Assessment of the effect of erdafitinib on cardiac safety: analysis of ECGs and exposure-QTc in patients with advanced or refractory solid tumors.

PURPOSE: To characterize the effect of erdafitinib on electrocardiogram (ECG) parameters and the relationship between erdafitinib plasma concentrations and QTc interval changes in patients with advanced or refractory solid tumors. METHODS: Triplicate ECGs and continuous 12-lead Holter data were collected in the dose escalation part (Part 1) of the first-in-human study, with doses ranging from 0.5 to 12 mg. Triplicate ECG monitoring continued in Parts 2-4 where 2 dose regimens selected from Part 1 were expanded in prespecified tumor types. Analyses of ECG data included central tendency analyses, identification of categorical outliers and morphological assessment. A concentration-QTc analysis was conducted using a linear mixed-effect model based on extracted time matching Holter data. RESULTS: Central tendency, categorical outlier, and ECG morphologic analyses from 187 patients revealed no clinically significant effect of erdafitinib on heart rate, atrioventricular conduction or cardiac depolarization (PR and QRS), and no effect on cardiac repolarization (QTc). Concentration-QTc analysis from 62 patients indicated that the slopes of relationship between total and free erdafitinib plasma concentrations and QTcI (mean exponent of 0.395) were estimated as - 0.00269 ms/(ng/mL) and - 1.138 ms/(ng/mL), respectively. The predicted change in QTcI at the observed geometric mean of total and free concentration at the highest therapeutic erdafitinib dose (9 mg daily) was < 10 ms at the upper bound of the two-sided 90% confidence interval. CONCLUSIONS: ECG data and the concentration-QTc relationships demonstrate that erdafitinib does not prolong QTc interval and has no effects on cardiac repolarization or other ECG parameters. Clinical trial registration numbers NCT01703481, EudraCT: 2012-000697-34.

Population pharmacokinetics of trabectedin in adolescent patients with cancer.

PURPOSE: To characterize the trabectedin population pharmacokinetics in children and adolescent patients with cancer and compare it with the trabectedin pharmacokinetics in adults. METHODS: Plasma concentrations from ten adolescent and three children with cancer (age range 4.0-17.0 years) treated with trabectedin at doses ranging from 1.1 to 1.7 mg/m2, administered as a 24-h continuous intravenous infusion every 3 weeks, were available for the analysis. An external model evaluation was performed to verify whether a previously developed adult population pharmacokinetic model was predictive of the pediatric plasma concentrations of trabectedin. The maximum a posteriori estimation of the individual pharmacokinetic parameters for pediatric patients was conducted, after successful completion of the external evaluation step. The relationships between pharmacokinetic parameters and body size were evaluated. RESULTS: External evaluation methods showed no major differences between the adult population and children and adolescent patients of this study. The mean ± standard deviation (SD) of the individual estimated clearance and central volume of distribution in these children/adolescent patients was 36.4 ± 16.1 L/h and 13.2 ± 6.54 L, respectively. These values were similar to the typical values reported for adult patients-37.6 L/h and 13.9 L (for females) and 16.1 L (for males). The median area under the plasma concentration versus time curve (AUC) in children/adolescent patients was 55.1 µg h/L, while in the adult population the median AUC was 61.3 µg h/L, both administered a 1.5 mg/m2 dose regimen with mean (range) BSA for adults = 1.86 (0.90-2.80) vs children/adolescent patients = 1.49 (0.66-2.54). CONCLUSIONS: The adult population pharmacokinetic model adequately described the trabectedin plasma concentrations and its variability in the pediatric population of patients involved in this assessment that mostly comprised adolescents. The trabectedin systemic exposure achieved in this population was comparable (within 12%) to the exposure obtained in adult population when the same dose, expressed in mg/m2, was administered.

Assessment of the drug interaction potential and single- and repeat-dose pharmacokinetics of the BRAF inhibitor dabrafenib.

The induction of CYP2C9 by dabrafenib using S-warfarin as a probe and the effects of a CYP3A inhibitor (ketoconazole) and a CYP2C8 inhibitor (gemfibrozil) on dabrafenib pharmacokinetics were evaluated in patients with BRAF V600 mutation-positive tumors. Dabrafenib single- and repeat-dose pharmacokinetics were also evaluated. S-warfarin AUC(0- ∞) decreased 37% and Cmax increased 18% with dabrafenib. Dabrafenib AUC(0- τ) and C(max) increased 71% and 33%, respectively, with ketoconazole. Hydroxy- and desmethyl-dabrafenib AUC(0-τ) increased 82% and 68%, respectively, and AUC for carboxy-dabrafenib decreased 16%. Dabrafenib AUC(0-τ) increased 47%, with no change in C(max), after gemfibrozil co-administration. Gemfibrozil did not affect systemic exposure to dabrafenib metabolites. Single- and repeat-dose dabrafenib pharmacokinetics were consistent with previous reports. All cohorts used the commercial capsules. More-frequent monitoring of international normalized ratios is recommended in patients receiving warfarin during initiation or discontinuation of dabrafenib. Substitution of strong inhibitors or strong inducers of CYP3A or CYP2C8 is recommended during treatment with dabrafenib.

Population pharmacokinetics of metronidazole evaluated using scavenged samples from preterm infants.

Pharmacokinetic (PK) studies in preterm infants are rarely conducted due to the research challenges posed by this population. To overcome these challenges, minimal-risk methods such as scavenged sampling can be used to evaluate the PK of commonly used drugs in this population. We evaluated the population PK of metronidazole using targeted sparse sampling and scavenged samples from infants that were ≤ 32 weeks of gestational age at birth and <120 postnatal days. A 5-center study was performed. A population PK model using nonlinear mixed-effect modeling (NONMEM) was developed. Covariate effects were evaluated based on estimated precision and clinical significance. Using the individual Bayesian PK estimates from the final population PK model and the dosing regimen used for each subject, the proportion of subjects achieving the therapeutic target of trough concentrations >8 mg/liter was calculated. Monte Carlo simulations were performed to evaluate the adequacy of different dosing recommendations per gestational age group. Thirty-two preterm infants were enrolled: the median (range) gestational age at birth was 27 (22 to 32) weeks, postnatal age was 41 (0 to 97) days, postmenstrual age (PMA) was 32 (24 to 43) weeks, and weight was 1,495 (678 to 3,850) g. The final PK data set contained 116 samples; 104/116 (90%) were scavenged from discarded clinical specimens. Metronidazole population PK was best described by a 1-compartment model. The population mean clearance (CL; liter/h) was determined as 0.0397 × (weight/1.5) × (PMA/32)²·49 using a volume of distribution (V) (liter) of 1.07 × (weight/1.5). The relative standard errors around parameter estimates ranged between 11% and 30%. On average, metronidazole concentrations in scavenged samples were 30% lower than those measured in scheduled blood draws. The majority of infants (>70%) met predefined pharmacodynamic efficacy targets. A new, simplified, postmenstrual-age-based dosing regimen is recommended for this population. Minimal-risk methods such as scavenged PK sampling provided meaningful information related to development of metronidazole PK models and dosing recommendations.

Modeling sleep data for a new drug in development using markov mixed-effects models.

PURPOSE: To characterize the time-course of sleep in insomnia patients as well as placebo and concentration-effect relationships of two hypnotic compounds, PD 0200390 and zolpidem, using an accelerated model-building strategy based on mixed-effects Markov models. METHODS: Data were obtained in a phase II study with the drugs. Sleep stages were recorded during eight hours of sleep for two nights per treatment for the five treatments. First-order Markov models were developed for one transition at a time in a sequential manner; first a baseline model, followed by placebo and lastly the drug models. To accelerate the process, predefined models were selected based on a priori knowledge of sleep, including inter-subject and inter-occasion variability. RESULTS: Baseline sleep was described using piece-wise linear models, depending on time of night and duration of sleep stage. Placebo affected light sleep stages; drugs also affected slow-wave sleep. Administering PD 0200390 30 min earlier than standard dosing was shown through simulations to reduce latency to persistent sleep by 40%. CONCLUSION: The proposed accelerated model-building strategy resulted in a model well describing sleep patterns of insomnia patients with and without treatments.

Benefit-risk assessment: the use of clinical utility index.

IMPORTANCE OF THE FIELD: Measurement of the tradeoff between efficacy and safety is rarely done in a quantitative fashion. The use of a clinical utility index (CUI) has been proposed as a tool to aid in this assessment. The methodology from multi-attribute decision analysis is in its early stage in drug development and can be used to understand the therapeutic index and the value relative to the competitive landscape. AREAS COVERED IN THIS REVIEW: Different examples and applications of the use of CUI are reviewed and key steps involved in the development described. These include: i) characterization of the exposure-response of efficacy and safety end points; ii) definition of clinically meaningful parameters; iii) selection and weighting of important attributes and iv) sensitivity analysis and measurement of uncertainty. WHAT THE READER WILL GAIN: An understanding of the value and limitations of CUI in drug development. TAKE HOME MESSAGE: The use of a CUI for quantitative assessment of benefit/risk is most useful when there are multiple attributes involved in a decision to better understand the relevance of each attribute and when differentiation from competitors is critical to the success of a compound. Although development of CUI may be time- and resource-consuming, it allows clear and transparent decision making.

Pharmacokinetics of quinapril in children: assessment during substitution for chronic angiotensin-converting enzyme inhibitor treatment.

Quinapril pharmacokinetics were studied in infants and children using a novel study design that allowed substitution of quinapril for one dose of the current chronic angiotensin-converting enzyme (ACE) inhibitor treatment. A total of 24 patients ranging in age from 2.5 to 82 months who were receiving an ACE inhibitor held their usual treatment on the study day and received a 0.2-mg/kg dose of quinapril syrup. Blood samples were collected through 24 hours postdose, and plasma was analyzed for quinapril and its active metabolite, quinaprilat. Quinapril was rapidly converted to quinaprilat. Quinaprilat concentrations generally peaked 1 to 2 hours postdose and declined with a mean half-life of 2.30 hours. Dosing on a mg/kg basis resulted in quinaprilat AUC and Cmax values that were generally comparable across the age range of patients in this study. The overall mean AUC0-infinity was 993 ng.h/mL (range: 533-1523), and mean Cmax was 260 ng/mL (range: 70.0-445.5). Quinaprilat CL/F correlated well with body size (body surface area or weight) and creatinine clearance (mL/min). Pharmacokinetic results after a 0.2-mg/kg dose in infants and children are comparable to those observed following a 10-mg dose in adults.