Metabolism and disposition of the anticancer quinolone derivative vosaroxin, a novel inhibitor of topoisomerase II.

Background Vosaroxin is a first-in-class anticancer quinolone derivative that is being investigated for patients with relapsed or refractory acute myeloid leukemia (AML). The primary objective of this study was to quantitatively determine the pharmacokinetics of vosaroxin and its metabolites in patients with advanced solid tumors. Methods This mass balance study investigated the pharmacokinetics (distribution, metabolism, and excretion) of vosaroxin in cancer patients after a single dose of 60 mg/m2 14C-vosaroxin, administered as short intravenous injection. Blood, urine and feces were collected over 168 h after injection or until recovered radioactivity over 24 h was less than 1% of the administered dose (whichever was earlier). Total radioactivity (TRA), vosaroxin and metabolites were studied in all matrices. Results Unchanged vosaroxin was the major species identified in plasma, urine, and feces. N-desmethylvosaroxin was the only circulating metabolite detected in plasma, accounting for <3% of the administered dose. However, in plasma, the combined vosaroxin + N-desmethylvosaroxin AUC0-∞ was 21% lower than the TRA AUC0-∞ , suggesting the possible formation of protein bound metabolites after 48 h when the concentration-time profiles diverged. The mean recovery of TRA in excreta was 81.3% of the total administered dose; 53.1% was excreted through feces and 28.2% through urine. Conclusions Unchanged vosaroxin was the major compound found in the excreta, although 10 minor metabolites were detected. The biotransformation reactions were demethylation, hydrogenation, decarboxylation and phase II conjugation including glucuronidation.

Using disease progression models as a tool to detect drug effect.

Generally, information required for approval of new drugs is dichotomous in that the drug is either efficacious and safe or not. Consequently, the purpose of most confirmatory clinical trials is to test the null hypothesis. The primary reasons for designing hypothesis testing trials are to provide the information required for approval using analyses techniques that are relatively straightforward and free of apparent assumptions. However, the information required for approval is very different from that used by prescribers for decision making. In the clinic, decisions must be made about dose adjustment for individual patients in the presence of additional therapies and co-morbidities. Choice of drug and dosing regimen is therefore a classical risk to benefit decision that is often poorly informed from the results of confirmatory trials. Therefore, providing answers to the more difficult question of how to use the drug in a clinical setting is essential.

Model Evaluation of Continuous Data Pharmacometric Models: Metrics and Graphics.

This article represents the first in a series of tutorials on model evaluation in nonlinear mixed effect models (NLMEMs), from the International Society of Pharmacometrics (ISoP) Model Evaluation Group. Numerous tools are available for evaluation of NLMEM, with a particular emphasis on visual assessment. This first basic tutorial focuses on presenting graphical evaluation tools of NLMEM for continuous data. It illustrates graphs for correct or misspecified models, discusses their pros and cons, and recalls the definition of metrics used.

Why therapeutic drug monitoring is needed for monoclonal antibodies and how do we implement this?

Monoclonal antibodies (mAbs) have improved clinical outcomes for many therapeutic indications. However, extensive between-subject variability (BSV) contributes to therapeutic failures through suboptimal exposure. Therapeutic drug monitoring (TDM) is routinely implemented for inflammatory diseases; improving outcomes and reducing treatment costs. BSV can be more extensive with anticancer mAbs. Clearance BSV is associated with patient factors and disease burden, suggesting that TDM could benefit anticancer mAbs, as was seen with inflammatory disease, however, there are many hurdles.

Clinical Decision Support Tools: The Evolution of a Revolution.

Dashboard systems for clinical decision support integrate data from multiple sources. These systems, the newest in a long line of dose calculators and other decision support tools, utilize Bayesian approaches to fully individualize dosing using information gathered through therapeutic drug monitoring. In the treatment of inflammatory bowel disease patients with infliximab, dashboards may reduce therapeutic failures and treatment costs. The history and future development of modern Bayesian dashboard systems is described.

Developing Exposure/Response Models for Anticancer Drug Treatment: Special Considerations.

Anticancer agents often have a narrow therapeutic index (TI), requiring precise dosing to ensure sufficient exposure for clinical activity while minimizing toxicity. These agents frequently have complex pharmacology, and combination therapy may cause schedule-specific effects and interactions. We review anticancer drug development, showing how integration of modeling and simulation throughout development can inform anticancer dose selection, potentially improving the late-phase success rate. This article has a companion article in Clinical Pharmacology & Therapeutics with practical examples.

Optimizing oncology therapeutics through quantitative translational and clinical pharmacology: challenges and opportunities.

Despite advances in biomedical research that have deepened our understanding of cancer hallmarks, resulting in the discovery and development of targeted therapies, the success rates of oncology drug development remain low. Opportunities remain for objective dose selection informed by exposure-response understanding to optimize the benefit-risk balance of novel therapies for cancer patients. This review article discusses the principles and applications of modeling and simulation approaches across the lifecycle of development of oncology therapeutics. Illustrative examples are used to convey the value gained from integration of quantitative clinical pharmacology strategies from the preclinical-translational phase through confirmatory clinical evaluation of efficacy and safety.

Simultaneous modeling of the pharmacokinetics and pharmacodynamics of midazolam and diazepam.

The pharmacokinetics and pharmacodynamics of midazolam and diazepam were compared after intravenous infusions of 0.03 and 0.07 mg/kg midazolam and 0.1 and 0.2 mg/kg diazepam on four separate occasions in 12 healthy male subjects in a randomized four-way crossover design. The Digit Symbol Substitution Test (DSST) was used as a measure of drug effect. Subjects performed three practice tests before dosing to account for any effects caused by familiarization ("learning curve") with the testing procedure. Pharmacokinetic and pharmacodynamic data were simultaneously fitted to a semiparametric model. In this model, a pharmacokinetic model related dose to plasma concentrations, a link model related plasma concentrations to the concentration at the effect site, and a pharmacodynamic model related the effect site concentration to the observed effect. The plasma-effect site equilibrium half-life was approximately 2 1/2 times longer for midazolam than for diazepam, which is in good agreement with previously published data. Based on the estimated effect site concentration at which half of the maximal effect was reached, midazolam had approximately a sixfold greater intrinsic potency than diazepam. This difference in potency was also observed in a previous study that used transformed electroencephalographic (EEG) data to assess pharmacodynamic activity. The findings reported here with a clinically relevant pharmacodynamic marker (DSST) confirm the utility of surrogate drug effect measures such as EEG. This work also shows the feasibility of conducting pharmacokinetic pharmacodynamic analysis during the drug development process.

A population pharmacokinetic-pharmacodynamic analysis of single doses of clenoliximab in patients with rheumatoid arthritis.

Clenoliximab (IDEC-151) is a macaque-human chimeric monoclonal antibody (immunoglobulin G4) specific for the CD4 molecule on the surface of T lymphocytes. It is being studied in patients with rheumatoid arthritis in which T cell activation orchestrates inflammation and tissue damage. In this initial study in humans, the pharmacokinetics and pharmacodynamics of clenoliximab were investigated after single intravenous infusion. Blood was collected up to 12 weeks after dose administration to measure clenoliximab concentration, CD4+ T-cell count, CD4 antigen coating, and CD4 cell surface density. Clenoliximab displayed nonlinear pharmacokinetic behavior and caused an 80% reduction in CD4 density for up to 3 weeks, without depleting T cells. A pharmacokinetic-pharmacodynamic model was developed that described the relationship between antibody concentration, antigen coating, and the observed decreases in CD4 cell surface density. This was used to anticipate the effects of clenoliximab in untested regimens and optimize the design of future clinical trials.

Determination of humanized anti-Tac in human serum by a sandwich enzyme linked immunosorbent assay.

A 'sandwich' enzyme-linked immunosorbent assay has been developed for measuring humanized anti-Tac (HAT), a humanized antibody to the IL-2 receptor on activated T cells (Tac), in human serum. The working range of this assay is 25-400 ng/ml with an overall precision of 5%. In this assay, the analyte, HAT, is sandwiched between Tac which is bound to a microtiter plate and biotinylated Tac that is conjugated to peroxidase labelled streptavidin. This assay was utilized to determine the pharmacokinetic parameters of HAT in patients with graft-versus-host disease.

Pretreatment with ranitidine does not reduce the bioavailability of orally administered topotecan.

PURPOSE: The purpose of this randomized, two-period crossover study was to determine the pharmacokinetics of orally administered topotecan in the presence and absence of oral ranitidine. METHODS: Patients with solid malignant tumors refractory to standard treatment were given topotecan orally on a daily times five schedule repeated every 21 days. Topotecan was given initially at 2.3 mg/m2 per day; dose adjustments were permitted after the first dose of course 2 if necessary. Blood samples for pharmacokinetic assessments were drawn at protocol-specified times for up to 10 h following oral administration of topotecan on day 1 of courses 1 and 2. Patients were randomly assigned to receive a total of nine doses of ranitidine: 150 mg twice daily for 4 days before day 1 of one of the first two courses and 150 mg given 2 h before the first topotecan dose. Plasma samples were assayed for concentrations of active topotecan lactone (TPT-L) and total topotecan (TPT-T, lactone plus open-ring carboxylate form) using high-performance liquid chromatography with fluorescence detection. After completion of courses 1 and 2, patients could continue on therapy for days 1-5 of every 21 days if not withdrawn due to unacceptable toxicity, disease progression, protocol violation, or by request. Patients continued on treatment for a maximum of six courses. RESULTS: No pharmacokinetic parameter for either TPT-L or TPT-T differed significantly during administration of topotecan with ranitidine compared with topotecan alone (n = 13). Geometric mean ratios (95% confidence intervals, CIs) of areas under the curve in the presence and absence of ranitidine were 0.94 (0.80, 1.10) for TPT-L and 0.97 (0.80, 1.16) for TPT-T. Corresponding ratios (CIs) of peak plasma concentrations in the presence and absence of ranitidine were 1.06 (0.78, 1.44) for TPT-L and 1.07 (0.84, 1.38) for TPT-T. The median difference in time to peak plasma concentration was 0.0 h for TPT-L and -0.5 h for TPT-T (i.e. slightly faster in the presence of ranitidine). CONCLUSIONS: Administration of ranitidine prior to oral topotecan resulted in a similar extent of absorption. A slightly faster rate of absorption of topotecan was also observed, which is unlikely to be of clinical significance. Dosage adjustments of orally administered topotecan should not be necessary in patients who are pretreated with ranitidine, an H2 antagonist, or another agent that comparably raises gastric pH.

Basic concepts in population modeling, simulation, and model-based drug development: part 3-introduction to pharmacodynamic modeling methods.

Population pharmacodynamic (PD) models describe the time course of drug effects, relating exposure to response, and providing a more robust understanding of drug action than single assessments. PD models can test alternative dose regimens through simulation, allowing for informed assessment of potential dose regimens and study designs. This is the third paper in a three-part series, providing an introduction into methods for developing and evaluating population PD models. Example files are available in the Supplementary Data.

Model-based meta-analysis: an important tool for making quantitative decisions during drug development.

Modeling during drug development is routinely used to integrate subject-level information, evaluate response, and inform clinical trials. Model-based meta-analysis (MBMA) combines aggregate safety and efficacy results from many trials. Because MBMA is based on results from large numbers of subjects, it increases the power to precisely detect small but clinically significant effects, providing a basis for quantitative drug development decisions and reducing time and cost. This Commentary describes an overview of MBMA and its application during drug development.

Models for disease progression: new approaches and uses.

Disease-progression models are useful tools in drug development. They increase the information obtained from clinical trials, improve study designs, and allow in silico evaluations of new treatment combinations and dose regimens. Disease-progression modeling can save time and strengthen "go/no-go" criteria. The use of meta-based modeling and the linking of disease progression to discrete clinical end points have improved the utility of this valuable approach. This article provides an overview of disease-progression evaluations using these new approaches.

Basic concepts in population modeling, simulation, and model-based drug development.

Modeling is an important tool in drug development; population modeling is a complex process requiring robust underlying procedures for ensuring clean data, appropriate computing platforms, adequate resources, and effective communication. Although requiring an investment in resources, it can save time and money by providing a platform for integrating all information gathered on new therapeutic agents. This article provides a brief overview of aspects of modeling and simulation as applied to many areas in drug development.CPT: Pharmacometrics & Systems Pharmacology (2012) 1, e6; doi:10.1038/psp.2012.4; advance online publication 26 September 2012.

Romiplostim dose response in patients with immune thrombocytopenia.

A pharmacodynamic model was developed for platelet counts in 52 patients with immune thrombocytopenia (ITP) receiving subcutaneous romiplostim in 3 phase I/II studies (dose range, 0.2-10 µg/kg). The model consisted of a drug-sensitive progenitor cell compartment linked to a peripheral blood compartment through 4 transition compartments. The baseline platelet count, mean transit time, and kinetics of drug effect constant were 11.1 × 10(9)/L, 170 hours, and 0.6 day(-1), respectively. The ITP patients had a shorter platelet life span and lower progenitor cell production rates than healthy volunteers. Romiplostim response was described for 2 subpopulations. The romiplostim stimulatory effect in ITP patients was 351%/100 µg/wk and 12%/100 µg/wk in 68% and 32% of patients, respectively. Visual and numerical predictive checks suggested accurate prediction of platelet time course and durable response rate in ITP patients. Model-based simulations confirmed the effectiveness of dose reduction to prevent platelet counts >400 × 10(9)/L.

A population pharmacokinetic and pharmacodynamic evaluation of pralatrexate in patients with relapsed or refractory non-Hodgkin's or Hodgkin's lymphoma.

In a pralatrexate phase I study, patients displayed a high incidence of mucositis of grades 3 and 4. Preliminary evaluations of the pharmacokinetics of the drug and its association with mucositis suggested that pralatrexate exposure (area under the concentration-time curve (AUC)) could be controlled with body size (e.g., weight or body surface area)-based dosing and that pretreatment with folic acid and vitamin B(12) might diminish the incidence and severity of mucositis. The study was amended, with revised dosing and vitamin B(12) administration. Data from 47 patients were evaluated using NONMEM. Weight and methylmalonic acid (MMA) level were predictive of pharmacokinetic (PK) variability. AUC and MMA level were positively correlated with the risk of developing mucositis. A lower AUC schedule with vitamin B(12) pretreatment may control mucositis without compromising efficacy. The covariates identified in this study are comparable with other antifolate analogs. The application of modeling was a critical step in the development of pralatrexate, yielding important suggestions for dose, scheduling, and pretreatment modifications.

Population pharmacokinetics-pharmacodynamics of alemtuzumab (Campath) in patients with chronic lymphocytic leukaemia and its link to treatment response.

AIMS: To characterize alemtuzumab pharmacokinetics and its exposure-response relationship with white blood cell (WBC) count in patients with B-cell chronic lymphocytic leukaemia (CLL). METHODS: Nonlinear mixed effects models were used to characterize plasma concentration-time data and WBC count-time data from 67 patients. Logistic regression was used to relate summary measures of drug exposure to tumour response. RESULTS: Alemtuzumab pharmacokinetics were best characterized by a two-compartment model with nonlinear elimination where V(max) (microg h(-1)) was [1020 x (WBC count/10 x 10(9) l(-1))(0.194)], K(m) was 338 microg l(-1), V(1) was 11.3 l, Q was 1.05 l h(-1) and V(2) was 41.5 l. Intersubject variability (ISV) in V(max), K(m), V(1) and V(2) was 32%, 145%, 84% and 179%, respectively. The reduction in WBC over time was modelled by a stimulatory loss indirect response model with values of 18.2 for E(max), 306 microg l(-1) for EC(50), 1.56 x 10(9) cells l(-1) h(-1) for K(in) and 0.029 per h for K(out). The probability of achieving a complete or partial response was >/=50% when the maximal trough concentration exceeded 13.2 microg ml(-1) or when AUC(0-tau) exceeded 484 microg h(-1) ml(-1). CONCLUSIONS: Alemtuzumab displayed time- and concentration-dependent pharmacokinetics with large interpatient variability, both in pharmacokinetics and pharmacodynamics, which was probably reflective of differences in tumour burden among patients. A direct relationship between maximal trough concentrations and clinical outcomes was observed, with increasing alemtuzumab exposure resulting in a greater probability of positive tumour response.