Transient lower esophageal sphincter relaxation pharmacokinetic-pharmacodynamic modeling: count model and repeated time-to-event model

A multistate platform model for time-to-event endpoints in oncology clinical trials

A multistate platform model was developed to describe time-to-event (TTE) endpoints in an oncology trial through the following states: initial, tumor response (TR), progressive disease (PD), overall survival (OS) event (death), censor to the last evaluable tumor assessment (progression-free survival [PFS] censor), and censor to study end (OS censor), using an ordinary differential equation framework. Two types of piecewise functions were used to describe the hazards for different events. Piecewise surge functions were used for events that require tumor assessments at the scheduled study visit times (TR, PD, and PFS censor). Piecewise constant functions were used to describe hazards for events that occur evenly throughout the study (OS event and OS censor). The multistate TTE model was applied to describe TTE endpoints from a published phase III study. The piecewise surge functions well-described the observed surges of hazards/events for TR, PD, PFS, and OS occurring near scheduled tumor assessments and showed good agreement with all Kaplan-Meier curves. With the flexibility of piecewise hazard functions, the model was able to evaluate covariate effects in a time-variant fashion to better understand the temporal patterns of disease prognosis through different disease states. This model can be applied to advance the field of oncology trial design and optimization by: (1) enabling robust estimations of baseline hazards and covariate effects for multiple TTE endpoints, (2) providing a platform model for understanding the composition and correlations between different TTE endpoints, and (3) facilitating oncology trial design optimization through clinical trial simulations.

Population repeated time-to-event analysis of exacerbations in asthma patients: A novel approach for predicting asthma exacerbations based on biomarkers, spirometry, and diaries/questionnaires

Identification of covariates, including biomarkers, spirometry, and diaries/questionnaires, that predict asthma exacerbations would allow better clinical predictions, shorter phase II trials and inform decisions on phase III design, and/or initiation (go/no-go). The objective of this work was to characterize asthma-exacerbation hazard as a function of baseline and time-varying covariates. A repeated time-to-event (RTTE) model for exacerbations was developed using data from a 52-week phase IIb trial, including 502 patients with asthma randomized to placebo or 70 mg, 210 mg, or 490 mg astegolimab every 4 weeks. Covariate analysis was performed for 20 baseline covariates using the full random effects modeling approach, followed by time-varying covariate analysis of nine covariates using the stepwise covariate model (SCM) building procedure. Following the SCM, an astegolimab treatment effect was explored. Diary-based symptom score (difference in objective function value [dOFV] of -83.7) and rescue medication use (dOFV = -33.5), and forced expiratory volume in 1 s (dOFV = -14.9) were identified as significant time-varying covariates. Of note, time-varying covariates become more useful with more frequent measurements, which should favor the daily diary scores over others. The most influential baseline covariates were exacerbation history and diary-based symptom score (i.e., symptom score was important as both time-varying and baseline covariate). A (nonsignificant) astegolimab treatment effect was included in the final model because the limited data set did not allow concluding the remaining effect size as irrelevant. Without time-varying covariates, the treatment effect was statistically significant (p < 0.01). This work demonstrated the utility of a population RTTE approach to characterize exacerbation hazard in patients with severe asthma.

Time-to-Event Analysis of Paclitaxel-Associated Peripheral Neuropathy in Advanced Non-Small-Cell Lung Cancer Highlighting Key Influential Treatment/Patient Factors

Paclitaxel-associated peripheral neuropathy (PN), a major dose-limiting toxicity, significantly impacts patients' quality of life/treatment outcome. Evaluation of risk factors often ignores time of PN onset, precluding the impact of time-dependent factors, e.g., drug exposure, needed to comprehensively characterize PN. We employed parametric time-to-event (TTE) analysis to describe the time course of risk of first occurrence of clinically relevant PN grades ≥2 (PN2+, n = 105, common terminology criteria v4.0) and associated patient/treatment characteristics, leveraging data from 365 patients (1454 cycles) receiving paclitaxel every 3 weeks (plus carboplatin AUC = 6 or cisplatin 80 mg/m2) for ≤6 cycles. Paclitaxel was intravenously administered (3 hours) as standard 200-mg/m2 doses (n = 182) or as pharmacokinetic-guided dosing (n = 183). A cycle-varying hazard TTE model linking surge in hazard of PN2+ to paclitaxel administration [PN2+ proportions (i.e., cases per 1000 patients), 1st day, cycle 1: 4.87 of 1000; cycle 6: 7.36 of 1000] and linear decline across cycle (last day, cycle 1: 1.64 of 1000; cycle 6: 2.48 of 1000) adequately characterized the time-varying hazard of PN2+. From joint covariate evaluation, PN2+ proportions (1st day, cycle 1) increased by 1.00 per 1000 with 5-μmol·h/l higher paclitaxel exposure per cycle (AUC between the start and end of a cycle, most relevant covariate), 0.429 per 1000 with 5-year higher age, 1.31 per 1000 (smokers vs. nonsmokers), and decreased by 0.670 per 1000 (females vs. males). Compared to 200 mg/m2 dosing every 3 weeks, model-predicted cumulative risk of PN2+ was significantly higher (42%) with 80 mg/m2 weekly dosing but reduced by 11% with 175 mg/m2 dosing every 3 weeks. The established TTE modeling framework enables quantification and comparison of patient's cumulative risks of PN2+ for different clinically relevant paclitaxel dosing schedules, sparing patients PN2+ to improve paclitaxel therapy. SIGNIFICANCE STATEMENT: Characterization of risk factors of paclitaxel-associated peripheral neuropathy (PN) typically involves time-independent comparison of PN odds in patient subpopulations, concealing the impact of time-dependent factors, e.g., changing paclitaxel exposure, required to comprehensively characterize PN. We developed a parametric time-to-event model describing the time course in risk of clinically relevant paclitaxel-associated PN, identifying the highest risk in older male smokers with higher paclitaxel area under the plasma concentration-time curve between the start and end of a cycle. The developed framework enabled quantification of patient's risk of PN for clinically relevant paclitaxel dosing schedules, facilitating future dosing decisions.

An Innovative Pharmacometric Approach for the Simultaneous Analysis of Frequency, Duration and Severity of Migraine Events

PURPOSE

To explore the use of a multistate repeated, time-to-categorical event model describing the frequency, severity and duration of migraines.

METHODS

Subject level data from patients in placebo arms from two efficacy trials for migraine-preventive treatments were used. Models were developed using NONMEM 7.3. A survival model was combined with an ordered categorical model to form the repeated-time-to-start of categorical migraine event model, which simultaneously described the time-to-start of migraines and the severity of the starting migraine event. This was linked to a repeated-time-to-end of migraine event model with different hazard functions depending on the severity of the ongoing migraine event. Model performance was internally and externally qualified.

RESULTS

The successfully qualified model showed that patients responding to placebo had a reduction in migraine incidence rate, and a decreased proportion of severe migraines. There was an increase in moderate migraine duration, an increased proportion of mild migraines and a reduction in proportion of severe migraines. Age was related to migraine duration.

CONCLUSIONS

The model represents an innovative framework for clinical trial modeling and simulation, and successfully describes placebo effect in migraine prevention. This approach can be adapted to investigate exposure-response relationship of drugs and can also be implemented in other therapeutic areas where the rate, duration and severity of disease episodes are relevant to trial outcomes.

Rotational thromboelastometry can predict the probability of bleeding events in a translational rat model of haemophilia A following gene-based FVIIa prophylaxis

INTRODUCTION

Monitoring of clinical effectiveness of bypassing agents in haemophilia patients is hampered by the lack of validated laboratory assays. Thromboelastography (TEG) and rotational thromboelastometry (ROTEM) have been evaluated for predicting clinical effectiveness of bypassing agents, however, with limited success.

AIM

Application of a longitudinal model-based approach may allow for a quantitative characterization of the link between ROTEM parameters and the probability of bleeding events.

METHODS

We analyse longitudinal data from haemophilia A rats receiving gene-based FVIIa prophylaxis in terms of total circulatory levels of FVII/FVIIa, clotting time (CT) measured using ROTEM and the probability of bleeding events.

RESULTS

Using population pharmacokinetic-pharmacodynamic (PKPD) modelling, a PK-CT-repeated time-to-event (RTTE) model was developed composed of three submodels (a) a FVII/FVIIa PK model, (b) a PK-CT model describing the relationship between predicted FVIIa expression and CT and (c) a RTTE model describing the probability of bleeding events as a function of CT. The developed PK-CT-RTTE model accurately described the vector dose-dependent plasma concentration-time profile of total FVII/FVIIa and the exposure-response relationship between AAV-derived FVIIa expression and CT. Importantly, the developed model accurately described the occurrence of bleeding events over time in a quantitative manner, revealing a linear relationship between predicted change from baseline CT and the probability of bleeding events.

CONCLUSION

Using PK-CT-RTTE modelling, we demonstrated that ROTEM parameters can accurately predict the probability of bleeding events in a translational animal model of haemophilia A.

A Pharmacometric Approach to Substitute for a Conventional Dose-Finding Study in Rare Diseases: Example of Phase III Dose Selection for Emicizumab in Hemophilia A

BACKGROUND

Emicizumab (ACE910) is a bispecific antibody mimicking the cofactor function of activated coagulation factor VIII. In phase I-I/II studies, emicizumab reduced the bleeding frequency in patients with severe hemophilia A, regardless of the presence of factor VIII inhibitors, at once-weekly subcutaneous doses of 0.3, 1, and 3 mg/kg.

METHODS

Using the phase I-I/II study data, population pharmacokinetic and repeated time-to-event (RTTE) modeling were performed to quantitatively characterize the relationship between the pharmacokinetics of emicizumab and reduction in bleeding frequency. Simulations were then performed to identify the minimal exposure expected to achieve zero bleeding events for 1 year in at least 50% of patients and to select the dosing regimens to be tested in phase III studies.

RESULTS

The RTTE model adequately predicted the bleeding onset over time as a function of plasma emicizumab concentration. Simulations suggested that plasma emicizumab concentrations of ≥  45 μg/mL should result in zero bleeding events for 1 year in at least 50% of patients. This efficacious exposure provided the basis for selecting previously untested dosing regimens of 1.5 mg/kg once weekly, 3 mg/kg every 2 weeks, and 6 mg/kg every 4 weeks for phase III studies.

CONCLUSIONS

A pharmacometric approach guided the phase III dose selection of emicizumab in hemophilia A, without conducting a conventional dose-finding study. Phase III studies with the selected dosing regimens are currently ongoing. This case study indicates that a pharmacometric approach can substitute for a conventional dose-finding study in rare diseases and will streamline the drug development process.

Review: The Role of Cannabinoids on Esophageal Function-What We Know Thus Far

The endocannabinoid system (ECS) primarily consists of cannabinoid receptors (CBRs), endogenous ligands, and enzymes for endocannabinoid biosynthesis and inactivation. Although the presence of CBRs, both CB1 and CB2, as well as a third receptor (G-protein receptor 55 [GPR55]), has been established in the gastrointestinal (GI) tract, few studies have focused on the role of cannabinoids on esophageal function. To date, studies have shown their effect on GI motility, inflammation and immunity, intestinal and gastric acid secretion, nociception and emesis pathways, and appetite control. Given the varying and sometimes limited efficacy of current medical therapies for diseases of the esophagus, further understanding and investigation into the interplay of the ECS on esophageal health and disease may present new therapeutic modalities that may help advance current treatment options. In this brief review, the current understanding of the ECS role in various esophageal functions and disorders is presented.

Repeated Time-to-event Analysis of Consecutive Analgesic Events in Postoperative Pain

BACKGROUND

Reduction in consumption of opioid rescue medication is often used as an endpoint when investigating analgesic efficacy of drugs by adjunct treatment, but appropriate methods are needed to analyze analgesic consumption in time. Repeated time-to-event (RTTE) modeling is proposed as a way to describe analgesic consumption by analyzing the timing of consecutive analgesic events.

METHODS

Retrospective data were obtained from 63 patients receiving standard analgesic treatment including morphine on request after surgery following hip fracture. Times of analgesic events up to 96 h after surgery were extracted from hospital medical records. Parametric RTTE analysis was performed with exponential, Weibull, or Gompertz distribution of analgesic events using NONMEM, version 7.2 (ICON Development Solutions, USA). The potential influences of night versus day, sex, and age were investigated on the probability.

RESULTS

A Gompertz distribution RTTE model described the data well. The probability of having one or more analgesic events within 24 h was 80% for the first event, 55% for the second event, 31% for the third event, and 18% for fourth or more events for a typical woman of age 80 yr. The probability of analgesic events decreased in time, was reduced to 50% after 3.3 days after surgery, and was significantly lower (32%) during night compared with day.

CONCLUSIONS

RTTE modeling described analgesic consumption data well and could account for time-dependent changes in probability of analgesic events. Thus, RTTE modeling of analgesic events is proposed as a valuable tool when investigating new approaches to pain management such as opioid-sparing analgesia.

N-acetyltransferase genotypes and the pharmacokinetics and tolerability of para-aminosalicylic acid in patients with drug-resistant pulmonary tuberculosis

The aim of this study was to examine the relationships between N-acetyltransferase genotypes, pharmacokinetics, and tolerability of granular slow-release para-aminosalicylic acid (GSR-PAS) in tuberculosis patients. The study was a randomized, two-period, open-label, crossover design wherein each patient received 4 g GSR-PAS twice daily or 8 g once daily alternately. The PAS concentration-time profiles were modeled by a one-compartment disposition model with three transit compartments in series to describe its absorption. Patients' NAT1 and NAT2 genotypes were determined by sequencing and restriction enzyme analysis, respectively. The number of daily vomits was modeled by a Poisson probability mass function. Comparisons of other tolerability measures by regimens, gender, and genotypes were evaluated by a linear mixed-effects model. The covariate effects associated with efavirenz, gender, and NAT1*3, NAT1*14, and NAT2*5 alleles corresponded to 25, 37, -17, -48, and -27% changes, respectively, in oral clearance of PAS. The NAT1*10 allele did not influence drug clearance. The time above the MIC of 1 mg/liter was significantly different between the two regimens but not influenced by the NAT1 or NAT2 genotypes. The occurrence and intensity of intolerance differed little between regimens. Four grams of GSR-PAS twice daily but not 8 g once daily ensured concentrations exceeding the MIC (1 mg/liter) throughout the dosing interval; PAS intolerance was not related to maximum PAS concentrations over the doses studied and was not more frequent after once-daily dosing. We confirm that the slow phenotype conferred by the NAT1*14 and NAT1*3 alleles resulted in higher PAS exposure but found no evidence of increased activity of the NAT1*10 allele.

Modeling and simulation of count data

Count data, or number of events per time interval, are discrete data arising from repeated time to event observations. Their mean count, or piecewise constant event rate, can be evaluated by discrete probability distributions from the Poisson model family. Clinical trial data characterization often involves population count analysis. This tutorial presents the basics and diagnostics of count modeling and simulation in the context of pharmacometrics. Consideration is given to overdispersion, underdispersion, autocorrelation, and inhomogeneity.

Evaluation of estimation methods and power of tests of discrete covariates in repeated time-to-event parametric models: application to Gaucher patients treated by imiglucerase

Analysis of repeated time-to-event data is increasingly performed in pharmacometrics using parametric frailty models. The aims of this simulation study were (1) to assess estimation performance of Stochastic Approximation Expectation Maximization (SAEM) algorithm in MONOLIX, Adaptive Gaussian Quadrature (AGQ), and Laplace algorithm in PROC NLMIXED of SAS and (2) to evaluate properties of test of a dichotomous covariate on occurrence of events. The simulation setting is inspired from an analysis of occurrence of bone events after the initiation of treatment by imiglucerase in patients with Gaucher Disease (GD). We simulated repeated events with an exponential model and various dropout rates: no, low, or high. Several values of baseline hazard model, variability, number of subject, and effect of covariate were studied. For each scenario, 100 datasets were simulated for estimation performance and 500 for test performance. We evaluated estimation performance through relative bias and relative root mean square error (RRMSE). We studied properties of Wald and likelihood ratio test (LRT). We used these methods to analyze occurrence of bone events in patients with GD after starting an enzyme replacement therapy. SAEM with three chains and AGQ algorithms provided good estimates of parameters much better than SAEM with one chain and Laplace which often provided poor estimates. Despite a small number of repeated events, SAEM with three chains and AGQ gave small biases and RRMSE. Type I errors were closed to 5%, and power varied as expected for SAEM with three chains and AGQ. Probability of having at least one event under treatment was 19.1%.

Inter-reflux and bolus clearance times in non-pathologic pediatric patients: data support computational models

Previous contributions suggested that gastroesophageal reflux can be modeled in terms of a Poisson process. This study aims to provide empirical data to validate this statement in pediatric patients so that computational models can be broadly used as an alternative for research. A retrospective review of 63 pediatric patients who underwent 24-hour impedance-pH monitoring to discard gastroesophageal reflux disease was conducted in this study. Patients were grouped by age as preterm (21), infants (21), and children (21). All the tracings were analyzed by a trained physician who identified the reflux entry and the bolus clearance time of each episode. The time between reflux episodes was tested against three probability distributions (gamma, exponential and inverse Gaussian) whereas the bolus clearance time was tested against a normal probability distribution. Parameters were estimated using the maximum likelihood method. The Kolmogorov-Smirnov test and the Kullback-Leibler divergence were computed to evaluate the goodness of fit. One-way analysis of variance was applied to compare results along the three groups. Exponential fitting for inter-reflux time was successful in 90.48% of children older than 1 year. The overall reflux rate was 57.58 reflux episodes per day whereas the mean bolus clearance time ranged between 10.87 in preterm subjects and 12.05 in children, showing a good Gaussian fitting. The time between reflux episodes can be modeled in terms of a Poisson process in non tube-fed patients, whereas the bolus clearance time follows a normal distribution in all cases.