Systems Biology, Systems Medicine, Systems Pharmacology: The What and The Why

The circadian rhythm: an influential soundtrack in the diabetes story

Type 2 Diabetes Mellitus (T2DM) has been the main category of metabolic diseases in recent years due to changes in lifestyle and environmental conditions such as diet and physical activity. On the other hand, the circadian rhythm is one of the most significant biological pathways in humans and other mammals, which is affected by light, sleep, and human activity. However, this cycle is controlled via complicated cellular pathways with feedback loops. It is widely known that changes in the circadian rhythm can alter some metabolic pathways of body cells and could affect the treatment process, particularly for metabolic diseases like T2DM. The aim of this study is to explore the importance of the circadian rhythm in the occurrence of T2DM via reviewing the metabolic pathways involved, their relationship with the circadian rhythm from two perspectives, lifestyle and molecular pathways, and their effect on T2DM pathophysiology. These impacts have been demonstrated in a variety of studies and led to the development of approaches such as time-restricted feeding, chronotherapy (time-specific therapies), and circadian molecule stabilizers.

Thinking in 3 dimensions: philosophies of the microenvironment in organoids and organs-on-chip

Organoids and organs-on-a-chip are currently the two major families of 3D advanced organotypic in vitro culture systems, aimed at reconstituting miniaturized models of physiological and pathological states of human organs. Both share the tenets of the so-called "three-dimensional thinking", a Systems Physiology approach focused on recapitulating the dynamic interactions between cells and their microenvironment. We first review the arguments underlying the "paradigm shift" toward three-dimensional thinking in the in vitro culture community. Then, through a historically informed account of the technical affordances and the epistemic commitments of these two approaches, we highlight how they embody two distinct experimental cultures. We finally argue that the current systematic effort for their integration requires not only innovative "synergistic" engineering solutions, but also conceptual integration between different perspectives on biological causality.

From Pharmacogenetics to Pharmaco-Omics:Milestones and Future Directions

The origins of pharmacogenetics date back to the 1950s, when it was established that inter-individual differences in drug response are partially determined by genetic factors. Since then, pharmacogenetics has grown into its own field, motivated by the translation of identified gene-drug interactions into therapeutic applications. Despite numerous challenges ahead, our understanding of the human pharmacogenetic landscape has greatly improved thanks to the integration of tools originating from disciplines as diverse as biochemistry, molecular biology, statistics, and computer sciences. In this review, we discuss past, present, and future developments of pharmacogenetics methodology, focusing on three milestones: how early research established the genetic basis of drug responses, how technological progress made it possible to assess the full extent of pharmacological variants, and how multi-dimensional omics datasets can improve the identification, functional validation, and mechanistic understanding of the interplay between genes and drugs. We outline novel strategies to repurpose and integrate molecular and clinical data originating from biobanks to gain insights analogous to those obtained from randomized controlled trials. Emphasizing the importance of increased diversity, we envision future directions for the field that should pave the way to the clinical implementation of pharmacogenetics.

Systematic analysis of the potential off-target activities of osimertinib by computational target fishing

Osimertinib is a third-generation epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor used to treat non-small cell lung cancer. However, its off-targets are obscure, and systematic analysis of off-target activities remains to be performed. Here, we identified the off-targets of osimertinib using PharmMapper and DRAR-CPI and analyzed the intersected targets using the GeneMANIA and DAVID servers. A drug-target-pathway network was constructed to visualize the associations. The results showed that osimertinib is associated with 31 off-targets, 40 Kyoto Encyclopedia of Genes and Genomes pathways, and 9 diseases. Network analysis revealed that the targets were involved in cancer and other physiological processes. In addition to EGFR, molecular docking analysis showed that seven proteins, namely Janus kinase 3, peroxisome proliferator-activated receptor alpha, renin, mitogen-activated protein kinases, lymphocyte-specific protein tyrosine kinase, cell division protein kinase 2 and proto-oncogene tyrosine-protein kinase Src, could also be potential targets of osimertinib. In conclusion, osimertinib is predicted to target multiple proteins and pathways, resulting in the formation of an action network via which it exerts systematic pharmacological effects.

Can Systems Biology Advance Clinical Precision Oncology?

Precision oncology is perceived as a way forward to treat individual cancer patients. However, knowing particular cancer mutations is not enough for optimal therapeutic treatment, because cancer genotype-phenotype relationships are nonlinear and dynamic. Systems biology studies the biological processes at the systems' level, using an array of techniques, ranging from statistical methods to network reconstruction and analysis, to mathematical modeling. Its goal is to reconstruct the complex and often counterintuitive dynamic behavior of biological systems and quantitatively predict their responses to environmental perturbations. In this paper, we review the impact of systems biology on precision oncology. We show examples of how the analysis of signal transduction networks allows to dissect resistance to targeted therapies and inform the choice of combinations of targeted drugs based on tumor molecular alterations. Patient-specific biomarkers based on dynamical models of signaling networks can have a greater prognostic value than conventional biomarkers. These examples support systems biology models as valuable tools to advance clinical and translational oncological research.

Patients' and professionals' views related to ethical issues in precision medicine: a mixed research synthesis

BACKGROUND

Precision medicine development is driven by the possibilities of next generation sequencing, information technology and artificial intelligence and thus, raises a number of ethical questions. Empirical studies have investigated such issues from the perspectives of health care professionals, researchers and patients. We synthesize the results from these studies in this review.

METHODS

We used a systematic strategy to search, screen and assess the literature for eligibility related to our research question. The initial search for empirical studies in five data bases provided 665 different records and we selected 92 of these publications for inclusion in this review. Data were extracted in a spreadsheet and categorized into different topics representing the views on ethical issues in precision medicine.

RESULTS

Many patients and professionals expect high benefits from precision medicine and have a positive attitude towards it. However, patients and professionals also perceive some risks. Commonly perceived risks include: lack of evidence for accuracy of tests and efficacy of treatments; limited knowledge of patients, which makes informed consent more difficult; possible unavailability of access to precision medicine for underprivileged people and ethnic minorities; misuse of data by insurance companies and employers, potential of racial stigmatization due to genetic information; unwanted communication of incidental findings; changes in doctor-patient-relationship through focusing on data; and the problem that patients could feel under pressure to optimize their health.

CONCLUSIONS

National legislation and guidelines already minimize many risks associated with precision medicine. However, from our perspective some problems require more attention. Should hopes for precision medicine's benefits be fulfilled, then the ethical principle of justice would require an unlimited access to precision medicine for all people. The potential for autonomous patients' decisions must be greatly enhanced by improvements in patient education. Harm from test results must be avoided in any case by the highest possible data security level and communication guidelines. Changes in the doctor-patient relationship and the impact of precision medicine on the quality of life should be further investigated. Additionally, the cost-effectiveness of precision medicine should be further examined, in order to avoid malinvestment.

Targeting Cellular DNA Damage Responses in Cancer: An In Vitro-Calibrated Agent-Based Model Simulating Monolayer and Spheroid Treatment Responses to ATR-Inhibiting Drugs

We combine a systems pharmacology approach with an agent-based modelling approach to simulate LoVo cells subjected to AZD6738, an ATR (ataxia–telangiectasia-mutated and rad3-related kinase) inhibiting anti-cancer drug that can hinder tumour proliferation by targeting cellular DNA damage responses. The agent-based model used in this study is governed by a set of empirically observable rules. By adjusting only the rules when moving between monolayer and multi-cellular tumour spheroid simulations, whilst keeping the fundamental mathematical model and parameters intact, the agent-based model is first parameterised by monolayer in vitro data and is thereafter used to simulate treatment responses in in vitro tumour spheroids subjected to dynamic drug delivery. Spheroid simulations are subsequently compared to in vivo data from xenografts in mice. The spheroid simulations are able to capture the dynamics of in vivo tumour growth and regression for approximately 8 days post-tumour injection. Translating quantitative information between in vitro and in vivo research remains a scientifically and financially challenging step in preclinical drug development processes. However, well-developed in silico tools can be used to facilitate this in vitro to in vivo translation, and in this article, we exemplify how data-driven, agent-based models can be used to bridge the gap between in vitro and in vivo research. We further highlight how agent-based models, that are currently underutilised in pharmaceutical contexts, can be used in preclinical drug development.

Scientific and regulatory evaluation of mechanistic in silico drug and disease models in drug development: Building model credibility

The value of in silico methods in drug development and evaluation has been demonstrated repeatedly and convincingly. While their benefits are now unanimously recognized, international standards for their evaluation, accepted by all stakeholders involved, are still to be established. In this white paper, we propose a risk‐informed evaluation framework for mechanistic model credibility evaluation. To properly frame the proposed verification and validation activities, concepts such as context of use, regulatory impact and risk‐based analysis are discussed. To ensure common understanding between all stakeholders, an overview is provided of relevant in silico terminology used throughout this paper. To illustrate the feasibility of the proposed approach, we have applied it to three real case examples in the context of drug development, using a credibility matrix currently being tested as a quick‐start tool by regulators. Altogether, this white paper provides a practical approach to model evaluation, applicable in both scientific and regulatory evaluation contexts.

Perspectives for systems biology in the management of tuberculosis

Standardised management of tuberculosis may soon be replaced by individualised, precision medicine-guided therapies informed with knowledge provided by the field of systems biology. Systems biology is a rapidly expanding field of computational and mathematical analysis and modelling of complex biological systems that can provide insights into mechanisms underlying tuberculosis, identify novel biomarkers, and help to optimise prevention, diagnosis and treatment of disease. These advances are critically important in the context of the evolving epidemic of drug-resistant tuberculosis. Here, we review the available evidence on the role of systems biology approaches - human and mycobacterial genomics and transcriptomics, proteomics, lipidomics/metabolomics, immunophenotyping, systems pharmacology and gut microbiomes - in the management of tuberculosis including prediction of risk for disease progression, severity of mycobacterial virulence and drug resistance, adverse events, comorbidities, response to therapy and treatment outcomes. Application of the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach demonstrated that at present most of the studies provide "very low" certainty of evidence for answering clinically relevant questions. Further studies in large prospective cohorts of patients, including randomised clinical trials, are necessary to assess the applicability of the findings in tuberculosis prevention and more efficient clinical management of patients.

Mathematical Modelling Based on In Vivo Imaging Suggests CD137-Stimulated Cytotoxic T Lymphocytes Exert Superior Tumour Control Due to an Enhanced Antimitotic Effect on Tumour Cells

Simple Summary

Cytotoxic T lymphocytes (CTLs) play an important role in controlling tumours, and an improved understanding of how they accomplish this will benefit immunotherapeutic cancer treatment strategies. Stimulation of CTLs by targeting their CD137 receptor is a strategy currently under investigation for enhancing responses against tumours, yet so far only limited quantitative knowledge regarding the effects of such stimulation upon CTLs has been obtained. Here, we develop mathematical models to describe dynamic in vivo two-photon imaging of tumour infiltrating CTLs, to characterise differences in their function either in the presence or absence of a CD137 agonist antibody. We showed that an increased antiproliferative effect and a more sustained presence of CTLs within the tumour were the most important effects associated with anti-CD137 treatment.

Abstract

Several immunotherapeutic strategies for the treatment of cancer are under development. Two prominent strategies are adoptive cell transfer (ACT) of CTLs and modulation of CTL function with immune checkpoint inhibitors or with costimulatory antibodies. Despite some success with these approaches, there remains a lack of detailed and quantitative descriptions of the events following CTL transfer and the impact of immunomodulation. Here, we have applied ordinary differential equation models to two photon imaging data derived from a B16F10 murine melanoma. Models were parameterised with data from two different treatment conditions: either ACT-only, or ACT with intratumoural costimulation using a CD137 targeted antibody. Model dynamics and best fitting parameters were compared, in order to assess the mode of action of the CTLs and examine how the CD137 antibody influenced their activities. We found that the cytolytic activity of the transferred CTLs was minimal without CD137 costimulation, and that the CD137 targeted antibody did not enhance the per-capita killing ability of the transferred CTLs. Instead, the results of our modelling study suggest that an antiproliferative effect of CTLs exerted upon the tumour likely accounted for the majority of the reduction in tumour growth after CTL transfer. Moreover, we found that CD137 most likely improved tumour control via enhancement of this antiproliferative effect, as well as prolonging the period in which CTLs were inside the tumour, leading to a sustained duration of their antitumour effects following CD137 stimulation.

IBD Systems Biology Is Here to Stay

BACKGROUND

Systems biology is a rapidly advancing field of science that allows us to look into disease mechanisms, patient diagnosis and stratification, and drug development in a completely new light. It is based on the utilization of unbiased computational systems free of the traditional experimental approaches based on personal choices of what is important and what select experiments should be performed to obtain the expected results.

METHODS

Systems biology can be applied to inflammatory bowel disease (IBD) by learning basic concepts of omes and omics and how omics-derived "big data" can be integrated to discover the biological networks underlying highly complex diseases like IBD. Once these biological networks (interactomes) are identified, then the molecules controlling the disease network can be singled out and specific blockers developed.

RESULTS

The field of systems biology in IBD is just emerging, and there is still limited information on how to best utilize its power to advance our understanding of Crohn disease and ulcerative colitis to develop novel therapeutic strategies. Few centers have embraced systems biology in IBD, but the creation of international consortia and large biobanks will make biosamples available to basic and clinical IBD investigators for further research studies.

CONCLUSIONS

The implementation of systems biology is indispensable and unavoidable, and the patient and medical communities will both benefit immensely from what it will offer in the near future.

Personalized Medicine for Neuroblastoma: Moving from Static Genotypes to Dynamic Simulations of Drug Response

High-risk neuroblastoma is an aggressive childhood cancer that is characterized by high rates of chemoresistance and frequent metastatic relapse. A number of studies have characterized the genetic and epigenetic landscape of neuroblastoma, but due to a generally low mutational burden and paucity of actionable mutations, there are few options for applying a comprehensive personalized medicine approach through the use of targeted therapies. Therefore, the use of multi-agent chemotherapy remains the current standard of care for neuroblastoma, which also conceptually limits the opportunities for developing an effective and widely applicable personalized medicine approach for this disease. However, in this review we outline potential approaches for tailoring the use of chemotherapy agents to the specific molecular characteristics of individual tumours by performing patient-specific simulations of drug-induced apoptotic signalling. By incorporating multiple layers of information about tumour-specific aberrations, including expression as well as mutation data, these models have the potential to rationalize the selection of chemotherapeutics contained within multi-agent treatment regimens and ensure the optimum response is achieved for each individual patient.

Exploring the Mechanism of Icariin in Osteoporosis Based on a Network Pharmacology Strategy

BACKGROUND With the aging of the world's population, the incidence of osteoporosis (OP) has become a public health problem of worldwide concern. Research shows that icariin may have a therapeutic effect on OP. MATERIAL AND METHODS PharmMapper was utilized to predict the potential targets of icariin. GeneCards and Online Mendelian Inheritance in Man (OMIM) were used for the collection of OP genes. The STRING database was utilized to obtain the protein-protein interaction (PPI) data. We used Cytoscape 3.7.2 to construct and analyze the networks. The genes and targets in the networks were input into the Database for Annotation, Visualization and Integrated Discovery (DAVID) to undergo Gene Ontology (GO) and pathway enrichment analysis. Finally, animal experiments were performed to verify the prediction results of this study. RESULTS A total of 297 icariin potential targets and 262 OP genes were obtained, and an icariin-OP PPI network was constructed and analyzed. The results of the GO enrichment analysis showed that icariin can regulate the steroid hormone-mediated signaling pathway, skeletal system development, extracellular space, cytosol, and steroid hormone receptor activity. The results of the pathway enrichment analysis showed that icariin can regulate osteoclast differentiation, FoxO, estrogen, and PPAR signaling pathways. The results of the experiments showed that icariin can increase estradiol, ß-catenin, and Receptor Activator of Nuclear Factor-к B Ligand (RANKL)/osteoprotegerin (OPG) ratio in postmenopausal OP rats (P<0.05). CONCLUSIONS This research found that the icariin can regulate OP-related biological processes, cell components, molecular functions, and signaling pathways.

From personalised nutrition to precision medicine: the rise of consumer genomics and digital health

Advances in genomics generated the concept that a better understanding of individual characteristics, e.g. genotype, will lead to improved tailoring of pharmaceutical and nutritional therapies. Subsequent developments in proteomics and metabolomics, in addition to wearable technologies for tracking parameters, such as dietary intakes, physical activity, heart rate and blood glucose, have further driven this idea. Alongside these innovations, there has been a rapid rise in companies offering direct-to-consumer genetic and/or microbiome testing, in combination with the marketing of personalised nutrition services. Key scientific questions include how disparate datasets are integrated, how accurate are current predictions and how these may be developed in the future. In this regard, lessons can be learned from systems biology, which aims both to integrate data from different levels of organisation (e.g. genomic, proteomic and metabolomic) and predict the emergent behaviours of biological systems or organisms as a whole. The present paper reviews the origins and recent advancement of 'big data' and systems approaches in medicine and nutrition. Conclusions are that systems integration of multiple technologies has generated mechanistic insights and informed the evolution of precision medicine and personalised nutrition. Pertinent ethical issues include who is entitled to access new technologies and how commercial companies are storing, using and/or re-mining consumer data. Questions about efficacy (both long-term behavioural change and health outcomes), cost-benefit and impacts on health inequalities remain to be fully addressed.

Applications of personalised signalling network models in precision oncology

As our ability to provide in-depth, patient-specific characterisation of the molecular alterations within tumours rapidly improves, it is becoming apparent that new approaches will be required to leverage the power of this data and derive the full benefit for each individual patient. Systems biology approaches are beginning to emerge within this field as a potential method of incorporating large volumes of network level data and distilling a coherent, clinically-relevant prediction of drug response. However, the initial promise of this developing field is yet to be realised. Here we argue that in order to develop these precise models of individual drug response and tailor treatment accordingly, we will need to develop mathematical models capable of capturing both the dynamic nature of drug-response signalling networks and key patient-specific information such as mutation status or expression profiles. We also review the modelling approaches commonly utilised within this field, and outline recent examples of their use in furthering the application of systems biology for a precision medicine approach to cancer treatment.

Rethinking the pragmatic systems biology and systems-theoretical biology divide: Toward a complexity-inspired epistemology of systems biomedicine

This paper examines some methodological and epistemological issues underlying the ongoing "artificial" divide between pragmatic-systems biology and systems-theoretical biology. The pragmatic systems view of biology has encountered problems and constraints on its explanatory power because pragmatic systems biologists still tend to view systems as mere collections of parts, not as "emergent realities" produced by adaptive interactions between the constituting components. As such, they are incapable of characterizing the higher-level biological phenomena adequately. The attempts of systems-theoretical biologists to explain these "emergent realities" using mathematics also fail to produce satisfactory results. Given the increasing strategic importance of systems biology, both from theoretical and research perspectives, we suggest that additional epistemological and methodological insights into the possibility of further integration between traditional experimental studies and complex modeling are required. This integration will help to improve the currently underdeveloped pragmatic-systems biology and system-theoretical biology. The "epistemology of complexity," I contend, acts as a glue that connects and integrates different and sometimes opposing viewpoints, perspectives, streams, and practices, thus maintaining intellectual and research coherence of systems research of life. It allows scientists to shift the focus from traditional experimental research to integrated, modeling-based holistic practices capable of providing a comprehensive knowledge of organizing principles of living systems. It also opens the possibility of the development of new practical and theoretical foundations of systems biology to build a better understanding of complex organismic functions.

From sugar to liver fat and public health: systems biology driven studies in understanding non-alcoholic fatty liver disease pathogenesis

Non-alcoholic fatty liver disease (NAFLD) is now a major public health concern with an estimated prevalence of 25-30% of adults in many countries. Strongly associated with obesity and the metabolic syndrome, the pathogenesis of NAFLD is dependent on complex interactions between genetic and environmental factors that are not completely understood. Weight loss through diet and lifestyle modification underpins clinical management; however, the roles of individual dietary nutrients (e.g. saturated and n-3 fatty acids; fructose, vitamin D, vitamin E) in the pathogenesis or treatment of NAFLD are only partially understood. Systems biology offers valuable interdisciplinary methods that are arguably ideal for application to the studying of chronic diseases such as NAFLD, and the roles of nutrition and diet in their molecular pathogenesis. Although present in silico models are incomplete, computational tools are rapidly evolving and human metabolism can now be simulated at the genome scale. This paper will review NAFLD and its pathogenesis, including the roles of genetics and nutrition in the development and progression of disease. In addition, the paper introduces the concept of systems biology and reviews recent work utilising genome-scale metabolic networks and developing multi-scale models of liver metabolism relevant to NAFLD. A future is envisioned where individual genetic, proteomic and metabolomic information can be integrated computationally with clinical data, yielding mechanistic insight into the pathogenesis of chronic diseases such as NAFLD, and informing personalised nutrition and stratified medicine approaches for improving prognosis.

pH as a potential therapeutic target to improve temozolomide antitumor efficacy : A mechanistic modeling study

Despite intensive treatments including temozolomide (TMZ) administration, glioblastoma patient prognosis remains dismal and innovative therapeutic strategies are urgently needed. A systems pharmacology approach was undertaken to investigate TMZ pharmacokinetics-pharmacodynamics (PK-PD) incorporating the effect of local pH, tumor spatial configuration and micro-environment. A hybrid mathematical framework was designed coupling ordinary differential equations describing the intracellular reactions, with a spatial cellular automaton to individualize the cells. A differential drug impact on tumor and healthy cells at constant extracellular pH was computationally demonstrated as TMZ-induced DNA damage was larger in tumor cells as compared to normal cells due to less acidic intracellular pH in cancer cells. Optimality of TMZ efficacy defined as maximum difference between damage in tumor and healthy cells was reached for extracellular pH between 6.8 and 7.5. Next, TMZ PK-PD in a solid tumor was demonstrated to highly depend on its spatial configuration as spread cancer cells or fragmented tumors presented higher TMZ-induced damage as compared to compact tumor spheroid. Simulations highlighted that smaller tumors were less acidic than bigger ones allowing for faster TMZ activation and their closer distance to blood capillaries allowed for better drug penetration. For model parameters corresponding to U87 glioma cells, inter-cell variability in TMZ uptake play no role regarding the mean drug-induced damage in the whole cell population whereas this quantity was increased by inter-cell variability in TMZ efflux which was thus a disadvantage in terms of drug resistance. Overall, this study revealed pH as a new potential target to significantly improve TMZ antitumor efficacy.