In order to optimize therapy, a full understanding of the pharmacokinetics of any systemic therapy is desired. We routinely model the pharmacokinetic (PK) data of agents being tested for antitumor activity and correlate that with activity and/or toxicity (pharmacodynamics modeling). The laboratory is currently collaborating on 80 clinical trials to characterize the clinical pharmacology of novel chemotherapy agents. We utilize compartmental and noncompartmental approaches to define the disposition of agents. Analysis of PK data (using concentration measurements provided by sample analysis using validated assays) allows for assessment of drug disposition, including the absorption, distribution, metabolism and excretion of a drug. Modeling this data, essentially describing these physiological processes as a mathematical equation, allows for optimization of drug administration (including dose and frequency of dosing,) in silico. Over the years, we have conducted population pharmacokinetic modeling of the following compounds: depsipeptide, romidepsin, sorafenib, olaparib, docetaxel in combination with the p-glycoprotein antagonist tariquidar, TRC105, and TRC102. Recent efforts have focused on building a population PK model to understand the disposition kinetics of mithramycin in the body to best optimize dose. We also performed population PK modeling and simulation of belinostat, a second-generation zinc-binding histone deacetylase inhibitor that is approved for peripheral T-cell lymphoma. It is currently being studied in small cell lung cancer and other advanced carcinomas as a 48-hour continuous intravenous infusion. Belinostat is predominantly metabolized by UGT1A1, which is polymorphic. Preliminary analyses revealed a difference in belinostat clearance based on UGT1A1 genotype. A 2-compartment population PK model was developed and validated that incorporated the UGT1A1 genotype, albumin, and creatinine clearance on the clearance parameter; body weight was a significant covariate on volume. Simulated doses of 600 and 400 mg/m(2) /24 h given to patients considered extensive or impaired metabolizers, respectively, provided equivalent AUCs. This model and subsequent simulations supported additional PK/toxicity and pharmacogenomics/toxicity analyses to suggest a UGT1A1 genotype-based dose adjustment to normalize belinostat exposure and allow for more tolerable therapy. In addition, global protein lysine acetylation was modeled with PK and demonstrated a reversible belinostat exposure/response relationship, consistent with previous reports. This population PK model of belinostat is currently being expanded to include a PD model. To further understand the mechanistic relationship between carboplatin and olaparib clearance, a population PK model was developed and validated by the CPP. Combining olaparib with carboplatin was recently shown to be active in both BRCA and non-BRCA mutant cancers in a recent phase I/Ib combination trial. The optimal drug sequence recommended was carboplatin 1-day before olaparib. However, carboplatin pre-treatment induced a 50% faster olaparib clearance. To further explore this drug interaction, a population pharmacokinetic (PK) model was designed that included a lag time parameter, a second absorption compartment from tablet formulation, a single distribution/elimination compartment, and covariance among the clearance and volume parameters. Clearance (6.8 L/h) and volume (33 L) estimates were comparable with literature. The only significant covariate was the presence of carboplatin on olaparib clearance, consistent with published noncompartmental PK and in vitro data. Simulations predicted lower steady-state peak/trough olaparib exposure through 24-36 h post carboplatin pre-treatment, but this effect was lost by day 2 and thus no dose adjustment is recommended.
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