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Four Phase 3 studies evaluated efficacy and safety of viloxazine extended-release in the treatment of attention-deficit/hyperactivity disorder (ADHD). The primary efficacy objective-change from baseline in ADHD Rating Scale-5 (ADHD-RS-5) Total score at end of study (EOS)-was not met in one of the studies (812P304). A band-pass analysis was performed to evaluate the impact of placebo response on the results.The distribution of placebo response at EOS of each trial was evaluated. The 2.5The 2.5This analysis indicated that a higher placebo response in Study 812P304 confounded the assessment of treatment effect. Application of the band-pass methodology confirmed the positive results of the 3 prior studies and the signal detection confounder in the fourth study.

TV-46000 is a long-acting subcutaneous antipsychotic that uses a novel copolymer drug delivery technology in combination with a well-characterized molecule, risperidone, that is in clinical development as a treatment for schizophrenia. A population pharmacokinetic (PPK) modeling and simulation approach was implemented to identify TV-46000 doses and dosing schedules for clinical development that would provide the best balance between clinical efficacy and safety. The PPK model was created by applying pharmacokinetic data from a phase 1 study of 97 patients with a diagnosis of schizophrenia or schizoaffective disorder who received either single or repeated doses of TV-46000. The PPK model was used to characterize the complex release profile of the total active moiety (TAM; the sum of the risperidone and 9-OH risperidone concentrations) concentration following subcutaneous injections of TV-46000. The PK profile was best described by a double Weibull function of the in vivo release rate and by a 2-compartment disposition and elimination model. Simulations were performed to determine TV-46000 doses and dosing schedules that maintained a median profile of TAM concentrations similar to published TAM exposure following oral risperidone doses that have been correlated to a 40% to 80% dopamine-D2 receptor occupancy therapeutic window. The simulations showed that therapeutic dose ranges for TV-46000 are 50 to 125 mg for once-monthly and 100 to 250 mg for the once every 2 months regimens. This PPK model provided a basis for prediction of patient-specific exposure and dopamine-D2 receptor occupancy estimates to support further clinical development and dose selection for the phase 3 studies.

The aim of this paper was to develop a new 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 new 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 LAI products and for deploying an effective model-informed drug development process (MIDD). 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-vivodrug 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.

IntroductionViloxazine ER (viloxazine extended-release capsules (Qelbree®) is a novel, nonstimulant, FDA-approved treatment for attention-deficit/hyperactivity disorder (ADHD) in adults and children ≥6 years of age. Viloxazine ER inhibits cytochrome P450-1A2, the enzyme responsible for caffeine metabolism. In a Phase 1 study, the peak exposure (Cmax) of caffeine was unchanged, while the systemic total exposure (AUC) was shown to increase ~5-fold following coadministration of caffeine (200mg) with viloxazine ER (900 mg/day x 4 days) compared to caffeine alone. Except for insomnia (44.4%), and possibly dizziness (8.3%), the incidence of adverse events (AEs) was not notably higher following coadministration vs. either caffeine or viloxazine ER alone. The objective of this analysis was to evaluate the impact of caffeine consumption on the incidence of AEs in adults with ADHD treated with viloxazine ER.MethodsData were analyzed from the Phase 3, double-blind (DB), placebo-controlled trial (NCT04016779) and ensuing (ongoing) open-label extension (OLE) safety trial (NCT04143217) supporting the viloxazine ER indication for adults with ADHD. Participants reported caffeine intake during the past week at each study visit.Correlation was assessed between viloxazine ER dose (mg/day) and weekly total caffeine consumption (mg) and between ADHD Investigator Symptom Rating Scale (AISRS) Total score and caffeine consumption. Fifteen AE preferred terms, known to be associated with caffeine consumption, were evaluated. For viloxazine ER-treated subjects (200–600 mg/day) who experienced a potentially caffeine-associated AE, the probability the AE occurred as a function of viloxazine ER dose and caffeine consumption during the DB or OLE trials was estimated using a logistic regression model for AEs with an incidence ≥5%.ResultsOf 372 enrolled subjects ~85% reported caffeine use during the DB trial; mean caffeine use was 1034 mg/week for the placebo group and 859 mg/week for the viloxazine ER group. There was no correlation between viloxazine ER dose and caffeine consumption (p=0.73), nor between AISRS total score and caffeine consumption (p=0.908). Of subjects reporting caffeine use, 44 (DB placebo), 79 (DB viloxazine ER), and 33 (OLE viloxazine ER) reported any of the pre-identified caffeine- associated AEs and were included in the regression analysis. For these subjects, insomnia-related AEs, fatigue, nausea, headache-related AEs, decreased appetite, and somnolence-related AEs occurred in ≥5% of viloxazine ER-treated subjects. Based on the regression analysis, caffeine consumption significantly increased the probability of experiencing insomnia-related AEs only (p=0.02).ConclusionsThis analysis suggests using caffeine concomitantly with viloxazine ER does not increase the likelihood of experiencing caffeine-related AEs except for insomnia. Still patients should be aware of the potential for viloxazine ER to augment caffeine exposure.FundingSupernus Pharmaceuticals, Inc.

Improvement in attention-deficit/hyperactivity disorder (ADHD) symptoms vs. placebo was reported in a series of pediatric clinical trials of viloxazine extended-release capsules (viloxazine ER; Qelbree™). This post hoc analysis of those studies evaluated the effect of viloxazine ER on learning and school problems (LSPs). We used data from four Phase 3 placebo-controlled trials of 100–600 mg/day viloxazine ER (N = 1354; 6–17 years of age). LSPs were evaluated using the School domain of the Weiss Functional Impairment Rating Scale-Parent Report (WFIRS-P-S) and the Learning Problems content scale of the Conners 3rd Edition-Parent Short Form (C3PS-LP) at baseline and end of study (≥ Week 6). ADHD symptoms were assessed weekly using the ADHD Rating Scale 5th Edition. The analyses were performed using the general linear mixed model with participant as a random effect. The responder analyses were performed using the Chi-square test. Viloxazine ER demonstrated significantly greater improvements in WFIRS-P-S (p p = 0.0113) scores vs. placebo. The response rate for the WFIRS-P-S was significantly greater for viloxazine ER vs. placebo (p = 0.001), and the number needed to treat (NNT) was 10.3 (effect size 0.7). Conversely, response rates for C3PS-LP did not differ between groups (p = 0.9069). In addition to ADHD symptoms improvement demonstrated in previous studies, viloxazine ER significantly reduced LSPs in pediatric subjects with ADHD. The responder analyses and NNT estimates indicate that a substantial number of children and adolescents with ADHD treated with viloxazine ER improved in clinically assessed LSPs.

Aim The aim of this study was to evaluate the effect of viloxazine extended-release capsules (viloxazine ER; Qelbree™) on executive function deficits (EFDs) in pediatric subjects (6–17 years of age) with attention-deficit/hyperactivity disorder (ADHD). Methods Data from four phase III placebo-controlled trials of 100–600 mg/day viloxazine ER (6–8 weeks of treatment) were used to evaluate the change from baseline (CFB) in the Conners 3rd Edition Parent Short Form—Executive Function (C3PS-EF) content scale T-score. Subjects were defined as EFD responders if they had C3PS-EF T-score > 70 at baseline and < 65 at end of study. ADHD symptoms were assessed with ADHD Rating Scale 5th Edition (ADHD-RS-5). Subjects were defined as ADHD symptom responders if they had a ≥ 50% reduction in CFB ADHD-RS-5 Total score at Week 6. The number needed to treat (NNT) and Cohen’s d effect sizes were estimated for EFD and ADHD symptoms. Results A total of 1154 subjects were included in the analysis. Statistically significant improvements in EFDs were observed with viloxazine ER versus placebo (p = 0.0002). There were 52.5% of EFD or ADHD symptom responders in the viloxazine ER treatment group and 35.4% in the placebo group (p

Purpose We conducted exposure-response modeling and simulations to compare the predicted efficacy of extended-release oxcarbazepine (OXC-XR), an oral once-daily (qd) antiepileptic drug, with that of immediate-release (IR) OXC twice-daily (bid) when the agents are used as monotherapy or adjunctive therapy in patients with epilepsy characterized by partial-onset seizures (POS). Methods Modeling assessed percent change from baseline 28-day seizure frequency (PCH) as a function of minimum concentration (Cmin) of monohydroxy derivative (MHD), the clinically relevant metabolite of OXC. For OXC-IR, the model used historical data; values for OXC-XR were derived from observed data. The model was simulated (N=100) to predict PCH at MHD Cmin concentrations achieved with 1200 and 2400 mg/day in adults and children receiving OXC-XR qd or OXC-IR bid. Mean PCH and 95% confidence intervals (CIs) were generated and compared. Results Predicted efficacy was not different (ie, 95% CI of mean PCH overlapped) for OXC-XR qd vs OXC-IR bid at mean MHD Cmin concentrations achieved with 1200 and 2400 mg/day adjunctive OXC-XR (47.4 and 76.4 µmol/L) and at target MHD Cmin concentrations for OXC-IR monotherapy (59.1 and 112 µmol/L) in adults. Predicted efficacy in adults vs children was not different between formulations. Depending on MHD Cmin, the predicted mean PCH in adults ranged from -51.4% to -73.4% with OXC-XR qd and -53.2% to -78.5% with OXC-IR bid. In children, the predicted mean PCH ranged from -48.4% to -58.1% (OXC-XR qd) and -32.5% to -70.4% (OXC-IR bid). Conclusion This model-based analysis predicted comparable efficacy for OXC-XR qd vs OXC-IR bid at MHD Cmin concentrations corresponding to 1200 and 2400 mg/day as monotherapy or adjunctive therapy. Based on this analysis, the US Food & Drug Administration approved OXC-XR for use as monotherapy in adults and children ≥6 years of age with POS.

OBJECTIVE: Extended-release methylphenidate (ER-MPH) formulations used to treat attention deficit hyperactivity disorder (ADHD) have complex pharmacokinetic (PK) profiles, resulting from differing ratios of immediate-release and extended-release components and/or their site of absorption. This study aimed to evaluate the smoothness of PK curves of ER-MPHs. DESIGN: The integral of the second derivative squared was evaluated for modeled PK curves, with smaller values indicating a smoother curve. The calculated smoothness of each PK curve was normalized by dividing by Cmax 2 to derive a normalized smoothness parameter appropriate across the dose range of each formulation. Calculations used modeled PK curves from 100mg delayed-release and ER-MPH (DR/ER-MPH), 54mg osmotic release oral system MPH (OROS MPH), 60mg MPH controlled-release delivery (MPH CD), 60mg ER-MPH oral suspension (MEROS), 20mg ER dexmethylphenidate (d-MPH ER), and 60mg multilayer-release MPH (MLR-MPH). RESULTS: The Cmax2-normalized smoothness value was consistent across DR/ER-MPH doses, allowing for relevant comparisons across formulations. Normalized smoothness values differed widely; the lowest normalized smoothness was 0.05 with DR/ER-MPH and ranged up to 9.56 with d-MPH ER. CONCLUSION: DR/ER-MPH demonstrated a smoother PK profile compared to the highest dose of other ER-MPH formulations. While the benefits of a smooth PK profile remain to be tested clinically, having fewer peaks and troughs has been hypothesized to reduce waxing and waning of therapeutic effects throughout the day, and more gradual changes in MPH plasma levels have been hypothesized to lower the risk of likeability and potentially abate afternoon symptom rebound.

Most stimulants used to treat attention-deficit/hyperactivity disorder are administered in the morning and absorbed in the upper gastrointestinal tract. DR/ER-MPH (formerly HLD200), an evening-dosed delayed-release and extended-release methylphenidate, is predicted to be absorbed in the proximal colon. The pharmacokinetic (PK) profile of DR/ER-MPH is characterized by an 8- to 10-hour delay in initial methylphenidate absorption and a subsequent gradual increase in plasma concentration, followed by a slow decline. To examine the relationship of absorption site to pharmacokinetics, the DR/ER-MPH formulation was altered to release methylphenidate in the small intestine and distal colon. The 3 formulations were administered in an open-label, 3-way, crossover study in healthy adults (N = 18). Compared with the small intestine formulation, the PK profile of the proximal colon (DR/ER-MPH) formulation exhibited a longer delay before initial methylphenidate absorption, decreased peak methylphenidate concentration, increased time to peak concentration, and decreased bioavailability; these characteristics were amplified in the distal colon formulation. Safety profiles fell within the expectations for methylphenidate products. Modeled PK profiles were similar between the small intestine formulation and a morning-dosed extended-release methylphenidate (both predicted to release methylphenidate in the upper gastrointestinal tract), providing additional evidence that the PK profile of DR/ER-MPH is shaped by colonic absorption.

Early response to viloxazine extended-release (viloxazine ER, Qelbree®) treatment predicted efficacy outcome in pediatric subjects with attention-deficit/hyperactivity disorder (ADHD). This study sought to determine whether the machine learning lasso model used in the pediatric study would predict response to viloxazine ER in an adult population based on early improvements in ADHD symptoms. We used data from a double-blind, placebo-controlled, flexible-dose (200-600 mg) study of viloxazine ER (N = 354; 18 to 60 years old). Area under the Receiver Operating Characteristic Curve (ROC AUC) statistics were computed using the lasso model from pediatric data to predict responder status in adults. Response was defined as ≥50% reduction from baseline in the Adult ADHD Investigator Symptoms Rating Scale (AISRS) Total score at Week 6. The adult study sample included 127 viloxazine ER-treated subjects with Week 6 data. Fifty-one subjects (40.2%) were categorized as responders. The ROC curves indicated that data collected up to Week 2 were sufficient to accurately predict treatment response at Week 6 with 68% positive predictive power, 80% sensitivity, and 74% specificity. This analysis demonstrated that the predictive model estimated from the child data generalizes to adults with ADHD, further supporting the consistency of viloxazine ER treatment across age groups.

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 net benefit of a treatment can be defined by the relationship between clinical improvement and risk of adverse events: the benefit-risk ratio. The optimization of the benefit-risk ratio can be achieved by identifying the most adequate dose (and/or dosage regimen) jointly with the best-performing in vivo release properties of a drug. A general in silico tool is presented for identifying the dose, the in vitro and the in vivo release properties that maximize the benefit-risk ratio using convolution-based modeling, an exposure-response model, and a surface response analysis. A case study is presented to illustrate how the benefit-risk ratio of methylphenidate for the treatment of attention deficit hyperactivity disorder can be maximized using the proposed strategy. The results of the analysis identified the characteristics of an optimized dose and in vitro/in vivo release suitable to provide a sustained clinical response with respect to the conventional dosage regimen and formulations.

The interest in the development and the therapeutic use of long‐acting injectable (LAI) products for chronic or long‐term treatments has grown exponentially. The complexity and the multiphase drug release process represent serious issues for an effective modeling of the PK properties of LAI products. The objective of this article is to show how convolution‐based models with piecewise‐linear approximation of the nonlinear drug release function can provide an enhanced modeling tool for (1) characterizing the complex PK profiles of LAI formulations with completely different drug release properties, and (2) addressing key questions supporting the optimal development of LAI products by simulating the PK time course resulting from different dosing strategies. Convolution‐based modeling and simulation were implemented in NONMEM, and 3 case studies were presented to assess the performances of this new modeling approach using PK data of LAI products developed using different technologies and administered using different routes: microsphere technology and aqueous nanosuspension intramuscularly administered and biodegradable polymer subcutaneously administered. The performance of the convolution‐based modeling approach was compared with the performance of conventional parametric models using a reference data set on theophylline. The results of the comparison indicated that the nonparametric input function provided a more accurate description of the data either in terms of global measure of goodness of fit (ie, Akaike information criterion and Bayesian information criterion) or in terms of performance of the fitted model (ie, the percent prediction error on Cma x and AUC0‐t).

Machine learning (ML) was used to determine whether early response can predict efficacy outcome in pediatric subjects with ADHD treated with SPN-812. We used data from four Phase 3 placebo-controlled trials of 100- to 600-mg/day SPN-812 (N=1397; 6–17 years of age). The treatment response was defined as having a ≥50% reduction in change from baseline (CFB) in ADHD Rating Scale-5 (ADHD-RS-5) Total score at Week 6. The variables used were: ADHD-RS-5 Total score, age, body weight, and body mass index at baseline; CFB ADHD-RS-5 Total score at Week 1, cumulative change in ADHD-RS-5 Total score at Week 2, and cumulative change in ADHD-RS-5 Total score at Week 3; Clinical Global Impressions-Improvement (CGI-I) score at Week 1, 2, and 3; and target dose. Using the best selected model, lasso regression, to generate importance scores, we found that change in ADHD-RS-5 Total score and CGI-I score were the best predictors of efficacy outcome. Change in ADHD-RS-5 Total score at Week 2 could predict treatment response at Week 6 (75% positive predictive power, 75% sensitivity, 74% specificity). Therefore, early response after two weeks of treatment with once-daily SPN-812 in pediatric patients with ADHD can predict efficacy outcome at Week 6.

Supplemental digital content is available in the text., Purpose/Background 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. Methods/Procedures 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. Findings/Results 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. Implications/Conclusions 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.

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 Cmax, 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 Cmax leading to failure of IVIVC validation, and (ii) larger %PE values for Cmax 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.

Purpose We conducted exposure-response modeling and simulations to compare the predicted efficacy of extended-release oxcarbazepine (OXC-XR), an oral once-daily (qd) antiepileptic drug, with that of immediate-release (IR) OXC twice-daily (bid) when the agents are used as monotherapy or adjunctive therapy in patients with epilepsy characterized by partial-onset seizures (POS). Methods Modeling assessed percent change from baseline 28-day seizure frequency (PCH) as a function of minimum concentration (Cmin) of monohydroxy derivative (MHD), the clinically relevant metabolite of OXC. For OXC-IR, the model used historical data; values for OXC-XR were derived from observed data. The model was simulated (N=100) to predict PCH at MHD Cmin concentrations achieved with 1200 and 2400 mg/day in adults and children receiving OXC-XR qd or OXC-IR bid. Mean PCH and 95% confidence intervals (CIs) were generated and compared. Results Predicted efficacy was not different (ie, 95% CI of mean PCH overlapped) for OXC-XR qd vs OXC-IR bid at mean MHD Cmin concentrations achieved with 1200 and 2400 mg/day adjunctive OXC-XR (47.4 and 76.4 µmol/L) and at target MHD Cmin concentrations for OXC-IR monotherapy (59.1 and 112 µmol/L) in adults. Predicted efficacy in adults vs children was not different between formulations. Depending on MHD Cmin, the predicted mean PCH in adults ranged from −51.4% to −73.4% with OXC-XR qd and −53.2% to −78.5% with OXC-IR bid. In children, the predicted mean PCH ranged from −48.4% to −58.1% (OXC-XR qd) and −32.5% to −70.4% (OXC-IR bid). Conclusion This model-based analysis predicted comparable efficacy for OXC-XR qd vs OXC-IR bid at MHD Cmin concentrations corresponding to 1200 and 2400 mg/day as monotherapy or adjunctive therapy. Based on this analysis, the US Food & Drug Administration approved OXC-XR for use as monotherapy in adults and children ≥6 years of age with POS.

AIM: A Phase 1 study was performed to evaluate safety, pharmacokinetics (PK) and pharmacodynamics (PD) of the selective histamine H4 receptor antagonist SENS-111, an oral small molecule. METHODS: One hundred healthy subjects were randomized in a placebo-controlled, double-blind study evaluating single-ascending doses (SAD; 100-500 mg) and multiple-ascending doses (MAD; 50-150 mg day-1 , 4 days; 200-250 mg day-1 , 7 days). Effects of SENS-111 on nystagmus and vertigo induced by modified caloric tests were measured in the MAD studies. Population PK and PK/PD models were developed using a nonlinear mixed-effects approach. RESULTS: SENS-111 was well tolerated with mild to moderate events. No sedation was reported. A maximal tolerated dose was not reached. Dose-proportional increases in concentrations were seen up to 200 mg and more than dose-proportional thereafter, with mean half-life between 24 and 56 h. The caloric test induced mild but measurable vertigo and nystagmus with large intra/inter-individual variation for all parameters. SENS-111 did not significantly impact nystagmus but significantly improved latency of vertigo appearance/disappearance, duration and European Evaluation of Vertigo questionnaire parameters vs. baseline. A two-compartment model with first-order absorption, distribution and elimination best fit the data. PK/PD indirect modelling applied to vertigo duration and latency of appearance indicated maximum activity between 100 and 500 ng ml-1 plasma concentrations, corresponding to 100 and 200 mg day-1 , which are appropriate for clinical efficacy evaluations in vestibular diseases. CONCLUSIONS: SENS-111 is a well-tolerated first-in-class H4 receptor antagonist with acceptable PK for oral daily dosing. PK/PD modelling determined plasma concentrations and doses for efficacy studies in patients with vertigo symptoms.

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.

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.

One of the main reasons for the inefficiency of multicenter randomized clinical trials (RCTs) in depression is the excessively high level of placebo response. The aim of this work was to propose a novel methodology to analyze RCTs based on the assumption that centers with high placebo response are less informative than the other centers for estimating the ‘true' treatment effect (TE). A linear mixed-effect modeling approach for repeated measures (MMRM) was used as a reference approach. The new method for estimating TE was based on a nonlinear longitudinal modeling of clinical scores (NLMMRM). NLMMRM estimates TE by associating a weighting factor to the data collected in each center. The weight was defined by the posterior probability of detecting a clinically relevant difference between active treatment and placebo at that center. Data from five RCTs in depression were used to compare the performance of MMRM with NLMMRM. The results of the analyses showed an average improvement of ~15% in the TE estimated with NLMMRM when the center effect was included in the analyses. Opposite results were observed with MMRM: TE estimate was reduced by ~4% when the center effect was considered as covariate in the analysis. The novel NLMMRM approach provides a tool for controlling the confounding effect of high placebo response, to increase signal detection and to provide a more reliable estimate of the ‘true' TE by controlling false negative results associated with excessively high placebo response.

This paper addresses the problem of modelling longitudinal data describing patients’ responses in clinical trials. In particular, a systematic approach relying on a system theoretic paradigm is proposed to deal with contexts where limited physiopathological knowledge is available on disease, drug response, and patients’ characteristics. The model relies on the notion of patient’s health state which summarizes the patient’s condition. In order to cope with the limited number of clinical data usually available, the paper considers a very parsimonious realization where the two state variables are the clinical endpoint and its derivative. Within a population framework, the individual response is modelled as the sum of an individual shift and the average response of subjects belonging to the same study, both described as Markovian processes and identified by empirical Bayes techniques. The proposed approach is validated with experimental data from a Phase II, flexible-dose, depression trial. The dose changes due to the flexible-dose scheme are handled as perturbations on the state. The connection between inter-individual variability and model stability is evaluated showing that the introduction of stable poles helps to describe populations whose range of individual responses does not diverge with time. In this way, good individual fittings and visual predictive checks were obtained for the clinical data. The proposed analysis provides a systematic approach to semi-mechanistic modelling when a precise knowledge of the physiological mechanisms of the disease is incomplete or missing.

The aim of this paper is to provide a systematic methodology for modelling longitudinal data to be used in contexts of limited or even absent knowledge of the physiological mechanism underlying the disease time course. Adopting a system-theoretic paradigm, a population response model is developed where the clinical endpoint is described as a function of the patient's health state. In particular, a continuous-time stochastic approach is proposed where the clinical score and its time-derivative summarize the patient's health state affected by a random term accounting for exogenous unpredictable factors. The proposed approach is validated on experimental data from the placebo and drug arms of a Phase II depression trial. Since some subjects in the trial may undergo changes in their treatment dose due to the flexible dosing scheme, dose escalations are modelled as instantaneous perturbations on the state. In its simplest form--an integrated Wiener process--was able to correctly capture the individual responses in both treatment arms. However, a better description of inter-individual variability was obtained by means of a stable Markovian model. Parameter estimation has been carried out according to the empirical Bayes method.

Band-pass filtering is a novel statistical methodology that proposes that filtering out data from trial sites generating non-plausible high or low levels of placebo response can yield a more accurate effect size and greater separation of active drug (when efficacious) from placebo. We applied band-pass filters to re-analyze data from a negative antidepressant trial (NCT00739908) evaluating CX157 (a reversible and selective monoamine oxidase inhibitor-A) versus placebo. 360 patients from 29 trial sites were randomized to either CX157 treatment (n=182) or placebo (n=178). We applied two filters of3 or7 points (filter #1) or3 and9 points (filter #2) mean change of the total MADRS placebo scores for each site. Trial sites that had mean placebo MADRS score changes exceeding the boundaries of these band-pass filter thresholds were considered non-informative and all of the data from these sites were excluded from the post-hoc re-analysis. The two band-pass filters reduced the sample of informative patients from 353 patients in the mITT population to 62 in filter #1 and 152 in the filter #2 group. The placebo response was reduced from 31.1% in the mITT population to 9.4% with filter #1 and 20.8% with filter #2. MMRM analysis revealed a non-statistically significant trend of p=0.13 and 0.16 for the two filters in contrast to the mITT population (p= 0.58). Our findings support the band-pass filter hypothesis and highlight issues related to site-based scoring variability and inappropriate subject selection that may contribute to trial failure.

This study implemented pharmacokinetic/pharmacodynamic modelling to support the clinical development of RBP-6000, a new, long-acting, sustained-release formulation of buprenorphine for the treatment of opioid dependence. Such a formulation could offer advantages over existing buprenorphine pharmacotherapy by improving patient compliance and reducing the diversion of the product. A population pharmacokinetic model was developed using 36 opioid-dependent subjects who received single subcutaneous doses of RBP-6000. Another pharmacokinetic/pharmacodynamic model was developed using μ-opioid receptor occupancy (µORO) data to predict efficacy of RBP-6000 after repeated doses. It was also assessed how buprenorphine plasma concentrations were correlated with opioid withdrawal symptoms and hydromorphone agonist blockade data from 15 heroin-dependent subjects. The resulting pharmacokinetic model accurately described buprenorphine and norbuprenorphine plasma concentrations. A saturable maximum effect (E max) model with 0.67 ng/mL effective concentration at 50 % of maximum (EC50) and 91 % E max best described µORO versus buprenorphine plasma concentrations. Linear relationships were found among µORO, withdrawal symptoms and blockade of agonist effects. Previously published findings have demonstrated µORO ≥70 % is needed to achieve withdrawal suppression and blockade of opioid agonist subjective effects. Model simulations indicated that a 200 mg RBP-6000 dose should achieve 2–3 ng/mL buprenorphine average concentrations and desired efficacy.

The objectives of this work were: (1) to characterize the placebo FEV1 response in asthma; (2) to identify the potential factors with strong influence on FEV1; (3) to determine the predictability of the early (week 2) placebo FEV1 response to the longer term (week 12) FEV1 response. Placebo FEV1 data of about 800 subjects from 11 randomized 12-week clinical trials in mild-to-moderate asthmatics were collected. Stepwise logistic regression methods using SAS were used to model the week 12 trough FEV1 change from baseline greater than a clinically relevant value (150 mL) and to select the predictive covariates. The study effect was assessed using hierarchical logistic regression models implemented in WinBUGS. The results indicated that the early (week 2) placebo response was significantly predictive of the FEV1 response at week 12. Age, baseline predicted FEV1, and season showed statistical significance in the model. The final model showed satisfactory predictability with the area under the receiver operating characteristic curve (ROC) of 80%. The late (week 12) FEV1 response with placebo was positively related to the early (week 2) FEV1 change. The use of the predictive modeling approach proposed in this article presents a valuable method to increase the efficiency of clinical trial design in asthma population.

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.

RBP-7000 is a sustained-release (once-monthly injection for subcutaneous administration) formulation of risperidone using the ATRIGEL® Delivery System, developed for treatment of schizophrenia to address compliance issues associated with oral administration. The objective of this analysis was to report the results of a population pharmacokinetic analysis and to describe the relationship between risperidone and 9-hydroxyrisperidone levels with dopamine (DA) D2-receptor occupancy, prolactin levels, and adverse events using data collected in 45 clinically stable schizophrenic patients receiving RBP-7000 in single ascending doses (risperidone) of 60, 90, and 120 mg. The population PK model accounted for an initial peak, a delayed and slow delivery, the disposition of risperidone, and the conversion of risperidone to 9-hydroxyrisperidone. BMI was a covariate affecting absorption of risperidone and ultimately formation of 9-hydroxyrisperidone. A logistic analysis indicated a correlation between the increase in Active Moiety (risperidone + 9-OH-risperidone) exposure (Cmax ) and the probability of observing GI disorders. An Emax population PK/prolactin model best described the relationship between the circulating Active Moiety and the serum prolactin levels. Gender was a significant covariate associated with Emax . These data provided a comprehensive characterization of the relationship between circulating Active Moiety and the efficacy/safety profile of RBP-7000 in clinically stable schizophrenic patients.

The selection of a therapeutically meaningful dose of a novel pharmaceutical is a crucial step in drug development. Positron emission tomography (PET) allows the in vivo estimation of the relationship between the plasma concentration of a drug and its target occupancy, optimizing dose selection and reducing the time and cost of early development. Triple reuptake inhibitors (TRIs), also referred to as serotonin-norepinephrine-dopamine reuptake inhibitors, enhance monoaminergic neurotransmission by blocking the action of the monoamine transporters, raising extracellular concentrations of those neurotransmitters. GSK1360707 [(1R,6S)-1-(3,4-dichlorophenyl)-6-(methoxymethyl)-4-azabicyclo[4.1.0]heptane] is a novel TRI that until recently was under development for the treatment of major depressive disorder; its development was put on hold for strategic reasons. We present the results of an in vivo assessment of the relationship between plasma exposure and transporter blockade (occupancy). Studies were performed in baboons (Papio anubis) to determine the relationship between plasma concentration and occupancy of brain serotonin reuptake transporter (SERT), dopamine reuptake transporter (DAT), and norepinephrine uptake transporter (NET) using the radioligands [(11)C]DASB [(N,N-dimethyl-2-(2-amino-4-cyanophenylthio) benzylamine], [(11)C]PE2I [N-(3-iodoprop-2E-enyl)-2β-carbomethoxy-3β-(4-methylphenyl)nortropane], and [(11)C]2-[(2-methoxyphenoxy)phenylmethyl]morpholine (also known as [(11)C]MRB) and in humans using [(11)C]DASB and [(11)C]PE2I. In P. anubis, plasma concentrations resulting in half-maximal occupancy at SERT, DAT, and NET were 15.16, 15.56, and 0.97 ng/ml, respectively. In humans, the corresponding values for SERT and DAT were 6.80 and 18.00 ng/ml. GSK1360707 dose-dependently blocked the signal of SERT-, DAT-, and NET-selective PET ligands, confirming its penetration across the blood-brain barrier and blockade of all three monoamine transporters in vivo.

Full, persistent blockade of central neurokinin-1 (NK1) receptors may be a potential antidepressant mechanism. The selective NK1 antagonist orvepitant (GW823296) was used to test this hypothesis. A preliminary positron emission tomography study in eight male volunteers drove dose selection for two randomized six week studies in patients with major depressive disorder (MDD). Displacement of central [11C]GR205171 binding indicated that oral orvepitant doses of 30–60 mg/day provided >99% receptor occupancy for ≥24 h. Studies 733 and 833 randomized patients with MDD and 17-item Hamilton Depression Rating Scale (HAM-D)≥22 to double-blind treatment with orvepitant 30 mg/day, orvepitant 60 mg/day or placebo (1:1:1). Primary outcome measure was change from baseline in 17-item HAM-D total score at Week 6 analyzed using mixed models repeated measures. Study 733 ( n=328) demonstrated efficacy on the primary endpoint (estimated drug-placebo differences of 30 mg: −2.41, 95% confidence interval (CI) (−4.50 to −0.31) p=0.0245; 60 mg: –2.86, 95% CI (−4.97 to −0.75) p=0.0082). Study 833 ( n=345) did not show significance (estimated drug-placebo differences of 30 mg: −1.67, 95% CI (−3.73 to 0.39) p=0.1122; 60 mg: −0.76, 95% CI (−2.85 to 1.32) p=0.4713). The results support the hypothesis that full, long lasting blockade of central NK1 receptors may be an efficacious mechanism for the treatment of MDD.

For psychiatric diseases, established mechanistic models are lacking and alternative empirical mathematical structures are usually explored by a trial-and-error procedure. To address this problem, one of the most promising approaches is an automated model-free technique that extracts the model structure directly from the statistical properties of the data. In this paper, a linear-in-parameter modelling approach is developed based on principal component analysis (PCA). The model complexity, i.e. the number of components entering the PCA-based model, is selected by either cross-validation or Mallows' Cp criterion. This new approach has been validated on both simulated and clinical data taken from a Phase II depression trial. Simulated datasets are generated through three parametric models: Weibull, Inverse Bateman and Weibull-and-Linear. In particular, concerning simulated datasets, it is found that the PCA approach compares very favourably with some of the popular parametric models used for analyzing data collected during psychiatric trials. Furthermore, the proposed method performs well on the experimental data. This approach can be useful whenever a mechanistic modelling procedure cannot be pursued. Moreover, it could support subsequent semi-mechanistic model building.

Emerging evidence indicates that the selection of patients and the selection of recruitment centers in placebo-controlled, randomized clinical trials (RCTs) in depression have a substantial impact on the probability of observing a treatment effect of a novel medication. The objective of this work was to evaluate the role of placebo in characterizing the exposure-response relationship and proposes a new methodology based on band-pass filtering for assessing the relationship between drug exposure, level of clinical response conditioned to the level of placebo response, and the potency of the antidepressant drug evaluated. Clinical trial simulation (CTS) was used to demonstrate that the conventional analyses of data generated in multi-centre RCTs including centres exhibiting heterogeneous placebo response can lead to contradictory and inappropriate assessment of the exposure-response relationship. To address this issue, a novel modelling approach has been developed for establishing the exposure-response relationship by using a pharmacodynamic model accounting for the placebo effect. The proposed model demonstrated that the expected drug related treatment effect in a placebo-controlled RCT can be predicted as a function of the dose, the drug potency and the level of placebo response when data from informative recruitment centres are considered. With this novel approach, a more accurate estimate of the dose/exposure response can be derived and more informed go/no-go decisions can be made in developing drugs for psychiatric disorders.

Methylphenidate (MPH) is currently used to treat children with attention deficit hyperactivity disorder (ADHD). Several extended-release (ER) formulations characterized by a dual release process were developed to improve efficacy over an extended duration. In this study, a model-based approach using literature data was developed to: 1) evaluate the most efficient pharmacokinetic (PK) model to characterize the complex PK profile of MPH ER formulations; 2) provide PK endpoint metrics for comparing ER formulations; 3) define criteria for optimizing development of ER formulations using a convolution-based model linking in vitro release, in vivo release, and hour-by-hour behavioral ratings of ADHD symptoms; and 4) define an optimized trial design for assessing the activity of MPH in pediatric populations. The convolution-based model accurately described the complex PK profiles of a variety of ER MPH products, providing a natural framework for establishing an in vitro/in vivo correlation and for defining criteria for assessing comparative bioequivalence of MPH ER products.

Performance of recruitment centers is a critical feature for the success of multicentric randomized clinical trials (RCT). An enrichment window strategy (EWS) was recently proposed to enhance signal detection in RCTs based on the identification of centers with non-plausible placebo response, leading to a Per Protocol exclusion of all data from these non-informative centers before statistical analysis. The risk of an inflated Type I error associated with EWS was assessed using Clinical Trial Simulation. Randomized two-arm placebo controlled trials were simulated under the assumption that the drug treatment was ineffective.The results obtained in the absence of population enrichment were compared to the results obtained after EWS application. The results indicated that EWS preserved the Type I error showing no bias, at variance with other procedures. These results were confirmed by simulating 13 three-arm placebo controlled RCTs from the GSK Clinical Database.

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.

The fact that there are high dropout rates in clinical trials of antipsychotic medications raises critical questions regarding the most appropriate method of designing new trials, analyzing efficacy data, and evaluating the clinical utility (CU) of novel treatments. In this article, we consider the use of a model-based approach to define an integrated CU criterion for better characterizing the clinical response to a treatment, for optimizing proof-of-concept trials, and for providing differentiating criteria for novel medications when complete information is not available.

Purpose There is growing recognition of the importance of integrating drug occupancy data acquired by positron emission tomography (PET) with the plasma pharmacokinetics of the drug, in order to establish proper dose selection in subsequent clinical trials. Here we present a study in human subjects of the occupancy of NK1 receptors achieved following different doses of casopitant, a selective NK1 antagonist.

Mixed-effect Markov chain models have been recently proposed to characterize the time course of transition probabilities between sleep stages in insomniac patients. The most recent one, based on multinomial logistic functions, was used as a base to develop a final model combining the strengths of the existing ones. This final model was validated on placebo data applying also new diagnostic methods and then used for the inclusion of potential age, gender, and BMI effects. Internal validation was performed through simplified posterior predictive check (sPPC), visual predictive check (VPC) for categorical data, and new visual methods based on stochastic simulation and estimation and called visual estimation check (VEC). External validation mainly relied on the evaluation of the objective function value and sPPC. Covariate effects were identified through stepwise covariate modeling within NONMEM VI. New model features were introduced in the model, providing significant sPPC improvements. Outcomes from VPC, VEC, and external validation were generally very good. Age, gender, and BMI were found to be statistically significant covariates, but their inclusion did not improve substantially the model’s predictive performance. In summary, an improved model for sleep internal architecture has been developed and suitably validated in insomniac patients treated with placebo. Thereafter, covariate effects have been included into the final model.

Hypnotic drug development calls for a better understanding of sleep physiology in order to improve and differentiate novel medicines for the treatment of sleep disorders. On this basis, a proper evaluation of polysomnographic data collected in clinical trials conducted to explore clinical efficacy of novel hypnotic compounds should include the assessment of sleep architecture and its drug-induced changes. This work presents a non-linear mixed-effect Markov-chain model based on multinomial logistic functions which characterize the time course of transition probabilities between sleep stages in insomniac patients treated with placebo. Polysomnography measurements were obtained from patients during one night treatment. A population approach was used to describe the time course of sleep stages (awake stage, stage 1, stage 2, slow-wave sleep and REM sleep) using a Markov-chain model. The relationship between time and individual transition probabilities between sleep stages was modelled through piecewise linear multinomial logistic functions. The identification of the model produced a good adherence of mean post-hoc estimates to the observed transition frequencies. Parameters were generally well estimated in terms of CV, shrinkage and distribution of empirical Bayes estimates around the typical values. The posterior predictive check analysis showed good consistency between model-predicted and observed sleep parameters. In conclusion, the Markov-chain model based on multinomial logistic functions provided an accurate description of the time course of sleep stages together with an assessment of the probabilities of transition between different stages.

Schizophrenia is a severe mental disorder characterized by definite specificities and complexities (heterogeneity of the disease symptoms, large between-subject variability in disease progression and response to therapeutic agents, placebo response, dropout, questionable preclinical models, importance of market differentiation, etc.) that make drug development in this field particularly difficult when compared to the other therapeutic areas. However, drug receptor binding (especially to D2, 5-HT2 and H1 receptors) can provide a useful quantitative framework that can be related to the downstream clinical (amelioration of disease-related scores) and unwanted (neurological effects and metabolic disregulation) effects.This paper reviews the pharmacokinetic, pharmacodynamic and disease progression approaches applied to the development of new drugs for the treatment of schizophrenia.Only model-based methodologies, able to integrate the diverse characteristics of a compound, can provide a rational approach to increase efficiency in drug development in this area, through the development of pharmacokinetic-pharmacodynamics models able to integrate quantitative descriptions of pharmacokinetics, desired and unwanted effects. These holistic approaches can be used in clinical trial simulations for reliably predicting the outcome of future trials. Meta-analyses of the competitor environment are also essential to position the new drug into a crowded competitive landscape.

Dropouts are common events in longitudinal studies in depression. Ignoring missing information may lead to biased and inconsistent assessment of study results. A non-linear model was recently developed to describe the time-course of HAMD-17 clinical score in the placebo arms of antidepressant clinical trials. In this paper we complemented this model by introducing an informative dropout component to jointly estimate HAMD-17 time-course and dropout mechanism. The aims of this work were to: (a) characterise typical placebo response in depression trials in presence of dropouts, (b) explore which dropout mechanism better describe the time-varying probability of a subject to dropout from the trial, and (c) define a framework for the development of clinical trial simulation in depression. A meta-analytic approach was used on placebo data collected in 6 clinical trials including 695 subjects suffering from Major Depressive Disorders. Alternative hypotheses for "missingness" were evaluated using different hazard models. The "Missing Not At Random" performed statistically (p