Comparative assessment of clinical response in patients with rheumatoid arthritis between PF-05280586, a proposed rituximab biosimilar, and rituximab.

AIMS: To evaluate potential differences between PF-05280586 and rituximab sourced from the European Union (rituximab-EU) and USA (rituximab-US) in clinical response (Disease Activity Score in 28 Joints [DAS28] and American College of Rheumatology [ACR] criteria), as part of the overall biosimilarity assessment of PF-05280586. METHODS: A randomised, double-blind, pharmacokinetic similarity trial was conducted in patients with active rheumatoid arthritis refractory to anti-tumour necrosis factor therapy on a background of methotrexate. Patients were treated with 1000 mg of PF-05280586, rituximab-EU or rituximab-US on days 1 and 15 and followed over 24 weeks for pharmacokinetic, clinical response and safety assessments. Key secondary end points were the areas under effect curves for DAS28 and ACR responses. Mean differences in areas under effect curves were compared against respective reference ranges established by observed rituximab-EU and rituximab-US responses using longitudinal nonlinear mixed effects models. RESULTS: The analysis included 214 patients. Demographics were similar across groups with exceptions in some baseline disease characteristics. Baseline imbalances and group-to-group variation were accounted for by covariate effects in each model. Predictions from the DAS28 and ACR models tracked the central tendency and distribution of observations well. No point estimates of mean differences were outside the reference range for DAS28 or ACR scores. The probabilities that the predicted differences between PF-05280586 vs. rituximab-EU or rituximab-US lie outside the reference ranges were low. CONCLUSIONS: No clinically meaningful differences were detected in DAS28 or ACR response between PF-05280586 and rituximab-EU or rituximab-US as the differences were within the pre-specified reference ranges. TRIAL REGISTRATION NUMBER: NCT01526057.

Evaluation of estimation, prediction and inference for autocorrelated latent variable modeling of binary data-a simulation study.

Longitudinal models of binary or ordered categorical data are often evaluated for adequacy by the ability of these to characterize the transition frequency and type between response states. Drug development decisions are often concerned with accurate prediction and inference of the probability of response by time and dose. A question arises on whether the transition probabilities need to be characterized adequately to ensure accurate response prediction probabilities unconditional on the previous response state. To address this, a simulation study was conducted to assess bias in estimation, prediction and inferences of autocorrelated latent variable models (ALVMs) when the transition probabilities are misspecified due to ill-posed random effects structures, inadequate likelihood approximation or omission of the autocorrelation component. The results may be surprising in that these suggest that characterizing autocorrelation in ALVMs is not as important as specifying a suitably rich random effects structure.

Exposure-response modeling of average daily pain score, and dizziness and somnolence, for mirogabalin (DS-5565) in patients with diabetic peripheral neuropathic pain.

Mirogabalin (DS-5565) is an α2δ-1 ligand being developed for pain associated with diabetic peripheral neuropathy, fibromyalgia, and postherpetic neuralgia. Nonlinear mixed-effects analyses were performed on average daily pain and on the incidence of the adverse events dizziness and somnolence. These models were used to predict the dose of mirogabalin equivalent to pregabalin and the probability of meaningful reduction in pain compared with placebo and pregabalin. In addition, regimen effects were evaluated for reductions of adverse events. Mirogabalin was estimated to be 17-fold more potent than pregabalin. The effectiveness of 150 mg pregabalin, dosed twice daily, attenuated by week 5. Therefore, the estimated mechanism-based equivalent dose (ED) of 17.7 mg mirogabalin was higher than that predicted to achieve comparable pain reduction. If attenuation of the pregabalin effect is real, mirogabalin doses lower than the ED could yield comparable pain reduction, albeit with less differentiation in pain from placebo. The incidence rate of dizziness and somnolence decreased over time. Twice-daily dosing of mirogabalin was predicted to yield a lower incidence rate of dizziness than once-daily dosing; thus, titration of dosages should reduce adverse event rates. These model results were used to influence phase 3 dosing selection.

Application of a hazard-based visual predictive check to evaluate parametric hazard models.

Parametric models used in time to event analyses are evaluated typically by survival-based visual predictive checks (VPC). Kaplan-Meier survival curves for the observed data are compared with those estimated using model-simulated data. Because the derivative of the log of the survival curve is related to the hazard--the typical quantity modeled in parametric analysis--isolation, interpretation and correction of deficiencies in the hazard model determined by inspection of survival-based VPC's is indirect and thus more difficult. The purpose of this study is to assess the performance of nonparametric hazard estimators of hazard functions to evaluate their viability as VPC diagnostics. Histogram-based and kernel-smoothing estimators were evaluated in terms of bias of estimating the hazard for Weibull and bathtub-shape hazard scenarios. After the evaluation of bias, these nonparametric estimators were assessed as a method for VPC evaluation of the hazard model. The results showed that nonparametric hazard estimators performed reasonably at the sample sizes studied with greater bias near the boundaries (time equal to 0 and last observation) as expected. Flexible bandwidth and boundary correction methods reduced these biases. All the nonparametric estimators indicated a misfit of the Weibull model when the true hazard was a bathtub shape. Overall, hazard-based VPC plots enabled more direct interpretation of the VPC results compared to survival-based VPC plots.

Integrated nonclinical and clinical risk assessment of the investigational proteasome inhibitor ixazomib on the QTc interval in cancer patients.

BACKGROUND: Ixazomib is the first oral, proteasome inhibitor to reach phase III trials. Here, we present an integrated nonclinical and clinical assessment of ixazomib's effect on QTc intervals. METHODS: Nonclinical studies assessed (1) the in vitro binding of ixazomib to the hERG channel and (2) its effect on QT/QTc in dogs (N = 4) via telemetry. Pharmacokinetic-matched triplicate electrocardiograms were collected in four clinical phase I studies of intravenous (0.125-3.11 mg/m(2), N = 125, solid tumors/lymphoma) or oral (0.24-3.95 mg/m(2), N = 120, multiple myeloma) ixazomib. The relationship between ixazomib plasma concentration and heart rate (HR)-corrected QT using Fridericia (QTcF) or population (QTcP) methods was analyzed using linear mixed-effects models with fixed effects for day and time. RESULTS: In vitro binding potency for ixazomib to the hERG channel was weak (K i 24.9 µM; IC50 59.6 µM), and nonclinical telemetry studies showed no QT/QTc prolongation at doses up to 4.2 mg/m(2). In cancer patients, ixazomib, when evaluated at doses yielding various plasma concentrations (with 26 % of data greater than mean C max for the 4 mg phase 3 dose), had no meaningful effect on QTc based on model-predicted mean change in QTcF/QTcP from baseline. There was no relationship between ixazomib concentration and RR, suggesting no effect on HR. CONCLUSIONS: Ixazomib has no clinically meaningful effects on QTc or HR. Integrating preclinical data and concentration-QTc modeling of phase 1 data may obviate the need for a dedicated QTc study in oncology. A framework for QT assessment in oncology drug development is proposed.

Population pharmacokinetics of pegaptanib sodium (Macugen(®)) in patients with diabetic macular edema.

OBJECTIVE: Population pharmacokinetic modeling of pegaptanib was undertaken to determine influence of renal function on apparent clearance. METHODS: In a randomized, double-masked multicenter trial, intravitreal pegaptanib (0.3, 1.0, or 3.0 mg/eye) was administered in patients with diabetic macular edema every 6 weeks for 12-30 weeks. A one-compartment model with first-order absorption, distribution volume, and clearance was used to characterize the pegaptanib plasma concentration-time profile. RESULTS: In 58 patients, increases in area under the concentration-time curve (AUC) to end of the dosing interval (AUC0-tau) and maximum concentration with repeat doses were <6%, indicating minimal plasma accumulation. Sex and race did not have clinically significant effects on pegaptanib exposure. In the final model, the AUC extrapolated to infinite time and maximum concentration increased by ≥50% in older patients (aged >68 years) relative to younger patients due to decreases in creatinine clearance (CRCL), a significant predictor of clearance. Pegaptanib clearance was reduced by 29% when CRCL decreased by 50%. The change in exposure with CRCL (range, 0-190 mL/minute) was < 10-fold with 0.3-3.0 mg doses. CONCLUSION: While pegaptanib clearance and AUC were significantly influenced by CRCL, the predicted exposure in patients with renal insufficiency or renal failure shows no evidence that a dose adjustment is warranted, given the tenfold margin of safety observed over the dose range of 0.3-3.0 mg.

Population dose-response analysis of daily seizure count following vigabatrin therapy in adult and pediatric patients with refractory complex partial seizures.

Vigabatrin is an irreversible inhibitor of γ-aminobutyric acid transaminase (GABA-T) and is used as an adjunctive therapy for adult patients with refractory complex partial seizures (rCPS). The purpose of this investigation was to describe the relationship between vigabatrin dosage and daily seizure rate for adults and children with rCPS and identify relevant covariates that might impact seizure frequency. This population dose-response analysis used seizure-count data from three pediatric and two adult randomized controlled studies of rCPS patients. A negative binomial distribution model adequately described daily seizure data. Mean seizure rate decreased with time after first dose and was described using an asymptotic model. Vigabatrin drug effects were best characterized by a quadratic model using normalized dosage as the exposure metric. Normalized dosage was an estimated parameter that allowed for individualized changes in vigabatrin exposure based on body weight. Baseline seizure rate increased with decreasing age, but age had no impact on vigabatrin drug effects after dosage was normalized for body weight differences. Posterior predictive checks indicated the final model was capable of simulating data consistent with observed daily seizure counts. Total normalized vigabatrin dosages of 1, 3, and 6 g/day were predicted to reduce seizure rates 23.2%, 45.6%, and 48.5%, respectively.

Covariate selection in pharmacometric analyses: a review of methods.

Covariate selection is an activity routinely performed during pharmacometric analysis. Many are familiar with the stepwise procedures, but perhaps not as many are familiar with some of the issues associated with such methods. Recently, attention has focused on selection procedures that do not suffer from these issues and maintain good predictive properties. In this review, we endeavour to put the main variable selection procedures into a framework that facilitates comparison. We highlight some issues that are unique to pharmacometric analyses and provide some thoughts and strategies for pharmacometricians to consider when planning future analyses.

Changes in serum creatinine in patients with active rheumatoid arthritis treated with tofacitinib: results from clinical trials.

INTRODUCTION: Small increases in mean serum creatinine (SCr) were observed in studies of rheumatoid arthritis patients during tofacitinib treatment. These SCr changes were investigated and potential mechanisms explored. METHODS: SCr values and renal adverse event data were pooled from five Phase 3 and two long-term extension (LTE) studies. Dose-response relationships and association with inflammation (C-reactive protein (CRP)) were explored using Phase 2 data and confirmed with Phase 3 data. RESULTS: In Phase 3, least squares mean SCr differences from placebo at Month 3 were 0.02 and 0.04 mg/dl for tofacitinib 5 and 10 mg twice daily (BID) (P <0.05), respectively. During Months 0 to 3, confirmed SCr ≥33% increases over baseline were reported in 17 (1.4%; 5 mg BID) and 23 (1.9%; 10 mg BID) patients. Generally, elevations plateaued and remained within normal limits throughout Phase 3 and LTE studies. Exposure-response modeling demonstrated small, reversible effects of tofacitinib on mean SCr, and significant (P <0.05) effects of CRP on model parameters. Phase 3 data confirmed that patients with higher baseline CRP or greater CRP decreases following tofacitinib treatment had the largest increases in SCr. Across Phase 3 and LTE studies, 22 tofacitinib-treated patients had clinical acute renal failure (ARF), predominantly in the setting of concurrent serious illness. CONCLUSIONS: Tofacitinib treatment was associated with small, reversible mean increases in SCr that plateaued early. The mechanism behind these SCr changes remains unknown, but may involve effects of tofacitinib on inflammation. ARF occurred infrequently, was associated with concurrent serious illness, and was unrelated to prior SCr increases.

Predicting the time to clinically worsening in mild cognitive impairment patients and its utility in clinical trial design by modeling a longitudinal clinical dementia rating sum of boxes from the ADNI database.

BACKGROUND: Growing interest in treating Alzheimer's disease (AD) patients in the earliest stages requires new clinical endpoints. Currently, there is no established clinical endpoint or treatment duration for mild cognitive impairment (MCI) trials. OBJECTIVE: This analysis attempts to answer "how long the MCI clinical trial would be necessary" using the Clinical Dementia Rating Sum of Boxes (CDR-SB) as a clinical endpoint, where CDR-SB is an example of a suitable tool to assess both cognition and function as a single primary efficacy outcome. METHODS: A longitudinal model was developed to predict the CDR-SB time-profile. The CDR-SB is considered ideal to assess both cognition and function as a single primary endpoint in MCI trials. The median time for clinically "worsening", defined using several thresholds for change from baseline, was calculated using individual CDR-SB predictions. Covariates predictive of worsening were also evaluated. RESULTS: The median time to a 1-point change in CDR-SB was approximately 2 years in MCI patients. Higher baseline severity in disease, lower hippocampal volume, and ApoE4 carrier status were significant covariates predicting shorter times to worsening (faster progress). The results indicate that at least a 2-year trial would be necessary with 30% (or more) disease modifying drug with a sample size of n = 350 to detect the significant difference from placebo (80% power) and to achieve the target mean effect size of 0.5 point change in CDR-SB. CONCLUSION: Predictions of CDR-SB changes from a longitudinal model are able to inform study design and possible enrichment strategies, based on covariate analyses, for prospective planning of clinical trials in MCI patients.

Predicting relapsing-remitting dynamics in multiple sclerosis using discrete distribution models: a population approach.

BACKGROUND: Relapsing-remitting dynamics are a hallmark of autoimmune diseases such as Multiple Sclerosis (MS). A clinical relapse in MS reflects an acute focal inflammatory event in the central nervous system that affects signal conduction by damaging myelinated axons. Those events are evident in T1-weighted post-contrast magnetic resonance imaging (MRI) as contrast enhancing lesions (CEL). CEL dynamics are considered unpredictable and are characterized by high intra- and inter-patient variability. Here, a population approach (nonlinear mixed-effects models) was applied to analyse of CEL progression, aiming to propose a model that adequately captures CEL dynamics. METHODS AND FINDINGS: We explored several discrete distribution models to CEL counts observed in nine MS patients undergoing a monthly MRI for 48 months. All patients were enrolled in the study free of immunosuppressive drugs, except for intravenous methylprednisolone or oral prednisone taper for a clinical relapse. Analyses were performed with the nonlinear mixed-effect modelling software NONMEM 7.2. Although several models were able to adequately characterize the observed CEL dynamics, the negative binomial distribution model had the best predictive ability. Significant improvements in fitting were observed when the CEL counts from previous months were incorporated to predict the current month's CEL count. The predictive capacity of the model was validated using a second cohort of fourteen patients who underwent monthly MRIs during 6-months. This analysis also identified and quantified the effect of steroids for the relapse treatment. CONCLUSIONS: The model was able to characterize the observed relapsing-remitting CEL dynamic and to quantify the inter-patient variability. Moreover, the nature of the effect of steroid treatment suggested that this therapy helps resolve older CELs yet does not affect newly appearing active lesions in that month. This model could be used for design of future longitudinal studies and clinical trials, as well as for the evaluation of new therapies.

Simultaneous pharmacokinetic model for rolofylline and both M1-trans and M1-cis metabolites.

Rolofylline is a potent, selective adenosine A1 receptor antagonist that was under development for the treatment of patients with acute congestive heart failure and renal impairment. Rolofylline is metabolized primarily to the pharmacologically active M1-trans and M1-cis metabolites (metabolites) by cytochrome P450 (CYP) 3A4. The aim of this investigation was to provide a pharmacokinetic (PK) model for rolofylline and metabolites following intravenous administration to healthy volunteers. Data included for this investigation came from a randomized, double-blind, dose-escalation trial in four groups of healthy volunteers (N=36) where single doses of rolofylline, spanning 1 to 60 mg ,were infused over 1-2 h. The rolofylline and metabolite data were analyzed simultaneously using NONMEM. The simultaneous PK model comprised, in part, a two-compartment linear PK model for rolofylline, with estimates of clearance and volume of distribution at steady-state of 24.4 L/h and 239 L, respectively. In addition, the final PK model contained provisions for both conversion of rolofylline to metabolites and stereochemical conversion of M1-trans to M1-cis. Accordingly, the final model captured known aspects of rolofylline metabolism and was capable of simultaneously describing the PK of rolofylline and metabolites in healthy volunteers.

Longitudinal FEV1 dose-response model for inhaled PF-00610355 and salmeterol in patients with chronic obstructive pulmonary disease.

The objective of this work was to characterize the dose-response relationship between two inhaled long-acting beta agonists (PF-00610355 and salmeterol) and the forced expiratory volume in one second (FEV1) in order to inform dosing recommendations for future clinical trials in patients with chronic obstructive pulmonary disease (COPD). This meta-analysis of four studies included 8,513 FEV1 measurements from 690 patients with moderate COPD. A longitudinal kinetic-pharmacodynamic (K-PD) model was developed and adequately described changes in FEV1 measurements over time, including circadian patterns within a day, as well as changes in FEV1 measurements elicited from administration of PF-00610355 or salmeterol. The fine-particle dose, the amount of drug present in particles small enough for lung delivery, was used as the exposure measure for PF-00610355. Greater reversibility following administration of a short-acting beta agonist during run-in was associated with increased FEV1 response to long-acting beta agonists (through an increased maximal response, E(max)). Simulations were conducted to better understand the response to PF-00610355 relative to placebo and salmeterol. The results of the simulations show that once daily fine-particle doses of 28.1 µg versus placebo have a moderate probability of providing an average improvement above 100 mL at trough. The 50 µg fine-particle dose, on the other hand, has a greater than 0.78 probability of achieving a 120 mL improvement versus placebo at trough. From an efficacy perspective and assuming a fine-particle fraction of 25 % for the Phase 3 formulation; 100 and 200 µg once daily nominal doses would be of interest to investigate in future Phase 3 trials.

Extending the latent variable model for extra correlated longitudinal dichotomous responses.

Since generalized nonlinear mixed-effects modeling methodology of ordered categorical data became available in the pharmacokinetic/pharmacodynamic (PK/PD) literature over a decade ago, pharmacometricians have been increasingly performing exposure-response analyses of such data to inform drug development. Also, as experiences with and scrutiny of these data have increased, pharmacometricians have noted fewer transitions (or greater correlations) between response values than predicted by the model. In this paper, we build on the latent variable (LV) approach, which is convenient for incorporating pharmacological concepts such as pharmacodynamic onset of drug effect, and present a PK/PD methodology which we term the multivariate latent variable (MLV) approach. This approach uses correlations between the latent residuals (LR) to address extra correlation or a fewer number of transitions, relative to if the LR were independent. Four approximation methods for handling dichotomous MLV data are formulated and then evaluated for accuracy and computation time using simulation studies. Some analytical results for models linear in the subject-specific random effects are also presented, and these provide insight into modeling such repeated measures data. Also, a case study previously analyzed using the LV approach is revisited using one of the MLV approximation methods and the results are discussed. Overall, consideration of the simulation and analytical results lead us to some conclusions we feel are applicable to many of the models and situations frequently encountered in analysis of such data: the MLV approach is a flexible method that can handle many different extra correlated data structures and therefore can more accurately predict the number of transitions between response values; incorrect modeling of the population covariances by implementing an LV model when extra correlation exists is not likely to (and in many cases does not) influence accuracy of the population (marginal) mean predictions; adequate prediction of the population mean probabilities achieves adequate predictions of the population variances, regardless of the correct specification of the population covariances--that is, if the LV model accurately predicts the means in the presence of extra correlation, it will accurately predict the variances; the between subject random effects component to the model describe the marginal covariances in responses--not the marginal variances as with continuous-type data. From these conclusions we make a general statement that it may not be necessary to model the extra correlation in every case using the MLV model, which requires technical implementation with currently available commercially or publically available software. The LV model may be sufficient for answering many of the typical questions arising during drug development. The MLV approach should be considered however if prediction or simulation of individual level data is an objective of the analysis.

Translational PK-PD modelling of molecular target modulation for the AMPA receptor positive allosteric modulator Org 26576.

INTRODUCTION: The α-amino-3-hydroxy-5-methylisoxazole-4-propionic acid (AMPA) receptor potentiator Org 26576 represents an interesting pharmacological tool to evaluate the utility of glutamatergic enhancement towards the treatment of psychiatric disorders. In this study, a rat-human translational pharmacokinetic-pharmacodynamic (PK-PD) model of AMPA receptor modulation was used to predict human target engagement and inform dose selection in efficacy clinical trials. METHODS: Modelling and simulation was applied to rat plasma and cerebrospinal fluid (CSF) pharmacokinetic and pharmacodynamic measurements to identify a target concentration (EC(80)) for AMPA receptor modulation. Human plasma pharmacokinetics was determined from 33 healthy volunteers and eight major depressive disorder patients. From four out of these eight patients, CSF PK was also determined. Simulations of human CSF levels were performed for several doses of Org 26576. RESULTS: Org 26576 (0.1-10 mg/kg, i.v.) potentiated rat hippocampal AMPA receptor responses in an exposure-dependant manner. The rat plasma and CSF PK data were fitted by one-compartment model each. The rat CSF PK-PD model yielded an EC(80) value of 593 ng/ml (90% confidence interval 406.8, 1,264.1). The human plasma and CSF PK data were simultaneously well described by a two-compartment model. Simulations showed that in humans at 100 mg QD, CSF levels of Org 26576 would exceed the EC(80) target concentration for about 2 h and that 400 mg BID would engage AMPA receptors for 24 h. CONCLUSION: The modelling approach provided useful insight on the likely human dose-molecular target engagement relationship for Org 26576. Based on the current analysis, 100 and 400 mg BID would be suitable to provide 'phasic' and 'continuous' AMPA receptor engagement, respectively.

Estimating transformations for repeated measures modeling of continuous bounded outcome data.

Continuous bounded outcome data are unlikely to meet the usual assumptions for mixed-effects models of normally distributed and independent subject-specific and residual random effects. Additionally, overly complicated model structures might be necessary to account adequately for non-drug (time-dependent) and drug treatment effects. A transformation strategy with a likelihood component for censoring is developed to promote the simplicity of model structures and to improve the plausibility of assumptions on the random effects. The approach is motivated by Health Assessment Questionnaire Disability Index (HAQ-DI) data from a study in subjects with rheumatoid arthritis and is evaluated using a simulation study.

Joint modeling of dizziness, drowsiness, and dropout associated with pregabalin and placebo treatment of generalized anxiety disorder.

Dizziness and drowsiness are cited as being predictors of dropout from clinical trials for the medicine pregabalin. These adverse events are typically recorded daily on a four point ordinal scale (0 = none, 1 = mild, 2 = moderate, 3 = severe), with most subjects never reporting either adverse event. We modeled the dizziness, drowsiness, and dropout associated with pregabalin use in generalized anxiety disorder using piecewise Weibull distributions for the time to first non-zero dizziness or drowsiness score, after which the dizziness or drowsiness was modeled with ordinal regression with a Markovian element. Dropout was modeled with a Weibull distribution. Platykurtosis was encountered in the estimated random effects distributions for the ordinal regression models and was addressed with dynamic John-Draper transformations. The only identified predictor for the time to first non-zero dizziness or drowsiness score was daily titrated dose. Predictors for dropout included creatinine clearance and maximum daily adverse event score. Tolerance to adverse events over time was modeled by including a non-stationary component for the dizziness ordinal Markov regression while the piecewise Weibull distributions allowed a change point in the median time to first non-zero dizziness or drowsiness score.

An alternative method for population pharmacokinetic data analysis under noncompliance.

Noncompliance presents a persistent problem while analyzing PK data from outpatient clinical studies. Ignoring dose omission or making uninformed assumptions about patient drug intake history can prove detrimental to the objectives of the analysis (e.g. determining the PK model parameters or identifying covariates) and ultimately compromise the interpretation of the data. In order to overcome this problem, an alternative method of handling noncompliant data is evaluated in this report. The proposed approach is based on the principle of superposition and works by separating the estimation of the elimination rate from the model based steady-state PK concentration. Simulations implementing this method under different scenarios of noncompliance demonstrate that it performs better than the conventional method of analyzing population PK data when compared on the basis of bias and imprecision in parameter estimation and power (and type I error) for covariate detection. Overall, the new method exhibits great potential to address the issue of uncertain/unreliable dosing histories frequently encountered in clinical trials.

Performance characteristics for some typical QT study designs under the ICH E-14 guidance.

The International Conference on Harmonization (ICH) guidance for clinical evaluation of QT prolongation (E14) affected drug development by advocating that a thorough QT study (TQT) be conducted during development to assess the QT prolongation liability of a compound. The ICH E14 Statistics Group shortly thereafter recommended that a noninferiority intersection-union test (IUT) be used to exclude a clinically worrisome QT prolongation. Recent analyses have indicated that the IUT might be overly conservative with respect to excluding QT prolongation. This report assesses the IUT false positive rate for 4 recently conducted TQT trials using simple simulation experiments. Positive TQT study rates ranged from negligible to nearly 60% depending on study design, sample size, and patient status, despite no drug effect. Addition of clinically nonmeaningful QT prolongations (up to 5 milliseconds) increased the positive study rate to 80% for 1 particular study design. Ultimately, these results reveal significant limitations of the IUT with respect to excluding an effect and study interpretation for certain trial designs.

Exposure-response modeling using latent variables for the efficacy of a JAK3 inhibitor administered to rheumatoid arthritis patients.

Currently, no general methods have been developed to relate pharmacologically based models, such as indirect response models, to discrete or ordered categorical data. We propose the use of an unobservable latent variable (LV), through which indirect response models can be linked with drug exposure. The resulting indirect latent variable response model (ILVRM) is demonstrated using a case study of a JAK3 inhibitor, which was administered to patients in a rheumatoid arthritis (RA) study. The clinical endpoint for signs and symptoms in RA is the American College of Rheumatology response criterion of 20%--a binary response variable. In this case study, four exposure-response models, which have different pharmacological interpretations, were constructed and fitted using the ILVRM method. Specifically, two indirect response models, an effect compartment model, and a model which assumes instantaneous (direct) drug action were assessed and compared for their ability to predict the response data. In general, different model interpretations can influence drug inference, such as time to drug effect onset, as well as affect extrapolations of responses to untested experimental conditions, and the underlying pharmacology that operates to generate key response features does not change because the response was measured discretely. Consideration of these model interpretations can impact future study designs and ultimately provide greater insight into drug development strategies.