Clinical Trial Simulation Research Articles

Evaluation of the effect of CYP2D6 and OCT1 polymorphisms on the pharmacokinetics of tramadol: Implications for clinical safety and dose rationale in paediatric chronic pain.

Our investigation aimed to assess the dose rationale of tramadol in paediatric patients considering the effect of CYP2D6/OCT1 polymorphisms on systemic exposure. Recommendations were made for the oral dose of tramadol to be used in a prospective study in children (3months to < 18 years old) with chronic pain. Intravenous pharmacokinetic and genotype data from neonatal patients (n = 46) were available for this analysis. The time course of tramadol and O-desmethyltramadol (M1) concentrations was characterized using a nonlinear mixed effects approach in conjunction with extrapolation principles. Clinical trial simulations were then implemented to explore the effects of polymorphism, maturation and developmental growth on the disposition of tramadol and M1. Reported efficacious exposure range in adult subjects were used as reference. The pharmacokinetics of tramadol and M1 was characterized by a two-compartment model. The total clearance of tramadol (CLPP) comprised CYP2D6-mediated metabolism (CLPM) and other pathways (CLPO). Age-related changes in CLPM, CLPO and M1 clearance (CLMO) were described by a sigmoid function, with CYP2D6 as a covariate on CLPP and CLPM, and OCT1 on CLMO. Simulation scenarios includingdifferent CYP2D6/OCT1 combinations revealed that steady-state concentrations are above the putative ranges for analgesia in >15% and >70% of subjects after doses of 3 and 8mg/kg, respectively. In the absence of genotyping, reference exposure ranges can be used to define the dose rationale for tramadol in paediatric chronic pain. However, a starting dose of 0.5mg/kg/day should be considered, followed by stepwise titration to the desired analgesic response.

Evaluation of ComBat harmonization for reducing across-tracer differences in regional amyloid PET analyses.

Introduction Differences in amyloid positron emission tomography (PET) radiotracer pharmacokinetics and binding properties lead to discrepancies in amyloid-β uptake estimates. Harmonization of tracer-specific biases is crucial for optimal performance of downstream tasks. Here, we investigated the efficacy of ComBat, a data-driven harmonization model, for reducing tracer-specific biases in regional amyloid PET measurements from [18F]-florbetapir (FBP) and [11C]-Pittsburgh Compound-B (PiB). Methods One-hundred-thirteen head-to-head FBP-PiB scan pairs, scanned from the same subject within ninety days, were selected from the Open Access Series of Imaging Studies 3 (OASIS-3) dataset. The Centiloid scale, ComBat with no covariates, ComBat with biological covariates, and GAM-ComBat with biological covariates were used to harmonize both global and regional amyloid standardized uptake value ratios (SUVR). Variants of ComBat, including longitudinal ComBat and PEACE, were also tested. Intraclass correlation coefficient (ICC) and mean absolute error (MAE) were computed to measure the absolute agreement between tracers. Additionally, longitudinal amyloid SUVRs from an anti-amyloid drug trial were simulated using linear mixed effects modeling. Differences in rates-of-change between simulated treatment and placebo groups were tested, and change in statistical power/Type-I error after harmonization was quantified. Results In the head-to-head tracer comparison, ComBat with no covariates was the best at increasing ICC and decreasing MAE of both global summary and regional amyloid PET SUVRs between scan pairs of the same group of subjects. In the clinical trial simulation, harmonization with both Centiloid and ComBat increased statistical power of detecting true rate-of-change differences between groups and decreased false discovery rate in the absence of a treatment effect. The greatest benefit of harmonization was observed when groups exhibited differing FBP-to-PiB proportions. Conclusion ComBat outperformed the Centiloid scale in harmonizing both global and regional amyloid estimates. Additionally, ComBat improved the detection of rate-of-change differences between clinical trial groups. Our findings suggest that ComBat is a viable alternative to Centiloid for harmonizing regional amyloid PET analyses.

A model-informed clinical trial simulation tool with a graphical user interface for Duchenne muscular dystrophy.

Quantitative model-based clinical trial simulation tools play a critical role in informing study designs through simulation before actual execution. These tools help drug developers explore various trial scenarios in silico to select a clinical trial design to detect therapeutic effects more efficiently, therefore reducing time, expense, and participants' burden. To increase the usability of the tools, user-friendly and interactive platforms should be developed to navigate various simulation scenarios. However, developing such tools challenges researchers, requiring expertise in modeling and interface development. This tutorial aims to address this gap by guiding developers in creating tailored R Shiny apps, using an example of a model-based clinical trial simulation tool that we developed for Duchenne muscular dystrophy (DMD). In this tutorial, the structural framework, essential controllers, and visualization techniques for analysis are described, along with key code examples such as criteria selection and power calculation. A virtual population was created using a machine learning algorithm to enlarge the available sample size to simulate clinical trial scenarios in the presented tool. In addition, external validation of the simulated outputs was conducted using a placebo arm of a recently published DMD trial. This tutorial will be particularly useful for developing clinical trial simulation tools based on DMD progression models for other end points and biomarkers. The presented strategies can also be applied to other diseases.

Optimizing β-lactam-containing antibiotic combination therapy for the treatment of Buruli ulcer.

The current treatment for Buruli ulcer is based on empirical evidence of efficacy. However, there is an opportunity for shortening its duration and improving response rates. Evolving understanding of the pharmacokinetic-pharmacodynamic relationships provides the basis for a stronger dose rationale for antibiotics. In conjunction with modelling and simulation, it is possible to identify dosing regimens with the highest probability of target attainment (PTA). This investigation aims to: (i) assess the dose rationale for a new combination therapy including amoxicillin/clavulanic acid (AMX/CLV) currently in clinical trials; and (ii) compare its performance with alternative dosing regimens including rifampicin, clarithromycin and AMX/CLV. In vitro estimates of the minimum inhibitory (MIC) concentration were selected as a measure of the antibacterial activity of different drug combinations. Clinical trial simulations were used to characterize the concentration vs. time profiles of rifampicin, clarithromycin and amoxicillin in a virtual cohort of adult and paediatric patients, considering the effect of baseline covariates on disposition parameters and interindividual variability in exposure. The PTA of each regimen was then assessed using different thresholds of the time above MIC. A weight-banded dosing regimen including 150-600 mg rifampicin once daily, 250-1000 mg clarithromycin and AMX/CLV 22.5mg/kg /1000 mg twice daily ensures higher PTA than the standard of care with AMX/CLV 45 mg/kg/2000 mg once daily. The higher PTA values support the proposed 4-drug combination (rifampicin, clarithromycin, AMX/CLV) currently under clinical investigation. Our findings also suggest that higher rifampicin doses might contribute to enhanced treatment efficacy.

Modeling of Parkinson's Disease Progression and Implications for Detection of Disease Modification in Treatment Trials.

Objectively measuring Parkinson's disease (PD) signs and symptoms over time is critical for the successful development of treatments aimed at halting the disease progression of people with PD. To create a clinical trial simulation tool that characterizes the natural history of PD progression and enables a data-driven design of randomized controlled studies testing potential disease-modifying treatments (DMT) in early-stage PD. Data from the Parkinson's Progression Markers Initiative (PPMI) were analyzed with nonlinear mixed-effect modeling techniques to characterize the progression of MDS-UPDRS part I (non-motor aspects of experiences of daily living), part II (motor aspects of experiences of daily living), and part III (motor signs). A clinical trial simulation tool was built from these disease models and used to predict probability of success as a function of trial design. MDS-UPDRS part III progresses approximately 3 times faster than MDS-UPDRS part II and I, with an increase of 3 versus 1 points/year. Higher amounts of symptomatic therapy is associated with slower progression of MDS-UPDRS part II and III. The modeling framework predicts that a DMT effect on MDS-UPDRS part III could precede effect on part II by approximately 2 to 3 years. Our clinical trial simulation tool predicted that in a two-year randomized controlled trial, MDS-UPDRS part III could be used to evaluate a potential novel DMT, while part II would require longer trials of a minimum duration of 3 to 5 years underscoring the need for innovative trial design approaches including novel patient-centric measures.

Application of Virtual Drug Study to New Drug Research and Development: Challenges and Opportunity.

In recent years, virtual drug study, as an emerging research strategy, has become increasingly important in guiding and promoting new drug research and development. Researchers can integrate a variety of technical methods to improve the efficiency of all phases of new drug research and development, including the use of artificial intelligence, modeling and simulation for target identification, compound screening and pharmacokinetic characteristics evaluation, and the application of clinical trial simulation to carry out clinical research. This paper aims to elaborate on the application of virtual drug study in the key stages of new drug research and development and discuss the opportunities and challenges it faces in supporting new drug research and development.

Model-Informed Assessment of Probability of Phase 3 Success for Ritlecitinib in Patients with Moderate-to-Severe Ulcerative Colitis.

Ritlecitinib, an oral Janus kinase 3/tyrosine kinase expressed in hepatocellular carcinoma family inhibitor, was evaluated in patients with ulcerative colitis (UC) in a phase 2b trial. Model-informed drug development strategies were applied to bridge observations from phase 2b to predictions for a proposed phase 3 study design to assess the probability of achieving the target efficacy outcome. A longitudinal exposure-response model of the time course of the 4 Mayo subscores (rectal bleeding, stool frequency, physician's global assessment, and endoscopic subscore) in patients with UC receiving placebo or ritlecitinib was developed using population modeling approaches and an item response theory framework. The quantitative relationships between the 4 Mayo subscores accommodated the prediction of composite endpoints such as total Mayo score and partial Mayo score (key endpoints from phase 2b), and modified clinical remission and endoscopic remission (proposed phase 3 endpoints). Clinical trial simulations using the final model assessed the probability of candidate ritlecitinib dosing regimens (including those tested in phase 2b and alternative) and phase 3 study designs for achieving target efficacy outcomes benchmarked against an approved treatment for moderate-to-severe UC. The probabilities of achieving target modified clinical remission and endoscopic improvement outcomes at both weeks 8 and 52 for ritlecitinib 100 mg once daily was 74.8%. Model-based assessment mitigated some of the risk associated with proceeding to pivotal phase 3 trials with dosing regimens of which there was limited clinical experience.

From Plan to Pivot: How Model-Informed Drug Development Shaped the Dose Strategy of the Zibotentan/Dapagliflozin ZENITH Trials.

Getting the dose right is a key challenge in drug development; model-informed drug development (MIDD) provides powerful tools to shape dose strategies and inform decision making. In this tutorial, the case study of the ZENITH trials showcases how a set of clinical pharmacology and MIDD approaches informed an impactful dose strategy. The endothelin A receptor antagonist zibotentan, combined with the sodium-glucose co-transporter-2 inhibitor dapagliflozin, has yielded a robust and significant albuminuria reduction in the Phase IIb trial ZENITH-CKD and is being investigated for reduction of kidney function decline in a high-risk chronic kidney disease population in the Phase III trial ZENITH High Proteinuria. Endothelin antagonist treatment has, until now, been limited by the class effect fluid retention. ZENITH-CKD investigated a wide range of zibotentan doses based on pharmacokinetics in renal impairment, competitor-data exposure-response modeling, and clinical trial simulations. Recruitment delays reduced interim analysis data availability; here, supportive dose-response modeling recovered decision-making confidence. At trial completion, the low-dose arm enabled Phase III dose selection between Phase IIb doses. Dose-response modeling of efficacy and Kaplan-Meier analyses of tolerability identified a kidney-function-based low-dose strategy of 0.25 or 0.75 mg zibotentan (with 10 mg dapagliflozin) to balance benefit/risk in ZENITH High Proteinuria. The applied clinical pharmacology and MIDD principles enabled successful Phase IIb dose finding, rationalized and built confidence in the innovative Phase III dosing strategy and identified a potential therapeutic window for zibotentan/dapagliflozin, providing the opportunity for a significant improvement in the treatment of chronic kidney disease with high proteinuria.

Model informed dose regimen optimizing in development of leritrelvir for the treatment of mild or moderate COVID-19.

Leritrelvir (RAY1216) acts as a main protease inhibitor that hinders the cleavage of viral precursor proteins, thereby inhibiting virus replication of SARS-CoV-2). This antiviral mechanism has shown significant efficacy against the novel coronavirus. Preclinical studies have demonstrated the potent antiviral activity and favorable safety profile of this compound. This study aims to develop a pharmacokinetic model for leritrelvir, with and without ritonavir as a pharmacokinetic enhancer and to evaluate the necessity of co-administration with ritonavir and to investigate different dosage regimens. The model establishment was based on plasma concentration data from a phase I trial involving 72 subjects in single-ascending dose (SAD), multiple-ascending dose (MAD), and a food effect cohort. Analysis was conducted using a nonlinear mixed-effects model, and clinical trial simulations were carried out. The findings of this study demonstrate a favorable safety profile for leritrelvir. With simulation suggests that a 400mg thrice-daily (TID) regimen may be optimal to maintain the trough concentrations (Ctrough) above levels required for inhibiiting viral replication. While ritonavir was found to enhance exposure, it was deemed unnecessary. Gender and food consumption were identified as significant covariates affecting pharmacokinetic parameters, however, no dose adjustments were deemed necessary. This findings supported by subsequent phase II and phase III trials validated the appropriateness of a 400mg TID regimen for the administration of leritrelvir.

Model-informed Evidence for Clinical Non-inferiority of Every-2-Weeks Versus Standard Weekly Dosing Schedule of Cetuximab in Metastatic Colorectal Cancer.

Cetuximab was initially developed and approved as a first-line treatment in patients with unresectable metastatic colorectal cancer (mCRC) for weekly administration (250 mg/m2 Q1W with 400 mg/m2 loading dose). An every-2-weeks schedule (500 mg/m2 Q2W) was approved recently by several health authorities. Being synchronized with chemotherapy, Q2W administration should improve patients' convenience and healthcare resource utilization. Herein, we present evidence of non-inferiority of Q2W cetuximab, compared with Q1W dosing using pharmacometrics modeling and clinical trial simulation (CTS). Pooled data from five phase I-III clinical trials in 852 patients with KRAS wild-type mCRC treated with Q1W or Q2W cetuximab were modeled using a population exposure-tumor size (TS) model linked to overall survival (OS); exposure was derived from a previously established population pharmacokinetic model. A semi-mechanistic TS model adapted from the Claret model incorporated killing rate proportional to cetuximab area under the concentration-time curve over 2 weeks (AUC) with Eastern Cooperative Oncology Group (ECOG) status as covariate on baseline TS. The OS was modeled with Weibull hazard using ECOG, baseline TS, primary tumor location, and predicted percent change in TS at 8 weeks as covariates. Model-based simulations revealed indistinguishable early tumor shrinkage and survival between Q2W vs. Q1W cetuximab. CTS evaluated OS non-inferiority (predefined margin of 1.25) in 1,000 trials, each with 2,000 virtual patients receiving Q2W or Q1W cetuximab (1:1), and demonstrated non-inferiority in 94% of cases. Taken together, these analyses provide model-based evidence for clinical non-inferiority of Q2W vs. Q1W cetuximab in mCRC with potential benefits to patients and healthcare providers.

Characterizing the clinical heterogeneity of early symptomatic Alzheimer's disease: a data-driven machine learning approach.

Alzheimer's disease (AD) is highly heterogeneous, with substantial individual variabilities in clinical progression and neurobiology. Amyloid deposition has been thought to drive cognitive decline and thus a major contributor to the variations in cognitive deterioration in AD. However, the clinical heterogeneity of patients with early symptomatic AD (mild cognitive impairment or mild dementia due to AD) already with evidence of amyloid abnormality in the brain is still unknown. Participants with a baseline diagnosis of mild cognitive impairment or mild dementia, a positive amyloid-PET scan, and more than one follow-up Alzheimer's Disease Assessment Scale-Cognitive Subscale-13 (ADAS-Cog-13) administration within a period of 5-year follow-up were selected from the Alzheimer's Disease Neuroimaging Initiative database (n = 421; age = 73±7; years of education = 16 ± 3; percentage of female gender = 43%; distribution of APOE4 carriers = 68%). A non-parametric k-means longitudinal clustering analysis in the context of the ADAS-Cog-13 data was performed to identify cognitive subtypes. We found a highly variable profile of cognitive decline among patients with early AD and identified 4 clusters characterized by distinct rates of cognitive progression. Among the groups there were significant differences in the magnitude of rates of changes in other cognitive and functional outcomes, clinical progression from mild cognitive impairment to dementia, and changes in markers presumed to reflect neurodegeneration and neuronal injury. A nomogram based on a simplified logistic regression model predicted steep cognitive trajectory with an AUC of 0.912 (95% CI: 0.88 - 0.94). Simulation of clinical trials suggested that the incorporation of the nomogram into enrichment strategies would reduce the required sample sizes from 926.8 (95% CI: 822.6 - 1057.5) to 400.9 (95% CI: 306.9 - 516.8). Our findings show usefulness in the stratification of patients in early AD and may thus increase the chances of finding a treatment for future AD clinical trials.

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

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 1second (FEV1) improvement with omalizumab dosing based on the US and EU asthma dosing tables. 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. The US and EU asthma dosing tables were predicted to result in generally comparable free IgE suppression and FEV1 improvement. 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.

New Horizons of Model Informed Drug Development in Rare Diseases Drug Development.

Model-informed approaches provide a quantitative framework to integrate all available nonclinical and clinical data, thus furnishing a totality of evidence approach to drug development and regulatory evaluation. Maximizing the use of all available data and information about the drug enables a more robust characterization of the risk-benefit profile and reduces uncertainty in both technical and regulatory success. This offers the potential to transform rare diseases drug development, where conducting large well-controlled clinical trials is impractical and/or unethical due to a small patient population, a significant portion of which could be children. Additionally, the totality of evidence generated by model-informed approaches can provide confirmatory evidence for regulatory approval without the need for additional clinical data. In the article, applications of novel quantitative approaches such as quantitative systems pharmacology, disease progression modeling, artificial intelligence, machine learning, modeling of real-world data using model-based meta-analysis and strategies such as external control and patient-reported outcomes as well as clinical trial simulations to optimize trials and sample collection are discussed. Specific case studies of these modeling approaches in rare diseases are provided to showcase applications in drug development and regulatory review. Finally, perspectives are shared on the future state of these modeling approaches in rare diseases drug development along with challenges and opportunities for incorporating such tools in the rational development of drug products.

Variational autoencoders for generative modeling of drug dosing determinants in renal, hepatic, metabolic, and cardiac disease states

AbstractPhysiological determinants of drug dosing (PDODD) are a promising approach for precision dosing. This study investigates the alterations of PDODD in diseases and evaluates a variational autoencoder (VAE) artificial intelligence model for PDODD. The PDODD panel contained 20 biomarkers, and 13 renal, hepatic, diabetes, and cardiac disease status variables. Demographic characteristics, anthropometric measurements (body weight, body surface area, waist circumference), blood (plasma volume, albumin), renal (creatinine, glomerular filtration rate, urine flow, and urine albumin to creatinine ratio), and hepatic (R‐value, hepatic steatosis index, drug‐induced liver injury index), blood cell (systemic inflammation index, red cell, lymphocyte, neutrophils, and platelet counts) biomarkers, and medical questionnaire responses from the National Health and Nutrition Examination Survey (NHANES) were included. The tabular VAE (TVAE) generative model was implemented with the Synthetic Data Vault Python library. The joint distributions of the generated data vs. test data were compared using graphical univariate, bivariate, and multidimensional projection methods and distribution proximity measures. The PDODD biomarkers related to disease progression were altered as expected in renal, hepatic, diabetes, and cardiac diseases. The continuous PDODD panel variables generated by the TVAE satisfactorily approximated the distribution in the test data. The TVAE‐generated distributions of some discrete variables deviated from the test data distribution. The age distribution of TVAE‐generated continuous variables was similar to the test data. The TVAE algorithm demonstrated potential as an AI model for continuous PDODD and could be useful for generating virtual populations for clinical trial simulations.

Prediction of therapeutic response and cancer outcomes in solid tumours via in silico clinical trials.

e13603 Background: Oncology therapeutic development has high failure rates with substantial costs. Advances in technology, including molecular characterisations of cancer and computational power, provide the opportunity to better model therapeutic response for existing and emerging therapies. Methods: Concr adapted Bayesian statistical principles used by astrophysicists to effectively integrate multi-dimensional data (genomic &amp; transcriptomic) for accurate modelling of cancer biology. Our proprietary computational framework was applied to predict clinical response and survival through construction of digital twins and in silico simulations of clinical trials. Predicted log odds ratios (LOR) were generated for drug response and compared to published data from 8 suitable historical clinical trials. Three distinct models underpin this framework: Drug Efficacy, Treatment Response &amp; Overall Survival (OS). Three publicly available datasets were used for training: Cancer Therapeutic Response Portal ( in vitro dose-response data), Cancer Cell Line Encyclopaedia and TCGA. Results: For all 8 clinical studies, the digital twin model accurately simulated both trial arms, compared drug efficacy across arms and predicted which treatment was most active. Unblinded evaluation: Our model accurately predicted gemcitabine would have greater clinical benefit than 5-FU (LOR -0.10, P &lt;0.0001) in metastatic pancreatic cancer. In advanced breast cancer, the model predicted docetaxel had a higher response rate than doxorubicin, and carboplatin response rates were similar to docetaxel in BRCA wild-type patients. In platinum-sensitive recurrent ovarian cancer, the model predicted cisplatin-based combination had a higher response rate than paclitaxel. Blinded evaluation: Using data for paclitaxel, the model correctly predicted that nab-paclitaxel+gemcitabine response rates were higher than gemcitabine in metastatic pancreatic cancer (predicted LOR -0.090, p = &lt;0.001). In all 3 early breast cancer studies, predictions mirrored clinical trial results, adjuvant ECF &gt; CMF, adjuvant TC &gt; AC and adjuvant tamoxifen was superior to capecitabine. The underlying pan-tumour Overall Survival model used a Random Survival Forest approach, trained on 23 cancers with predictive accuracy of 0.78 AUC ROC and C-index 0.71. To enable cohort enrichment for drug response, the model segmented patient cohorts into treatment responder and non-responder. Using this approach, the log odds increased for drug response rate for 16 of the 19 different cancers and 14 of the different 17 drugs. Conclusions: Our digital twin model represents transformative technology for clinical trial simulation with strong potential for accelerating and de-risking clinical drug development, and utility for synthetic control arms.

#362 Dosing low, winning high: unlocking the promise of endothelin antagonism for the treatment of CKD in the zibotentan/dapagliflozin ZENITH trials

Abstract Background and Aims The Phase 2b ZENITH-CKD trial demonstrated that combined treatment with the endothelin A receptor (ETAR) antagonist zibotentan and the sodium-glucose co-transporter-2 inhibitor (SGLT2i) dapagliflozin yields a robust and significant reduction in albuminuria in individuals with chronic kidney disease (CKD), compared to dapagliflozin treatment alone [1]. This is hypothesized to translate into a significant reduction of kidney function decline and may further confer cardiometabolic benefits including reducing blood pressure, LDL cholesterol and (in type 2 diabetes) HbA1c in the ongoing Phase 3 ZENITH High Proteinuria trial. The usage of ETAR antagonists has until now been limited by the class effect of fluid retention, which is associated with an increased risk of heart failure in the at-risk CKD population. Zibotentan has previously been investigated at 10 mg per day in Phase 3 for the treatment of prostate cancer. Method Model-Informed Drug Development (MIDD) was integrated with clinical pharmacology data to develop the dose strategy for both ZENITH trials. A low-dose strategy was selected considering the increased plasma exposure of zibotentan in kidney impairment. The Phase 2b dose steps were selected based on an exposure response model for the efficacy marker urinary albumin to creatinine ratio (uACR) and were supported using clinical trial simulations. The dose strategy for Phase 3 was defined based on a combined analysis of ZENITH-CKD results for both efficacy and tolerability: efficacy (uACR reduction on top of dapagliflozin) was analysed with a Dose-Response Mixed Model for Repeated Measures, and tolerability (fluid retention events, defined as a combination of specified body weight and BNP increase) was evaluated with Kaplan Meier analyses. Results Zibotentan doses of 0.25 mg and 1.5 mg (in a first, discontinued study part also 5 mg), in combination with dapagliflozin 10 mg, were selected for ZENITH-CKD. Clinical trial simulations indicated that inclusion of the low dose of 0.25 mg was crucial to characterise the dose-response relationship. Dose-uACR response modelling of ZENITH-CKD data predicted a zibotentan dose of 0.75 mg to result in a mean uACR reduction of around 25% in addition to the reduction with dapagliflozin. A post-hoc regression analysis of uACR mean changes from baseline, stratified by dose, showed a trend towards lower uACR reduction with higher eGFR levels. Kaplan-Meier analyses of the fluid retention risk in different eGFR subgroups indicated that the fluid retention risk increased with increasing zibotentan dose for eGFR levels &amp;lt;45 mL/min/1.73 m2, while for higher eGFR levels, no differences were observed between treatments with dapagliflozin alone and added zibotentan 0.25 mg or 1.5 mg. Therefore, a kidney-function based zibotentan dose strategy was selected for the ZENITH High Proteinuria trial: Combined zibotentan/dapagliflozin is being compared against dapagliflozin alone; within the combination treatment arm, 0.25 mg zibotentan is selected for participants with baseline eGFR levels &amp;lt;45 mL/min/1.73 m2, and 0.75 mg is selected for higher eGFR level, with a tolerance eGFR window for participants whose eGFR declines below this cut-off, and with the possibility to reduce the dose to 0.25 mg in case of tolerability findings. Conclusion The favourable benefit/risk profile that was demonstrated for combined zibotentan/dapagliflozin in ZENITH-CKD was enabled by the zibotentan low-dose strategy. The wide dose ranging and MIDD further enabled selection of a kidney-function based zibotentan dosing strategy for ZENITH High Proteinuria which is designed to further balance benefit/risk. The low-dose zibotentan strategy and the combination with dapagliflozin opened for the first time a therapeutic window for ETAR antagonists, with the potential to redefine their value for the treatment of CKD. This work was sponsored by AstraZeneca.

The Calibrated Bayesian Hypothesis Test for Directional Hypotheses of the Odds Ratio in $$2\times 2$$ Contingency Tables

AbstractThe $$\chi ^{2}$$ χ 2 test is among the most widely used statistical hypothesis tests in medical research. Often, the statistical analysis deals with the test of row-column independence in a $$2\times 2$$ 2 × 2 contingency table, and the statistical parameter of interest is the odds ratio. A novel Bayesian analogue to the frequentist $$\chi ^{2}$$ χ 2 test is introduced. The test is based on a Dirichlet-multinomial model under a joint sampling scheme and works with balanced and unbalanced randomization. The test focusses on the quantity of interest in a variety of medical research, the odds ratio in a $$2\times 2$$ 2 × 2 contingency table. A computational implementation of the test is developed and R code is provided to apply the test. To meet the demands of regulatory agencies, a calibration of the Bayesian test is introduced which allows to calibrate the false-positive rate and power. The latter provides a Bayes-frequentist compromise which ensures control over the long-term error rates of the test. Illustrative examples using clinical trial data and simulations show how to use the test in practice. In contrast to existing Bayesian tests for $$2\times 2$$ 2 × 2 tables, calibration of the acceptance threshold for the hypothesis of interest allows to achieve a bound on the false-positive rate and minimum power for a prespecified odds ratio of interest. The novel Bayesian test provides an attractive choice for Bayesian biostatisticians who face the demands of regulatory agencies which usually require formal control over false-positive errors and power under the alternative. As such, it constitutes an easy-to-apply addition to the arsenal of already existing Bayesian tests.

Effectiveness of simulation-based clinical research curriculum for undergraduate medical students - a pre-post intervention study with external control

BackgroundSimulation is widely utilized in medical education. Exploring the effectiveness of high-fidelity simulation of clinical research within medical education may inform its integration into clinical research training curricula, finally cultivating physician-scientist development.MethodsStandard teaching scripts for both clinical trial and cross-sectional study simulation were designed. We recruited undergraduates majoring in clinical medicine at 3th grade into a pre-post intervention study. Additionally, a cross-sectional survey randomly selected medical undergraduates at 4th or 5th grade, medical students in master and doctor degree as external controls. Self-assessment scores of knowledge and practice were collected using a 5-point Likert scale. Changes in scores were tested by Wilcoxon signed-rank test and group comparisons were conducted by Dunn’s tests with multiple corrections. Multivariable quantile regressions were used to explore factors influencing the changes from baseline.ResultsSeventy-eight undergraduates involved the clinical trial simulation and reported improvement of 1.60 (95% CI, 1.48, 1.80, P < 0.001) in knowledge and 1.82 (95% CI, 1.64, 2.00, P < 0.001) in practice score. 83 undergraduates involved in the observational study simulation and reported improvement of 0.96 (95% CI, 0.79, 1.18, P < 0.001) in knowledge and 1.00 (95% CI, 0.79, 1.21, P < 0.001) in practice. All post-intervention scores were significantly higher than those of the three external control groups, P < 0.001. Higher agreement on the importance of clinical research were correlated with greater improvements in scores. Undergraduates in pre-post study showed high confidence in doing a future clinical research.ConclusionOur study provides evidence supporting the integration of simulation into clinical research curriculum for medical students. The importance of clinical research can be emphasized during training to enhance learning effect.

Impact of covariate model building methods on their clinical relevance evaluation in population pharmacokinetic analyses: comparison of the full model, stepwise covariate model (SCM) and SCM+ approaches.

Covariate analysis in population pharmacokinetics is key for adjusting doses for patients. The main objective of this work was to compare the adequacy of various modeling approaches on covariate clinical relevance decision-making. The full model, stepwise covariate model (SCM) and SCM+ PsN algorithms were compared in a clinical trial simulation of a 383-patient population pharmacokinetic study mixing rich and sparse designs. A one-compartment model with first-order absorption was used. A base model including a body weight effect on CL/F and V/F and a covariate model including 4 additional covariates-parameters relationships were simulated. As for forest plots, ratios between covariates at a specific value and that of a typical individual were calculated with their 90% confidence interval (CI90) using standard errors. Covariates on CL, V and KA were considered relevant if their CI90 fell completely outside the reference area [0.8-1.2]. All approaches provided unbiased covariate ratio estimates. For covariates with a simulated effect, the 3 approaches correctly identify their clinical relevance. However, significant covariates were missed in up to 15% of cases with SCM/SCM+. For covariate with no simulated effects, the full model mainly identified them as non-relevant or with insufficient information while SCM/SCM+ mainly did not select them. SCM/SCM+ assume that non-selected covariates are non-relevant when it could be due to insufficient information, whereas the full model does not make this assumption and is faster. This study must be extended to other methods and completed by a more complex high-dimensional simulation framework.

Development of a target concentration intervention to individualize paroxysmal nocturnal hemoglobinuria treatment with pegcetacoplan

Pegcetacoplan (Aspaveli®/Empaveli™) is a factor C3 inhibitor that is approved for the treatment of paroxysmal nocturnal hemoglobinuria. An individualized dosing strategy might be useful to improve patient-friendliness and cost-effectiveness of this very expensive drug. Therefore, the aim of this study was to develop an individualized treatment regimen for pegcetacoplan based on the pharmacokinetic-pharmacodynamic data of the manufacturer. We conducted a clinical trial simulation with the approved dosing regimen of 1080 mg twice-weekly and a target concentration intervention-based dosing regimen in patients with and without prior eculizumab use. For eculizumab-naïve patients, the target concentration intervention-based dosing regimen resulted in a comparable fraction of patients with LDH normalization (LDH < 226 U/L) and hemoglobulin normalization (> 12 g/dL) compared to the approved regimen (LDH 50.2% and 50.0% respectively and hemoglobulin 45.6% and 44.4%). A modest dose reduction of ~ 5% was possible with target concentration intervention-based dosing. An intensified dosing interval was necessary in 2.3% of the patients however an interval prolongation was possible in 28.2% of the patients. Similar results were obtained for patients prior treated with eculizumab. In this study we show the potential of an individualized dosing regimen of pegcetacoplan with can improve patient friendliness in approximately 30% of the patients and improve therapy in approximately 2% of the patients at slightly reduced costs.