Receptor/gene/protein-mediated signaling connects methylprednisolone exposure to metabolic and immune-related pharmacodynamic actions in liver

High-quality and robust protein quantification in large clinical/pharmaceutical cohorts with IonStar proteomics investigation

Robust, reliable quantification of large sample cohorts is often essential for meaningful clinical or pharmaceutical proteomics investigations, but it is technically challenging. When analyzing very large numbers of samples, isotope labeling approaches may suffer from substantial batch effects, and even with label-free methods, it becomes evident that low-abundance proteins are not reliably measured owing to unsufficient reproducibility for quantification. The MS1-based quantitative proteomics pipeline IonStar was designed to address these challenges. IonStar is a label-free approach that takes advantage of the high sensitivity/selectivity attainable by ultrahigh-resolution (UHR)-MS1 acquisition (e.g., 120-240k full width at half maximum at m/z = 200) which is now widely available on ultrahigh-field Orbitrap instruments. By selectively and accurately procuring quantitative features of peptides within precisely defined, very narrow m/z windows corresponding to the UHR-MS1 resolution, the method minimizes co-eluted interferences and substantially enhances signal-to-noise ratio of low-abundance species by decreasing noise level. This feature results in high sensitivity, selectivity, accuracy and precision for quantification of low-abundance proteins, as well as fewer missing data and fewer false positives. This protocol also emphasizes the importance of well-controlled, robust experimental procedures to achieve high-quality quantification across a large cohort. It includes a surfactant cocktail-aided sample preparation procedure that achieves high/reproducible protein/peptide recoveries among many samples, and a trapping nano-liquid chromatography-mass spectrometry strategy for sensitive and reproducible acquisition of UHR-MS1 peptide signal robustly across a large cohort. Data processing and quality evaluation are illustrated using an example dataset ( http://proteomecentral.proteomexchange.org ), and example results from pharmaceutical project and one clinical project (patients with acute respiratory distress syndrome) are shown. The complete IonStar pipeline takes ~1-2 weeks for a sample cohort containing ~50-100 samples.

Transitioning from Basic toward Systems Pharmacodynamic Models: Lessons from Corticosteroids

Technology in bioanalysis, -omics, and computation have evolved over the past half century to allow for comprehensive assessments of the molecular to whole body pharmacology of diverse corticosteroids. Such studies have advanced pharmacokinetic and pharmacodynamic (PK/PD) concepts and models that often generalize across various classes of drugs. These models encompass the "pillars" of pharmacology, namely PK and target drug exposure, the mass-law interactions of drugs with receptors/targets, and the consequent turnover and homeostatic control of genes, biomarkers, physiologic responses, and disease symptoms. Pharmacokinetic methodology utilizes noncompartmental, compartmental, reversible, physiologic [full physiologically based pharmacokinetic (PBPK) and minimal PBPK], and target-mediated drug disposition models using a growing array of pharmacometric considerations and software. Basic PK/PD models have emerged (simple direct, biophase, slow receptor binding, indirect response, irreversible, turnover with inactivation, and transduction models) that place emphasis on parsimony, are mechanistic in nature, and serve as highly useful "top-down" methods of quantitating the actions of diverse drugs. These are often components of more complex quantitative systems pharmacology (QSP) models that explain the array of responses to various drugs, including corticosteroids. Progressively deeper mechanistic appreciation of PBPK, drug-target interactions, and systems physiology from the molecular (genomic, proteomic, metabolomic) to cellular to whole body levels provides the foundation for enhanced PK/PD to comprehensive QSP models. Our research based on cell, animal, clinical, and theoretical studies with corticosteroids have provided ideas and quantitative methods that have broadly advanced the fields of PK/PD and QSP modeling and illustrates the transition toward a global, systems understanding of actions of diverse drugs. SIGNIFICANCE STATEMENT: Over the past half century, pharmacokinetics (PK) and pharmacokinetics/pharmacodynamics (PK/PD) have evolved to provide an array of mechanism-based models that help quantitate the disposition and actions of most drugs. We describe how many basic PK and PK/PD model components were identified and often applied to the diverse properties of corticosteroids (CS). The CS have complications in disposition and a wide array of simple receptor-to complex gene-mediated actions in multiple organs. Continued assessments of such complexities have offered opportunities to develop models ranging from simple PK to enhanced PK/PD to quantitative systems pharmacology (QSP) that help explain therapeutic and adverse CS effects. Concurrent development of state-of-the-art PK, PK/PD, and QSP models are described alongside experimental studies that revealed diverse CS actions.

Modeling Corticosteroid Pharmacokinetics and Pharmacodynamics, Part III: Estrous Cycle and Estrogen Receptor-Dependent Antagonism of Glucocorticoid-Induced Leucine Zipper (GILZ) Enhancement by Corticosteroids

Our previous report examined the pharmacokinetics (PK) of methylprednisolone (MPL) and adrenal suppression after a 50 mg/kg IM bolus in male and female rats, and we described in detail the development of a minimal physiologically based pharmacokinetic/pharmacodynamic (mPBPK/PD) model. In continuation of such assessments, we investigated sex differences in genomic MPL responses (PD). Message expression of the glucocorticoid-induced leucine zipper (GILZ) was chosen as a multitissue biomarker of glucocorticoid receptor (GR)-mediated drug response. Potential time-dependent interplay between sex hormone and glucocorticoid signaling in vivo was assessed by comparing the enhancement of GILZ by MPL in the uterus [high estrogen receptor (ER) density] and in liver (lower ER density) from male and female rats dosed within the proestrus (high estradiol/progesterone) and estrus (low estradiol/progesterone) phases of the rodent estrous cycle. An expanded-systems PD model of MPL considering circadian rhythms, multireceptor (ER and GR) control, and estrous variations delineated the determinants controlling receptor/gene-mediated steroid responses. Hepatic GILZ response was ∼3-fold greater in females, regardless of estrous stage, compared with males, driven predominantly by increased MPL exposure in females and a negligible influence of estrogen interaction. In contrast, GILZ response in the uterus during proestrus in females was 60% of that observed in estrus-phased females, despite no PK or receptor differences, providing in vivo support to the hypothesis of estrogen-mediated antagonism of glucocorticoid signaling. The developed model offers a mechanistic platform to assess the determinants of sex and tissue specificity in corticosteroid actions and, in turn, reveals a unique PD drug-hormone interaction occurring in vivo. SIGNIFICANCE STATEMENT: Mechanisms relating to sex-based pharmacodynamic variability in genomic responses to corticosteroids have been unclear. Using combined experimental and systems pharmacology modeling approaches, sex differences in both pharmacokinetic and pharmacodynamic mechanisms controlling the enhancement of a sensitive corticosteroid-regulated biomarker, the glucocorticoid-induced leucine zipper (GILZ), were clarified in vivo. The multiscale minimal physiologically based pharmacokinetics/pharmacodynamic model successfully captured the experimental observations and quantitatively discerned the roles of the rodent estrous cycle (hormonal variation) and tissue specificity in mediating the antagonistic coregulation of GILZ gene synthesis. These findings collectively support the hypothesis that estrogens antagonize pharmacodynamic signaling of genomic corticosteroid actions in vivo in a time- and estrogen receptor-dependent manner.

Guiding dose selection of monoclonal antibodies using a new parameter (AFTIR) for characterizing ligand binding systems

Guiding the dose selection for monoclonal antibody oncology drugs is often done using methods for predicting the receptor occupancy of the drug in the tumor. In this manuscript, previous work on characterizing target inhibition at steady state using the AFIR metric (Stein and Ramakrishna in CPT Pharmacomet Syst Pharmacol 6(4):258-266, 2017) is extended to include a "target-tissue" compartment and the shedding of membrane-bound targets. A new potency metric average free tissue target to initial target ratio (AFTIR) at steady state is derived, and it depends on only four key quantities: the equilibrium binding constant, the fold-change in target expression at steady state after binding to drug, the biodistribution of target from circulation to target tissue, and the average drug concentration in circulation. The AFTIR metric is useful for guiding dose selection, for efficiently performing sensitivity analyses, and for building intuition for more complex target mediated drug disposition models. In particular, reducing the complex, physiological model to four key parameters needed to predict target inhibition helps to highlight specific parameters that are the most important to estimate in future experiments to guide drug development.

Modeling Corticosteroid Pharmacogenomics and Proteomics in Rat Liver

Corticosteroids (CS) regulate the expression of numerous genes at the mRNA and protein levels. The time course of CS pharmacogenomics and proteomics were examined in livers obtained from adrenalectomized rats given a 50-mg/kg bolus dose of methylprednisolone. Microarrays and mass spectrometry-based proteomics were employed to quantify hepatic transcript and protein dynamics. One-hundred, sixty-three differentially expressed mRNA and their corresponding proteins (163 genes) were clustered into two dominant groups. The temporal profiles of most proteins were delayed compared with their mRNA, attributable to synthesis delays and slower degradation kinetics. On the basis of our fifth-generation model of CS, mathematical models were developed to simultaneously describe the emergent time patterns for an array of steroid-responsive mRNA and proteins. The majority of genes showed time-dependent increases in mRNA and protein expression before returning to baseline. A model assuming direct, steroid-mediated stimulation of mRNA synthesis was applied. Some mRNAs and their proteins displayed down-regulation following CS. A model assuming receptor-mediated inhibition of mRNA synthesis was used. More complex patterns were observed for other genes (e.g., biphasic behaviors and opposite directionality in mRNA and protein). Models assuming either stimulation or inhibition of mRNA synthesis coupled with dual secondarily induced regulatory mechanisms affecting mRNA or protein turnover were derived. These findings indicate that CS-regulated gene expression manifested at the mRNA and protein levels are controlled via mechanisms affecting key turnover processes. Our quantitative models of CS pharmacogenomics were expanded from mRNA to proteins and provide extended hypotheses for understanding the direct, secondary, and downstream mechanisms of CS actions.

Mathematical Models in the Description of Pregnane X Receptor (PXR)-Regulated Cytochrome P450 Enzyme Induction

The pregnane X receptor (PXR) is a drug/xenobiotic-activated transcription factor of crucial importance for major cytochrome P450 xenobiotic-metabolizing enzymes (CYP) expression and regulation in the liver and the intestine. One of the major target genes regulated by PXR is the cytochrome P450 enzyme (CYP3A4), which is the most important human drug-metabolizing enzyme. In addition, PXR is supposed to be involved both in basal and/or inducible expression of many other CYPs, such as CYP2B6, CYP2C8, 2C9 and 2C19, CYP3A5, CYP3A7, and CYP2A6. Interestingly, the dynamics of PXR-mediated target genes regulation has not been systematically studied and we have only a few mechanistic mathematical and biologically based models describing gene expression dynamics after PXR activation in cellular models. Furthermore, few indirect mathematical PKPD models for prediction of CYP3A metabolic activity in vivo have been built based on compartmental models with respect to drug–drug interactions or hormonal crosstalk. Importantly, several negative feedback loops have been described in PXR regulation. Although current mathematical models propose these adaptive mechanisms, a comprehensive mathematical model based on sufficient experimental data is still missing. In the current review, we summarize and compare these models and address some issues that should be considered for the improvement of PXR-mediated gene regulation modelling as well as for our better understanding of the quantitative and spatial dynamics of CYPs expression.