Mathematical modeling of mammalian circadian clocks affecting drug and disease responses.

To align with daily environmental changes, most physiological processes in mammals exhibit a time-of-day rhythmicity. This circadian control of physiology is intrinsically driven by a cell-autonomous clock gene network present in almost all cells of the body that drives rhythmic expression of genes that regulate numerous molecular and cellular processes. Accordingly, many aspects of pharmacology and toxicology also oscillate in a time-of-day manner giving rise to diverse effects on pharmacokinetics and pharmacodynamics. Genome-wide studies and mathematical modeling are available tools that have significantly improved our understanding of these nonlinear aspects of physiology and therapeutics. In this manuscript current literature and our prior work on the model-based approaches that have been used to explore circadian genomic systems of mammals are reviewed. Such basic understanding and having an integrative approach may provide new strategies for chronotherapeutic drug treatments and yield new insights for the restoration of the circadian system when altered by diseases.

Seeking Nonspecific Binding: Assessing the Reliability of Tissue Dilutions for Calculating Fraction Unbound.

It has become commonplace (270+ article citations to date) to measure the fraction unbound (FrUn) of drugs in tissue homogenates and diluted plasma and then use a Correction Factor Equation (CFE) to extrapolate to the undiluted state. The CFE is based on assumptions of nonspecific binding with experimental use of very low drug concentrations. There are several possible determinants of apparent nonspecific binding as measured by methods such as equilibrium dialysis: true macromolecule binding and lipid partitioning along with receptor, enzyme, and transporter interactions. Theoretical calculations based on nonlinear protein binding indicate that the CFE will be most reliable to obtain FrUn when added drug concentration is small, binding constants are weak, protein concentrations are relatively high, and tissue dilution is minimal. When lipid partitioning is the sole factor determining apparent tissue binding, the CFE should be perfectly accurate. Use of very low drug concentrations, however, makes it more likely that specific binding to receptors and other targets may occur, and thus FrUn may reflect some binding to such components. Inclusion of trapped blood can clearly cause minor to marked discrepancies from purely tissue binding alone, which can be corrected. Furthermore, assessment of the occurrence of ionization/pH shifts, drug instability, and tissue metabolism may be necessary. Caution is needed in the use and interpretation of results from tissue dilution studies and other assessments of nonspecific binding, particularly for very strongly bound drugs with very small FrUn values and in tissues with metabolic enzymes, receptors, and trapped blood. SIGNIFICANCE STATEMENT: The use of tissue, plasma, and cell preparations to help obtain fraction unbound and tissue-to-plasma partition coefficients in pharmacokinetics has grown commonplace, especially for brain. This report examines theoretical, physiological, and experimental issues that need consideration before trusting such measurements and calculations.

Physiologically Based Pharmacokinetics of Dexamethasone in Rats.

Blood and multitissue concentration-time profiles for dexamethasone (DEX), a synthetic corticosteroid, were measured in male rats after subcutaneous bolus and infusion dosing. A physiologically based pharmacokinetics (PBPK) model was applied for 12 measured tissues. Tissue partition coefficients (K p ) and metabolic clearance were assessed from infusion studies. Blood cell to plasma partitioning (0.664) and plasma free fraction (0.175) for DEX were found to be moderate. DEX was extensively partitioned into liver (K p = 6.76), whereas the calculated K p values of most tissues ranged between 0.1 and 1.5. Despite the moderate lipophilicity of DEX (log P = 1.8), adipose exhibited very limited distribution (K p = 0.17). Presumably due to P-glycoprotein-mediated efflux, DEX concentrations were very low in brain compared with its expected high permeability. Infusion studies yielded K p values from male and female rats at steady state that were similar. In silico K p values calculated for different tissues by using GastroPlus software were similar to in vivo values except for adipose and liver. Glucocorticoid receptors are found in diverse tissues, and these PBPK modeling results may help provide exposure profiles driving pharmacodynamic effects of DEX. SIGNIFICANCE STATEMENT: Our physiologically based pharmacokinetics model describes the experimentally determined tissue and plasma dexamethasone (DEX) pharmacokinetics (PK) profiles in rats reasonably well. This model can serve for further investigation of DEX tissue distribution in rats as the PK driving force for PD effects in different tissues. No major sex differences were found for DEX tissue distribution. Knowledge gained in this study may be translatable to higher-order species including humans.

Pharmacokinetics and pharmacodynamics of liposomal chemophototherapy with short drug-light intervals.

Chemophototherapy (CPT) merges photodynamic therapy with chemotherapy and can substantially enhance drug delivery. Using a singular liposomal formulation for CPT, we describe a semi-mechanistic pharmacokinetic-pharmacodynamic (PK/PD) model to investigate observed antitumor effects. Long-circulating, sterically-stabilized liposomes loaded with doxorubicin (Dox) stably incorporate small amounts of a porphyrin-phospholipid (PoP) photosensitizer in the bilayer. These were administered intravenously to mice bearing low-passage, patient-derived pancreatic cancer xenografts (PDX). Dox PK was described with a two-compartment model and tumor drug disposition kinetics were modeled with first-order influx and efflux rates. Tumor irradiation with 665 nm laser light (200 J/cm2) 1 h after liposome administration increased tumor vascular permeabilization and drug accumulation, which was accounted for in the PK/PD model with increased tumor influx and efflux rates by approximately 12- and 4- fold, respectively. This modeling approach provided an overall 7-fold increase in Dox area under the curve in the tumor, matching experimental data (7.4-fold). A signal transduction model based on nonlinear direct cell killing accounted for observed tumor growth patterns. This PK/PD model adequately describes the CPT anti-PDX tumor response based on enhanced drug delivery at the short drug-light interval used.

Multi-Scale Network Model Supported by Proteomics for Analysis of Combined Gemcitabine and Birinapant Effects in Pancreatic Cancer Cells.

Gemcitabine combined with birinapant, an inhibitor of apoptosis protein antagonist, acts synergistically to reduce pancreatic cancer cell proliferation. A large-scale proteomics dataset provided rich time-series data on proteome-level changes that reflect the underlying biological system and mechanisms of action of these drugs. A multiscale network model was developed to link the signaling pathways of cell cycle regulation, DNA damage response, DNA repair, apoptosis, nuclear factor-kappa ß (NF-κß), and mitogen-activated protein kinase (MAPK)-p38 to cell cycle progression, proliferation, and death. After validating the network model under different conditions, the Sobol Sensitivity Analysis was applied to identify promising targets to enhance gemcitabine efficacy. The effects of p53 silencing and combining curcumin with gemcitabine were also tested with the developed model. Merging proteomics analysis with systems modeling facilitates the characterization of quantitative relations among relevant signaling pathways in drug action and resistance, and such multiscale network models could be applied for prediction of combination efficacy and target selection.

Population pharmacokinetic-pharmacodynamic-disease progression model for effects of anakinra in Lewis rats with collagen-induced arthritis.

A population pharmacokinetic-pharmacodynamic-disease progression (PK/PD/DIS) model was developed to characterize the effects of anakinra in collagen-induced arthritic (CIA) rats and explore the role of interleukin-1ß (IL-1ß) in rheumatoid arthritis. The CIA rats received either vehicle, or anakinra at 100 mg/kg for about 33 h, 100 mg/kg for about 188 h, or 10 mg/kg for about 188 h by subcutaneous infusion. Plasma concentrations of anakinra were assayed by enzyme-linked immunosorbent assay. Swelling of rat hind paws was measured. Population PK/PD/DIS parameters were computed for the various groups using non-linear mixed-effects modeling software (NONMEM® Version VI). The final model was assessed using visual predictive checks and nonparameter stratified bootstrapping. A two-compartment PK model with two sequential absorption processes and linear elimination was used to capture PK profiles of anakinra. A transduction-based feedback model incorporating logistic growth rate captured disease progression and indirect response model I captured drug effects. The PK and paw swelling versus time profiles in CIA rats were fitted well. Anakinra has modest effects (I ( max ) = 0.28) on paw edema in CIA rats. The profiles are well-described by our PK/PD/DIS model which provides a basis for future mechanism-based assessment of anakinra dynamics in rheumatoid arthritis.

Translational biomarkers: from preclinical to clinical a report of 2009 AAPS/ACCP Biomarker Workshop.

There have been some successes in qualifying biomarkers and applying them to drug development and clinical treatment of various diseases. A recent success is illustrated by a collaborative effort among the US Food and Drug Administration, the European Medicines Agency, and the pharmaceutical industry to provide a set of seven preclinical kidney toxicity biomarkers for drug development. Other successes include, but are not limited to, clinical biomarkers for cancer treatment and clinical management of heart transplant patients. The value of fully qualified surrogate endpoints in facilitating successful drug development is undisputed, especially for diseases in which the traditional clinical outcome can only be assessed in large, multi-year trials. Emerging biomarkers, including chemical genomic or imaging biomarkers, and measurement of circulating tumor cells hold great promise for early diagnosis of disease and as prognostic tests for managing treatment of chronic diseases such as osteoarthritis, Alzheimer disease, cardiovascular disease, and cancer. To advance the success of treating and managing these diseases, efforts are needed to establish the temporal relationship between changes in inflammatory or imaging biomarkers with the progression of the chronic disease, and in the case of cancer, between the extent of circulating cancer cells and tumor progression or remission.

Scaling pharmacodynamics from in vitro and preclinical animal studies to humans.

An important feature of mechanism-based pharmacokinetic/pharmacodynamic (PK/PD) models is the identification of drug- and system-specific factors that determine the intensity and time-course of pharmacological effects. This provides an opportunity to integrate information obtained from in vitro bioassays and preclinical pharmacological studies in animals to anticipate the clinical and adverse responses to drugs in humans. The fact that contemporary PK/PD modeling continues to evolve and seeks to emulate systems level properties should provide enhanced capabilities to scale-up pharmacodynamic data. Critical steps in drug discovery and development, such as lead compound and first in human dose selection, may become more efficient with the implementation and further refinement of translational PK/PD modeling. In this review, we highlight fundamental principles in pharmacodynamics and the basic expectations for in vitro bioassays and traditional allometric scaling in PK/PD modeling. Discussion of PK/PD modeling efforts for recombinant human erythropoietin is also included as a case study showing the potential for advanced systems analysis to facilitate extrapolations and improve understanding of inter-species differences in drug responses.

The influence of St. John's wort on the pharmacokinetics and protein binding of imatinib mesylate.

STUDY OBJECTIVE: To determine the effect of St. John's wort on the pharmacokinetics of imatinib mesylate. DESIGN: Open-label, complete crossover, fixed-sequence, pharmacokinetic study. SETTING: Clinical research center. SUBJECTS: Ten healthy adult volunteers. INTERVENTION: Single 400-mg oral doses of imatinib were administered before and after 2 weeks of treatment with St. John's wort 300 mg 3 times/day. MEASUREMENTS AND MAIN RESULTS: The pharmacokinetics of imatinib were significantly altered by St. John's wort, with reductions of 32% in the median area under the concentration-time curve from time zero to infinity (p=0.0001), 29% in maximum observed concentration (p=0.005), and 21% in half-life (p=0.0001). Protein binding ranged from 97.7-90.3% (mean 94.9%), was concentration independent, and was not altered by St. John's wort. Therapeutic outcomes of imatinib have been shown to correlate with both dose and drug concentrations. CONCLUSION: Coadministration of imatinib with St. John's wort may compromise imatinib's clinical efficacy.