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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. 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. 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.

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.

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.

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; IC 50 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.

We propose an efficient algorithm for screening covariates in population model building using Wald's approximation to the likelihood ratio test (LRT) statistic in conjunction with Schwarz's Bayesian criterion. The algorithm can be applied to a full model fit of k covariate parameters to calculate the approximate LRT for all 2k - 1 possible restricted models. The algorithm's efficiency also permits internal validation of the model selection process via bootstrap methods. We illustrate the use of this algorithm for both model selection and validation with data from a Daypro pediatric study. The algorithm is easily implemented using standard statistical software such as SAS/IML and S-Plus. A SAS/IML macro to perform the algorithm is provided.

Population pharmacokinetic modeling of pegaptanib was undertaken to determine influence of renal function on apparent clearance. 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. 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. 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.

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.

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.

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.