles of CRD patients for potential disease-specific biomarkers using unlabeled and labeled metabolomics using low and high-resolution mass spectrometry (MS), respectively. Dried-blood spot (DBS) and serum samples, collected from CRD patients and healthy controls, were analyzed using the unlabeled and labeled method. The metabolomics profiles for both sets of samples and groups were collected, and their data were processed using the lab’s standard protocol. The univariate analysis showed (FDR p < 0.05 and fold change 2) was significant to show a group of potential biomarkers for CRD discovery, including uridine diphosphate, cystine-5-diphosphate, and morpholine. Several pathways were involved in CRD patients based on the metabolic profile, including aminoacyl-tRNA biosynthesis, purine and pyrimidine, glutathione, TCA cycle, and some amino acid metabolism (alanine, aspartate and glutamate, arginine and tryptophan), which have the most impact. In conclusion, early CRD detection and treatment is possible using a metabolomics approach that targets alanine, aspartate, and glutamate pathway metabolites.

Enhanced childhood diseases treatment using computational models: Systematic review of intelligent experiments heading to precision medicine

INTRODUCTION

Precision or personalized Medicine (PM) is used for the prevention and treatment of diseases by considering a huge amount of information about individuals variables. Due to high volume of information, AI-based computational models are required. A large set of studies conducted to examine the PM approach to improve childhood clinical outcomes. Thus, the main goal of this study was to review the application of health information technology and especially artificial intelligence (AI) methods for the treatment of childhood disease using PM.

METHODS

PubMed, Scopus, Web of Science, and EMBASE databases were searched up to December 18, 2019. Articles that focused on informatics applications for childhood disease PM included in this study. Included papers were classified for qualitative analysis and interpreting results. The results were analyzed using Microsoft Excel 2019.

RESULTS

From 341 citations, 62 papers met our inclusion criteria. The number of published papers that used AI methods to apply for PM in childhood diseases increased from 2010 to 2019. Our results showed that most applied methods were related to machine learning discipline. In terms of clinical scope, the largest number of clinical articles are devoted to oncology. Besides, the analysis showed that genomics was the most PM approach used regarding childhood disease.

CONCLUSION

This systematic review examined papers that used AI methods for applying PM approaches in childhood diseases from medical informatics perspectives. Thus, it provided new insight to researchers who are interested in knowing research needs in this field.

The aging gut microbiome and its impact on host immunity

The microbiome plays a fundamental role in the maturation, function, and regulation of the host-immune system from birth to old age. In return, the immune system has co-evolved a mutualistic relationship with trillions of beneficial microbes residing our bodies while mounting efficient responses to fight invading pathogens. As we age, both the immune system and the gut microbiome undergo significant changes in composition and function that correlate with increased susceptibility to infectious diseases and reduced vaccination responses. Emerging studies suggest that targeting age-related dysbiosis can improve health- and lifespan, in part through reducing systemic low-grade inflammation and immunosenescence-two hallmarks of the aging process. However-a cause and effect relationship of age-related dysbiosis and associated functional declines in immune cell functioning have yet to be demonstrated in clinical settings. This review aims to (i) give an overview on hallmarks of the aging immune system and gut microbiome, (ii) discuss the impact of age-related changes in the gut commensal community structure (introduced as microb-aging) on host-immune fitness and health, and (iii) summarize prebiotic- and probiotic clinical intervention trials aiming to reinforce age-related declines in immune cell functioning through microbiome modulation or rejuvenation.

Contribution of Multiplex Immunoassays to Rheumatoid Arthritis Management: From Biomarker Discovery to Personalized Medicine

Rheumatoid arthritis (RA) is a multifactorial, inflammatory and progressive autoimmune disease that affects approximately 1% of the population worldwide. RA primarily involves the joints and causes local inflammation and cartilage destruction. Immediate and effective therapies are crucial to control inflammation and prevent deterioration, functional disability and unfavourable progression in RA patients. Thus, early diagnosis is critical to prevent joint damage and physical disability, increasing the chance of achieving remission. A large number of biomarkers have been investigated in RA, although only a few have made it through the discovery and validation phases and reached the clinic. The single biomarker approach mostly used in clinical laboratories is not sufficiently accurate due to its low sensitivity and specificity. Multiplex immunoassays could provide a more complete picture of the disease and the pathways involved. In this review, we discuss the latest proposed protein biomarkers and the advantages of using protein panels for the clinical management of RA. Simultaneous analysis of multiple proteins could yield biomarker signatures of RA subtypes to enable patients to benefit from personalized medicine.

A Review of Challenges and Opportunities in Machine Learning for Health

Modern electronic health records (EHRs) provide data to answer clinically meaningful questions. The growing data in EHRs makes healthcare ripe for the use of machine learning. However, learning in a clinical setting presents unique challenges that complicate the use of common machine learning methodologies. For example, diseases in EHRs are poorly labeled, conditions can encompass multiple underlying endotypes, and healthy individuals are underrepresented. This article serves as a primer to illuminate these challenges and highlights opportunities for members of the machine learning community to contribute to healthcare.

Global variability analysis of mRNA and protein concentrations across and within human tissues

Genes and proteins show variable expression patterns throughout the human body. However, it is not clear whether relative differences in mRNA concentrations are retained on the protein level. Furthermore, inter-individual protein concentration variability within single tissue types has not been comprehensively explored. Here, we used the Gini index for in-depth concentration variability analysis of publicly available transcriptomics and proteomics data, and of an in-house proteomics dataset of human liver and jejunum from 38 donors. We found that the transfer of concentration variability from mRNA to protein is limited, that established 'reference genes' for data normalization vary markedly at the protein level, that protein concentrations cover a wide variability spectrum within single tissue types, and that concentration variability analysis can be a convenient starting point for identifying disease-associated proteins and novel biomarkers. Our results emphasize the importance of considering individual concentration levels, as opposed to population averages, for personalized systems biology analysis.

Dynamical systems approaches to personalized medicine

The complexity of the human body is a major roadblock to diagnosis and treatment of disease. Individuals may be diagnosed with the same disease but exhibit different biomarker profiles or physiological changes and, importantly, they may respond differently to the same risk factors and the same treatment. There is no doubt that computational methods of data analysis and interpretation must be developed for medicine to evolve from the traditional population-based approaches to personalized treatment strategies. We discuss how computational systems biology is contributing to this current evolution.

A Systems Biology Approach for Personalized Medicine in Refractory Epilepsy

Epilepsy refers to a common chronic neurological disorder that affects all age groups. Unfortunately, antiepileptic drugs are ineffective in about one-third of patients. The complex interindividual variability influences the response to drug treatment rendering the therapeutic failure one of the most relevant problems in clinical practice also for increased hospitalizations and healthcare costs. Recent advances in the genetics and neurobiology of epilepsies are laying the groundwork for a new personalized medicine, focused on the reversal or avoidance of the pathophysiological effects of specific gene mutations. This could lead to a significant improvement in the efficacy and safety of treatments for epilepsy, targeting the biological mechanisms responsible for epilepsy in each individual. In this review article, we focus on the mechanism of the epilepsy pharmacoresistance and highlight the use of a systems biology approach for personalized medicine in refractory epilepsy.

Therapeutic targeting of angiogenesis molecular pathways in angiogenesis-dependent diseases

Angiogenesis is a critical step in the progression of almost all human malignancies and some other life-threatening diseases. Anti-angiogenic therapy is a novel and effective approach for treatment of angiogenesis-dependent diseases such as cancer, diabetic retinopathy, and age-related macular degeneration. In this article, we will review the main strategies developed for anti-angiogenic therapies beside their clinical applications, the major challenges, and the latest advances in the development of anti-angiogenesis-based targeted therapies.

Early diagnosis of disease using microbead array technology: A review

Early diagnosis of diseases (before they become advanced and incurable) is essential to reduce morbidity and mortality rates. With the advent of novel technologies in clinical laboratory diagnosis, microbead-based arrays have come to be recognized as an efficient approach, that demonstrates useful advantages over traditional assay methods for multiple disease-related biomarkers. Multiplexed microbead assays provide a robust, rapid, specific, and cost-effective approach for high-throughput and simultaneous screening of many different targets. Biomolecular binding interactions occur after applying a biological sample (such as blood plasma, saliva, cerebrospinal fluid etc.) containing the target analyte(s) to a set of microbeads with different ligand-specificities that have been coded in planar or suspension arrays. The ligand-receptor binding activity is tracked by optical signals generated by means of flow cytometry analysis in the case of suspension arrays, or by image processing devices in the case of planar arrays. In this review paper, we discuss diagnosis of cancer, neurological and infectious diseases by using optically-encoded microbead-based arrays (both multiplexed and single-analyte assays) as a reliable tool for detection and quantification of various analytes.

Data assimilation and multisource decision-making in systems biology based on unobtrusive Internet-of-Things devices

Biological and medical diagnoses depend on high-quality measurements. A wearable device based on Internet of Things (IoT) must be unobtrusive to the human body to encourage users to accept continuous monitoring. However, unobtrusive IoT devices are usually of low quality and unreliable because of the limitation of technology progress that has slowed down at high peak. Therefore, advanced inference techniques must be developed to address the limitations of IoT devices. This review proposes that IoT technology in biological and medical applications should be based on a new data assimilation process that fuses multiple data scales from several sources to provide diagnoses. Moreover, the required technologies are ready to support the desired disease diagnosis levels, such as hypothesis test, multiple evidence fusion, machine learning, data assimilation, and systems biology. Furthermore, cross-disciplinary integration has emerged with advancements in IoT. For example, the multiscale modeling of systems biology from proteins and cells to organs integrates current developments in biology, medicine, mathematics, engineering, artificial intelligence, and semiconductor technologies. Based on the monitoring objectives of IoT devices, researchers have gradually developed ambulant, wearable, noninvasive, unobtrusive, low-cost, and pervasive monitoring devices with data assimilation methods that can overcome the limitations of devices in terms of quality measurement. In the future, the novel features of data assimilation in systems biology and ubiquitous sensory development can describe patients' physical conditions based on few but long-term measurements.

A Clinician's Perspective on Biomarkers

Psychiatrists and mental health professionals regularly perform various clinical tasks (e.g., detection, differential diagnosis, prognostication, treatment selection and implementation). How well they perform each of these tasks has a direct impact on patient outcomes. Measurement-based care has brought greater precision to these tasks and has improved outcomes. This article provides an overview of the types of biomeasures and biomarkers, the clinical uses of biomarkers, and the challenges in their development and clinical use. Although still in their infancy, biomarkers hold the promise of bringing even greater precision and even better outcomes in mental health. Biomeasures that could become biomarkers include genetic, proteomic, metabolomic, and immunologic measures, as well as physiological, functional, and brain structural measures. Mechanistic markers reflect and are based on the specific pathobiological processes that are involved in the development of a clinically defined condition. Some clinically relevant biomarkers may rely on this mechanistic understanding while others may not. Clinical biomarkers serve three broadly defined goals. Diagnostic markers define what is wrong. Prognostic markers define what will happen in the natural course of the condition, although they may also predict the course of illness during treatment. Theranostic markers address issues pertinent to treatment by defining whether, when, whom, and how to treat. Other biomarkers may be used to monitor the overall effect of treatment regardless of the therapeutic effects or to monitor the specific therapeutic effects of the intervention on the disorder itself. Biomarkers can also be used to estimate susceptibility/risk of developing the condition or the biological consequences of having had the disorder.

High-Performance Agent-Based Modeling Applied to Vocal Fold Inflammation and Repair

Fast and accurate computational biology models offer the prospect of accelerating the development of personalized medicine. A tool capable of estimating treatment success can help prevent unnecessary and costly treatments and potential harmful side effects. A novel high-performance Agent-Based Model (ABM) was adopted to simulate and visualize multi-scale complex biological processes arising in vocal fold inflammation and repair. The computational scheme was designed to organize the 3D ABM sub-tasks to fully utilize the resources available on current heterogeneous platforms consisting of multi-core CPUs and many-core GPUs. Subtasks are further parallelized and convolution-based diffusion is used to enhance the performance of the ABM simulation. The scheme was implemented using a client-server protocol allowing the results of each iteration to be analyzed and visualized on the server (i.e., in-situ) while the simulation is running on the same server. The resulting simulation and visualization software enables users to interact with and steer the course of the simulation in real-time as needed. This high-resolution 3D ABM framework was used for a case study of surgical vocal fold injury and repair. The new framework is capable of completing the simulation, visualization and remote result delivery in under 7 s per iteration, where each iteration of the simulation represents 30 min in the real world. The case study model was simulated at the physiological scale of a human vocal fold. This simulation tracks 17 million biological cells as well as a total of 1.7 billion signaling chemical and structural protein data points. The visualization component processes and renders all simulated biological cells and 154 million signaling chemical data points. The proposed high-performance 3D ABM was verified through comparisons with empirical vocal fold data. Representative trends of biomarker predictions in surgically injured vocal folds were observed.

E-health beyond technology: analyzing the paradigm shift that lies beneath

Information and computer technology has come to play an increasingly important role in medicine, to the extent that e-health has been described as a disruptive innovation or revolution in healthcare. The attention is very much focused on the technology itself, and advances that have been made in genetics and biology. This leads to the question: What is changing in medicine today concerning e-health? To what degree could these changes be characterized as a 'revolution'? We will apply the work of Thomas Kuhn, Larry Laudan, Michel Foucault and other philosophers-which offers an alternative understanding of progress and revolution in medicine to the classic discovery-oriented approach-to our analysis. Nowadays, the long-standing curative or reactive paradigm in medicine is facing a crisis due to an aging population, a significant increase in chronic diseases and the development of more expensive diagnostic tools and therapies. This promotes the evolution towards a new paradigm with an emphasis on preventive medicine. E-health constitutes an essential part of this new paradigm that seeks to solve the challenges presented by an aging population, skyrocketing costs and so forth. Our approach changes the focus from the technology itself toward the underlying paradigm shift in medicine. We will discuss the relevance of this approach by applying it to the surge in digital self-tracking through health apps and wearables: the recognition of the underlying paradigm shift leads to a more comprehensive understanding of self-tracking than a solely discovery-oriented or technology-focused view can provide.

Metabolomics: Challenges and Opportunities in Systems Biology Studies

Metabolomics has the capability of providing predisposition, diagnostic, prognostic, and therapeutic biomarker profiles of individual patients, since a large number of metabolites can be measured in an unbiased manner from biological samples. In this setting, ¹H-Nuclear Magnetic Resonance (NMR) spectroscopy of biofluids such as plasma, urine, and fecal water offers the opportunity to identify patterns of biomarker changes that reflects the physiological or pathological status of an individual patient.In this chapter, we show as a metabolomics study can be used to diagnose a disease, classifying patients as healthy or as pathological taking into account individual variability.

Personalized medicine-a modern approach for the diagnosis and management of hypertension

The main goal of treating hypertension is to reduce blood pressure to physiological levels and thereby prevent risk of cardiovascular disease and hypertension-associated target organ damage. Despite reductions in major risk factors and the availability of a plethora of effective antihypertensive drugs, the control of blood pressure to target values is still poor due to multiple factors including apparent drug resistance and lack of adherence. An explanation for this problem is related to the current reductionist and ‘trial-and-error’ approach in the management of hypertension, as we may oversimplify the complex nature of the disease and not pay enough attention to the heterogeneity of the pathophysiology and clinical presentation of the disorder. Taking into account specific risk factors, genetic phenotype, pharmacokinetic characteristics, and other particular features unique to each patient, would allow a personalized approach to managing the disease. Personalized medicine therefore represents the tailoring of medical approach and treatment to the individual characteristics of each patient and is expected to become the paradigm of future healthcare. The advancement of systems biology research and the rapid development of high-throughput technologies, as well as the characterization of different –omics, have contributed to a shift in modern biological and medical research from traditional hypothesis-driven designs toward data-driven studies and have facilitated the evolution of personalized or precision medicine for chronic diseases such as hypertension.

Côte d'Ivoire Dual Burden of Disease (CoDuBu): Study Protocol to Investigate the Co-occurrence of Chronic Infections and Noncommunicable Diseases in Rural Settings of Epidemiological Transition

BACKGROUND

Individual-level concomitance of infectious diseases and noncommunicable diseases (NCDs) is poorly studied, despite the reality of this dual disease burden for many low- and middle-income countries (LMICs).

OBJECTIVE

This study protocol describes the implementation of a cohort and biobank aiming for a better understanding of interrelation of helminth and Plasmodium infections with NCD phenotypes like metabolic syndrome, hypertension, and diabetes.

METHODS

A baseline cross-sectional population-based survey was conducted over one year, in the Taabo health and demographic surveillance system (HDSS) in south-central Côte d'Ivoire. We randomly identified 1020 consenting participants aged ≥18 years in three communities (Taabo-Cité, Amani-Ménou, and Tokohiri) reflecting varying stages of epidemiological transition. Participants underwent health examinations consisting of NCD phenotyping (anthropometry, blood pressure, renal function, glycemia, and lipids) and infectious disease testing (infections with soil-transmitted helminths, schistosomes, and Plasmodium). Individuals identified to have elevated blood pressure, glucose, lipids, or with infections were referred to the central/national health center for diagnostic confirmation and treatment. Aliquots of urine, stool, and venous blood were stored in a biobank for future exposome/phenome research. In-person interviews on sociodemographic attributes, risk factors for infectious diseases and NCDs, medication, vaccinations, and health care were also conducted. Appropriate statistical techniques will be applied in exploring the concomitance of infectious diseases and NCDs and their determinants. Participants' consent for follow-up contact was obtained.

RESULTS

Key results from this baseline study, which will be published in peer-reviewed literature, will provide information on the prevalence and co-occurrence of infectious diseases, NCDs, and their risk factors. The Taabo HDSS consists of rural and somewhat more urbanized areas, allowing for comparative studies at different levels of epidemiological transition. An HDSS setting is ideal as a basis for longitudinal studies since their sustainable field work teams hold close contact with the local population.

CONCLUSIONS

The collaboration between research institutions, public health organizations, health care providers, and staff from the Taabo HDSS in this study assures that the synthesized evidence will feed into health policy towards integrated infectious disease-NCD management. The preparation of health systems for the dual burden of disease is pressing in low- and middle-income countries. The established biobank will strengthen the local research capacity and offer opportunities for biomarker studies to deepen the understanding of the cross-talk between infectious diseases and NCDs.

TRIAL REGISTRATION

International Standard Randomized Controlled Trials Number (ISRCTN): 87099939; http://www.isrctn.com/ISRCTN87099939 (Archived by WebCite at http://www.webcitation.org/6uLEs1EsX).

Research and Discovery Science and the Future of Dental Education and Practice

Dental graduates of 2040 will face new and complex challenges. If they are to meet these challenges, dental schools must develop a research and discovery mission that will equip graduates with the new knowledge required to function in a modern health care environment. The dental practitioner of 2040 will place greater emphasis on risk assessment, disease prevention, and health maintenance; and the emerging discipline of precision medicine and systems biology will revolutionize disease diagnosis and reveal new targeted therapies. The dental graduate of 2040 will be expected to function effectively in a collaborative, learning health care system and to understand the impact of health care policy on local, national, and global communities. Emerging scientific fields such as big data analytics, stem cell biology, tissue engineering, and advanced biomimetics will impact dental practice. Despite all the warning signs indicating how the changing scientific and heath care landscape will dramatically alter dental education and dental practice, dental schools have yet to reconsider their research and educational priorities and clinical practice objectives. Until dental schools and the practicing community come to grips with these challenges, this persistent attitude of complacency will likely be at the dental profession's peril. This article was written as part of the project "Advancing Dental Education in the 21^st Century."

Novel Biomarkers in the Diagnosis of Chronic Kidney Disease and the Prediction of Its Outcome

In its early stages, symptoms of chronic kidney disease (CKD) are usually not apparent. Significant reduction of the kidney function is the first obvious sign of disease. If diagnosed early (stages 1 to 3), the progression of CKD can be altered and complications reduced. In stages 4 and 5 extensive kidney damage is observed, which usually results in end-stage renal failure. Currently, the diagnosis of CKD is made usually on the levels of blood urea and serum creatinine (sCr), however, sCr has been shown to be lacking high predictive value. Due to the development of genomics, epigenetics, transcriptomics, proteomics, and metabolomics, the introduction of novel techniques will allow for the identification of novel biomarkers in renal diseases. This review presents some new possible biomarkers in the diagnosis of CKD and in the prediction of outcome, including asymmetric dimethylarginine (ADMA), symmetric dimethylarginine (SDMA), uromodulin, kidney injury molecule-1 (KIM-1), neutrophil gelatinase-associated lipocalin (NGAL), miRNA, ncRNA, and lincRNA biomarkers and proteomic and metabolomic biomarkers. Complicated pathomechanisms of CKD development and progression require not a single marker but their combination in order to mirror all types of alterations occurring in the course of this disease. It seems that in the not so distant future, conventional markers may be exchanged for new ones, however, confirmation of their efficacy, sensitivity and specificity as well as the reduction of analysis costs are required.

Living on the edge: substrate competition explains loss of robustness in mitochondrial fatty-acid oxidation disorders

Background

Defects in genes involved in mitochondrial fatty-acid oxidation (mFAO) reduce the ability of patients to cope with metabolic challenges. mFAO enzymes accept multiple substrates of different chain length, leading to molecular competition among the substrates. Here, we combined computational modeling with quantitative mouse and patient data to investigate whether substrate competition affects pathway robustness in mFAO disorders.

Results

First, we used comprehensive biochemical analyses of wild-type mice and mice deficient for medium-chain acyl-CoA dehydrogenase (MCAD) to parameterize a detailed computational model of mFAO. Model simulations predicted that MCAD deficiency would have no effect on the pathway flux at low concentrations of the mFAO substrate palmitoyl-CoA. However, high concentrations of palmitoyl-CoA would induce a decline in flux and an accumulation of intermediate metabolites. We proved computationally that the predicted overload behavior was due to substrate competition in the pathway. Second, to study the clinical relevance of this mechanism, we used patients’ metabolite profiles and generated a humanized version of the computational model. While molecular competition did not affect the plasma metabolite profiles during MCAD deficiency, it was a key factor in explaining the characteristic acylcarnitine profiles of multiple acyl-CoA dehydrogenase deficient patients. The patient-specific computational models allowed us to predict the severity of the disease phenotype, providing a proof of principle for the systems medicine approach.

Conclusion

We conclude that substrate competition is at the basis of the physiology seen in patients with mFAO disorders, a finding that may explain why these patients run a risk of a life-threatening metabolic catastrophe.

Electronic supplementary material

The online version of this article (doi:10.1186/s12915-016-0327-5) contains supplementary material, which is available to authorized users.

Precision medicine and molecular imaging: new targeted approaches toward cancer therapeutic and diagnosis

This paper presents a review of the importance and role of precision medicine and molecular imaging technologies in cancer diagnosis with therapeutics and diagnostics purposes. Precision medicine is progressively becoming a hot topic in all disciplines related to biomedical investigation and has the capacity to become the paradigm for clinical practice. The future of medicine lies in early diagnosis and individually appropriate treatments, a concept that has been named precision medicine, i.e. delivering the right treatment to the right patient at the right time. Molecular imaging is quickly being recognized as a tool with the potential to ameliorate every aspect of cancer treatment. On the other hand, emerging high-throughput technologies such as omics techniques and systems approaches have generated a paradigm shift for biological systems in advanced life science research. In this review, we describe the precision medicine, difference between precision medicine and personalized medicine, precision medicine initiative, systems biology/medicine approaches (such as genomics, radiogenomics, transcriptomics, proteomics, and metabolomics), P4 medicine, relationship between systems biology/medicine approaches and precision medicine, and molecular imaging modalities and their utility in cancer treatment and diagnosis. Accordingly, the precision medicine and molecular imaging will enable us to accelerate and improve cancer management in future medicine.

Gene Expression Profiles of HIV/AIDS Patients with Qi-Yin Deficiency and Dampness-Heat Retention

Objectives: Traditional Chinese Medicine (TCM) applied in the clinic as a complementary and alternative therapy has helped improve immunity and reduce side effects and symptomatic treatment in patients with HIV/AIDS. However, the mechanisms of TCM syndromes are not clear. Transcriptomics enables the study of such TCM syndromes.

Design: This study compared the messenger RNA (mRNA) expressions of healthy persons and patients with HIV/AIDS who had two common TCM syndromes, qi-yin deficiency and dampness-heat retention, to find the difference in HIV/AIDS with TCM syndromes.

Results: Comparison with healthy persons identified 113 mRNAs—41 enhanced and 72 decreased—in the qi-yin deficiency group. Additionally, 76 mRNAs were found in the dampness-heat retention group: 14 increased and 62 decreased. Functional genetic analysis of the mRNAs indicated that two TCM syndromes were correlated with cell apoptosis, immunoinflammatory responses, and lymphocyte activation. Differentially expressed mRNAs in the qi-yin deficiency group were obviously associated with cellular activity, communication, protein localization, cellular ion homeostasis, and regulation of cell motion, whereas mRNAs in the dampness-heat retention group were associated with sequence-specific DNA binding, cellular response to stress, and hemopoietic or lymphoid organ development.

Conclusions: These results suggest that the formation of different TCM syndromes in patients with HIV/AIDS were founded on biological transcriptomics, which reveal mechanisms of the formation of these syndromes in HIV/AIDS. Differentially expressed mRNAs in two TCM syndrome groups tended to normalize after TCM intervention, which indicates that TCM might remit symptoms by changing genetic expression.

A simple grid implementation with Berkeley Open Infrastructure for Network Computing using BLAST as a model

Development of high-throughput technologies, such as Next-generation sequencing, allows thousands of experiments to be performed simultaneously while reducing resource requirement. Consequently, a massive amount of experiment data is now rapidly generated. Nevertheless, the data are not readily usable or meaningful until they are further analysed and interpreted. Due to the size of the data, a high performance computer (HPC) is required for the analysis and interpretation. However, the HPC is expensive and difficult to access. Other means were developed to allow researchers to acquire the power of HPC without a need to purchase and maintain one such as cloud computing services and grid computing system. In this study, we implemented grid computing in a computer training center environment using Berkeley Open Infrastructure for Network Computing (BOINC) as a job distributor and data manager combining all desktop computers to virtualize the HPC. Fifty desktop computers were used for setting up a grid system during the off-hours. In order to test the performance of the grid system, we adapted the Basic Local Alignment Search Tools (BLAST) to the BOINC system. Sequencing results from Illumina platform were aligned to the human genome database by BLAST on the grid system. The result and processing time were compared to those from a single desktop computer and HPC. The estimated durations of BLAST analysis for 4 million sequence reads on a desktop PC, HPC and the grid system were 568, 24 and 5 days, respectively. Thus, the grid implementation of BLAST by BOINC is an efficient alternative to the HPC for sequence alignment. The grid implementation by BOINC also helped tap unused computing resources during the off-hours and could be easily modified for other available bioinformatics software.

The Significance of an Enhanced Concept of the Organism for Medicine

Recent developments in evolutionary biology, comparative embryology, and systems biology suggest the necessity of a conceptual shift in the way we think about organisms. It is becoming increasingly evident that molecular and genetic processes are subject to extremely refined regulation and control by the cell and the organism, so that it becomes hard to define single molecular functions or certain genes as primary causes of specific processes. Rather, the molecular level is integrated into highly regulated networks within the respective systems. This has consequences for medical research in general, especially for the basic concept of personalized medicine or precision medicine. Here an integrative systems concept is proposed that describes the organism as a multilevel, highly flexible, adaptable, and, in this sense, autonomous basis for a human individual. The hypothesis is developed that these properties of the organism, gained from scientific observation, will gradually make it necessary to rethink the conceptual framework of physiology and pathophysiology in medicine.

Animal-Free Chemical Safety Assessment

The exponential growth of the Internet of Things and the global popularity and remarkable decline in cost of the mobile phone is driving the digital transformation of medical practice. The rapidly maturing digital, non-medical world of mobile (wireless) devices, cloud computing and social networking is coalescing with the emerging digital medical world of omics data, biosensors and advanced imaging which offers the increasingly realistic prospect of personalized medicine. Described as a potential “seismic” shift from the current “healthcare” model to a “wellness” paradigm that is predictive, preventative, personalized and participatory, this change is based on the development of increasingly sophisticated biosensors which can track and measure key biochemical variables in people. Additional key drivers in this shift are metabolomic and proteomic signatures, which are increasingly being reported as pre-symptomatic, diagnostic and prognostic of toxicity and disease. These advancements also have profound implications for toxicological evaluation and safety assessment of pharmaceuticals and environmental chemicals. An approach based primarily on human in vivo and high-throughput in vitro human cell-line data is a distinct possibility. This would transform current chemical safety assessment practice which operates in a human “data poor” to a human “data rich” environment. This could also lead to a seismic shift from the current animal-based to an animal-free chemical safety assessment paradigm.