Concentration-QTc and cardiac safety analysis of single and multiple zavegepant nasal spray doses in healthy participants to support approval.

Zavegepant is a novel gepant administered as a nasal spray approved in the United States at a 10 mg dose for the acute treatment of migraine with or without aura in adults. The cardiovascular safety of zavegepant nasal spray was assessed in both single-ascending dose (SAD) and multiple-ascending dose (MAD) studies in healthy participants. The SAD study included 72 participants (54 active/18 placebo) who received 0.1-40 mg zavegepant or placebo. The MAD study included 72 participants (56 active/16 placebo) who received 5-40 mg zavegepant or placebo for 1-14 days. Plasma zavegepant pharmacokinetics and electrocardiographic (ECG) parameters (Fridericia-corrected QT interval [QTcF], heart rate, PR interval, ventricular depolarization [QRS], T-wave morphology, and U-wave presence) were analyzed pre- and post-zavegepant administration. Using pooled data from the SAD and MAD studies, the relationship between time-matched plasma zavegepant concentrations and QTc interval was assessed using a linear mixed-effects model to evaluate the potential for QTc interval prolongation. Results showed that single and multiple doses of zavegepant had no significant impact on ECG parameters versus placebo, and there was no concentration-dependent effect on QTcF interval. The estimated slope of the plasma zavegepant concentration-QTcF model was -0.053 ms per ng/mL with a 90% confidence interval of -0.0955 to -0.0110 (p = 0.0415), which is not considered clinically meaningful. At doses up to four times the recommended daily dose, zavegepant does not prolong the QT interval to any clinically relevant extent.

Employing zero-inflated beta distribution in an exposure-response analysis of TYK2/JAK1 inhibitor brepocitinib in patients with plaque psoriasis.

Brepocitinib is an oral selective dual TYK2/JAK1 inhibitor and based on its cytokine inhibition profile is expected to provide therapeutic benefit in the treatment of plaque psoriasis. Efficacy data from a completed Phase 2a study in patients with moderate-to-severe plaque psoriasis were utilized to develop a population exposure-response model that can be employed to inform dose selection decisions for further clinical development. A modeling approach that employs the zero-inflated beta distribution was used to account for the bounded nature and distributional characteristics of the Psoriasis Area and Severity Index (PASI) score data. The developed exposure-response model provided an adequate description of the observed PASI scores across all the treatment arms tested and across both the induction and maintenance dosing periods of the study. In addition, the developed model exhibited a good predictive capacity with regard to the derived responder metrics (e.g., 75%/90%/100% improvement in PASI score [PASI75/90/100]). Clinical trial simulations indicated that the induction/maintenance dosing paradigm explored in this study does not offer any advantages from an efficacy perspective and that doses of 10, 30, and 60 mg once-daily may be suitable candidates for clinical evaluation in subsequent Phase 2b studies.

Population Pharmacokinetics of Oral Brepocitinib in Healthy Volunteers and Patients.

Brepocitinib is a tyrosine kinase 2 and Janus kinase 1 inhibitor in development for treatment of inflammatory autoimmune diseases. This analysis aimed to add to the pharmacokinetic knowledge of the medication, through development of a population pharmacokinetic model and identification of factors that affect drug disposition. Plasma samples from 5 clinical trials were collated, composed of healthy volunteers, patients with psoriasis and patients with alopecia areata taking oral brepocitinib. NONMEM was used to develop a population pharmacokinetic model, and patient demographics were tested as covariates. The final model was a 1-compartment model with first-order absorption. The typical values for apparent clearance and apparent volume of distribution were 18.7 L/h (78% coefficient of variation [CV]) and 136 L (60.5% CV), respectively. Absorption was rapid with an absorption constant of 3.46 h, with an absorption lag of 0.24 hours observed with the oral tablet formulation. The proportional residual error was found to be 52.7% CV in healthy volunteers and 87.5% CV in patients. High-fat meals were associated with a reduction in both the rate (69.9% lower) and extent (28.3% lower) of absorption, while Asian populations had reduced clearance (24.3% lower). Nonlinear pharmacokinetics were observed at doses of 175 mg and above, with a 35.1% higher relative bioavailability at these doses. There were insufficient data to describe this nonlinearity as a continuous relationship. This initial description of the population pharmacokinetics will act as a foundation for the model-informed drug development of brepocitinib and will facilitate future modeling of this medicine. ClinicalTrials.gov numbers NCT02310750 NCT03236493 NCT03656952 NCT02969018 NCT02974868.

Your best day: An interactive app to translate how time reallocations within a 24-hour day are associated with health measures.

Reallocations of time between daily activities such as sleep, sedentary behavior and physical activity are differentially associated with markers of physical, mental and social health. An individual's most desirable allocation of time may differ depending on which outcomes they value most, with these outcomes potentially competing with each other for reallocations. We aimed to develop an interactive app that translates how self-selected time reallocations are associated with multiple health measures. We used data from the Australian Child Health CheckPoint study (n = 1685, 48% female, 11-12 y), with time spent in daily activities derived from a validated 24-h recall instrument, %body fat from bioelectric impedance, psychosocial health from the Pediatric Quality of Life Inventory and academic performance (writing) from national standardized tests. We created a user-interface to the compositional isotemporal substitution model with interactive sliders that can be manipulated to self-select time reallocations between activities. The time-use composition was significantly associated with body fat percentage (F = 2.66, P < .001), psychosocial health (F = 4.02, P < .001), and academic performance (F = 2.76, P < .001). Dragging the sliders on the app shows how self-selected time reallocations are associated with the health measures. For example, reallocating 60 minutes from screen time to physical activity was associated with -0.8 [95% CI -1.0 to -0.5] %body fat, +1.9 [1.4 to 2.5] psychosocial score and +4.5 [1.8 to 7.2] academic performance. Our app allows the health associations of time reallocations to be compared against each other. Interactive interfaces provide flexibility in selecting which time reallocations to investigate, and may transform how research findings are disseminated.

Predictions of Systemic, Intracellular, and Lung Concentrations of Azithromycin With Different Dosing Regimens Used in COVID-19 Clinical Trials.

Azithromycin (AZ), a broad-spectrum macrolide antibiotic, is being investigated in patients with coronavirus disease 2019 (COVID-19). A population pharmacokinetic model was implemented to predict lung, intracellular poly/mononuclear cell (peripheral blood monocyte (PBM)/polymorphonuclear leukocyte (PML)), and alveolar macrophage (AM) concentrations using published data and compared against preclinical effective concentration 90% (EC90 ) for severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2). The final model described the data reported in eight publications adequately. Consistent with its known properties, concentrations were higher in AM and PBM/PML, followed by lung tissue, and lowest systemically. Simulated PBM/PML concentrations exceeded EC90 following the first dose and for ~ 14 days following 500 mg q.d. for 3 days or 500 mg q.d. for 1 day/250 mg q.d. on days 2-5, 10 days following a single 1,000 mg dose, and for > 20 days with 500 mg q.d. for 10 days. AM concentrations exceeded the 90% inhibitory concentration for > 20 days for all regimens. These data will better inform optimization of dosing regimens for AZ clinical trials.

Development of a physiologically based pharmacokinetic model for intravenous lenalidomide in mice.

PURPOSE: Lenalidomide is used widely in B-cell malignancies for its immunomodulatory activity. It is primarily eliminated via the kidneys, with a significant proportion of renal elimination attributed to active processes. Lenalidomide is a weak substrate of P-glycoprotein (P-gp), though it is unclear whether P-gp is solely responsible for lenalidomide transport. This study aimed to determine whether the current knowledge of lenalidomide was sufficient to describe the pharmacokinetics of lenalidomide in multiple tissues. METHODS: A physiologically based pharmacokinetic model was developed using the Open Systems Pharmacology Suite to explore the pharmacokinetics of lenalidomide in a variety of tissues. Data were available for mice dosed intravenously at 0.5, 1.5, 5, and 10 mg/kg, with concentrations measured in plasma, brain, heart, kidney, liver, lung, muscle, and spleen. P-gp expression and activity were sourced from the literature. RESULTS: The model predictions in plasma, liver, and lung were representative of the observed data (median prediction error 13%, - 10%, and 30%, respectively, with 90% confidence intervals including zero), while other tissue predictions showed sufficient similarity to the observed data. Contrary to the data, model predictions for the brain showed no drug reaching brain tissue when P-gp was expressed at the blood-brain barrier. The data were better described by basolateral transporters at the intracellular wall. Local sensitivity analysis showed that transporter activity was the most sensitive parameter in these models for exposure. CONCLUSION: As P-gp transport at the blood-brain barrier did not explain the observed brain concentrations alone, there may be other transporters involved in lenalidomide disposition.

Optimising time samples for determining area under the curve of pharmacokinetic data using non-compartmental analysis.

OBJECTIVES: The selection of sample times for a pharmacokinetic study is important when trapezoidal integration (e.g. non-compartmental analysis) is used to determine the area under the concentration-time curve (AUC). The aim of this study was to develop an algorithm that determines optimal times that provide the most accurate AUC by minimising trapezoidal integration error. METHODS: The algorithm required initial single individual or mean pooled concentration data but did not specifically require a prior pharmacokinetic model. Optimal sample intervals were determined by minimising trapezoidal error using a genetic algorithm followed by a quasi-Newton method. The method was evaluated against simulated and clinical datasets to determine the method's ability to estimate the AUC. KEY FINDINGS: The sample times produced by the algorithm were able to accurately estimate the AUC of pharmacokinetic profiles, with the relative AUC having 90% confidence intervals of 0.919-1.05 for profiles with two-compartment kinetics. When comparing the algorithm with rich sampling (e.g. phase I trial), the algorithm provided equivalent or superior sample times with fewer observations. CONCLUSIONS: The creation of the algorithm and its companion web application allows users with limited pharmacometric or programming training can obtain optimal sampling times for pharmacokinetic studies.

Population pharmacokinetics of lenalidomide in patients with B-cell malignancies.

AIMS: Lenalidomide is an immunomodulatory imide drug used broadly in the treatment of multiple myeloma and lymphoma. It continues to be evaluated in chronic lymphocytic leukaemia (CLL) at lower doses due to dose-related toxicities including tumour flare and tumour lysis syndrome. This study aimed to develop a population pharmacokinetic model for lenalidomide in multiple cancers, including CLL, to identify any disease-related differences in disposition. METHODS: Lenalidomide concentrations from 4 clinical trials were collated (1999 samples, 125 subjects), covering 4 cancers (multiple myeloma, CLL, acute myeloid leukaemia and acute lymphoblastic leukaemia) and a large dose range (2.5-75 mg). A population pharmacokinetic model was developed with NONMEM and patient demographics were tested as covariates. RESULTS: The data were best fitted by a 1-compartment kinetic model with absorption described by 7 transit compartments. Clearance and volume of distribution were allometrically scaled for fat-free mass. The population parameter estimates for apparent clearance, apparent volume of distribution and transit rate constant were 12 L/h (10.8-13.6), 68.8 L (61.8-76.3), and 13.5 h-1 (11.9-36.8) respectively. Patients with impaired renal function (creatinine clearance <30 mL/min) exhibited a 22% reduction in lenalidomide clearance compared to patients with creatinine clearance of 90 mL/min. Cancer type had no discernible effect on lenalidomide disposition. CONCLUSIONS: This is the first report of a lenalidomide population pharmacokinetic model to evaluate lenalidomide pharmacokinetics in patients with CLL and compare its pharmacokinetics with other B-cell malignancies. As no differences in pharmacokinetics were found between the observed cancer-types, the unique toxicities observed in CLL may be due to disease-specific pharmacodynamics.

Comparison of non-compartmental and mixed effect modelling methods for establishing bioequivalence for the case of two compartment kinetics and censored concentrations.

Non-compartmental analysis (NCA) is regarded as the standard for establishing bioequivalence, despite its limitations and the existence of alternative methods such as non-linear mixed effects modelling (NLMEM). Comparisons of NCA and NLMEM in bioequivalence testing have been limited to drugs with one-compartment kinetics and have included a large number of different approaches. A simulation tool was developed with the ability to rapidly compare NCA and NLMEM methods in determining bioequivalence using both R and NONMEM and applied to a drug with two-compartment pharmacokinetics. Concentration-time profiles were simulated where relative bioavailability, random unexplained variability (RUV) at the lower limit of quantification (LLOQ) differed between simulations. NLMEM analyses employed either the M1 or M3 methods for dealing with values below the LLOQ. It was used to elucidate the impact of changes in (i) RUV at the LLOQ, (ii) the extent of censoring data below the LLOQ and (iii) the concentration sampling times. The simulations showed NLMEM having a consistent 20-40% higher accuracy and sensitivity in identifying bioequivalent studies when compared to NCA, while NCA was found to have a 1-10% higher specificity than NLMEM. Increasing data censoring by increasing the LLOQ resulted in decreases of ~10% to the accuracy and sensitivity of NCA, with minimal effects on NLMEM. The tool provides a platform for comparing NCA and NLMEM methods and its use can be extended beyond the scenarios reported here. In the situations examined it is seen that NLMEM is more accurate than NCA and may offer some advantages in the determination of bioequivalence.