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Quantitative systems pharmacology (QSP) modeling is applied to address essential questions in drug development, such as the mechanism of action of a therapeutic agent and the progression of disease. Meanwhile, machine learning (ML) approaches also contribute to answering these questions via the analysis of multi-layer ‘omics’ data such as gene expression, proteomics, metabolomics, and high-throughput imaging. Furthermore, ML approaches can also be applied to aspects of QSP modeling. Both approaches are powerful tools and there is considerable interest in integrating QSP modeling and ML. So far, a few successful implementations have been carried out from which we have learned about how each approach can overcome unique limitations of the other. The QSP + ML working group of the International Society of Pharmacometrics QSP Special Interest Group was convened in September, 2019 to identify and begin realizing new opportunities in QSP and ML integration. The working group, which comprises 21 members representing 18 academic and industry organizations, has identified four categories of current research activity which will be described herein together with case studies of applications to drug development decision making. The working group also concluded that the integration of QSP and ML is still in its early stages of moving from evaluating available technical tools to building case studies. This paper reports on this fast-moving field and serves as a foundation for future codification of best practices.

Computational systems biology (CSB) is a field that emerged primarily as the product of research activities. As such, it grew in several directions in a distributed and uncoordinated manner making the area appealing and fascinating. The idea of not having to follow a specific path but instead creating one fueled innovation. As the field matured, several interdisciplinary graduate programs emerged attempting to educate future generations of computational systems biologists. These educational initiatives coordinated the dissemination of information across student populations that had already decided to specialize in this field. However, we are now entering an era where CSB, having established itself as a valuable research discipline, is attempting the next major step: Entering undergraduate curricula. As interesting as this endeavor may sound, it has several difficulties, mainly because the field is not uniformly defined. In this manuscript, we argue that this diversity is a significant advantage and that several incarnations of an undergraduate-level CSB biology course could, and should, be developed tailored to programmatic needs. In this manuscript, we share our experiences creating a course as part of a Biomedical Engineering program.

A computational framework is developed to enable the characterization of genome-wide, multi-tissue circadian dynamics at the level of “functional groupings of genes” defined in the context of signaling, cellular/genetic processing and metabolic pathways in rat and mouse. Our aim is to identify how individual genes come together to generate orchestrated rhythmic patterns and how these may vary within and across tissues. We focus our analysis on four tissues (adipose, liver, lung, and muscle). A genome-wide pathway-centric analysis enables us to develop a comprehensive picture on how the observed circadian variation at the individual gene level, orchestrates functional responses at the pathway level. Such pathway-based “meta-data” analysis enables the rational integration and comparison across platforms and/or experimental designs evaluating emergent dynamics, as opposed to comparisons of individual elements. One of our key findings is that when considering the dynamics at the pathway level, a complex behavior emerges. Our work proposes that tissues tend to coordinate gene’s circadian expression in a way that optimizes tissue-specific pathway activity, depending of each tissue’s broader role in homeostasis.

The hypothalamic-pituitary-adrenal (HPA) axis orchestrates the physiological response stress. Moreover, the HPA axis exhibits prominent circadian activity and synchronizes peripheral circadian clocks to daily environmental cycles, thereby promoting homeostasis. Persistent disruption of homeostatic glucocorticoid circadian rhythmicity due to chronic stress exposure is correlated with the incidence of various pathological conditions including depression, diabetes and cancer. Allostatic habituation of the HPA axis can therefore confer fitness advantages by preventing the sustained dysregulation of glucocorticoid-responsive signaling pathways. However, such allostatic adaptation results in a physiological cost (allostatic load) that might impair the homeostatic stress-responsive and synchronizing functions of the HPA axis. We use mathematical modeling to characterize specific chronic stress-induced allostatic adaptations in the HPA network. We predict the existence of multiple personalized regulatory strategies enabling the maintenance of homeostatic glucocorticoid rhythms, while allowing for flexible HPA response characteristics. We show that this regulatory variability produces a trade-off between the stress-responsive and time-keeping properties of the HPA axis. Finally, allostatic regulatory adaptations are predicted to cause a time-of-day dependent sensitization of the acute stress response and impair the entrainability of the HPA axis

Gout, the most common inflammatory arthritis worldwide, is an auto-inflammatory metabolic disease that leads to monosodium urate crystal deposition. Hyperuricaemia is a significant risk factor for the development of gout; however, hyperuricaemia alone is not sufficient to induce gout. Gout flares have circadian rhythms. Gout flares vary during the day and have strong seasonality, with flares being more common in the spring. The reasons for the predominance of flares in the spring are unclear since serum urate (SU) levels show seasonal variation; however, SU levels are highest in the summer. Immune function varies significantly throughout the year, with enhanced immune responses increasing during the winter. In addition, chronic disruption of circadian rhythms is associated with metabolic syndrome and diseases driven by metabolism. The most telling example relates to Xanthine oxidase (XOD/XDH). The analysis of XOD/XDH established its circadian regulation and demonstrated that inhibition of the activity of XOD is characterised by distinct, cross-regulating diurnal/seasonal patterns of activity. The gastrointestinal microbiota of gout patients is highly distinct from healthy individuals. In a small series of gout patients, Bacteroides caccae and Bacteroides xylanisolvens were found to be enriched. Bacteroidales levels were highest during the spring and summer, and loading values were highest in the spring. Our review discusses gout’s circadian rhythm and seasonality, possible influences of the microbiome on gout due to our new knowledge that Bacteroidales levels were highest during spring when gout is most common, and potential opportunities for treatment based on our current understanding of this interaction.

Glucocorticoids influence a wide array of metabolic, anti-inflammatory, immunosuppressive, and cognitive signaling processes, playing an important role in homeostasis and preservation of normal organ function. Synthesis is regulated by the hypothalamic-pituitary-adrenal (HPA) axis of which cortisol is the primary glucocorticoid in humans. Synthetic glucocorticoids are important pharmacological agents that augment the anti-inflammatory and immunosuppressive properties of endogenous cortisol and are widely used for the treatment of asthma, Crohn’s disease, and rheumatoid arthritis, amongst other chronic conditions. The homeostatic activity of cortisol is disrupted by the administration of synthetic glucocorticoids and so there is interest in developing treatment options that minimize HPA axis disturbance while maintaining the pharmacological effects. Studies suggest that optimizing drug administration time can achieve this goal. The present review provides an overview of endogenous glucocorticoid activity and recent advances in treatment options that have further improved patient safety and efficacy with an emphasis on chronopharmacology.

Chronic skin wounds are hypoxic and are stalled in a pro-inflammatory state. Hemoglobin (Hb)-based oxygen carriers have shown potential in increasing oxygen delivery to aid wound healing. Macrophages also take up Hb, thus altering their phenotype and the regulation of inflammation. Herein, we compared the effect of Hb and polymerized Hbs (PolyHbs) on the phenotype of human macrophages. Macrophages were incubated with Hb or different forms of PolyHbs, and the inflammatory secretion profile was analyzed. PolyHbs were produced by polymerizing Hb in the relaxed (R) or tense (T) quaternary state and by varying the molar ratio of the glutaraldehyde crosslinking agent to Hb. Hb decreased the secretion of most measured factors. PolyHb treatment led to generally similar secretion profiles; however, Hb had more similar trends to R-state PolyHb. Ingenuity pathway analysis predicted positive outcomes in wound healing and angiogenesis for T-state PolyHb prepared with a 30:1 (glutaraldehyde:Hb) polymerization ratio. When tested in diabetic mouse wounds, T-state PolyHb resulted in the greatest epidermal thickness and vascular endothelial CD31 staining. Thus, the effects of PolyHb on macrophages are affected by the polymerization ratio and the quaternary state, and T-state PolyHb yields secretion profiles that are most beneficial in wound healing.

The shift towards non-communicable, chronic, diseases has placed emphasis on understanding the dominant role our everyday activities and broader environment as a major determinant of health. The realization that genetics is but one of the drivers of maintaining health and developing chronic diseases, necessitates that we refocus our attention on the patient and the interactions between life style and environment and the biology and physiology underlying health and chronic diseases. In that respect, we begin to better appreciate that health and disease are fundamentally systems properties and need to be treated as such. In this review, we attempt to first, define the “patient” eco-system, its boundaries and its components and then discuss how to establish and assess the multitude of interactions across the system components. We particularly focus on three environmental and life-style factors: mistimed rest/activity patterns, mistimed feeding patterns and chronic stress.

Disruption of circadian rhythms has been associated with metabolic syndromes, including obesity and diabetes. A variety of metabolic activities are under circadian modulation, as local and global clock gene knockouts result in glucose imbalance and increased risk of metabolic diseases. Insulin release from the pancreatic β cells exhibits daily variation, and recent studies have found that insulin secretion, not production, is under circadian modulation. As consideration of daily variation in insulin secretion is necessary to accurately describe glucose-stimulated insulin secretion, we describe a mathematical model that incorporates the circadian modulation via insulin granule trafficking. We use this model to understand the effect of oscillatory characteristics on insulin secretion at different times of the day. Furthermore, we integrate the dynamics of clock genes under the influence of competing environmental signals (light/dark cycle and feeding/fasting cycle) and demonstrate how circadian disruption and meal size distribution change the insulin secretion pattern over a 24-h day.

This study describes a new approach to discern early divergence in stem cell lineage progression via temporal dynamics of the cytoskeletal protein, F-actin. The approach involves real-time labeling of human mesenchymal stem cells (MSCs) and longitudinal tracking of the turnover dynamics of a fluorogenic F-actin specific probe, SiR-actin (SA). Cells cultured in media with distinct lineage factors and labeled with SA showed lineage specific reduction in the actin turnover shortly after adipogenic (few minutes) and chondrogenic (3–4 hours) commitment in contrast to osteogenic and basal cultured conditions. Next, composite staining of SA along with the competing F-actin specific fluorescent conjugate, phalloidin, and high-content image analysis of the complementary labels showed clear phenotypic parsing of the sub-populations as early as 1-hour post-induction across all three lineages. Lastly, the potential of SA-based actin turnover analysis to distinguish cellular aging was explored. In-vitro aged cells were found to have reduced actin turnover within 1-hour of simultaneous analysis in comparison to cells of earlier passage. In summary, SiR-actin fluorescent reporter imaging offers a new platform to sensitively monitor emergent lineage phenotypes during differentiation and aging and resolve some of the earliest evident differences in actin turnover dynamics.

Pharmacokinetic models are particularly useful to study the underlying and complex physiological mechanisms contributing to clinical differences across patient subgroups or special populations. Unfortunately, the inherent variability of biological systems and knowledge gaps in physiological data limit confidence in model predictions for special populations. Sourcing data to reflect the desired physiologies can be resource intensive, particularly for a larger model. Thus, a critical step in model development for special populations involves an in-depth analysis of model inputs, which can be guided by Monte Carlo simulations. Such an approach enables the generation of parameter values by stochastic sampling that are subsequently restricted to the combinations that describe biologically plausible or target model output. Our approach utilized a published pharmacokinetic compartmental model to demonstrate how sampling in conjunction with global sensitivity analysis can be used to explore sexual dimorphism and inter-individual variability in multivariate parameter spaces for differentiation of model input and behavior across phenotypes. Despite limiting the model output to relatively narrow ranges, male and female phenotypes were associated with wide variability in both individual parameter values and combinations of parameters. Through an integrated approach using a support vector machine, principal component analysis and global sensitivity analysis, our approach revealed that specific combinations of parameters gave rise to a certain phenotype, while individual parameters influenced the shape of plasma concentration profile. Augmenting analysis of the model input with global sensitivity analysis enabled an understanding of both sexual dimorphism and inter-individual variability in pharmacokinetics. While the current study revealed how model input could be separated by sex for a simple compartment model, the approach could be extended to other patient factors, such as age or disease, and to a more complex physiologically-based model that describes absorption, distribution, metabolism, and elimination with more detail.

Cancer is a devastating disease that takes the lives of hundreds of thousands of people every year. Due to disease heterogeneity, standard treatments, such as chemotherapy or radiation, are effective in only a subset of the patient population. Tumors can have different underlying genetic causes and may express different proteins in one patient versus another. This inherent variability of cancer lends itself to the growing field of precision and personalized medicine (PPM). There are many ongoing efforts to acquire PPM data in order to characterize molecular differences between tumors. Some PPM products are already available to link these differences to an effective drug. It is clear that PPM cancer treatments can result in immense patient benefits, and companies and regulatory agencies have begun to recognize this. However, broader changes to the healthcare and insurance systems must be addressed if PPM is to become part of standard cancer care.

The circadian timing system comprises a network of time-keeping clocks distributed across a living host whose responsibility is to allocate resources and distribute functions temporally to optimize fitness. The molecular structures generating these rhythms have evolved to accommodate the rotation of the earth in an attempt to primarily match the light/dark periods during the 24-hr day. To maintain synchrony of timing across and within tissues, information from the central clock, located in the suprachiasmatic nucleus, is conveyed using systemic signals. Leading among those signals are endocrine hormones, and while the hypothalamic-pituitary-adrenal axis through the release of glucocorticoids is a major pacesetter. Interestingly, the fundamental units at the molecular and physiological scales that generate local and systemic signals share critical structural properties. These properties enable time-keeping systems to generate rhythmic signals and allow them to adopt specific properties as they interact with each other and the external environment. The purpose of this review is to provide a broad overview of these structures, discuss their functional characteristics, and describe some of their fundamental properties as these related to health and disease. This article is categorized under: Immune System Diseases > Computational Models Immune System Diseases > Biomedical Engineering.

The evolution of complex genetic networks is shaped over the course of many generations through multiple mechanisms. These mechanisms can be broken into two predominant categories: adaptive forces, such as natural selection, and non-adaptive forces, such as recombination, genetic drift, and random mutation. Adaptive forces are influenced by the environment, where individuals better suited for their ecological niche are more likely to reproduce. This adaptive force results in a selective pressure which creates a bias in the reproduction of individuals with beneficial traits. Non-adaptive forces, in contrast, are not influenced by the environment: Random mutations occur in offspring regardless of whether they improve the fitness of the offspring. Both adaptive and non-adaptive forces play critical roles in the development of a species over time, and both forces are intrinsically linked to one another. We hypothesize that even under a simple sexual reproduction model, selective pressure will result in changes in the mutation rate and genome size. We tested this hypothesis by evolving Boolean networks using a modified genetic algorithm. Our results demonstrate that changes in environmental signals can result in selective pressure which affects mutation rate., Evolution; Genetic Algorithms; Adaptation.

A model-based approach for the assessment of pathway dynamics is explored to characterize metabolic and signaling pathway activity changes characteristic of the dosing-dependent differences in response to methylprednisolone in muscle. To consistently compare dosing-induced changes we extend the principles of pharmacokinetics and pharmacodynamics and introduce a novel representation of pathway-level dynamic models of activity regulation. We hypothesize the emergence of dosing-dependent regulatory interactions is critical to understanding the mechanistic implications of MPL dosing in muscle. Our results indicate that key pathways, including amino acid and lipid metabolism, signal transduction, endocrine regulation, regulation of cellular functions including growth, death, motility, transport, protein degradation, and catabolism are dependent on dosing, exhibiting diverse dynamics depending on whether the drug is administered acutely of continuously. Therefore, the dynamics of drug presentation offer the possibility for the emergence of dosing-dependent models of regulation. Finally, we compared acute and chronic MPL response in muscle with liver. The comparison revealed systematic response differences between the two tissues, notably that muscle appears more prone to adapt to MPL.

Endogenous glucocorticoids have diverse physiological effects and are important regulators of metabolism, immunity, cardiovascular function, musculoskeletal health and central nervous system activity. Synthetic glucocorticoids have received widespread attention for their potent anti-inflammatory activity and have become an important class of drugs used to augment endogenous glucocorticoid activity for the treatment of a host of chronic inflammatory conditions. Chronic use of synthetic glucocorticoids is associated with a number of adverse effects as a result of the persistent dysregulation of glucocorticoid sensitive pathways. A failure to consider the pronounced circadian rhythmicity of endogenous glucocorticoids can result in either supraphysiological glucocorticoid exposure or severe suppression of endogenous glucocorticoid secretion, and is thought be a causal factor in the incidence of adverse effects during chronic glucocorticoid therapy. Furthermore, given that synthetic glucocorticoids have potent feedback effects on the hypothalamic-pituitary-adrenal (HPA) axis, physiological factors which can give rise to individual variability in HPA axis activity such as sex, age, and disease state might also have substantial implications for therapy. We use a semi-mechanistic mathematical model of the rodent HPA axis to study how putative sex differences and individual variability in HPA axis regulation can influence the effects of long-term synthetic exposure on endogenous glucocorticoid circadian rhythms. Model simulations suggest that for the same drug exposure, simulated females exhibit less endogenous suppression than males considering differences in adrenal sensitivity and negative feedback to the hypothalamus and pituitary. Simulations reveal that homeostatic regulatory variability and chronic stress-induced regulatory adaptations in the HPA axis network can result in substantial differences in the effects of synthetic exposure on the circadian rhythm of endogenous glucocorticoids. In general, our results provide insight into how the dosage and exposure profile of synthetic glucocorticoids could be manipulated in a personalized manner to preserve the circadian dynamics of endogenous glucocorticoids during chronic therapy, thus potentially minimizing the incidence of adverse effects associated with long-term use of glucocorticoids.

The circadian rhythms influence the metabolic activity from molecular level to tissue, organ, and host level. Disruption of the circadian rhythms manifests to the host’s health as metabolic syndromes, including obesity, diabetes, and elevated plasma glucose, eventually leading to cardiovascular diseases. Therefore, it is imperative to understand the mechanism behind the relationship between circadian rhythms and metabolism. To start answering this question, we propose a semimechanistic mathematical model to study the effect of circadian disruption on hepatic gluconeogenesis in humans. Our model takes the light-dark cycle and feeding-fasting cycle as two environmental inputs that entrain the metabolic activity in the liver. The model was validated by comparison with data from mice and rat experimental studies. Formal sensitivity and uncertainty analyses were conducted to elaborate on the driving forces for hepatic gluconeogenesis. Furthermore, simulating the impact of Clock gene knockout suggests that modification to the local pathways tied most closely to the feeding-fasting rhythms may be the most efficient way to restore the disrupted glucose metabolism in liver.

The triterpenoid ursolic acid (UA) has been proposed as a potential cancer chemopreventive agent in many preclinical and clinical studies. In the present work, we aimed to characterize the pharmacokinetics (PK) of UA and to quantitatively assess the anti-oxidative and anti-inflammatory effects of UA, which are potentially linked to its chemopreventive efficacy. UA was administered intravenously (i.v., 20 mg/kg) or by oral gavage (100 mg/kg) to male Sprague-Dawley rats, and blood samples were collected at a series of designated time points. The plasma concentration of UA was determined using a validated liquid chromatography-mass spectrometry (LC-MS) approach. A biexponential decline in the UA plasma concentration was observed after i.v. dosing and was fitted to a two-compartmental model. The expression levels of phase II drug metabolism (DM)/anti-oxidant genes and the inflammatory iNos gene in corresponding treatment arms were measured using qPCR as a pharmacodynamic (PD) marker. The expression of phase II DM/anti-oxidant genes increased and peaked approximately 3 h after 20 mg/kg UA treatment. In a lipopolysaccharide (LPS)-induced acute inflammation model, UA inhibited LPS-stimulated iNos expression and that of the epigenetic markers the DNA methyltransferases (DNMTs) and histone deacetylases (HDACs) in leukocytes. A PK–PD model using Jusko's indirect response model (IDR) with transition compartments (TC) was established to describe the time delay and magnitude of the gene expression elicited by UA. The PK-PD model provided reasonable fitting linking the plasma concentration of UA simultaneously with the PD response based on leukocyte mRNA expression. Overall, our results indicate that UA is effective at inducing various phase II DM/anti-oxidant genes and inhibiting pro-inflammatory genes in vivo. This PK-PD modeling approach may provide a conceptual framework for the future clinical evaluation of dietary chemopreventive agents in humans.

The hypothalamic-pituitary-adrenal (HPA) axis orchestrates the physiological response to unpredictable acute stressors. Moreover, the HPA axis exhibits prominent circadian activity and synchronizes peripheral circadian clocks to daily environmental cycles, thereby promoting homeostasis. Persistent disruption of homeostatic glucocorticoid circadian rhythmicity due to chronic stress exposure is correlated with the incidence of various pathological conditions including depression, diabetes and cancer. Allostatic habituation of the HPA axis, such that glucocorticoid levels retain homeostatic levels upon chronic exposure to stress, can therefore confer fitness advantages by preventing the sustained dysregulation of glucocorticoid-responsive signaling pathways. However, such allostatic adaptation results in a physiological cost (allostatic load) that might impair the homeostatic stress-responsive and synchronizing functions of the HPA axis. We use mathematical modeling to characterize specific chronic stress-induced allostatic adaptations in the HPA network. We predict the existence of multiple individualized regulatory strategies enabling the maintenance of homeostatic glucocorticoid rhythms, while allowing for flexible HPA response characteristics. We show that this regulatory variability produces a trade-off between the stress-responsive and time-keeping properties of the HPA axis. Finally, allostatic regulatory adaptations are predicted to cause a time-of-day dependent sensitization of the acute stress response and impair the entrainability of the HPA axis.

Pharmacological time-series data, from comparative dosing studies, are critical to characterizing drug effects. Reconciling the data from multiple studies is inevitably difficult; multiple in vivo high-throughput -omics studies are necessary to capture the global and temporal effects of the drug, but these experiments, though analogous, differ in (microarray or other) platforms, time-scales, and dosing regimens and thus cannot be directly combined or compared. This investigation addresses this reconciliation issue with a meta-analysis technique aimed at assessing the intrinsic activity at the pathway level. The purpose of this is to characterize the dosing effects of methylprednisolone (MPL), a widely used anti-inflammatory and immunosuppressive corticosteroid (CS), within the liver. A multivariate decomposition approach is applied to analyze acute and chronic MPL dosing in male adrenalectomized rats and characterize the dosing-dependent differences in the dynamic response of MPL-responsive signaling and metabolic pathways. We demonstrate how to deconstruct signaling and metabolic pathways into their constituent pathway activities, activities which are scored for intrinsic pathway activity. Dosing-induced changes in the dynamics of pathway activities are compared using a model-based assessment of pathway dynamics, extending the principles of pharmacokinetics/pharmacodynamics (PKPD) to describe pathway activities. The model-based approach enabled us to hypothesize on the likely emergence (or disappearance) of indirect dosing-dependent regulatory interactions, pointing to likely mechanistic implications of dosing of MPL transcriptional regulation. Both acute and chronic MPL administration induced a strong core of activity within pathway families including the following: lipid metabolism, amino acid metabolism, carbohydrate metabolism, metabolism of cofactors and vitamins, regulation of essential organelles, and xenobiotic metabolism pathway families. Pathway activities alter between acute and chronic dosing, indicating that MPL response is dosing dependent. Furthermore, because multiple pathway activities are dominant within a single pathway, we observe that pathways cannot be defined by a single response. Instead, pathways are defined by multiple, complex, and temporally related activities corresponding to different subgroups of genes within each pathway.

Personalized medicine strives to deliver the ‘right drug at the right dose’ by considering inter-person variability, one of the causes for therapeutic failure in specialized populations of patients. Physiologically-based Pharmacokinetic (PBPK) modeling is a key tool in the advancement of personalized medicine to evaluate complex clinical scenarios, making use of physiological information as well as physicochemical data to simulate various physiological states to predict the distribution of pharmacokinetic responses. The increased dependency on PBPK models to address regulatory questions is aligned with the ability of PBPK models to minimize ethical and technical difficulties associated with pharmacokinetic and toxicology experiments for special patient populations. Subpopulation modeling can be achieved through an iterative and integrative approach using an adopt, adapt, develop, assess, amend, and deliver [(AAD)2] methodology. PBPK modeling has two valuable applications in personalized medicine: (1) determining the importance of certain subpopulations within a distribution of pharmacokinetic responses for a given drug formulation and (2) establishing the formulation design space needed to attain a targeted drug plasma concentration profile. This review article focuses on model development for physiological differences associated with sex (male vs. female), age (pediatric vs. young adults vs. elderly), disease state (healthy vs. unhealthy), and temporal variation (influence of biological rhythms), connecting them to drug product formulation development within the Quality by Design framework. Although PBPK modeling has come a long way, there is still a lengthy road before it can be fully accepted by pharmacologists, clinicians, and the broader industry.

In this short review, we discuss evidence supporting the modulation of peripheral circadian systems as a therapeutic strategy for rheumatoid arthritis (RA). We first review the role of proinflammatory cytokines and oxidative stress, two of the primary mediators of chronic inflammation in RA, and their regulation by circadian clock machinery. We further highlight the role of environmental and metabolic signals in regulating the central and peripheral circadian clocks, with an emphasis on seasonal variations in photoperiod and rhythmic metabolic input, respectively. Finally, we hypothesize that the entrainment and realignment of peripheral clock rhythms have the ability to modulate these mediators, improving clinical outcomes in RA patients. Our discussion emphasizes the use of light therapy and time-restricted feeding for entraining peripheral clocks either via the entrainment of the central circadian clock in suprachiasmatic nuclei (SCN) or directly by uncoupling the peripheral circadian clocks from SCN. In doing so, we highlight the use of nonpharmacologic interventions as a potential strategy for improving clinical outcomes in chronic inflammatory conditions such as RA.

Circadian time-keeping mechanisms preserve homeostasis by synchronizing internal physiology with predictable variations in the environment and temporally organize the activation of physiological signaling mechanisms to promote survival and optimize the allocation of energetic resources. In this paper, we highlight the importance of the robust circadian dynamics of allostatic mediators, with a focus on the hypothalamic-pituitary-adrenal (HPA) axis, for the optimal regulation of host physiology and in enabling organisms to adequately respond and adapt to physiological stressors. We review studies showing how the chronic disruption of circadian rhythms can result in the accumulation of allostatic load, which impacts the appropriate functioning of physiological systems and diminishes the resilience of internal systems to adequately respond to subsequent stressors. A careful consideration of circadian rhythm dynamics leads to a more comprehensive characterization of individual variability in allostatic load and stress resilience. Finally, we suggest that the restoration of circadian rhythms after pathological disruption can enable the re-engagement of allostatic mechanisms and re-establish stress resilience.

Natural glucocorticoids, a class of cholesterol-derived hormones, modulate an array of metabolic, anti-inflammatory, immunosuppressive and cognitive signaling. The synthesis of natural glucocorticoids, largely cortisol in humans, is regulated by the hypothalamic-pituitary-adrenal (HPA) axis and exhibits pronounced circadian variation. Considering the central regulatory function of endogenous glucocorticoids, maintenance of the circadian activity of the HPA axis is essential to host survival and chronic disruption of such activity leads to systemic complications. There is a great deal of interest in synthetic glucocorticoids due to the immunosuppressive and anti-inflammatory properties and the development of novel dosing regimens that can minimize the disruption of endogenous activity, while still maintaining the pharmacological benefits of long-term synthetic glucocorticoid therapy. Synthetic glucocorticoids are associated with an increased risk of developing the pathological disorders related to chronic suppression of cortisol rhythmicity as a result of the potent negative feedback by synthetic glucocorticoids on the HPA axis precursors. In this study, a mathematical model was developed to explore the influence of chronopharmacological dosing of exogenous glucocorticoids on the endogenous cortisol rhythm considering intra-venous and oral dosing. Chronic daily dosing resulted in modification of the circadian rhythmicity of endogenous cortisol with the amplitude and acrophase of the altered rhythm dependent on the administration time. Simulations revealed that the circadian features of the endogenous cortisol rhythm can be preserved by proper timing of administration. The response following a single dose was not indicative of the response following long-term, repeated chronopharmacological dosing of synthetic glucocorticoids. Furthermore, simulations revealed the inductive influence of long-term treatment was only associated with low to moderate doses, while high doses generally led to suppression of endogenous activity regardless of the chronopharmacological dose. Finally, chronic daily dosing was found to alter the responsiveness of the HPA axis, such that a decrease in the amplitude of the cortisol rhythm resulted in a partial loss in the time-of-day dependent response to CRH stimulation, while an increase in the amplitude was associated with a more pronounced time-of-day dependence of the response.

Chronic inflammation is a key driver of cancer development. Nitrite levels, which are regulated by inducible nitric oxide synthase (iNOS), play a critical role in inflammation. While the anti-oxidant and anti-inflammatory effects of curcumin, a natural product present in the roots of Curcuma longa have been widely studied, the acute pharmacokinetics (PK) and pharmacodynamics (PD) of curcumin in suppressing pro-inflammatory markers and epigenetic modulators remain unclear. In this study, we evaluated the PK and PD of curcumin-induced suppression of lipopolysaccharide (LPS)-mediated inflammation in rat lymphocytes. LPS was administered intravenously either alone or with curcumin to female Sprague-Dawley rats. Plasma samples were analyzed for curcumin concentration and mRNA expression was quantified in lymphocytes. Relative gene expression of several inflammatory and epigenetic modulators was analyzed. To investigate the relationship between curcumin concentration and iNOS, TNF-α, and IL-6 gene expression, PK/PD modeling using Jusko’s indirect response model (IDR) integrating transit compartments (TC) describing the delayed response was conducted. The concentration-time profile of curcumin exhibited a bi-exponential decline, which was well described by a two-compartmental pharmacokinetic model. Importantly our results demonstrate that LPS induced gene expression of pro-inflammatory markers in lymphocytes, with peak expression at approximately 3 h and curcumin suppressed the gene expression in animals administered with LPS. These effects were well captured using the IDR model and an IDR model with the transit compartments. In summary, the PK/PD modeling approach could potentially provide a robust quantitative framework for evaluating the acute anti-inflammatory and epigenetic effects of curcumin in future clinical trials.

The use of models in biology has become particularly relevant as it enables investigators to develop a mechanistic framework for understanding the operating principles of living systems as well as in quantitatively predicting their response to both pathological perturbations and pharmacological interventions. This application has resulted in a synergistic convergence of systems biology and pharmacokinetic-pharmacodynamic modeling techniques that has led to the emergence of quantitative systems pharmacology (QSP). In this review, we discuss how the foundational principles of chemical process systems engineering inform the progressive development of more physiologically-based systems biology models.

Breakthroughs in high-throughput technologies have provided vast information related to the genome, transcriptome, proteome, and metabolome that enable human physiology to be described mathematically. The collection of biological data at various organizationally complex levels may be translated into a network which describes how these genes, transcripts, proteins, and metabolites collectively function. Quantitative systems pharmacology (QSP) models relate data from the -omics levels to an observable phenotype, associated with a disease and the impact of a drug on the disease, bridging the gap between cell-level biochemical pathways and systemic-level pharmacokinetic–pharmacodynamic (PKPD) models. This systems view promises to decipher the interactions between drug-response pathways and the homeostatic mechanisms of the host to be explored more extensively in healthy and diseased states. These multiscale models transcend the temporal and spatial scales to explain a drug's effect on the network and disease physiology, while also considering the influences of external stimuli such as environmental cues or social structure on systemic function. With appropriate implementation, the “one-gene, one-target, one-mechanism” paradigm is slowly replaced with a more detailed, mechanistic understanding. The concepts and example models presented throughout this chapter highlight the need for QSP in the development of new disease treatments. Although much work remains before human physiology and disease progression are fully understood, the current state of QSP models has successfully explored the link between drug exposure and systemic response.

Synchronization of biological functions to environmental signals enables organisms to anticipate and appropriately respond to daily external fluctuations and is critical to the maintenance of homeostasis. Misalignment of circadian rhythms with environmental cues is associated with adverse health outcomes. Cortisol, the downstream effector of hypothalamic-pituitary-adrenal (HPA) activity, facilitates synchronization of peripheral biological processes to the environment. Cortisol levels exhibit substantial seasonal rhythmicity, with peak levels occurring during the short-photoperiod winter months and reduced levels occurring in the long-photoperiod summer season. Seasonal changes in cortisol secretion could therefore alter its entraining capabilities, resulting in a season-dependent modification in the alignment of biological activities with the environment. We develop a mathematical model to investigate the influence of photoperiod-induced seasonal differences in the circadian rhythmicity of the HPA axis on the synchronization of the peripheral circadian clock and cell cycle in a heterogeneous cell population. Model simulations predict that the high-amplitude cortisol rhythms in winter result in the greatest entrainment of peripheral oscillators. Furthermore, simulations predict a circadian gating of the cell cycle with respect to the expression of peripheral clock genes. Seasonal differences in cortisol rhythmicity are also predicted to influence mitotic synchrony, with a high-amplitude winter rhythm resulting in the greatest synchrony and a shift in timing of the cell cycle phases, relative to summer. Our results highlight the primary interactions among the HPA axis, the peripheral circadian clock, and the cell cycle and thereby provide an improved understanding of the implications of circadian misalignment on the synchronization of peripheral regulatory processes.

Quantitative systems pharmacology aims at systematizing, in a model-based manner, the integration of systems biology and pharmacology in an effort to rationalize the process of assessing the ability of a drug to enhance well-being by off-setting the effects of a disease. Systems engineering, on the other hand, has enabled us to develop principles and methodologies for designing and operating engineered networks of structures exploring the integration of the underlying governing (design) laws. Although the computational tools which have resulted in major advances in the design, analysis, and operation of complex engineered structures have had tremendous success in the analysis of systems pharmacology models, it is argued in this opinion paper, that exploring the underlying conceptual foundation of complex systems engineering will enable us to move toward integrated models at the host level to explore, and possibly, induce synergies between low-level drug targets and higher level, systemic, defense mechanisms. This is an approach which would require refocusing of the key activities; however, it is likely the more promising approach as we enter the new era of personalized and precision medicine. We finally argue for the development of an allostatic approach to quantitative systems pharmacology and the development of an integrated framework for considering drugs in their broader context, beyond their local site of action. WIREs Syst Biol Med 2015, 7:101–112. doi: 10.1002/wsbm.1294 For further resources related to this article, please visit the WIREs website. Conflict of interest: The author has declared no conflicts of interest for this article.