Clinical Trial Simulation Research Articles

A two-step deconvolution-analysis-informed population pharmacodynamic modeling approach for drugs targeting pulsatile endogenous compounds

Pharmacodynamic modeling of pulsatile endogenous compounds (e.g. growth hormone [GH]) is currently limited to the identification of a low number of pulses. Commonly used pharmacodynamic models are not able to capture the complexity of pulsatile secretion and therefore non-compartmental analyses are performed to extract summary statistics (mean, AUC, Cmax). The aim of this study was to develop a new quantification method that deals with highly variable pulsatile data by using a deconvolution-analysis-informed population pharmacodynamic modeling approach. Pulse frequency and pulse times were obtained by deconvolution analysis of 24 h GH profiles. The estimated pulse times then informed a non-linear mixed effects population pharmacodynamic model in NONMEM V7.3. The population parameter estimates were used to perform simulations that show agonistic and antagonistic drug effects on the secretion of GH. Additionally, a clinical trial simulation shows the application of this method in the quantification of a hypothetical drug effect that inhibits GH secretion. The GH profiles were modeled using a turnover compartment in which the baseline secretion, kout, pulse secretion width, amount at time point 0 and pulse amplitude were estimated as population parameters. Population parameters were estimated with low relative standard errors (ranging from 2 to 5%). Total body water (%) was identified as a covariate for pulse amplitude, baseline secretion and the pulse secretion width following a power relationship. Simulations visualized multiple gradients of a hypothetical drug that influenced the endogenous secretion of GH. The established model was able to fit and quantify the highly variable individual 24 h GH profiles over time. This pharmacodynamic model can be used to quantify drug effects that target other endogenous pulsatile compounds.

A pharmacodynamic modelling and simulation study identifying gender differences of daily olanzapine dose and dopamine D2-receptor occupancy

Background: Gender differences in treatment response rates for patients treated with antipsychotics are known. However, the literature lacks a pharmacodynamic model to allow for gender-based clinical trial simulations from modelling parameters for Olanzapine and dopamine D2 receptor occupancy. Thus, the primary aim of this analysis is to test and quantify the effect of gender on the pharmacodynamics of Olanzapine.Methods: Population pharmacodynamic modelling was performed using non-linear mixed effects modelling in MONOLIX, while the Clinical Trial Simulations were performed using R for statistical programming. The pharmacometric analysis is based on a pooled data approach from three clinical studies where patients were diagnosed with schizophrenia and one clinical study where the patients were diagnosed with bipolar disorder.Results: Olanzapine D2RO was modelled using an Emax model in a study population of 70 patients. Population pharmacodynamic parameters were estimated to be: Emax = 85.6% (RSE = 3%), ED50-Men = 5.15 mg/day (RSE = 14) and ED50-Women = 2.38 mg/day (RSE = 34%), with the p-value = 0.037 for the gender-stratified ED50 results.Conclusion: The pharmacometrics analysis and model-based dosing simulations suggest that, in order to achieve 70% D2RO, women require a 10 mg/day dose and men require approximately a 20 mg/day dose of Olanzapine. Further, clinical implications exist suggesting that clinicians should factor patient gender when considering both a starting dose, as well a, a maintenance dose for patients prescribed Olanzapine due to quantifiable gender-differences of striatal dopamine D2 receptor occupancy.

The Role of Computational Modeling and Simulation in the Total Product Life Cycle of Peripheral Vascular Devices.

The total product life cycle (TPLC) of medical devices has been defined by four stages: discovery and ideation, regulatory decision, product launch, and postmarket monitoring. Manufacturers of medical devices intended for use in the peripheral vasculature, such as stents, inferior vena cava (IVC) filters, and stent-grafts, mainly use computational modeling and simulation (CM&S) to aid device development and design optimization, supplement bench testing for regulatory decisions, and assess postmarket changes or failures. For example, computational solid mechanics and fluid dynamics enable the investigation of design limitations in the ideation stage. To supplement bench data in regulatory submissions, manufactures can evaluate the effects of anatomical characteristics and expected in vivo loading environment on device performance. Manufacturers might also harness CM&S to aid root-cause analyses that are necessary when failures occur postmarket, when the device is exposed to broad clinical use. Once identified, CM&S tools can then be used for redesign to address the failure mode and re-establish the performance profile with the appropriate models. The Center for Devices and Radiological Health (CDRH) wants to advance the use of CM&S for medical devices and supports the development of virtual physiological patients, clinical trial simulations, and personalized medicine. Thus, the purpose of this paper is to describe specific examples of how CM&S is currently used to support regulatory submissions at different phases of the TPLC and to present some of the stakeholder-led initiatives for advancing CM&S for regulatory decision-making.

Application of a PK-PD Modeling and Simulation-Based Strategy for Clinical Translation of Antibody-Drug Conjugates: a Case Study with Trastuzumab Emtansine (T-DM1).

Successful clinical translation of antibody-drug conjugates (ADCs) can be challenging due to complex pharmacokinetics and differences between preclinical and clinical tumors. To facilitate this translation, we have developed a general pharmacokinetic-pharmacodynamic (PK-PD) modeling and simulation (M&S)-based strategy for ADCs. Here we present the validation of this strategy using T-DM1 as a case study. A previously developed preclinical tumor disposition model for T-DM1 (Singh and Shah, AAPSJ. 2015; 18(4):861-875) was used to develop a PK-PD model that can characterize in vivo efficacy of T-DM1 in preclinical tumor models. The preclinical data was used to estimate the efficacy parameters for T-DM1. Human PK of T-DM1 was a priori predicted using allometric scaling of monkey PK parameters. The predicted human PK, preclinically estimated efficacy parameters, and clinically observed volume and growth parameters for breast cancer were combined to develop a translated clinical PK-PD model for T-DM1. Clinical trial simulations were performed using the translated PK-PD model to predict progression-free survival (PFS) and objective response rates (ORRs) for T-DM1. The model simulated PFS rates for HER2 1+ and 3+ populations were comparable to the rates observed in three different clinical trials. The model predicted only a modest improvement in ORR with an increase in clinically approved dose of T-DM1. However, the model suggested that a fractionated dosing regimen (e.g., front loading) may provide an improvement in the efficacy. In general, the PK-PD M&S-based strategy presented here is capable of a priori predicting the clinical efficacy of ADCs, and this strategy has been now retrospectively validated for all clinically approved ADCs.

Characterisation of noise and sharpness of images from four digital breast tomosynthesis systems for simulation of images for virtual clinical trials

In-depth evaluation of the noise and sharpness characteristics of FujiFilm Innovality, GE SenoClaire, Hologic Selenia Dimensions and Siemens Inspiration digital breast tomosynthesis (DBT) systems was performed with the intention of improving image simulation for virtual clinical trials. Noise power spectra (NPS) and modulation transfer function curves (MTF) were measured for planar modes and for the first and central projections for DBT modes. In DBT mode, the x-ray beam was blocked for the projections before the central projection in order to remove the influence of lag and ghosting from the previous images. A quadratic fit between the NPS and linearised pixel value gave the noise coefficients for planar and DBT imaging modes. The spatial frequencies corresponding to an MTF of 0.5 (MTF0.5) were calculated from the MTF measurements made on the breast support and at 40 mm above the breast support. This was done for the first and the central projections. The percentage of signal carried over from the first projection to subsequent images (lag) was measured using a slit. The noise associated with lag was also evaluated. The DBT modes typically had lower electronic noise coefficients but higher structural noise coefficients compared to the respective planar mode MTF0.5 measured 40 mm above the table was between 6% and 47% lower for continuous scanning systems compared to 1% lower for step and shoot systems. For wide angle DBT, the MTF0.5 of the first projection was 18% (FujiFilm) and 28% (Siemens) lower than for the central projection. Lag in the second projection was 2.2%, 0.3%, 0.8% for the FujiFilm, GE and Hologic systems respectively. In all cases, the noise associated with lag was negligible. Current modelling frameworks for virtual clinical trials of breast DBT systems need to be adapted to account for signals from lag and variations in the MTF at wide angles.

Clinical Trial Simulations and Pharmacometric Analysis in Pediatrics: Application to Inhaled Loxapine in Children and Adolescents

Loxapine for inhalation is a drug-device combination product approved in adults for the acute treatment of agitation associated with schizophrenia or bipolar I disorder. The primary objective of this study was to develop a clinical trial protocol to support a phase I pharmacokinetic study in children aged 10years and older. In addition, this report details the results of the clinical study in relation to the predicted likelihood of achieving the target exposure associated with therapeutic effect in adults. A nonlinear mixed-effects population pharmacokinetic model was developed using adult data and was adjusted for the targeted pediatric age groups by applying allometric scaling to account for body size effects. Based on this pediatric model, age-appropriate regimens to achieve loxapine exposures similar to the ones associated with therapeutic effect in the adult studies were identified via trial simulation. D-optimal design and power analysis were conducted to identify optimal pharmacokinetic sampling times and sample size, respectively. The developed clinical trial design formed the basis of a phase I study to assess the safety and pharmacokinetics of loxapine for inhalation in children aged 10years and older (ClinicalTrials.gov ID: NCT02184767). The results of the study indicated that overall loxapine exposures were consistent with what had been predicted by the trial simulations. The presented approach illustrates how modeling and simulation can assist in the design of informative clinical trials to identify safe and effective doses and dose ranges in children and adolescents.

Characterizing QT interval prolongation in early clinical development: a case study with methadone.

Recently, we have shown how pharmacokinetic–pharmacodynamic (PKPD) modeling can be used to assess the probability of QT interval prolongation both in dogs and humans. A correlation between species has been identified for a drug‐specific parameter, making it possible to prospectively evaluate nonclinical signals. Here, we illustrate how nonclinical data on methadone can be used to support the evaluation of dromotropic drug effects in humans. ECG and drug concentration data from a safety pharmacology study in dogs were analyzed using nonlinear mixed effects modeling. The slope of the PKPD model describing the probability of QT interval prolongation was extrapolated from dogs to humans and subsequently combined with methadone pharmacokinetic data as input for clinical trial simulations. Concentration versus time profiles were simulated for doses between 5 and 500 mg. Predicted peak concentrations in humans were then used as reference value to assess the probability of an increase in QT interval of ≥5 and ≥10 ms. Point estimates for the slope in dogs suggested low probability of ≥10 ms prolongation in humans, whereas an effect of approximately 5 ms increase is predicted when accounting for the 90% credible intervals of the drug‐specific parameter in dogs. Interspecies differences in drug disposition appear to explain the discrepancies between predicted and observed QT prolonging effects in humans. Extrapolation of the effects of racemic compound may not be sufficient to describe the increase in QT interval observed after administration of methadone to patients. Assessment of the contribution of enantioselective metabolism and active metabolites is critical.

Markov Mixed Effects Modeling Using Electronic Adherence Monitoring Records Identifies Influential Covariates to HIV Preexposure Prophylaxis

Adherence is a major factor in the effectiveness of preexposure prophylaxis (PrEP) for HIV prevention. Modeling patterns of adherence helps to identify influential covariates of different types of adherence as well as to enable clinical trial simulation so that appropriate interventions can be developed. We developed a Markov mixed-effects model to understand the covariates influencing adherence patterns to daily oral PrEP. Electronic adherence records (date and time of medication bottle cap opening) from the Partners PrEP ancillary adherence study with a total of 1147 subjects were used. This study included once-daily dosing regimens of placebo, oral tenofovir disoproxil fumarate (TDF), and TDF in combination with emtricitabine (FTC), administered to HIV-uninfected members of serodiscordant couples. One-coin and first- to third-order Markov models were fit to the data using NONMEM® 7.2. Model selection criteria included objective function value (OFV), Akaike information criterion (AIC), visual predictive checks, and posterior predictive checks. Covariates were included based on forward addition (α= 0.05) and backward elimination (α= 0.001). Markov models better described the data than 1-coin models. A third-order Markov model gave the lowest OFV and AIC, but the simpler first-order model was used for covariate model building because no additional benefit on prediction of target measures was observed for higher-order models. Female sex and older age had a positive impact on adherence, whereas Sundays, sexual abstinence, and sex with a partner other than the study partner had a negative impact on adherence. Our findings suggest adherence interventions should consider the role of these factors.

A Genomic Rule Predicting HMA Treatment Response in MDS Identified By Protein Network Mapping and Validated By Clinical Trial Simulation

Background: Hypomethyating agents (HMAs) for the treatment of the myelodysplastic syndromes (MDS) fail to achieve clinical improvement in nearly 60% of patients. We previously reported a genomics-informed computational method that had >80% accuracy in predicting HMA treatment response in higher risk MDS patients.Aim: To define novel genomic signature rules to predict MDS response to HMA therapy and validate the rules in a clinical trial simulation.Methods: We analyzed a cohort of 213 high-risk MDS patients treated with an HMA and assessed for disease response by IWG criteria (Bejar, et al. Blood 2014). Bone marrow cells from these MDS patients were examined by conventional cytogenetics and next generation sequencing of target myeloid genes. For each patient, every available genomic abnormality was entered into a computational biology program (Cellworks Group) that uses PubMed and other online resources to generate patient-specific protein network maps of activated and inactivated protein pathways. Digital drug simulations with HMAs were conducted by quantitatively measuring drug effect on a composite MDS disease inhibition score (i.e., cell proliferation, viability, and apoptosis). This information was then used to create virtual Kaplan Meier-type estimates plotting the percent of responders (Y axis) to their disease inhibition score (X axis). A database was developed to house the clinical, genomic, and demographic information for each patient, and used to run clinical trial simulations.Results: A training cohort of 80 MDS patients was used to identify genomic signature rules capable of predicting clinical response to HMA treatment. The 3 rules identified were (1) null hypothesis, no rule for selection of MDS patients for HMA response; (2) TET2 mutation alone, which is known to influence response to HMA in MDS; and (3) TET2 function exclusion, defined as TET2 mutation without the presence of ASXL1 mutation, harboring wild-type SRSF2, and wild-type EZH2. Next, we used these rules in a clinical trial simulation involving 109 different patients in the test dataset. 14 of 203 patients lacked adequate genomic or clinical data for the virtual trial. The 3 rules identified in the training set served as inclusion criteria for 3 HMA treatment arms. The data enabled us to generate the predictive equivalent to a Kaplan-Meier estimate for each arm. Using rule (1) as a control treatment arm, we used rule (2) as an inclusion criterion for a second treatment arm and found a non-significant probability that a randomly selected patient obtained a better MDS disease inhibition score using the rule (2) inclusion criterion (P=0.08) (Figure 1). When rule (3) was used as inclusion criteria, the probability that a randomly selected patient obtained a better MDS disease inhibition score was significantly increased (P=0.0023) (Figure 1).Conclusions: In this study we identified a new four-gene rule predicting MDS response to HMA treatment. We also present a novel method of clinical trial simulation to validate biomarker rules. This unique computer-based approach is intended to inform the design of phase 2 and 3 clinical trials with the ultimate goal of improving drug development time and expense by establishing and validating inclusion criteria for precision enrollment. [Display omitted] DisclosuresKumar Singh:Cellworks: Employment. Tiwari:Cellworks: Employment. Vali:Cellworks Group: Employment. Abbasi:Cellworks: Employment. Bejar:Celgene: Consultancy, Honoraria; Genoptix: Consultancy, Honoraria, Patents & Royalties: No royalties; Foundation Medicine: Consultancy.

Critical Path to Tuberculosis Drug Regimens: Global collaboration to accelerate development of novel drug regimens and rapid drug susceptibility tests for tuberculosis

The Critical Path to Tuberculosis Drug Regimens (CPTR) initiative aims to support the rational deployment of new tuberculosis (TB) therapies by speeding the development and impact of new and markedly improved drug regimens as well as rapid drug susceptibility tests. Co-founded by the Bill & Melinda Gates Foundation, the Critical Path Institute, and the TB Alliance in 2010, CPTR is a coalition comprising the world’s leading pharmaceutical companies, product development sponsors, diagnostic companies, regulatory agencies, and civil society organizations which support and catalyze advances in regulatory science, the development of infrastructure, and other progress needed to accelerate the pace of development and introduction of novel regimens and rapid drug susceptibility tests. This manuscript summarizes the work of two subgroups within CPTR, the Regulatory Sciences and Rapid Drug Susceptibility Test consortia, and their efforts to drive innovation. These consortia are supported by a robust TB clinical data platform, which continues to evolve through contributions of contemporary TB clinical trial data sets as well as whole genome sequence level data from isolates across the globe. Examples of innovation are described and include a recently-qualified drug development tool and emerging programs to support the development of clinical trial simulation tools.

Stratification, Hypothesis Testing, and Clinical Trial Simulation in Pediatric Drug Development.

Pediatric drug development is plagued by small sample sizes, unvalidated clinical endpoints, and limited studies. The objective of this study was to determine whether age stratification within the pediatric population could be used to (1) assess response to a pharmacologic intervention and to (2) design future trials based upon published stratified disease data using clinical trial simulation (CTS). Data available from the literature for Kawasaki disease (KD) was used in the model. Age-stratified CTS for a theoretical new drug was conducted. Population-specific differences due to age might affect trial success if not taken into account. CTS predicted inflammatory indices, and inclusion cutoff significantly altered the trial outcome. Finally, altered pharmacokinetics/pharmacodynamics in varying age groups of KD patients may alter drug exposure and response. If assumptions regarding a pediatric disease process, such as KD, do not include age stratification with inclusion or response, then the wrong decision could result with regard to age-appropriateness or approval of a drug.

Translating QT interval prolongation from conscious dogs to humans.

In spite of screening procedures in early drug development, uncertainty remains about the propensity of new chemical entities (NCEs) to prolong the QT/QTc interval. The evaluation of proarrhythmic activity using a comprehensive in vitro proarrhythmia assay does not fully account for pharmacokinetic-pharmacodynamic (PKPD) differences in vivo. In the present study, we evaluated the correlation between drug-specific parameters describing QT interval prolongation in dogs and in humans. Using estimates of the drug-specific parameter, data on the slopes of the PKPD relationships of nine compounds with varying QT-prolonging effects (cisapride, sotalol, moxifloxacin, carabersat, GSK945237, SB237376 and GSK618334, and two anonymized NCEs) were analysed. Mean slope estimates varied between -0.98ms μM-1 and 6.1ms μM-1 in dogs and -10ms μM-1 and 90ms μM-1 in humans, indicating a wide range of effects on the QT interval. Linear regression techniques were then applied to characterize the correlation between the parameter estimates across species. For compounds without a mixed ion channel block, a correlation was observed between the drug-specific parameter in dogs and humans (y=-1.709+11.6x; R2 =0.989). These results show that per unit concentration, the drug effect on the QT interval in humans is 11.6-fold larger than in dogs. Together with information about the expected therapeutic exposure, the evidence of a correlation between the compound-specific parameter in dogs and in humans represents an opportunity for translating preclinical safety data before progression into the clinic. Whereas further investigation is required to establish the generalizability of our findings, this approach can be used with clinical trial simulations to predict the probability of QT prolongation in humans.

A Combination Regimen Design Program Based on Pharmacodynamic Target Setting for Childhood Tuberculosis: Design Rules for the Playground.

Children with tuberculosis are treated with drug regimens copied from adults despite significant differences in antibiotic pharmacokinetics, pathology, and the microbial burden between childhood and adult tuberculosis. We sought to develop a new and effective oral treatment regimen specific to children of different ages. We investigated and validated the concept that target drug concentrations associated with therapy failure and death in children are different from those of adults. On that basis, we proposed a 4-step program to rapidly develop treatment regimens for children. First, target drug concentrations for optimal efficacy are derived from preclinical models of disseminated tuberculosis that recapitulate pediatric pharmacokinetics, starting with monotherapy. Second, 2-drug combinations were examined for zones of synergy, antagonism, and additivity based on a whole exposure–response surface. Exposures associated with additivity or synergy were then combined and the regimen was compared to standard therapy. Third, several exposures of the third drug were added, and a 3-drug regimen was identified based on kill slopes in comparison to standard therapy. Fourth, computer-aided clinical trial simulations are used to identify clinical doses that achieve these kill rates in children in different age groups. The proposed program led to the development of a 3-drug combination regimen for children from scratch, independent of adult regimens, in <2 years. The regimens and doses can be tested in animal models and in clinical trials.

Model-Based Assessment of Alternative Study Designs in Pediatric Trials. Part II: Bayesian Approaches.

This study presents a pharmacokinetic‐pharmacodynamic based clinical trial simulation framework for evaluating the performance of a fixed‐sample Bayesian design (BD) and two alternative Bayesian sequential designs (BSDs) (i.e., a non‐hierarchical (NON‐H) and a semi‐hierarchical (SEMI‐H) one). Prior information was elicited from adult trials and weighted based on the expected similarity of response to treatment between the pediatric and adult populations. Study designs were evaluated in terms of: type I and II errors, sample size per arm (SS), trial duration (TD), and estimate precision. No substantial differences were observed between NON‐H and SEMI‐H. BSDs require, on average, smaller SS and TD compared to the BD, which, on the other hand, guarantees higher estimate precision. When large differences between children and adults are expected, BSDs can return very large SS. Bayesian approaches appear to outperform their frequentist counterparts in the design of pediatric trials even when little weight is given to prior information from adults.

Critical Path to Tuberculosis Drug Regimens: Global collaboration to accelerate development of novel drug regimens and rapid drug susceptibility tests for tuberculosis

The Critical Path to Tuberculosis Drug Regimens (CPTR) initiative aims to support the rational deployment of new tuberculosis (TB) therapies by speeding the development and impact of new and markedly improved drug regimens as well as rapid drug susceptibility tests. Co-founded by the Bill &amp; Melinda Gates Foundation, the Critical Path Institute, and the TB Alliance in 2010, CPTR is a coalition comprising the world’s leading pharmaceutical companies, product development sponsors, diagnostic companies, regulatory agencies, and civil society organizations which support and catalyze advances in regulatory science, the development of infrastructure, and other progress needed to accelerate the pace of development and introduction of novel regimens and rapid drug susceptibility tests. This manuscript summarizes the work of two subgroups within CPTR, the Regulatory Sciences and Rapid Drug Susceptibility Test consortia, and their efforts to drive innovation. These consortia are supported by a robust TB clinical data platform, which continues to evolve through contributions of contemporary TB clinical trial data sets as well as whole genome sequence level data from isolates across the globe. Examples of innovation are described and include a recently-qualified drug development tool and emerging programs to support the development of clinical trial simulation tools.

Abstract CT112: Exposure-response analysis of pembrolizumab in patients with advanced melanoma and non-small cell lung cancer enrolled in KEYNOTE-001, -002, and -006

Abstract Background: The anti-PD-1 monoclonal antibody pembrolizumab has demonstrated durable antitumor activity against several advanced malignancies and is generally well tolerated. In the KEYNOTE-001, -002, and -006 trials, patients with advanced/metastatic melanoma and non-small cell lung cancer (NSCLC) were given pembrolizumab doses ranging from 2 mg/kg Q3W to 10 mg/kg Q2W. The objective of this current analysis was to explore the relationship between exposure to pembrolizumab and safety/efficacy in patients with advanced melanoma or NSCLC to help select an appropriate therapeutic dose. Methods: Exposure-response analyses (using graphical exploration and nonlinear mixed effects modeling) of tumor size data were used to characterize the relationship between pembrolizumab exposure and tumor size reduction. Data for these analyses were derived from 1366 patients with melanoma and 496 with NSCLC; exposure-response analyses were performed separately for each indication. Tumor size, defined as the sum of longest dimensions (SLD) of target lesions, was the response readout and individual steady-state area under the curve (AUC) estimates from a population PK model were used as an integrated exposure measure across all concentration data for each patient. Additionally, exposure-adverse event (AE) logistic regression was performed on the integrated dataset, focusing on AEs of special interest (AEOSIs), defined as a broad category of potentially immune-related AEs. Results: The graphical analysis for both melanoma (stratified by IPI pretreatment status) and NSCLC (stratified by PD-L1 expression status) identified an almost flat relationship between exposure and change in tumor size from baseline at 24 wk (melanoma) or 18 wk (NSCLC), with substantial overlap in confidence intervals. In agreement with the exploratory graphical analyses, individual pembrolizumab exposures showed a small and statistically insignificant influence on the final model estimated tumor decay parameter for both melanoma (slope = 0.131, P = 0.20 for IPI-naive; and slope = 0.1, P = 0.25 for IPI-experienced), and NSCLC (slope = 0.196, P = 0.54) patients. Clinical trial simulations, using the tumor size model to normalize for covariates, predicted little variation and considerable overlap in the confidence intervals for response across the dose regimens studied for both populations, confirming lack of clinically meaningful differences between 2 mg/kg and 10 mg/kg. The exposure-AEOSI analysis did not identify exposure to pembrolizumab as a significant predictor of the occurrence of AEOSIs. Conclusions: The exposure range associated with 2 mg/kg Q3W dose provides near maximal efficacy with acceptable tolerability, and thus there is no clear benefit to higher dosing. The findings of this analysis support a common regimen of 2 mg/kg Q3W for pembrolizumab in melanoma and NSCLC. Citation Format: Manash S. Chatterjee, David C. Turner, Malidi Ahamadi, Rik de Greef, Tomoko Freshwater, Kapil Mayawala, David Dong, Julie Stone, Dinesh de Alwis, Anna Kondic. Exposure-response analysis of pembrolizumab in patients with advanced melanoma and non-small cell lung cancer enrolled in KEYNOTE-001, -002, and -006. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr CT112.

LIMITATIONS OF THE FDA-APPROVED ‘AD CLINICAL TRIAL SIMULATION TOOL’

LIMITATIONS OF THE FDA-APPROVED ‘AD CLINICAL TRIAL SIMULATION TOOL’

Using Clinical Trial Simulators to Analyse the Sources of Variance in Clinical Trials of Novel Therapies for Acute Viral Infections.

BackgroundAbout 90% of drugs fail in clinical development. The question is whether trials fail because of insufficient efficacy of the new treatment, or rather because of poor trial design that is unable to detect the true efficacy. The variance of the measured endpoints is a major, largely underestimated source of uncertainty in clinical trial design, particularly in acute viral infections. We use a clinical trial simulator to demonstrate how a thorough consideration of the variability inherent in clinical trials of novel therapies for acute viral infections can improve trial design.Methods and FindingsWe developed a clinical trial simulator to analyse the impact of three different types of variation on the outcome of a challenge study of influenza treatments for infected patients, including individual patient variability in the response to the drug, the variance of the measurement procedure, and the variance of the lower limit of quantification of endpoint measurements. In addition, we investigated the impact of protocol variation on clinical trial outcome. We found that the greatest source of variance was inter-individual variability in the natural course of infection. Running a larger phase II study can save up to $38 million, if an unlikely to succeed phase III trial is avoided. In addition, low-sensitivity viral load assays can lead to falsely negative trial outcomes.ConclusionsDue to high inter-individual variability in natural infection, the most important variable in clinical trial design for challenge studies of potential novel influenza treatments is the number of participants. 100 participants are preferable over 50. Using more sensitive viral load assays increases the probability of a positive trial outcome, but may in some circumstances lead to false positive outcomes. Clinical trial simulations are powerful tools to identify the most important sources of variance in clinical trials and thereby help improve trial design.

An MCMC method for the evaluation of the Fisher information matrix for non-linear mixed effect models.

Non-linear mixed effect models (NLMEMs) are widely used for the analysis of longitudinal data. To design these studies, optimal design based on the expected Fisher information matrix (FIM) can be used instead of performing time-consuming clinical trial simulations. In recent years, estimation algorithms for NLMEMs have transitioned from linearization toward more exact higher-order methods. Optimal design, on the other hand, has mainly relied on first-order (FO) linearization to calculate the FIM. Although efficient in general, FO cannot be applied to complex non-linear models and with difficulty in studies with discrete data. We propose an approach to evaluate the expected FIM in NLMEMs for both discrete and continuous outcomes. We used Markov Chain Monte Carlo (MCMC) to integrate the derivatives of the log-likelihood over the random effects, and Monte Carlo to evaluate its expectation w.r.t. the observations. Our method was implemented in R using Stan, which efficiently draws MCMC samples and calculates partial derivatives of the log-likelihood. Evaluated on several examples, our approach showed good performance with relative standard errors (RSEs) close to those obtained by simulations. We studied the influence of the number of MC and MCMC samples and computed the uncertainty of the FIM evaluation. We also compared our approach to Adaptive Gaussian Quadrature, Laplace approximation, and FO. Our method is available in R-package MIXFIM and can be used to evaluate the FIM, its determinant with confidence intervals (CIs), and RSEs with CIs.

Clinical Trial Simulations From a Model-Based Meta-Analysis of Studies in Patients With Advanced Hepatocellular Carcinoma Receiving Antiangiogenic Therapy.

A mixed effect model describing median overall survival (mOS) in patients with advanced hepatocellular carcinoma (aHCC) treated with antiangiogenic therapy (AAT) was developed from literature data. Data were extracted from 59 studies, representing 4,813 patients. The final model included estimates of mOS after AAT (8.5 months) or placebo (7.1 months) administration. The mOS increased 21% when the AAT was sorafenib (SOR) or 42% when locoregional therapy was coadministered. The mOS decreased when patients received prior systemic therapy (↓7%) or concomitant chemotherapy (↓4%) or the percentage of patients with hepatitis B increased (↓∼0.4%/%). Clinical trial simulations of a phase II comparative trial predicted an mOS ratio (placebo:AAT) of 0.687 or 0.831, with a 65% or 22% probability of demonstrating superiority, for SOR or other AATs, respectively. Additionally, the 95% confidence interval (CI) of the simulated median mOS ratio for non‐SOR AATs was similar to the 95% CI of the hazard ratio (HR) observed in the trial.