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.

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.

In this paper, we discuss approaches for integrating biological information reflecting diverse physiologic levels. In particular, we explore statistical and model-based methods for integrating transcriptomic, proteomic and metabolomics data. Our case studies reflect responses to a systemic inflammatory stimulus and in response to an anti-inflammatory treatment. Our paper serves partly as a review of existing methods and partly as a means to demonstrate, using case studies related to human endotoxemia and response to methylprednisolone (MPL) treatment, how specific questions may require specific methods, thus emphasizing the non-uniqueness of the approaches. Finally, we explore novel ways for integrating -omics information with PKPD models, toward the development of more integrated pharmacology models.

Homeostasis posits that physiological systems compensate setpoint deviations in an attempt to maintain a state of internal constancy. Allostasis, on the other hand, suggests that physiological regulation is more appropriately described by predictive modulatory actions that, by adjusting setpoints, anticipate and react to changes in internal and external demand. In other words, “maintaining stability through change.” The allostatic perspective enabled the rationalization of predictive and reactive homeostasis. While the latter reflects external perturbations, the former refers to systemic adaptation in response to anticipated changes − not necessarily related to unexpected external disturbances. Therefore, the concept of allostasis accounts also for adaptation to circadian variations (seasonal, circannual or other predictive variability) and interprets the system’s adaptation of its setpoints not as reactive/subnormal adjustments, but rather as a proper response. Therefore, systemic entrainment to periodic demands is handled by predicting and implementing setpoint changes. Given the important role of circadian variability and regulation in maintaining health, and the loss of circadian entrainment as a predisposing factor and sequel of stress, we elaborate on an allostasis model which demonstrates the ability of the systems to adapt to circadian demands and quantifies the deteriorative natural wear and tear of a system constantly adapting, i.e. the irreversible damage and its consequences on system function and overall survival. While developing a system of cascaded nature, we demonstrate the importance of phase coordination and the implications of maintaining proper phase relations. The disruption of these relations is a hallmark of circadian disruption, a predisposing factor to increased vulnerability and/or a sequel to chronic stress.