Model-based comparisons of post-treatment free IgE and FEV1 between omalizumab asthma dosing tables in the United States and European Union.

AIMS: Omalizumab is an anti-immunoglobulin E (IgE) monoclonal antibody that was first approved by the United States (US) Food and Drug Administration (FDA) for the treatment of allergic asthma in 2003. The pivotal trials supporting the initial approval of omalizumab used dosing determined by patient's baseline IgE and body weight, with the goal of reducing the mean free IgE level to approximately 25 ng/mL or less. While the underlying parameters supporting the dosing table remained the same, subsequent studies and analyses have resulted in approved alternative versions of the dosing table, including the European Union (EU) asthma dosing table, which differs in weight bands and maximum allowable baseline IgE and omalizumab dose. In this study, we leveraged modelling and simulation approaches to predict and compare the free IgE reduction and forced expiratory volume in 1 second (FEV1) improvement with omalizumab dosing based on the US and EU asthma dosing tables. METHODS: Previously established population pharmacokinetic-IgE and IgE-FEV1 models were used to predict and compare post-treatment free IgE and FEV1 based on the US and EU dosing tables. Clinical trial simulations (with virtual asthma populations) and Monte Carlo simulations were performed to provide both breadth and depth in the comparisons. RESULTS: The US and EU asthma dosing tables were predicted to result in generally comparable free IgE suppression and FEV1 improvement. CONCLUSIONS: Despite the similar free IgE and FEV1 outcomes from simulations, this has not been clinically validated with respect to the registrational endpoint of reduction in annualized asthma exacerbations.

Five multivariate Duchenne muscular dystrophy progression models bridging six-minute walk distance and MRI relaxometry of leg muscles.

The study aimed to provide quantitative information on the utilization of MRI transverse relaxation time constant (MRI-T2) of leg muscles in DMD clinical trials by developing multivariate disease progression models of Duchenne muscular dystrophy (DMD) using 6-min walk distance (6MWD) and MRI-T2. Clinical data were collected from the prospective and longitudinal ImagingNMD study. Disease progression models were developed by a nonlinear mixed-effect modeling approach. Univariate models of 6MWD and MRI-T2 of five muscles were developed separately. Age at assessment was the time metric. Multivariate models were developed by estimating the correlation of 6MWD and MRI-T2 model variables. Full model estimation approach for covariate analysis and five-fold cross validation were conducted. Simulations were performed to compare the models and predict the covariate effects on the trajectories of 6MWD and MRI-T2. Sigmoid Imax and Emax models best captured the profiles of 6MWD and MRI-T2 over age. Steroid use, baseline 6MWD, and baseline MRI-T2 were significant covariates. The median age at which 6MWD is half of its maximum decrease in the five models was similar, while the median age at which MRI-T2 is half of its maximum increase varied depending on the type of muscle. The models connecting 6MWD and MRI-T2 successfully quantified how individual characteristics alter disease trajectories. The models demonstrate a plausible correlation between 6MWD and MRI-T2, supporting the use of MRI-T2. The developed models will guide drug developers in using the MRI-T2 to most efficient use in DMD clinical trials.

Bayesian methods: a potential path forward for sepsis trials.

BACKGROUND: Given the success of recent platform trials for COVID-19, Bayesian statistical methods have become an option for complex, heterogenous syndromes like sepsis. However, study design will require careful consideration of how statistical power varies using Bayesian methods across different choices for how historical data are incorporated through a prior distribution and how the analysis is ultimately conducted. Our objective with the current analysis is to assess how different uses of historical data through a prior distribution, and type of analysis influence results of a proposed trial that will be analyzed using Bayesian statistical methods. METHODS: We conducted a simulation study incorporating historical data from a published multicenter, randomized clinical trial in the US and Canada of polymyxin B hemadsorption for treatment of endotoxemic septic shock. Historical data come from a 179-patient subgroup of the previous trial of adult critically ill patients with septic shock, multiple organ failure and an endotoxin activity of 0.60-0.89. The trial intervention consisted of two polymyxin B hemoadsorption treatments (2 h each) completed within 24 h of enrollment. RESULTS: In our simulations for a new trial of 150 patients, a range of hypothetical results were observed. Across a range of baseline risks and treatment effects and four ways of including historical data, we demonstrate an increase in power with the use of clinically defensible incorporation of historical data. In one possible trial result, for example, with an observed reduction in risk of mortality from 44 to 37%, the probability of benefit is 96% with a fixed weight of 75% on prior data and 90% with a commensurate (adaptive-weighting) prior; the same data give an 80% probability of benefit if historical data are ignored. CONCLUSIONS: Using Bayesian methods and a biologically justifiable use of historical data in a prior distribution yields a study design with higher power than a conventional design that ignores relevant historical data. Bayesian methods may be a viable option for trials in critical care medicine where beneficial treatments have been elusive.

Predicting outcomes of phase III oncology trials with Bayesian mediation modeling of tumor response.

Pivotal cancer trials often fail to yield evidence in support of new therapies thought to offer promising alternatives to standards-of-care. Conducting randomized controlled trials in oncology tends to be considerably more expensive than studies of other diseases with comparable sample size. Moreover, phase III trial design often takes place with a paucity of survival data for experimental therapies. Experts have explained the failures on the basis of design flaws which produce studies with unrealistic expectations. This article presents a framework for predicting outcomes of phase III oncology trials using Bayesian mediation models. Predictions, which arise from interim analyses, derive from multivariate modeling of the relationships among treatment, tumor response, and their conjoint effects on survival. Acting as a safeguard against inaccurate pre-trial design assumptions, the methodology may better facilitate rapid closure of negative studies. Additionally the models can be used to inform re-estimations of sample size for under-powered trials that demonstrate survival benefit via tumor response mediation. The methods are applied to predict the outcomes of two colorectal cancer studies. Simulation is used to evaluate and compare models in the absence versus presence of reliable surrogate markers of survival.

Population pharmacokinetic-pharmacodynamic model-based exploration of alternative ustekinumab dosage regimens for patients with Crohn's disease.

AIMS: In the UNITI endoscopy sub-study, only 17.4% of patients with Crohn's disease (CD) on ustekinumab achieved endoscopic response and 10.9% of patients achieved endoscopic remission at week (w)44. We aimed to evaluate the impact of alternative ustekinumab dosage regimens on endoscopic outcomes based on population pharmacokinetic-pharmacodynamic (popPK-PD) modelling and simulation analysis. METHODS: Real-world data were obtained from 83 patients with moderate-to-severe CD (95% biological-refractory) enrolled in a prospective cohort study receiving intravenous ustekinumab (~6 mg/kg) followed by every eight-week (q8w) subcutaneous maintenance therapy (90 mg). Three sequential models were developed: a two-compartment popPK model linking ustekinumab dose to ustekinumab exposure, an indirect response popPK-PD model describing the effect of ustekinumab exposure on fecal calprotectin (fCal), and a logistic regression outcome model linking fCal to endoscopic outcomes. RESULTS: Ustekinumab clearance increased with decreasing serum albumin and increasing bodyweight. fCal decreased with increasing ustekinumab exposure. The probability of endoscopic response at w24 increased from 10.0% to 17.9% with fCal at w8 decreasing from 1800 µg/g to 694 µg/g (EC50 ). The probability of endoscopic remission at w24 increased from 2.1% to 10.0% with fCal at w8 decreasing from 1800 µg/g to 214 µg/g (EC50 ). Simulation-based comparison of q8w and q4w maintenance dosing regimens predicted 16.7% and 22.2% endoscopic response rates, respectively. Endoscopic remission rates were estimated to be 4.2% on q8w dosing and 6.7% on q4w dosing. CONCLUSIONS: The developed models can guide clinical trial design and support model-informed dose optimization (stratified or individualized dosing) to improve endoscopic outcomes.

Decision rules for identifying combination therapies in open-entry, randomized controlled platform trials.

Platform trials have become increasingly popular for drug development programs, attracting interest from statisticians, clinicians and regulatory agencies. Many statistical questions related to designing platform trials-such as the impact of decision rules, sharing of information across cohorts, and allocation ratios on operating characteristics and error rates-remain unanswered. In many platform trials, the definition of error rates is not straightforward as classical error rate concepts are not applicable. For an open-entry, exploratory platform trial design comparing combination therapies to the respective monotherapies and standard-of-care, we define a set of error rates and operating characteristics and then use these to compare a set of design parameters under a range of simulation assumptions. When setting up the simulations, we aimed for realistic trial trajectories, such that for example, a priori we do not know the exact number of treatments that will be included over time in a specific simulation run as this follows a stochastic mechanism. Our results indicate that the method of data sharing, exact specification of decision rules and a priori assumptions regarding the treatment efficacy all strongly contribute to the operating characteristics of the platform trial. Furthermore, different operating characteristics might be of importance to different stakeholders. Together with the potential flexibility and complexity of a platform trial, which also impact the achieved operating characteristics via, for example, the degree of efficiency of data sharing this implies that utmost care needs to be given to evaluation of different assumptions and design parameters at the design stage.

Estimating design operating characteristics in Bayesian adaptive clinical trials.

Bayesian adaptive designs have gained popularity in all phases of clinical trials with numerous new developments in the past few decades. During the COVID-19 pandemic, the need to establish evidence for the effectiveness of vaccines, therapeutic treatments, and policies that could resolve or control the crisis emphasized the advantages offered by efficient and flexible clinical trial designs. In many COVID-19 clinical trials, because of the high level of uncertainty, Bayesian adaptive designs were considered advantageous. Designing Bayesian adaptive trials, however, requires extensive simulation studies that are generally considered challenging, particularly in time-sensitive settings such as a pandemic. In this article, we propose a set of methods for efficient estimation and uncertainty quantification for design operating characteristics of Bayesian adaptive trials. Specifically, we model the sampling distribution of Bayesian probability statements that are commonly used as the basis of decision making. To showcase the implementation and performance of the proposed approach, we use a clinical trial design with an ordinal disease-progression scale endpoint that was popular among COVID-19 trials. However, the proposed methodology may be applied generally in the clinical trial context where design operating characteristics cannot be obtained analytically.

Les plans adaptatifs bayésiens ont gagné en popularité dans toutes les phases d'essais cliniques grâce à d'importants développements réalisés au cours des dernières décennies. Pendant la pandémie COVID­19, la nécessité d'établir des preuves de l'efficacité des vaccins, des traitements thérapeutiques et des politiques susceptibles de résoudre ou de contrôler la crise a mis en évidence les avantages offerts par des plans d'essais cliniques efficaces et flexibles. En raison du niveau élevé d'incertitude présent dans de nombreux essais cliniques COVID­19, les plans adaptatifs bayésiens ont été considérés comme avantageux. Cela dit, la conception d'essais adaptatifs bayésiens nécessite de vastes études de simulation qui sont généralement considérées comme difficiles, en particulier dans des contextes sensibles au facteur temps comme lors d'une pandémie. Les auteurs de cet article proposent un ensemble de méthodes d'estimation efficace et de quantification de l'incertitude pour la conception d'essais adaptatifs bayésiens. En particulier, une modélisation de la distribution d'échantillonnage des énoncés de probabilité bayésienne est proposée. Cette dernière est couramment requise lors de la prise de décisions. Pour illustrer la mise en œuvre et la performance de l'approche proposée, les auteurs ont utilisé un plan d'essai clinique avec un critère d'évaluation ordinal de l'évolution de la maladie, plan relativement populaire dans les essais COVID­19. Aussi, la méthodologie proposée est assez générale pour être appliquée dans le contexte d'essais cliniques dont les caractéristiques opérationnelles du plan correspondant ne peuvent pas être obtenues de manière analytique.

Bayesian adaptive design for clinical trials in Duchenne muscular dystrophy.

A Bayesian adaptive design is proposed for a clinical trial in Duchenne muscular dystrophy. The trial was designed to demonstrate treatment efficacy on an ambulatory-based clinical endpoint and to identify early success on a biomarker (dystrophin protein levels) that can serve as a basis for accelerated approval in the United States. The trial incorporates placebo augmentation using placebo data from past clinical trials. A thorough simulation study was conducted to understand the operating characteristics of the trial. This trial design was selected for the US FDA Complex Innovative Trial Design Pilot Meeting Program and the experience in that program is summarized.

Simulation-based design of pragmatic trials in psoriatic arthritis using propensity scores.

BACKGROUND/AIMS: Design of clinical trials requires careful decision-making across several dimensions, including endpoints, eligibility criteria, and subgroup enrichment. Clinical trial simulation can be an informative tool in trial design, providing empirical evidence by which to evaluate and compare the results of hypothetical trials with varying designs. We introduce a novel simulation-based approach using observational data to inform the design of a future pragmatic trial. METHODS: We utilize propensity score-adjusted models to simulate hypothetical trials under alternative endpoints and enrollment criteria. We apply our approach to the design of pragmatic trials in psoriatic arthritis, using observational data embedded within the Tight Control of Inflammation in Early Psoriatic Arthritis study to simulate hypothetical open-label trials comparing treatment with tumor necrosis factor-α inhibitors to methotrexate. We first validate our simulations of a trial with traditional enrollment criteria and endpoints against a recently published trial. Next, we compare simulated treatment effects in patient populations defined by traditional and broadened enrollment criteria, where the latter is consistent with a future pragmatic trial. In each trial, we also consider five candidate primary endpoints. RESULTS: Our results highlight how changes in the enrolled population and primary endpoints may qualitatively alter study findings and the ability to detect heterogeneous treatment effects between clinical subgroups. For treatments of interest in the study of psoriatic arthritis, broadened enrollment criteria led to diluted estimated treatment effects. Endpoints with greater responsiveness to treatment compared with a traditionally used endpoint were identified. These considerations, among others, are important for designing a future pragmatic trial aimed at having high external validity with relevance for real-world clinical practice. CONCLUSION: Observational data may be leveraged to inform design decisions in pragmatic trials. Our approach may be generalized to the study of other conditions where existing trial data are limited or do not generalize well to real-world clinical practice, but where observational data are available.

Impact of disease progression on individual IPSS trajectories and consequences of immediate versus delayed start of treatment in patients with moderate or severe LUTS associated with BPH.

PURPOSE: Despite superiority of tamsulosin-dutasteride combination therapy versus monotherapy for lower urinary tract symptoms due to benign prostatic hyperplasia (LUTS/BPH), patients at risk of disease progression are often initiated on α-blockers. This study evaluated the impact of initiating tamsulosin monotherapy prior to switching to tamsulosin-dutasteride combination therapy versus immediate combination therapy using a longitudinal model describing International Prostate Symptom Score (IPSS) trajectories in moderate/severe LUTS/BPH patients at risk of disease progression. METHODS: Clinical trial simulations (CTS) were performed using data from 10,238 patients from Phase III/IV dutasteride trials. The effect of varying disease progression rates was explored by comparing profiles on- and off-treatment. CTS scenarios were investigated, including a reference (immediate combination therapy) and six alternative virtual treatment arms (delayed combination therapy of 1-24 months). Clinical response (≥ 25% IPSS reduction relative to baseline) was analysed using log-rank test. Differences in IPSS relative to baseline at various on-treatment time points were assessed by t tests. RESULTS: Delayed combination therapy initiation led to significant (p < 0.01) decreases in clinical response. At month 48, clinical response rate was 79.7% versus 74.1%, 70.3% and 71.0% and IPSS was 6.3 versus 7.6, 8.1 and 8.0 (switchers from tamsulosin monotherapy after 6, 12 and 24 months, respectively) with immediate combination therapy. More patients transitioned from severe/moderate to mild severity scores by month 48. CONCLUSIONS: CTS allows systematic evaluation of immediate versus delayed combination therapy. Immediate response to α-blockers is not predictive of long-term symptom improvement. Observed IPSS differences between immediate and delayed combination therapy (6-24 months) are statistically significant.

Multistate modeling and simulation of patient trajectories after allogeneic hematopoietic stem cell transplantation to inform drug development.

We present a case study for developing clinical trial scenarios in a complex progressive disease with multiple events of interest. The idea is to first capture the course of the disease in a multistate Markov model, and then to simulate clinical trials from this model, including a variety of hypothesized drug effects. This case study focuses on the prevention of graft-versus-host disease (GvHD) after allogeneic hematopoietic stem cell transplantation (HSCT). The patient trajectory after HSCT is characterized by a complex interplay of various events of interest, and there is no established best method of measuring and/or analyzing treatment benefits. We characterized patient trajectories by means of multistate models that we fitted to a subset of the Center for International Blood and Marrow Transplant Research (CIBMTR) database. Events of interest included acute GvHD of grade III or IV, severe chronic GvHD, relapse of the underlying disease, and death. The transition probability matrix was estimated using the Aalen-Johansen estimator, and patient characteristics were identified that were associated with different transition rates. In a second step, clinical trial scenarios were simulated from the model assuming various drug effects on the background transition rates, and the operating characteristics of different endpoints and analysis strategies were compared in these scenarios. This helped devise a drug development strategy in GvHD prevention after allogeneic HSCT. More generally, multistate models provide a rich framework for exploring complex progressive diseases, and the availability of a corresponding simulation machinery provides great flexibility for clinical trial planning.

Assessment of Bayesian expected power via Bayesian bootstrap.

The Bayesian expected power (BEP) has become increasingly popular in assessing the probability of success for a future trial. While the traditional power assumes a single value for the unknown effect size Δ and is thus highly dependent on the assumed value, the BEP embraces the uncertainty around Δ given the prior information and is therefore a less subjective measure for the probability of success than the traditional power especially when the prior information is not rich. Current methods for assessing BEP are often based in a parametric framework by imposing a model on the pilot data to derive and sample from the posterior distributions of Δ. The model-based approach can be analytically challenging and computationally costly especially for multivariate data sets, and it also runs the risk of generating misleading BEP if the model is misspecified. We propose an approach based on the Bayesian bootstrap (BBS) technique to simulate future trials in the presence of individual-level pilot data, based on which the empirical BEP can be calculated. The BBS approach is model-free with no assumptions about the distribution of the prior data and also circumvents the analytical and computational complexity associated with obtaining the posterior distribution of the Δ. Information from multiple pilot studies is also straightforward to combine. We also propose the double bootstrap technique, a frequentist counterpart to the BBS, that shares similar properties and achieves the same goal as the BBS for BEP assessment. Simulation and case studies are presented to demonstrate the implementation of the BBS technique and the double bootstrap technique and to compare the BEP results with model-based approach.

Leveraging model-informed approaches for drug discovery and development in the cardiovascular space.

Cardiovascular disease remains a significant global health burden, and development of cardiovascular drugs in the current regulatory environment often demands large and expensive cardiovascular outcome trials. Thus, the use of quantitative pharmacometric approaches which can help enable early Go/No Go decision making, ensure appropriate dose selection, and increase the likelihood of successful clinical trials, have become increasingly important to help reduce the risk of failed cardiovascular outcomes studies. In addition, cardiovascular safety is an important consideration for many drug development programs, whether or not the drug is designed to treat cardiovascular disease; modeling and simulation approaches also have utility in assessing risk in this area. Herein, examples of modeling and simulation applied at various stages of drug development, spanning from the discovery stage through late-stage clinical development, for cardiovascular programs are presented. Examples of how modeling approaches have been utilized in early development programs across various therapeutic areas to help inform strategies to mitigate the risk of cardiovascular-related adverse events, such as QTc prolongation and changes in blood pressure, are also presented. These examples demonstrate how more informed drug development decisions can be enabled by modeling and simulation approaches in the cardiovascular area.

A big data approach to the development of mixed-effects models for seizure count data.

OBJECTIVE: Our objective was to develop a generalized linear mixed model for predicting seizure count that is useful in the design and analysis of clinical trials. This model also may benefit the design and interpretation of seizure-recording paradigms. Most existing seizure count models do not include children, and there is currently no consensus regarding the most suitable model that can be applied to children and adults. Therefore, an additional objective was to develop a model that accounts for both adult and pediatric epilepsy. METHODS: Using data from SeizureTracker.com, a patient-reported seizure diary tool with >1.2 million recorded seizures across 8 years, we evaluated the appropriateness of Poisson, negative binomial, zero-inflated negative binomial, and modified negative binomial models for seizure count data based on minimization of the Bayesian information criterion. Generalized linear mixed-effects models were used to account for demographic and etiologic covariates and for autocorrelation structure. Holdout cross-validation was used to evaluate predictive accuracy in simulating seizure frequencies. RESULTS: For both adults and children, we found that a negative binomial model with autocorrelation over 1 day was optimal. Using holdout cross-validation, the proposed model was found to provide accurate simulation of seizure counts for patients with up to four seizures per day. SIGNIFICANCE: The optimal model can be used to generate more realistic simulated patient data with very few input parameters. The availability of a parsimonious, realistic virtual patient model can be of great utility in simulations of phase II/III clinical trials, epilepsy monitoring units, outpatient biosensors, and mobile Health (mHealth) applications.

In Silico Modeling Approach for the Evaluation of Gastrointestinal Dissolution, Supersaturation, and Precipitation of Posaconazole.

The aim of this study was to evaluate gastrointestinal (GI) dissolution, supersaturation, and precipitation of posaconazole, formulated as an acidified (pH 1.6) and neutral (pH 7.1) suspension. A physiologically based pharmacokinetic (PBPK) modeling and simulation tool was applied to simulate GI and systemic concentration-time profiles of posaconazole, which were directly compared with intraluminal and systemic data measured in humans. The Advanced Dissolution Absorption and Metabolism (ADAM) model of the Simcyp Simulator correctly simulated incomplete gastric dissolution and saturated duodenal concentrations of posaconazole in the duodenal fluids following administration of the neutral suspension. In contrast, gastric dissolution was approximately 2-fold higher after administration of the acidified suspension, which resulted in supersaturated concentrations of posaconazole upon transfer to the upper small intestine. The precipitation kinetics of posaconazole were described by two precipitation rate constants, extracted by semimechanistic modeling of a two-stage medium change in vitro dissolution test. The 2-fold difference in exposure in the duodenal compartment for the two formulations corresponded with a 2-fold difference in systemic exposure. This study demonstrated for the first time predictive in silico simulations of GI dissolution, supersaturation, and precipitation for a weakly basic compound in part informed by modeling of in vitro dissolution experiments and validated via clinical measurements in both GI fluids and plasma. Sensitivity analysis with the PBPK model indicated that the critical supersaturation ratio (CSR) and second precipitation rate constant (sPRC) are important parameters of the model. Due to the limitations of the two-stage medium change experiment the CSR was extracted directly from the clinical data. However, in vitro experiments with the BioGIT transfer system performed after completion of the in silico modeling provided an almost identical CSR to the clinical study value; this had no significant impact on the PBPK model predictions.

Analysis of opioid consumption in clinical trials: a simulation based analysis of power of four approaches.

Inconsistent trial design and analysis is a key reason that few advances in postoperative pain management have been made from clinical trials analyzing opioid consumption data. This study aimed to compare four different approaches to analyze opioid consumption data. A repeated time-to-event (RTTE) model in NONMEM was used to simulate clinical trials of morphine consumption with and without a hypothetical adjuvant analgesic in doses equivalent to 15-62% reduction in morphine consumption. Trials were simulated with duration of 24-96 h. Monte Carlo simulation and re-estimation were performed to determine sample size required to demonstrate efficacy with 80% power using t test, Mann-Whitney rank sum test, time-to-event (TTE) modeling and RTTE modeling. Precision of efficacy estimates for RTTE models were evaluated in 500 simulations. A sample size of 50 patients was required to detect 37% morphine sparing effect with at least 80% power in a 24 h trial with RTTE modeling whereas the required sample size was 200 for Mann-Whitney, 180 for t-test and 76 for TTE models. Extending the trial duration from 24 to 96 h reduced the required sample size by 3.1 fold with RTTE modeling. Precise estimate of potency was obtained with a RTTE model accounting for both morphine effects and time-varying covariates on opioid consumption. An RTTE analysis approach proved better suited for demonstrating efficacy of opioid sparing analgesics than traditional statistical tests as a lower sample size was required due the ability to account for time-varying factors including PK.

Modeling and simulation of maintenance treatment in first-line non-small cell lung cancer with external validation.

BACKGROUND: Maintenance treatment (MTx) in responders following first-line treatment has been investigated and practiced for many cancers. Modeling and simulation may support interpretation of interim data and development decisions. We aimed to develop a modeling framework to simulate overall survival (OS) for MTx in NSCLC using tumor growth inhibition (TGI) data. METHODS: TGI metrics were estimated using longitudinal tumor size data from two Phase III first-line NSCLC studies evaluating bevacizumab and erlotinib as MTx in 1632 patients. Baseline prognostic factors and TGI metric estimates were assessed in multivariate parametric models to predict OS. The OS model was externally validated by simulating a third independent NSCLC study (n = 253) based on interim TGI data (up to progression-free survival database lock). The third study evaluated pemetrexed + bevacizumab vs. bevacizumab alone as MTx. RESULTS: Time-to-tumor-growth (TTG) was the best TGI metric to predict OS. TTG, baseline tumor size, ECOG score, Asian ethnicity, age, and gender were significant covariates in the final OS model. The OS model was qualified by simulating OS distributions and hazard ratios (HR) in the two studies used for model-building. Simulations of the third independent study based on interim TGI data showed that pemetrexed + bevacizumab MTx was unlikely to significantly prolong OS vs. bevacizumab alone given the current sample size (predicted HR: 0.81; 95 % prediction interval: 0.59-1.09). Predicted median OS was 17.3 months and 14.7 months in both arms, respectively. These simulations are consistent with the results of the final OS analysis published 2 years later (observed HR: 0.87; 95 % confidence interval: 0.63-1.21). Final observed median OS was 17.1 months and 13.2 months in both arms, respectively, consistent with our predictions. CONCLUSIONS: A robust TGI-OS model was developed for MTx in NSCLC. TTG captures treatment effect. The model successfully predicted the OS outcomes of an independent study based on interim TGI data and thus may facilitate trial simulation and interpretation of interim data. The model was built based on erlotinib data and externally validated using pemetrexed data, suggesting that TGI-OS models may be treatment-independent. The results supported the use of longitudinal tumor size and TTG as endpoints in early clinical oncology studies.

Gastrointestinal Motility Variation and Implications for Plasma Level Variation: Oral Drug Products.

The oral route of administration is still by far the most ubiquitous method of drug delivery. Development in this area still faces many challenges due to the complexity and inhomogeneity of the gastrointestinal environment. In particular, dosing unpredictably relative to motility phase means the gastrointestinal environment is a random variable within a defined range. Here, we present a mass balance analysis that captures this variation and highlights the effects of gastrointestinal motility, exploring what impacts it ultimately has on plasma levels and the relationship to bioequivalence for high solubility products with both high and low permeability (BCS I and III). Motility-dependent compartmental absorption and transit (MDCAT) mechanistic analysis is developed to describe the underlying fasted state cyclical motility and how the contents of the gastrointestinal tract are propelled.

Model-based clinical drug development in the past, present and future: a commentary.

Clinical drug development remains a mostly empirical, costly enterprise, in which decision-making is often based on qualitative assessment of risk, without properly leveraging all the relevant data collected throughout the development programme. Model-based drug development (MBDD) has been proposed by regulatory agencies, academia and pharmaceutical companies as a paradigm to modernize drug research through the quantification of risk and combination of information from different sources across time. We present here a historical account of the use of MBDD in clinical drug development, the current challenges and further opportunities for its application in the pharmaceutical industry.

Design of a drug-drug interaction study of vincristine with azole antifungals in pediatric cancer patients using clinical trial simulation.

BACKGROUND: The aim of the current work was to perform a clinical trial simulation (CTS) analysis to optimize a drug-drug interaction (DDI) study of vincristine in children who also received azole antifungals, taking into account challenges of conducting clinical trials in this population, and, to provide a motivating example of the application of CTS in the design of pediatric oncology clinical trials. PROCEDURE: A pharmacokinetic (PK) model for vincristine in children was used to simulate concentration-time profiles. A continuous model for body surface area versus age was defined based on pediatric growth curves. Informative sampling time windows were derived using D-optimal design. The CTS framework was used to different magnitudes of clearance inhibition (10%, 25%, or 40%), sample size (30-500), the impact of missing samples or sampling occasions, and the age distribution, on the power to detect a significant inhibition effect, and in addition, the relative estimation error (REE) of the interaction effect. RESULTS: A minimum group specific sample size of 38 patients with a total sample size of 150 patients was required to detect a clearance inhibition effect of 40% with 80% power, while in the case of a lower effect of clearance inhibition, a substantially larger sample size was required. However, for the majority of re-estimated drug effects, the inhibition effect could be estimated precisely (REE < 25%) in even smaller sample sizes and with lower effect sizes. CONCLUSION: This work demonstrated the utility of CTS for the evaluation of PK clinical trial designs in the pediatric oncology population.