Highlights • Use predictive models to characterize the drug's PK and PK/PD. • Identify factors that could affect outcomes of clinical trial through clinical trial simulation. • Use models to identify ...prognostic factors that could affect the disease progression. • Use of optimal and adaptive clinical trial to design more informative trials in CNS. • Use predictive models to control the level of placebo response.
Different approaches based on deconvolution and convolution analyses have been proposed to establish IVIVC. A new implementation of the convolution-based model was used to evaluate the time-scaled ...IVIVC using the convolution (method 1) and the deconvolution-based (method 2) approaches. With the deconvolution-based approach, time-scaling was detected and estimated using Levy’s plots while with the convolution-based approach, time-scaling was directly determined by a time-scaling sub-model of the convolution integral model by nonlinear regression. The objectives of this study were (i) to show how time-scaled deconvolution and convolution-based approaches can be implemented using population modeling approach using standard nonlinear mixed-effect modeling software such as NONMEM and R, and (ii) to compare the performances of the two methods for assessing IVIVC using complex
in vivo
drug release process. The impact of different PK scenarios (linear and nonlinear PK disposition models, and increasing levels of inter-individual variability (IIV) on
in vivo
drug release process) was considered. The performances of the methods were assessed by computing the prediction error (%PE) on
C
max
, AUC, and partial AUC values. The mean %PE values estimated with the two methods were compliant with the IVIVC validation criteria. However, different from convolution-based, deconvolution-based approach showed that (i) the increase of IIV on
in vivo
drug release significantly affects the maximal %PE values of
C
max
leading to failure of IVIVC validation, and (ii) larger %PE values for
C
max
were associated to complex nonlinear PK disposition models. These results suggest that convolution-based approach could be considered at preferred approach for assessing time-scaled IVIVC.
The aim of this paper was to develop a convolution-based modeling approach for describing the paliperidone PK resulting from the administration of extended-release once-a-day oral dose, and once- and ...three monthly long-acting injectable products and to compare the performances of this approach to the traditional modeling strategy. The results of the analyses indicated that the traditional and convolution-based models showed comparable performances in the characterization of the paliperidone PK. However, the convolution-based approach showed several appealing features that justify the choice of this modeling as a preferred tool for modeling Long Acting Injectable (LAI) products and for deploying an effective model-informed drug development process. In particular, the convolution-based modeling can (a) facilitate the development of in vitro/in vivo correlation, (b) be used to identify formulations with optimal in vivo release properties, and (c) be used for optimizing the clinical benefit of a treatment by supporting the implementation of integrated models connecting in vitro and in vivo drug release, in vivo drug release to PK, and PK to PD and biomarker endpoints. A case study was presented to illustrate the benefits and the flexibility of the convolution-based modeling outcomes. The model was used to evaluate the in vivo drug release properties associated with a hypothetical once-a-year administration of a LAI product with the assumption that the expected paliperidone exposure during a 3-year treatment overlays the exposure expected after repeated administrations of a 3-month LAI product.
The convolution-based modeling approach has been shown to be flexible and easy to implement for performing a deconvolution analysis and for assessing
in vitro
/
in vivo
correlation using non-linear ...regression and a pre-specified model describing the
in vivo
drug absorption. A generalization of this method has been developed using a nonparametric description of the
in vivo
drug absorption process in replacement of a model-based definition. A comparison of the parametric and nonparametric deconvolution and convolution analyses was conducted on the pharmacokinetic (PK) data observed in four published studies after the administration of an extended-release formulation of methylphenidate at the dose of 18 mg. All the analyses were conducted using a conventional non-linear regression software (NONMEM). The results of the deconvolution analysis indicated that the parametric and nonparametric approaches performed similarly. The parametric approach described the input function using a double Weibull equation (6 parameters) while the nonparametric approach described the input function using a piecewise approximation (12–13 parameters). The validation of the results of the deconvolution analysis was conducted by comparing observed and predicted PK concentrations by the convolution analysis. The performance of the parametric and nonparametric approaches for assessing deconvolution was evaluated using the Akaike and the Bayesian information criteria. These criteria indicated that, despite the similar results obtained with the two approaches, the nonparametric approach provided better results. In conclusion, these results indicated that the nonparametric approach should be considered as the preferred approach for conducting a deconvolution analysis.
Abstract Dropouts impact clinical trial outcome analyses. Ignoring missing data is not an acceptable option when planning, conducting or interpreting the analysis of a clinical trial. Treatment ...related efficacy and safety data observed in the trial may not always be sufficient in explaining the dropouts' mechanism. Nevertheless, these dropout data may carry important treatment-related information and present as an outcome by itself. Traditional analyses involve the use of the time-to-event approach assuming that the dropouts' hazard is solely related to the efficacy or safety profiles observed in a study. A latent variable approach was developed to generalize this approach and to implement a more flexible dropout hazard function in a schizophrenia trial. This unobserved latent variable was used to jointly model the longitudinal efficacy data and dropout profiles across treatments. The analysis provides a framework to model informative dropouts simultaneously with primary efficacy outcomes and make intelligent decisions in drug development.
One of the most important reasons for failure of placebo‐controlled randomized controlled clinical trials (RCTs) is the lack of appropriate methodologies for detecting treatment effect (TE; ...difference between placebo and active treatment response) in the presence of excessively low/high levels of placebo response. Although, the higher the level of placebo response in a trial, the lower the apparent detectable TE. TE is usually estimated in a conventional analysis of an RCT as an “apparent” TE value conditional to the level of placebo response in that RCT. A model‐informed methodology is proposed to establish a relationship between level of placebo response and TE. This relationship is used to estimate the “typical” TE associated with a “typical” level of placebo response, irrespective of the level of placebo response observed. The approach can be valuable for providing a reliable estimate of TE, for conducting risk/benefit analysis, and for determining dosage recommendations.
The conventional statistical methodologies for evaluating treatment effect are based on hypothesis testing (P‐value). Alternative measurements of treatment effect have been proposed for ...anti‐infective treatments using the probability of target attainment. A general framework is proposed to extend this methodology to other therapeutic areas. A disease trial model is used for estimating the probability of reaching a treatment effect associated with relevant clinical benefits, in complement to the evaluation of the probability of rejecting the null hypothesis. A case study is presented in depression, where disease status is evaluated using bounded clinical scores (Hamilton Depression Rating Scale), and detectable treatment effect is inversely proportional to placebo response. The β‐regression approach is used to model Hamilton scale scores, and a placebo‐related criterion is proposed for determining the clinical benefit. The probability of reaching a clinical benefit represents a reliable criterion for replacing the P‐value paradigm in the assessment of the outcomes of clinical trials.
Model‐based approach is recognized as a tool to make drug development more productive and to better support regulatory and therapeutic decisions. The objective of this study was to develop a novel ...model‐based methodology based on the response surface analysis and a nonlinear optimizer algorithm to maximize the clinical performances of drug treatments. The treatment response was described using a drug‐disease model accounting for multiple components such as the dosage regimen, the pharmacokinetic characteristics of a drug (including the mechanism and the rate of drug delivery), and the exposure‐response relationship. Then, the clinical benefit of a treatment was defined as a function of the diseases and the clinical endpoints and was estimated as a function of the target pharmacodynamic endpoints used to evaluate the treatment effect. A case study is presented to illustrate how the treatment performances of paliperidone extended release (ER) and paliperidone long‐acting injectable (LAI) can be improved. A convolution‐based approach was used to characterize the pharmacokinetics of ER and LAI paliperidone. The drug delivery properties and the dosage regimen maximizing the clinical benefit (defined as the target level of D2 receptor occupancy) were estimated using a nonlinear optimizer. The results of the analysis indicated that a substantial improvement in clinical benefit (from 15% to 27% for the optimization of the in vivo release and from ∼30% to ∼70% for the optimization of dosage regimen) was obtained when optimal strategies were deployed either for optimizing the in vivo drug delivery properties of ER formulations or for optimizing the dosage regimen of LAI formulations.
HLD200 is an evening-dosed, delayed-release and extended-release methylphenidate (DR/ER-MPH) that provides a consistent delay in initial drug release to target onset of therapeutic effect from ...awakening and maintain it into the evening. Building on a modeling framework established with other extended-release methylphenidate formulations, pharmacokinetic (PK) and PK/pharmacodynamic (PD) models for DR/ER-MPH were developed to describe the time course of effect in response to a range of doses and administration times.
Using available PK data from healthy adults, a population PK model was developed using a 1-compartment model with a time-varying absorption rate described by a single Weibull function. A PK/PD model was then developed using Swanson, Kotkin, Agler, M-Flynn, and Pelham combined scores from a phase 3 trial of children with attention-deficit/hyperactivity disorder and simulated plasma concentration-time data. Simulations using the PK/PD model were performed for doses of 60, 80, and 100 mg of DR/ER-MPH, administered 4 to 14 hours before the classroom day.
The PK/PD model predicts that DR/ER-MPH produces a clinical response from early morning into the late afternoon or evening, with increased duration of response occurring with increasing doses. Furthermore, the PK/PD model predicts that maximal clinical effect is achieved with DR/ER-MPH administered 12 hours before the start of the classroom day.
Model-predicted duration of benefit with DR/ER-MPH is consistent with trial data documenting improvements in functional impairment during the early morning and evening. This model may facilitate dosage optimization by predicting changes in clinical benefit with dose and administration time adjustment.
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