Corticosteroid (CS) pharmacogenomics was studied using gene microarrays in rat liver. Methylprednisolone (MPL) was administered intravenously at 50 mg/kg. Rats were sacrificed and liver excised at 17 time points over 72 h. RNAs from individual livers were used to query Affymetrix GeneChips that contain sequences for 8000 genes. Cluster analysis revealed six temporal patterns consisting of 197 CS-responsive probes representing 143 genes. Based on our fifth-generation model of CS pharmacokinetics/pharmacodynamics (PK/PD), mechanistic models were developed to describe the time pattern for each CS-responsive gene. Two clusters showed increased expression with different effect duration. PK/PD models assuming CS stimulation of mRNA synthesis were applied. Another two clusters showed an initial decline followed by delayed increase, suggesting two mechanisms might be involved jointly. The initial suppression was captured by CS inhibition of mRNA synthesis or stimulation of degradation. CS may also stimulate the production of a biosignal (transcription factors or other hormones), which can cause secondary induction of the target mRNA. One cluster showed a very abrupt increase in message followed by rapid decrease. These genes were lymphocytic in origin and were modeled combining the fast gene induction effect of CS in lymphoid cells and its direct lymphocyte trafficking effect. Another cluster showed reduction persisting for 18 h, which was described by CS inhibition of mRNA synthesis. Our results reveal the marked diversity of genes regulated by CS via a limited array of mechanisms. These PK/PD models provide quantitation of CS pharmacogenomics and new hypotheses regarding understanding of diverse mechanisms of CS receptor-gene mediated action.

Purpose In a phase I trial for patients with refractory solid tumors, hedgehog pathway inhibitor vismodegib (GDC-0449) showed little decline in plasma concentrations over 7 days after a single oral dose and nonlinearity with respect to dose and time after single and multiple dosing. We studied the role of GDC-0449 binding to plasma protein alpha-1-acid glycoprotein (AAG) to better understand these unusual pharmacokinetics. Experimental Design Sixty-eight patients received GDC-0449 at 150 (n = 41), 270 (n = 23), or 540 (n = 4) mg/d, with pharmacokinetic (PK) sampling at multiple time points. Total and unbound (dialyzed) GDC-0449 plasma concentrations were assessed by liquid chromatography/tandem mass spectrometry, binding kinetics by surface plasmon resonance–based microsensor, and AAG levels by ELISA. Results A linear relationship between total GDC-0449 and AAG plasma concentrations was observed across dose groups (R2 = 0.73). In several patients, GDC-0449 levels varied with fluctuations in AAG levels over time. Steady-state, unbound GDC-0449 levels were less than 1% of total, independent of dose or total plasma concentration. In vitro, GDC-0449 binds AAG strongly and reversibly (KD = 13 μmol/L) and human serum albumin less strongly (KD = 120 μmol/L). Simulations from a derived mechanistic PK model suggest that GDC-0449 pharmacokinetics are mediated by AAG binding, solubility-limited absorption, and slow metabolic elimination. Conclusions GDC-0449 levels strongly correlated with AAG levels, showing parallel fluctuations of AAG and total drug over time and consistently low, unbound drug levels, different from previously reported AAG-binding drugs. This PK profile is due to high-affinity, reversible binding to AAG and binding to albumin, in addition to solubility-limited absorption and slow metabolic elimination properties.

Potential differences in pharmacokinetics (PK) between healthy subjects and patients with cancer were investigated using a physiologically based pharmacokinetic approach integrating demographic and physiological data from patients with cancer. Demographic data such as age, sex and body weight, and clinical laboratory measurements such as albumin, alpha-1 acid glycoprotein (AAG) and hematocrit were collected in ~2500 patients with cancer. A custom oncology population profile was built using the observed relationships among demographic variables and laboratory measurements in Simcyp® software, a population based ADME simulator. Patients with cancer were older compared with the age distribution in a built-in healthy volunteer profile in Simcyp. Hematocrit and albumin levels were lower and AAG levels were higher in patients with cancer. The custom population profile was used to investigate the disease effect on the pharmacokinetics of two probe substrates, saquinavir and midazolam. Higher saquinavir exposure was predicted in patients relative to healthy subjects, which was explained by the altered drug binding due to elevated AAG levels in patients with cancer. Consistent with historical clinical data, similar midazolam exposure was predicted in patients and healthy subjects, supporting the hypothesis that the CYP3A activity is not altered in patients with cancer. These results suggest that the custom oncology population profile is a promising tool for the prediction of PK in patients with cancer. Further evaluation and extension of this population profile with more compounds and more data will be needed.