Cooperation of structural motifs controls drug selectivity in cyclin-dependent kinases: an advanced theoretical analysis.

Understanding drug selectivity mechanism is a long-standing issue for helping design drugs with high specificity. Designing drugs targeting cyclin-dependent kinases (CDKs) with high selectivity is challenging because of their highly conserved binding pockets. To reveal the underlying general selectivity mechanism, we carried out comprehensive analyses from both the thermodynamics and kinetics points of view on a representative CDK12 inhibitor. To fully capture the binding features of the drug-target recognition process, we proposed to use kinetic residue energy analysis (KREA) in conjunction with the community network analysis (CNA) to reveal the underlying cooperation effect between individual residues/protein motifs to the binding/dissociating process of the ligand. The general mechanism of drug selectivity in CDKs can be summarized as that the difference of structural cooperation between the ligand and the protein motifs leads to the difference of the energetic contribution of the key residues to the ligand. The proposed mechanisms may be prevalent in drug selectivity issues, and the insights may help design new strategies to overcome/attenuate the drug selectivity associated problems.

Meaningful Endpoints for Idiopathic Pulmonary Fibrosis (IPF) Clinical Trials: Emphasis on 'Feels, Functions, Survives'. Report of a Collaborative Discussion in a Symposium with Direct Engagement from Representatives of Patients, Investigators, the National Institutes of Health, a Patient Advocacy Organization, and a Regulatory Agency.

Background: Idiopathic pulmonary fibrosis (IPF) carries significant mortality and unpredictable progression, with limited therapeutic options. Designing trials with patient-meaningful endpoints, enhancing the reliability and interpretability of results, and streamlining the regulatory approval process are of critical importance to advancing clinical care in IPF. Methods: A landmark in-person symposium in June 2023 assembled 43 participants from the US and internationally, including patients with IPF, investigators, and regulatory representatives, to discuss the immediate future of IPF clinical trial endpoints. Patient advocates were central to discussions, which evaluated endpoints according to regulatory standards and the FDA's 'feels, functions, survives' criteria. Results: Three themes emerged: 1) consensus on endpoints mirroring the lived experiences of patients with IPF; 2) consideration of replacing forced vital capacity (FVC) as the primary endpoint, potentially by composite endpoints that include 'feels, functions, survives' measures or FVC as components; 3) support for simplified, user-friendly patient-reported outcomes (PROs) as either components of primary composite endpoints or key secondary endpoints, supplemented by functional tests as secondary endpoints and novel biomarkers as supportive measures (FDA Guidance for Industry (Multiple Endpoints in Clinical Trials) available at: https://www.fda.gov/media/162416/download). Conclusions: This report, detailing the proceedings of this pivotal symposium, suggests a potential turning point in designing future IPF clinical trials more attuned to outcomes meaningful to patients, and documents the collective agreement across multidisciplinary stakeholders on the importance of anchoring IPF trial endpoints on real patient experiences-namely, how they feel, function, and survive. There is considerable optimism that clinical care in IPF will progress through trials focused on patient-centric insights, ultimately guiding transformative treatment strategies to enhance patients' quality of life and survival.

Definition of Virological Endpoints Improving the Design of Human Immunodeficiency Virus (HIV) Cure Strategies Using Analytical Antiretroviral Treatment Interruption.

BACKGROUND: Analytical treatment interruption (ATI) is the gold standard in HIV research for assessing the capability of new therapeutic strategies to control viremia without antiretroviral treatment (ART). The viral setpoint is commonly used as endpoint to evaluate their efficacy. However, in line with recommendations from a consensus meeting, to minimize the risk of increased viremia without ART, trials often implement short ATI phases and stringent virological ART restart criteria. This approach can limit the accurate observation of the setpoint. METHODS: We analyzed viral dynamics in 235 people with HIV from 3 trials, examining virological criteria during ATI phases. Time-related (eg time to rebound, peak, and setpoint) and viral load magnitude-related criteria (peak, setpoint, and time-averaged AUC [nAUC]) were described. Spearman correlations were analyzed to identify (1) surrogate endpoints for setpoint and (2) optimal virological ART restart criteria mitigating the risks of ART interruption and the evaluation of viral control. RESULTS: Comparison of virological criteria between trials showed strong dependencies on ATI design. Similar correlations were found across trials, with nAUC the most strongly correlated with the setpoint, with correlations >0.70. A threshold >100 000 copies/mL for 2 consecutive measures is requested as a virological ART restart criterion. CONCLUSIONS: Our results are in line with recommendations and emphasize the benefits of an ATI phase >12 weeks, with regular monitoring, and a virological ART restart criterion of 10 000 copies/mL to limit the risk for patients while capturing enough information to keep nAUC as an optimal proxy to the setpoint.

Comparing the prognostic performance of iBOX and biopsy-proven acute rejection for long-term kidney graft survival.

Biopsy-proven acute rejection (BPAR) occurs in approximately 10% of kidney transplant recipients in the first year, making superiority trials unfeasible. iBOX, a quantitative composite of estimated glomerular filtration rate, proteinuria, antihuman leukocyte antigen donor-specific antibody, and + full/- abbreviated kidney histopathology, is a new proposed surrogate endpoint. BPAR's prognostic ability was compared with iBOX in a pooled cohort of 1534 kidney transplant recipients from 4 data sets, including 2 prospective randomized controlled trials. Discrimination analyses showed mean c-statistic differences between both iBOX compared with BPAR of 0.25 (95% confidence interval: 0.17-0.32) for full iBOX and 0.24 (95% confidence interval: 0.16-0.32) for abbreviated iBOX, indicating statistically significantly higher c-statistic values for the iBOX prognosis of death-censored graft survival. Mean (± standard error) c-statistics were 0.81 ± 0.03 for full iBOX, 0.80 ± 0.03 for abbreviated iBOX, and 0.57 ± 0.03 for BPAR. In calibration analyses, predicted graft loss events from both iBOX models were not significantly different from those observed. However, for BPAR, the predicted events were significantly (P < .01) different (observed: 64; predicted: 70; full iBOX: 76; abbreviated iBOX: 173 BPAR). IBOX at 1-year posttransplant is superior to BPAR in the first year posttransplant in graft loss prognostic performance, providing valuable additional information and facilitating the demonstration of superiority of novel immunosuppressive regimens.

Characterizing the stabilization effects of stabilizers in protein-protein systems with end-point binding free energy calculations.

Drug design targeting protein-protein interactions (PPIs) associated with the development of diseases has been one of the most important therapeutic strategies. Besides interrupting the PPIs with PPI inhibitors/blockers, increasing evidence shows that stabilizing the interaction between two interacting proteins may also benefit the therapy, such as the development of various types of molecular glues/stabilizers that mostly work by stabilizing the two interacting proteins to regulate the downstream biological effects. However, characterizing the stabilization effect of a stabilizer is usually hard or too complicated for traditional experiments since it involves ternary interactions [protein-protein-stabilizer (PPS) interaction]. Thus, developing reliable computational strategies will facilitate the discovery/design of molecular glues or PPI stabilizers. Here, by fully analyzing the energetic features of the binary interactions in the PPS ternary complex, we systematically investigated the performance of molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) and molecular mechanics generalized Born surface area (MM/GBSA) methods on characterizing the stabilization effects of stabilizers in 14-3-3 systems. The results show that both MM/PBSA and MM/GBSA are powerful tools in distinguishing the stabilizers from the decoys (with area under the curves of 0.90-0.93 for all tested cases) and are reasonable for ranking protein-peptide interactions in the presence or absence of stabilizers as well (with the average Pearson correlation coefficient of ~0.6 at a relatively high dielectric constant for both methods). Moreover, to give a detailed picture of the stabilization effects, the stabilization mechanism is also analyzed from the structural and energetic points of view for individual systems containing strong or weak stabilizers. This study demonstrates a potential strategy to accelerate the discovery of PPI stabilizers.

Mechanism of action, potency and efficacy: considerations for cell therapies.

One of the most challenging aspects of developing advanced cell therapy products (CTPs) is defining the mechanism of action (MOA), potency and efficacy of the product. This perspective examines these concepts and presents helpful ways to think about them through the lens of metrology. A logical framework for thinking about MOA, potency and efficacy is presented that is consistent with the existing regulatory guidelines, but also accommodates what has been learned from the 27 US FDA-approved CTPs. Available information regarding MOA, potency and efficacy for the 27 FDA-approved CTPs is reviewed to provide background and perspective. Potency process and efficacy process charts are introduced to clarify and illustrate the relationships between six key concepts: MOA, potency, potency test, efficacy, efficacy endpoint and efficacy endpoint test. Careful consideration of the meaning of these terms makes it easier to discuss the challenges of correlating potency test results with clinical outcomes and to understand how the relationships between the concepts can be misunderstood during development and clinical trials. Examples of how a product can be "potent but not efficacious" or "not potent but efficacious" are presented. Two example applications of the framework compare how MOA is assessed in cell cultures, animal models and human clinical trials and reveals the challenge of establishing MOA in humans. Lastly, important considerations for the development of potency tests for a CTP are discussed. These perspectives can help product developers set appropriate expectations for understanding a product's MOA and potency, avoid unrealistic assumptions and improve communication among team members during the development of CTPs.

Comparing Analytical Methods for Composite Endpoints in Clinical Trials: Insights from the Vericiguat Global Study in Subjects with Heart Failure with Reduced Ejection Fraction (VICTORIA) Trial.

BACKGROUND AND AIMS: In the VICTORIA trial of participants with heart failure (HF) and reduced ejection fraction, vericiguat (V) reduced the primary composite outcome [time to first HF hospitalization (HFH) or cardiovascular death (CVD)] (897 events) compared to placebo (P) (972 events) (hazard ratio, 0.90; 95% confidence interval [CI], 0.82-0.98; p=0.02). In this prespecified secondary analysis, we applied the weighted composite endpoint (WCE) and the win ratio (WR) methods to provide complementary assessments of treatment effect. METHODS AND RESULTS: The WCE method estimated the mean HFH-adjusted survival based on prespecified weights from a Delphi panel of the VICTORIA executive committee and national leaders: mild (weight per event: 0.39), moderate (0.5) or severe (0.67) HFH, and CVD (1.0). The unmatched WR was estimated for the descending hierarchy of CVD, then recurrent HFH. The WCE used all 3412 primary clinical events: 875(V:416/P:459) severe HFH, 1614(767/847) moderate HFH and 68(38/30) mild HFH, 855(414/441) CVD. Improved HFH-adjusted survival occurred with vericiguat [mean 78.2% vs. 75.6%; difference (95% CI): 2.4% (1.7%-3.2%); p<0.0001]. Based on the comparison of 6,375,624 pairs, the WR of 1.13 (95% CI, 1.03-1.24, p=0.01) also indicated improved clinical outcomes with vericiguat. CONCLUSIONS: The results of the WCE and WR methods were consistent with the primary analysis of the time to first HFH or CVD. Whereas both WCE and WR assessed recurrent events, the WCE allowed inclusion of all recurrent events, insights on the severity of HFH events, and an absolute measure of the participant-treatment experience. This approach complements conventional assessment, better informing consumers of new therapeutics and future trial designs.

PD-1 inhibitor combined with albumin paclitaxel and apatinib as second-line treatment for patients with metastatic gastric cancer: a single-center, single-arm, phase II study.

BACKGROUND: Immune checkpoint inhibitors have been approved for first- and third-line treatment of advanced gastric cancer. However, pembrolizumab alone in the second line did not improve overall survival compared to chemotherapy in the KEYNOTE-061 study. In this study, we aimed to explore the efficacy and safety of a three-drug regimen of PD-1 inhibitor combined with albumin paclitaxel and apatinib (a VEGFR inhibitor) for the second-line treatment of patients with metastatic gastric cancer (mGC). METHODS: This was a single-center, single-arm, phase II clinical study. Patients with mGC with stable microsatellite and negative HER-2 expression who failed first-line chemotherapy were enrolled. The enrolled patients were treated with PD-1 inhibitor (selected according to patients' requirements) in combination with albumin paclitaxel (125 mg/m2, intravenously, days 1 and 8, or 250 mg/m2, intravenously, day 1) and apatinib (250 or 500 mg, orally, days 1-21) every 3 weeks. The primary endpoint was progression-free survival (PFS), and the secondary endpoints were overall survival (OS), objective response rate (ORR), disease control rate (DCR), duration of response, and adverse events (AEs). RESULTS: From July 11, 2019, to October 13, 2022, a total of 43 patients were enrolled, of whom 10 were PD-L1 negative, 11 were PD-L1 positive, and 22 had unknown PD-L1 expression. As of the data cutoff on April 1st, 2023, nine patients had partial response, 29 had stable disease, and five experienced progressive disease, with the ORR of 20.9% and DCR of 88.3%. The median PFS was 6.2 months (95% CI, 3.9-9.3), and the median OS was 10.1 months (95% CI, 7.5-14.1). All patients suffered from alopecia and neurotoxicity. The other main AEs of grade 1 or 2 were bone marrow suppression (N = 21, 48.8%), hand-foot reaction (N = 19, 44.2%), hypertension (N = 18, 41.9%), hypothyroidism (N = 11, 25.6%), gastrointestinal bleeding (N = 3, 7.0%), and liver function damage (N = 5, 11.6%). Two patients reported grade 3-4 immune-related liver damage. CONCLUSION: Second-line PD-1 inhibitor combined with albumin paclitaxel and apatinib showed certain efficacy and safety in patients with mGC. TRIAL REGISTRATION: Clinical trials, NCT04182724. Registered 27 November 2019; retrospectively registered, https://clinicaltrials.gov/study/NCT04182724.

Role of Estimated Glomerular Filtration Rate in Clinical Research: The Never-Ending Matter.

Background: Chronic kidney disease (CKD) burden is crucial both on a global scale and at individual patient level, affecting morbidity and mortality directly and through its effect on both cardiovascular damage and CKD progression to end-stage-kidney-disease (ESKD). Unfortunately, the awareness of CKD is poor, with few CKD patients conscious of the severity of their health status. The principal biomarker of kidney function is estimated glomerular filtration rate (eGFR). Methods: We searched the literature and present a review article with the aim of summarizing the role of eGFR in clinical research. In particular, we report the eGFR role as a prognostic, enrichment and endpoint biomarker and its role in the early detection of CKD. Results: eGFR has a major role as a biomarker in clinical research. As a prognostic marker, eGFR reduction is associated with cardiovascular events, ESKD and mortality. As an enrichment biomarker, eGFR values are pivotal for selecting patients to be included in randomized and observational studies; it helps to test a pre-defined drug in early CKD or in more advanced CKD allowing also to avoid screening failures and to shorten the duration of clinical trials. Moreover, eGFR decline (expressed as a percentage of reduction from baseline or continuous slope) can be considered a good endpoint in clinic trials overcoming delays whilst waiting for hard endpoints to develop. Conclusions: eGFR is a strong clinical measure for both observational and intervention studies. It is also helpful in screening the general population for kidney disease and, in particular, to increase awareness of CKD.

Hot topics in allergen immunotherapy, 2023: Current status and future perspective.

The importance of allergen immunotherapy (AIT) is multifaceted, encompassing both clinical and quality-of-life improvements and cost-effectiveness in the long term. Key mechanisms of allergen tolerance induced by AIT include changes in memory type allergen-specific T- and B-cell responses towards a regulatory phenotype with decreased Type 2 responses, suppression of allergen-specific IgE and increased IgG1 and IgG4, decreased mast cell and eosinophil numbers in allergic tissues and increased activation thresholds. The potential of novel patient enrolment strategies for AIT is taking into account recent advances in biomarkers discoveries, molecular allergy diagnostics and mobile health applications contributing to a personalized approach enhancement that can increase AIT efficacy and compliance. Artificial intelligence can help manage and interpret complex and heterogeneous data, including big data from omics and non-omics research, potentially predict disease subtypes, identify biomarkers and monitor patient responses to AIT. Novel AIT preparations, such as synthetic compounds, innovative carrier systems and adjuvants, are also of great promise. Advances in clinical trial models, including adaptive, complex and hybrid designs as well as real-world evidence, allow more flexibility and cost reduction. The analyses of AIT cost-effectiveness show a clear long-term advantage compared to pharmacotherapy. Important research questions, such as defining clinical endpoints, biomarkers of patient selection and efficacy, mechanisms and the modulation of the placebo effect and alternatives to conventional field trials, including allergen exposure chamber studies are still to be elucidated. This review demonstrates that AIT is still in its growth phase and shows immense development prospects.

Surrogacy of one-year survival for overall survival in advanced hepatocellular carcinoma.

BACKGROUND: The increasing number of sequential treatments complicates the evaluation of overall survival (OS) in clinical trials for hepatocellular carcinoma (HCC), therefore, reliable surrogate endpoints (SEs) are required. This study aimed to evaluate the surrogacy of progression-free survival (PFS) and one-year (1-yr) milestone survival for OS in HCC trials. METHODS: We systematically searched databases for randomized clinical trials that evaluated systemic treatments for advanced HCC. Individual patient data were reconstructed to calculate the 1-yr survival rate. We adopted a two-stage meta-analytic validation model to evaluate the correlation between SEs and OS, and the correlation between treatment effects on SEs and OS. The hazard ratio (HR) was calculated to assess the treatment effects on PFS and OS, and the 1-yr survival ratio was calculated to evaluate the treatment effects on the 1-yr milestone survival. RESULTS: Thirty-two HCC trials involving 13,808 patients were included. A weak correlation was detected between the median PFS and median OS (R2 = 0.32), whereas the correlation improved between PFS HR and OS HR (R2 = 0.58). We identified strong correlations between the 1-yr survival rate and median OS and between the 1-yr survival ratio and OS HR (R2 = 0.74 and 0.65, respectively). In subgroup analyses, PFS HR strongly correlated with OS HR in trials relevant to immune checkpoint inhibitors (ICIs). Although the correlation remained weak between PFS and OS even in trials with PFS HR ≤ 0.6, the 1-yr survival rate and 1-yr survival ratio were strong surrogates for median OS and OS HR, respectively (R2 = 0.77 and 0.75). CONCLUSIONS: One-year milestone survival outperformed PFS as a SE for OS in HCC, indicating the application of 1-yr survival as a secondary endpoint. In particular, PFS HR was a potential SE for OS HR in the ICI trials.

Progression-free survival as a surrogate endpoint for overall survival in patients with relapsed or refractory multiple myeloma.

OBJECTIVES: The goal of the research was to assess the quantitative relationship between median progression-free survival (PFS) and median overall survival (OS) specifically among patients with relapsed/refractory multiple myeloma (RRMM) based on published randomized controlled trials (RCTs). METHODS: Two bibliographic databases (PubMed and Embase, 1970-2017) were systematically searched for RCTs in RRMM that reported OS and PFS, followed by an updated search of studies published between 2010 and 2022 in 3 databases (Embase, MEDLINE, and EBM Reviews, 2010-2022). The association between median PFS and median OS was assessed using the nonparametric Spearman rank and parametric Pearson correlation coefficients. Subsequently, the quantitative relationship between PFS and OS was assessed using weighted least-squares regression adjusted for covariates including age, sex, and publication year. Study arms were weighted by the number of patients in each arm. RESULTS: A total of 31 RCTs (56 treatment arms, 10,450 patients with RRMM) were included in the analysis. The average median PFS and median OS were 7.1 months (SD 5.5) and 28.1 months (SD 11.8), respectively. The Spearman and Pearson correlation coefficients between median PFS and median OS were 0.80 (P < 0.0001) and 0.79 (P < 0.0001), respectively. In individual treatment arms of RRMM trials, each 1-month increase in median PFS was associated with a 1.72-month (95% CI 1.26-2.17) increase in median OS. CONCLUSION: Analysis of the relationship between PFS and OS incorporating more recent studies in RRMM further substantiates the use of PFS to predict OS in RRMM.

An integrated in vitro carcinogenicity test that distinguishes between genotoxic carcinogens, non-genotoxic carcinogens, and non-carcinogens.

Chemical safety testing plays a crucial role in product and pharmacological development, as well as chemoprevention; however, in vitro genotoxicity safety tests do not always accurately predict the chemicals that will be in vivo carcinogens. If chemicals test positive in vitro for genotoxicity but negative in vivo, this can contribute to unnecessary testing in animals used to confirm erroneous in vitro positive results. Current in vitro tests typically evaluate only genotoxicity endpoints, which limits their potential to detect non-genotoxic carcinogens. The frequency of misleading in vitro positive results can be high, leading to a requirement for more informative in vitro tests. It is now recognized that multiple-endpoint genotoxicity testing may aid more accurate detection of carcinogens and non-carcinogens. The objective of this review was to evaluate the utility of our novel, multiple-endpoint in vitro test, which uses multiple cancer-relevant endpoints to predict carcinogenic potential. The tool assessed micronucleus frequency, p53 expression, p21 expression, mitochondrial respiration, cell cycle abnormalities and, uniquely, cell morphology changes in human lymphoblastoid cell lines, TK6 and MCL-5. The endpoints were used to observe cellular responses to 18 chemicals within the following categories: genotoxic carcinogens, non-genotoxic carcinogens, toxic non-carcinogens, and misleading in vitro positive and negative agents. The number of endpoints significantly altered for each chemical was considered, alongside the holistic Integrated Signature of Carcinogenicity score, derived from the sum of fold changes for all endpoints. Following the calculation of an overall score from these measures, carcinogens exhibited greater potency than non-carcinogens. Genotoxic carcinogens were generally more potent than non-genotoxic carcinogens. This novel approach therefore demonstrated potential for correctly predicting whether chemicals with unknown mechanism may be considered carcinogens. Overall, while further validation is recommended, the test demonstrates potential for the identification of carcinogenic compounds. Adoption of the approach could enable reduced animal use in carcinogenicity testing.

Effects of hypoglycaemic agents on reducing surrogate metabolic parameters for the prevention of cardiovascular events and death in patients with type 2 diabetes: A systematic review and meta-analysis.

AIMS: To investigate the impact of glucose-lowering therapy-induced glycated haemoglobin (HbA1c) reduction on the risk of major clinical events according to body weight change and, as a secondary objective, to evaluate the impact of concomitant reductions in HbA1c and body weight on major clinical events. MATERIALS AND METHODS: We searched the MEDLINE and EMBASE databases up to June 30, 2022, for large-scale studies on glucose-lowering therapies in which more than 1000 patient-years of follow-up in each randomized group were completed. The primary outcome was all-cause mortality. The study was registered in PROSPERO (CRD42022355479). RESULTS: Thirty-four trials involving 227 220 patients with type 2 diabetes were meta-analysed using a random-effects model. Each 1% reduction in HbA1c was associated with a different risk of mortality depending on the ability of glucose-lowering therapies to induce body weight loss or gain. When glucose-lowering therapies were associated with weight gain, the risk of mortality increased by 8% (hazard ratio [HR] 1.08, 95% confidence interval [CI] 1.00-1.16) for each 1% reduction in HbA1c. When glucose-lowering therapies were associated with weight loss, the risk of mortality was reduced by 22% (HR 0.78, 95% CI 0.72-0.85) for each 1% reduction in HbA1c. In addition, concomitant reductions in HbA1c and body weight were associated with a significantly lower risk of mortality and vascular events. CONCLUSIONS: In patients with type 2 diabetes, concomitant reductions in HbA1c and body weight might be more effective in preventing the risk of vascular events and mortality.

A Bayesian phase II proof-of-concept design for clinical trials with longitudinal endpoints.

Existing phase II clinical trial designs focus on a single scalar endpoint, such as a binary, continuous, or survival endpoint. In some clinical trials, such as pain management studies, the efficacy endpoint of interest is measured longitudinally. We propose a Bayesian phase II design for such clinical trials. We model the longitudinal measurement process using Bayesian hierarchical model, where subject-specific trajectory shrinks toward the population trajectory to borrow information across subjects. The Bayesian penalized spline is used to model subject-specific and population trajectories without making strong parametric assumption on their shapes. We use the area under the curve of the trajectory as the summary of the treatment effect over time. The design takes a group sequential approach and takes into account both statistical significance and clinical relevance. Bayesian criteria is proposed to make interim and final decisions based on the evidence of statistical significance and clinical relevance. The proposed design is highly flexible and can accommodate trials with one or multiple longitudinal endpoints, as well as a longitudinal primary endpoint with a secondary endpoint. Simulation study shows that the proposed design is robust with desirable operating characteristics.

Novel non-linear models for clinical trial analysis with longitudinal data: A tutorial using SAS for both frequentist and Bayesian methods.

Longitudinal data from clinical trials are commonly analyzed using mixed models for repeated measures (MMRM) when the time variable is categorical or linear mixed-effects models (ie, random effects model) when the time variable is continuous. In these models, statistical inference is typically based on the absolute difference in the adjusted mean change (for categorical time) or the rate of change (for continuous time). Previously, we proposed a novel approach: modeling the percentage reduction in disease progression associated with the treatment relative to the placebo decline using proportional models. This concept of proportionality provides an innovative and flexible method for simultaneously modeling different cohorts, multivariate endpoints, and jointly modeling continuous and survival endpoints. Through simulated data, we demonstrate the implementation of these models using SAS procedures in both frequentist and Bayesian approaches. Additionally, we introduce a novel method for implementing MMRM models (ie, analysis of response profile) using the nlmixed procedure.

Bayesian response adaptive randomization design with a composite endpoint of mortality and morbidity.

Allocating patients to treatment arms during a trial based on the observed responses accumulated up to the decision point, and sequential adaptation of this allocation, could minimize the expected number of failures or maximize total benefits to patients. In this study, we developed a Bayesian response-adaptive randomization (RAR) design targeting the endpoint of organ support-free days (OSFD) for patients admitted to the intensive care units. The OSFD is a mixture of mortality and morbidity assessed by the number of days of free of organ support within a predetermined post-randomization time-window. In the past, researchers treated OSFD as an ordinal outcome variable where the lowest category is death. We propose a novel RAR design for a composite endpoint of mortality and morbidity, for example, OSFD, by using a Bayesian mixture model with a Markov chain Monte Carlo sampling to estimate the posterior probability distribution of OSFD and determine treatment allocation ratios at each interim. Simulations were conducted to compare the performance of our proposed design under various randomization rules and different alpha spending functions. The results show that our RAR design using Bayesian inference allocated more patients to the better performing arm(s) compared to other existing adaptive rules while assuring adequate power and type I error rate control across a range of plausible clinical scenarios.

Extension of a conditional performance score for sample size recalculation rules to the setting of binary endpoints.

BACKGROUND: Sample size calculation is a central aspect in planning of clinical trials. The sample size is calculated based on parameter assumptions, like the treatment effect and the endpoint's variance. A fundamental problem of this approach is that the true distribution parameters are not known before the trial. Hence, sample size calculation always contains a certain degree of uncertainty, leading to the risk of underpowering or oversizing a trial. One way to cope with this uncertainty are adaptive designs. Adaptive designs allow to adjust the sample size during an interim analysis. There is a large number of such recalculation rules to choose from. To guide the choice of a suitable adaptive design with sample size recalculation, previous literature suggests a conditional performance score for studies with a normally distributed endpoint. However, binary endpoints are also frequently applied in clinical trials and the application of the conditional performance score to binary endpoints is not yet investigated. METHODS: We extend the theory of the conditional performance score to binary endpoints by suggesting a related one-dimensional score parametrization. We moreover perform a simulation study to evaluate the operational characteristics and to illustrate application. RESULTS: We find that the score definition can be extended without modification to the case of binary endpoints. We represent the score results by a single distribution parameter, and therefore derive a single effect measure, which contains the difference in proportions [Formula: see text] between the intervention and the control group, as well as the endpoint proportion [Formula: see text] in the control group. CONCLUSIONS: This research extends the theory of the conditional performance score to binary endpoints and demonstrates its application in practice.

Optimal futility stopping boundaries for binary endpoints.

BACKGROUND: Group sequential designs incorporating the option to stop for futility at the time point of an interim analysis can save time and resources. Thereby, the choice of the futility boundary importantly impacts the design's resulting performance characteristics, including the power and probability to correctly or wrongly stop for futility. Several authors contributed to the topic of selecting good futility boundaries. For binary endpoints, Simon's designs (Control Clin Trials 10:1-10, 1989) are commonly used two-stage designs for single-arm phase II studies incorporating futility stopping. However, Simon's optimal design frequently yields an undesirably high probability of falsely declaring futility after the first stage, and in Simon's minimax design often a high proportion of the planned sample size is already evaluated at the interim analysis leaving only limited benefit in case of an early stop. METHODS: This work focuses on the optimality criteria introduced by Schüler et al. (BMC Med Res Methodol 17:119, 2017) and extends their approach to binary endpoints in single-arm phase II studies. An algorithm for deriving optimized futility boundaries is introduced, and the performance of study designs implementing this concept of optimal futility boundaries is compared to the common Simon's minimax and optimal designs, as well as modified versions of these designs by Kim et al. (Oncotarget 10:4255-61, 2019). RESULTS: The introduced optimized futility boundaries aim to maximize the probability of correctly stopping for futility in case of small or opposite effects while also setting constraints on the time point of the interim analysis, the power loss, and the probability of stopping the study wrongly, i.e. stopping the study even though the treatment effect shows promise. Overall, the operating characteristics, such as maximum sample size and expected sample size, are comparable to those of the classical and modified Simon's designs and sometimes better. Unlike Simon's designs, which have binding stopping rules, the optimized futility boundaries proposed here are not adjusted to exhaust the full targeted nominal significance level and are thus still valid for non-binding applications. CONCLUSIONS: The choice of the futility boundary and the time point of the interim analysis have a major impact on the properties of the study design. Therefore, they should be thoroughly investigated at the planning stage. The introduced method of selecting optimal futility boundaries provides a more flexible alternative to Simon's designs with non-binding stopping rules. The probability of wrongly stopping for futility is minimized and the optimized futility boundaries don't exhibit the unfavorable properties of an undesirably high probability of falsely declaring futility or a high proportion of the planned sample evaluated at the interim time point.

Comparing Bayesian hierarchical meta-regression methods and evaluating the influence of priors for evaluations of surrogate endpoints on heterogeneous collections of clinical trials.

BACKGROUND: Surrogate endpoints, such as those of interest in chronic kidney disease (CKD), are often evaluated using Bayesian meta-regression. Trials used for the analysis can evaluate a variety of interventions for different sub-classifications of disease, which can introduce two additional goals in the analysis. The first is to infer the quality of the surrogate within specific trial subgroups defined by disease or intervention classes. The second is to generate more targeted subgroup-specific predictions of treatment effects on the clinical endpoint. METHODS: Using real data from a collection of CKD trials and a simulation study, we contrasted surrogate endpoint evaluations under different hierarchical Bayesian approaches. Each approach we considered induces different assumptions regarding the relatedness (exchangeability) of trials within and between subgroups. These include partial-pooling approaches, which allow subgroup-specific meta-regressions and, yet, facilitate data adaptive information sharing across subgroups to potentially improve inferential precision. Because partial-pooling models come with additional parameters relative to a standard approach assuming one meta-regression for the entire set of studies, we performed analyses to understand the impact of the parameterization and priors with the overall goals of comparing precision in estimates of subgroup-specific meta-regression parameters and predictive performance. RESULTS: In the analyses considered, partial-pooling approaches to surrogate endpoint evaluation improved accuracy of estimation of subgroup-specific meta-regression parameters relative to fitting separate models within subgroups. A random rather than fixed effects approach led to reduced bias in estimation of meta-regression parameters and in prediction in subgroups where the surrogate was strong. Finally, we found that subgroup-specific meta-regression posteriors were robust to use of constrained priors under the partial-pooling approach, and that use of constrained priors could facilitate more precise prediction for clinical effects in trials of a subgroup not available for the initial surrogacy evaluation. CONCLUSION: Partial-pooling modeling strategies should be considered for surrogate endpoint evaluation on collections of heterogeneous studies. Fitting these models comes with additional complexity related to choosing priors. Constrained priors should be considered when using partial-pooling models when the goal is to predict the treatment effect on the clinical endpoint.