Monitoring Of Clinical Trials Research Articles

Upstrapping to determine futility: predicting future outcomes nonparametrically from past data

BackgroundClinical trials often involve some form of interim monitoring to determine futility before planned trial completion. While many options for interim monitoring exist (e.g., alpha-spending, conditional power), nonparametric based interim monitoring methods are also needed to account for more complex trial designs and analyses. The upstrap is one recently proposed nonparametric method that may be applied for interim monitoring.MethodsUpstrapping is motivated by the case resampling bootstrap and involves repeatedly sampling with replacement from the interim data to simulate thousands of fully enrolled trials. The p-value is calculated for each upstrapped trial and the proportion of upstrapped trials for which the p-value criteria are met is compared with a pre-specified decision threshold. To evaluate the potential utility for upstrapping as a form of interim futility monitoring, we conducted a simulation study considering different sample sizes with several different proposed calibration strategies for the upstrap. We first compared trial rejection rates across a selection of threshold combinations to validate the upstrapping method. Then, we applied upstrapping methods to simulated clinical trial data, directly comparing their performance with more traditional alpha-spending and conditional power interim monitoring methods for futility.ResultsThe method validation demonstrated that upstrapping is much more likely to find evidence of futility in the null scenario than the alternative across a variety of simulations settings. Our three proposed approaches for calibration of the upstrap had different strengths depending on the stopping rules used. Compared to O’Brien-Fleming group sequential methods, upstrapped approaches had type I error rates that differed by at most 1.7% and expected sample size was 2–22% lower in the null scenario, while in the alternative scenario power fluctuated between 15.7% lower and 0.2% higher and expected sample size was 0–15% lower.ConclusionsIn this proof-of-concept simulation study, we evaluated the potential for upstrapping as a resampling-based method for futility monitoring in clinical trials. The trade-offs in expected sample size, power, and type I error rate control indicate that the upstrap can be calibrated to implement futility monitoring with varying degrees of aggressiveness and that performance similarities can be identified relative to considered alpha-spending and conditional power futility monitoring methods.

Enhancing Dementia Risk Mitigation: Standardized Digital Monitoring in Clinical Trials and Platform Design for At-Risk Elderly Individuals.

Dementia is becoming a significant public health concern, affecting approximately 130,000 individuals in Austria, whereby nearly 40% of the cases are attributed to modifiable risk factors. Multidomain lifestyle interventions have thereby demonstrated significant effects in reducing the risk of dementia. The goal was to define an interoperability framework to conduct standardized monitoring in clinical trials for enhancing dementia risk mitigation. In addition, the identified standards should be integrated into the components of the project. A step-by-step approach was used, where initially data collection, aggregation and harmonization was carried out with retrospective data from various clinical centers. Afterwards, the interoperability framework was defined including the prospective data that is gathered during a clinical trial. A guideline for integrating healthcare standards was developed and incorporated into the technical components for the clinical trial. The interoperability framework was designed in a scalable way and will be regularly updated for future needs.

Miniaturized inkjet-printed flexible ion-selective sensing electrodes with the addition of graphene in PVC layer for fast response real-time monitoring applications

In this letter, we propose a miniaturization scheme of inkjet printed ionic sensing electrodes by adding graphene into the ion-selective PVC film not only to reduce the impedance of the ionic liquid layer of the electrode but also to increase the electrode capacitance for the reduction of the response time. Based on the scheme, we present a fully inkjet-printed electrochemical ion-selective sensor comprising a working electrode and reference electrode, which are inkjet-printed Ag NPs/PEDOT:PSS-graphene/PVC-graphene and Ag/AgCl(s)/ionic liquid PVC-graphene layer structures, respectively. The printed ion-selective working electrode has been miniaturized to a size of 22,400 μm2 equivalent to a square shape of ∼150 × 150 μm2 comparable to the size of a human cell. By adding graphene to the ion selective PVC film, more than 90 % charge transfer resistance reduction can be achieved and the shunt capacitance is increased by 3.4-fold in shunt capacitance compared to the film without graphene, thereby more than 33 % reduction of the response time required to reach equilibrium. Meanwhile, these miniaturized potassium sensors using the working electrodes with/without adding graphene have been integrated with in-lab signal-processing and wireless-transmission module to yield similar results to the one measured by commercial electrochemical workstation showing a great potential for real-time monitoring in portable clinical trials. Specifically, the proposed sensor utilizing graphene-enhanced electrodes demonstrates a linearity uncertainty of 2.9 mV, which is approximately half of the uncertainty observed in the sensors lacking graphene integration.

Challenges implementing the FDA guidance RE diet stabilization and monitoring in clinical trials for IEMs

Challenges implementing the FDA guidance RE diet stabilization and monitoring in clinical trials for IEMs

Abstract 7625: The liquid biomarker thymidine kinase activity reflecting proliferation rate can provide guidance in drug development and address Project Optimus

Abstract Thymidine Kinase (TK) plays a basic role in DNA synthesis and cell proliferation. The enzyme is highly cell cycle dependent and correlates with cell growth state. Numerous studies have consistently shown elevated TK activity (TKa) in the blood of patients with solid tumors and active disease. DiviTum® TKa, a clinically and analytically validated FDA cleared assay, measures and quantifies TKa in serum or plasma in humans and animals. TKa as a biomarker offers valuable information at all stages of drug development, from drug efficacy and dose response studies in cell culture and xenograft models, to patient selection and treatment monitoring in clinical trials. The launch of FDA Project Optimus highlights the need for new biomarkers to assist in drug development to identify optimal dose. Using TKa to measure the impact on cell proliferation as a drug evaluation endpoint can confirm mechanism of action efficacy and a minimally effective dose of a new compound rather than a maximally tolerated dose. TKa can be used as a pharmacodynamic tool to improve study endpoints. TKa data in cultured cells and mouse xenografts demonstrates that a complete shutdown of tumor cell proliferation can be observed well before and at much lower drug-doses than when cell death or toxicity is used as the dose determination endpoint. Increased dosing of CDK4/6 inhibitors demonstrates immediate impact on TKa levels in cell lines and provides signals of drug effect at doses where cell viability is unaffected. After initiation of samuraciclib treatment, a CDK7 inhibitor, a significant reduction in TKa levels in blood from metastatic breast cancer (MBC) patients was observed, indicating inhibition of cell cycle progression. For patients on samuraciclib therapy and with stable disease before progression, TKa levels were significantly lower than pre-treatment (p=0.0038). Post-progression TKa levels increased to significantly higher levels than during therapy including the last sample measured before progression (p=0.014). Baseline blood TKa levels are predictive for progression free survival (PFS) allowing for the early stratification of patients with long vs short PFS. In patients with MBC, TKa levels during the first treatment cycle are strongly predictive for PFS. Suppressed TKa levels after 2 and 4 weeks of CDK4/6 inhibitor therapy predict longer PFS than high TKa levels at the same time points (HR 5.65; p< 0.0001). MBC patients monitored >3 months and showing low TKa levels have very low likelihood of progression within 30- to 60 days, <2% and <6%, respectively. TKa is a translational liquid biomarker that bridges results between preclinical and clinical studies, providing fundamental information for drug development decision making. Citation Format: Mattias Bergqvist, Hanna Ritzén, Amy Williams. The liquid biomarker thymidine kinase activity reflecting proliferation rate can provide guidance in drug development and address Project Optimus [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7625.

Design and monitoring of clinical trials with an interim analysis and a negative binomial endpoint

There are very rich publications devoted to group sequential design, adaptive design and trial monitoring for continuous, binary and time to event endpoints. Many authors also discuss fixed design, blinded sample size re-estimation design and group sequential design for studies with a negative binomial outcome. Nonetheless, literature is sparse in adaptive design for a trial with a negative binomial endpoint. The features of such an endpoint in a flexible trial design setting remains inadequately understood. In this research, we seek to bridge this knowledge gap by offering a thorough examination of utilizing data components from a two-stage adaptive design for unblinded conditional power calculation and corresponding sample size re-estimation. We also provide expression for calculating the probability of meeting the futility criterion to determine the appropriate timing for the interim analysis. To evaluate the performance of the design, we conduct simulations to assess its operation characteristics. Finally, we provide a helpful and illustrative example to demonstrate the practical applications of the methods.

Frequent response monitoring in chronic lymphocytic leukemia clinical trials: what is the value?

Frequent response monitoring in chronic lymphocytic leukemia clinical trials: what is the value?

Wearable Accelerometer and Gyroscope Sensors for Estimating the Severity of Essential Tremor.

Several validated clinical scales measure the severity of essential tremor (ET). Their assessments are subjective and can depend on familiarity and training with scoring systems. We propose a multi-modal sensing using a wearable inertial measurement unit for estimating scores on the Fahn-Tolosa-Marin tremor rating scale (FTM) and determine the classification accuracy within the tremor type. 17 ET participants and 18 healthy controls were recruited for the study. Two movement disorder neurologists who were blinded to prior clinical information viewed video recordings and scored the FTM. Participants drew a guided Archimedes spiral while wearing an inertial measurement unit placed at the mid-point between the lateral epicondyle of the humerus and the anatomical snuff box. Acceleration and gyroscope recordings were analyzed. The ratio of the power spectral density between frequency bands 0.5-4 Hz and 4-12 Hz, and the sum of power spectrum density over the entire spectrum of 2-74 Hz, for both accelerometer and gyroscope data, were computed. FTM was estimated using regression model and classification using SVM was validated using the leave-one-out method. Regression analysis showed a moderate to good correlation when individual features were used, while correlation was high ([Formula: see text] = 0.818) when suitable features of the gyro and accelerometer were combined. The accuracy for two-class classification of the combined features using SVM was 91.42% while for four-class it was 68.57%. Potential applications of this novel wearable sensing method using a wearable Inertial Measurement Unit (IMU) include monitoring of ET and clinical trials of new treatments for the disorder.

LightNet: a novel lightweight convolutional network for brain tumor segmentation in healthcare.

Diagnosis, treatment planning, surveillance, and the monitoring of clinical trials for brain diseases all benefit greatly from neuroimaging-based tumor segmentation. Recently, Convolutional Neural Networks (CNNs) have demonstrated promising results in enhancing the efficiency of image-based brain tumor segmentation. Most current work on CNNs, however, is devoted to creating increasingly complicated convolution modules to improve performance, which in turn raises the computing cost of the model. This work proposes a simple and effective feed-forward CNN, LightNet (Light Network). Based on multi-path and multi-level, it replaces traditional convolutional methods with light operations, which reduces network parameters and redundant feature maps. In the up-sampling stage, a light channel attention module is added to achieve richer multi-scale and spatial semantic feature information extraction of brain tumor. The performance of the network is evaluated in the Multimodal Brain Tumor Segmentation Challenge (BraTS 2015) dataset, and results are presented here alongside other high-performing CNNs. Results show comparable accuracy with other methods but with increased efficiency, segmentation performance, and reduced redundancy and computational complexity. The result is a high-performing network with a balance between efficiency and accuracy, allowing, for example, better energy performance on mobile devices.

Digital is the New Blood: Enabling the Present and the Future

AbstractBackgroundDigital is the new blood, where collection, use, and storage of raw digital data streams enabled by technology‐embedded sensors are replacing analog methods. Analyses of digital data extend beyond current applications, analogous to previously stored blood currently be used for omics/biomarker research. Today digital technologies are used to measure Alzheimer’s disease (AD)‐related clinical symptoms (e.g., cognition, function, and behavior), but future possibilities remain to be determined. Importantly, digital data collection can go beyond current intermittent sampling by employing internet‐connected applications and devices that allow real‐time monitoring and accurate detection of changes, even within a widely heterogeneous spectrum.MethodBeginning in 2005, the Framingham Heart Study (FHS) began digitally recording participants’ spoken responses to neuropsychological (NP) test questions, and in 2011 extended to a digital pen to record written responses. The emergence of wearable devices and smartphone applications has led to an expanded use of digital technologies beyond recordings during NP assessments. The Boston University Alzheimer’s Disease Research Center (BU ADRC) leverages digital sensors in multiple internet‐connected devices that longitudinally collect digital data, in both in‐person and remote settings.ResultThe first publication involving FHS digital data was in 2016; the remaining (n> 17) have been from 2020‐current. Digital data measuring cognition, function and behavior are being collected through the BU ADRC, where results to date indicate nearly 100% and 75% uptake of active and passive engagement applications, respectively, and 95% uptake for wearable devices (n = 60). Longitudinal assessments include > 3 months for ∼50 participants; > 1 year for n∼21 participants.ConclusionOpportunities for improved trial outcome measurement are evident in the broad range of outcomes digital sensors capture beyond traditional research methodologies. Digital tools are being deployed for rapid screening for clinical trial inclusion, but more transformative opportunities are still to come. Replacing the sporadic and highly heterogeneous measures of cognition and related behaviors with more accurate measures that track treatment impact in real‐time at the individual level will shift the concept of clinically meaningful outcomes from group indicators to personalized ones. Digital is central to the realization of virtual clinical trial monitoring for precision treatment effects.

A code for clinical trials centralized monitoring, sharing open-science solutions to high-quality data.

Monitoring of clinical trials is critical to the protection of human subjects and the conduct of high-quality research. Even though the adoption of risk-based monitoring (RBM) has been suggested for many years, the RBM approach has been less widespread than expected. Centralized monitoring is one of the RMB pillars, together with remote-site monitoring visits, reduced Source Data Verification (SDV) and Source Document Reviews (SDR). The COVID-19 pandemic promoted disruptions in the conduction of clinical trials, as on-site monitoring visits were adjourned. In this context, the transition to RBM by all actors involved in clinical trials has been encouraged. In order to ensure the highest quality of data within a COVID-19 clinical trial, a centralized monitoring tool alongside Case Report Forms (CRFs) and synchronous automated routines were developed at the clinical research platform, Fiocruz, Brazilian Ministry of Health. This paper describes how these tools were developed, their features, advantages, and limitations. The software codes, and the CRFs are available at the Fiocruz Data Repository for Research-Arca Dados, reaffirming Fiocruz's commitment to Open Science practices.

Specifications of qPCR based epigenetic immune cell quantification.

Immune monitoring is an important aspect indiagnostics and clinical trials for patients with compromised immune systems. Flow cytometry is the standard method forimmune cell counting but faces limitations. Bestpracticeguidelines are available, but lack of standardization complicates compliance with e.g., invitro diagnostic regulations. Limited sample availability forces immune monitoring to predominantly use population-based reference intervals. Epigenetic qPCR has evolved asalternative with broad applicability and low logistical demands. Analytical performance specifications (APS) havebeen defined for qPCR in several regulated fields including testing of genetically modified organisms or vector-shedding. APS were characterized using five epigenetic qPCR-based assays quantifying CD3+, CD4+, CD8+ T, B and NK cells in light of regulatory requirements. Epigenetic qPCR meets all specifications including bias, variability, linearity, ruggedness and sample stability as suggested by pertinent guidelines and regulations. The assays were subsequently applied to capillary blood from 25 normal donors over a 28-day period. Index of individuality (IoI) and reference change values were determined to evaluate potential diagnostic gains of individual reference intervals. Analysis of the IoI suggests benefits for individual over population-based references. Reference change values (RCVs) show that changes of approx. Fifty percent from prior measurement are suggestive for clinically relevant changes in any of the 5 cell types. The demonstrated precision, long-term stability and obtained RCVs render epigenetic cell counting a promising tool for immune monitoring in clinical trials and diagnosis.

Statistical rules for safety monitoring in clinical trials.

Protecting patient safety is an essential component of the conduct of clinical trials. Rigorous safety monitoring schemes are implemented for these studies to guard against excess toxicity risk from study therapies. They often include protocol-specified stopping rules dictating that an excessive number of safety events will trigger a halt of the study. Statistical methods are useful for constructing rules that protect patients from exposure to excessive toxicity while also maintaining the chance of a false safety signal at a low level. Several statistical techniques have been proposed for this purpose, but the current literature lacks a rigorous comparison to determine which method may be best suitable for a given trial design. The aims of this article are (1) to describe a general framework for repeated monitoring of safety events in clinical trials; (2) to survey common statistical techniques for creating safety stopping criteria; and (3) to provide investigators with a software tool for constructing and assessing these stopping rules. The properties and operating characteristics of stopping rules produced by Pocock and O'Brien-Fleming tests, Bayesian Beta-Binomial models, and sequential probability ratio tests (SPRTs) are studied and compared for common scenarios that may arise in phase II and III trials. We developed the R package "stoppingrule" for constructing and evaluating stopping rules from these methods. Its usage is demonstrated through a redesign of a stopping rule for BMT CTN 0601 (registered at Clinicaltrials.gov as NCT00745420), a phase II, single-arm clinical trial that evaluated outcomes in pediatric sickle cell disease patients treated by bone marrow transplant. Methods with aggressive stopping criteria early in the trial, such as the Pocock test and Bayesian Beta-Binomial models with weak priors, have permissive stopping criteria at late stages. This results in a trade-off where rules with aggressive early monitoring generally will have a smaller number of expected toxicities but also lower power than rules with more conservative early stopping, such as the O-Brien-Fleming test and Beta-Binomial models with strong priors. The modified SPRT method is sensitive to the choice of alternative toxicity rate. The maximized SPRT generally has a higher number of expected toxicities and/or worse power than other methods. Because the goal is to minimize the number of patients exposed to and experiencing toxicities from an unsafe therapy, we recommend using the Pocock or Beta-Binomial, weak prior methods for constructing safety stopping rules. At the design stage, the operating characteristics of candidate rules should be evaluated under various possible toxicity rates in order to guide the choice of rule(s) for a given trial; our R package facilitates this evaluation.

Smartwatch-derived sleep and heart rate measures complement step counts in explaining established metrics of MS severity

BackgroundPassive remote monitoring of patients with MS (PwMS) with sensor-based wearable technologies promises near-continuous evaluation with high ecological validity. Step counts correlate strongly with traditional measures of MS severity. We hypothesized that remote monitoring of sleep and heart rate will yield complementary information. MethodsWe recruited 31 PwMS and 31 age- and sex-matched healthy volunteers (HV) as part of the dreaMS feasibility study (NCT04413032). Fitbit Versa 2 smartwatches were worn for 6 weeks and provided a total of 25 features for activity, heart rate, and sleep. Features were selected based on their pairwise intercorrelation (Pearson |r| < 0.6), test-retest reliability (intraclass correlation coefficient ≥ 0.6 or median coefficient of variation < 0.2) and group comparisons between HV and PwMS with moderate disability (expanded disability status scale (EDSS) ≥ 3.5) (rank-biserial |r| ≥ 0.5). These selected features were correlated with clinical reference tests (EDSS, timed 25-foot walk (T25FW), MS-walking scale (MSWS-12)) in PwMS, and multivariate models adjusted for age, sex, and disease duration were compared. ResultsWe analyzed 28 PwMS (68% female, mean age 44 years, median EDSS 3.0) and 26 HV in our primary analysis. The objectively selected features discriminated well between HV and PwMS with moderate disability with rank-biserial r = 0.83 for Total number of steps, 0.51 for Deep sleep proportion, -0.51 for Median heart rate, 0.85 for Proportion very active, and 0.65 for Total number of floors. In PwMS they correlated strongly with the three clinical reference tests EDSS (strongest Spearman ρ = -0.75 for Proportion very active), T25FW (-0.75 for Total number of floors), and MSWS-12 (-0.72 for Total number of floors). Deep sleep proportion and Median heart rate complemented Total number of steps in explaining the variance of reference tests. ConclusionsActivity, deep sleep and heart rate measures can be derived reliably from smartwatches and contain independent clinically meaningful information about MS severity, highlighting their potential for continuous passive monitoring in both clinical trials and clinical care of PwMS.

Using 18F-AV-133 VMAT2 PET Imaging to Monitor Progressive Nigrostriatal Degeneration in Parkinson Disease.

There are limited validated biomarkers in Parkinson disease (PD) which substantially hinders the ability to monitor disease progression and consequently measure the efficacy of disease-modifying treatments. Imaging biomarkers, such as vesicular monoamine transporter type 2 (VMAT2) PET, enable enhanced diagnostic accuracy and detect early neurodegenerative changes associated with prodromal PD. This study sought to assess whether 18F-AV-133 VMAT2 PET is sensitive enough to monitor and quantify disease progression over a 2-year window. 18F-AV-133 PET scans were performed on participants with PD and REM sleep behavior disorder (RBD) and neurologic controls (NC). All participants were scanned twice ∼26 months apart. Regional tracer retention was calculated with a primary visual cortex reference region and expressed as the standard uptake volume ratio. Regions of interest included caudate, anterior, and posterior putamen. At the time of scanning, participants underwent clinical evaluation including UPDRSMOTOR test, Sniffin' Sticks, and Hospital Anxiety and Depression Score. Over the 26-month interval, a significant decline in PET signal was observed in all 3 regions in participants with PD (N = 26) compared with NC (N = 12), consistent with a decrease in VMAT2 level and ongoing neurodegeneration. Imaging trajectory calculations suggest that the neurodegeneration in PD occurs over ∼33 years [CI: 27.2-39.5], with ∼10.5 years [CI: 9.1-11.3] of degeneration in the posterior putamen before it becomes detectable on a VMAT2 PET scan, a further ∼6.5 years [CI: 1.6-12.7] until symptom onset, and a further ∼3 years [CI: 0.3-8.7] until clinical diagnosis. Over a 2-year period, 18F-AV-133 VMAT2 PET was able to detect progression of nigrostriatal degeneration in participants with PD, and it represents a sensitive tool to identify individuals at risk of progression to PD, which are currently lacking using clinical readouts. Trajectory models propose that there is nigrostriatal degeneration occurring for 20 years before clinical diagnosis. These data demonstrate that VMAT2 PET provides a sensitive measure to monitor neurodegenerative progression of PD which has implications for PD diagnostics and subsequently clinical trial patient stratification and monitoring. This study provides Class IV evidence that VMAT2 PET can detect patients with Parkinson disease and quantify progression over a 2-year window.

Expectancies for Subjective and Antidepressant Effects in Psilocybin Users

Expectancy effects for many psychoactive substances appear to play a role in consumption, problematic use, subjective responses to acute administration, and subsequent effects. Expectancies of psychedelics have received little attention in published research despite their reputation for creating dramatic changes in subjective state. Psilocybin-assisted treatment (PAT) improves depression, but details of associated expected effects remain incomplete. Previous work suggests that PAT-induced changes in depression and other forms of well-being covary with specific subjective effects of psilocybin. Self-reports from over 500 psilocybin-using individuals revealed correlations with relevant subjective effects that appeared to mediate antidepressant effects in previous work (e.g., Mystical Experiences, Ego dissolution, and Emotional Breakthrough). Correlations with demographic variables, current depressive symptoms, and general hallucinogen involvement were markedly smaller. Expectancies on specific depressive symptoms also paralleled retrospective reports of other psychedelic-induced antidepressant effects. Regression revealed that current depressive symptoms, ego dissolution, and emotional breakthroughs accounted for unique variance in expected antidepressant effects, but expectancies on mystical effects did not. Although limitations suggest cautious interpretation, psilocybin-using individuals appear to hold relevant expectancies about subjective and antidepressant effects, which might play a role in treatment outcomes worthy of monitoring in clinical trials.

Application of Statistical Methods for Central Statistical Monitoring and Implementations on the German Multiple Sclerosis Registry

Monitoring of clinical trials is a fundamental process required by regulatory agencies. It assures the compliance of a center to the required regulations and the trial protocol. Traditionally, monitoring teams relied on extensive on-site visits and source data verification. However, this is costly, and the outcome is limited. Thus, central statistical monitoring (CSM) is an additional approach recently embraced by the International Council for Harmonisation (ICH) to detect problematic or erroneous data by using visualizations and statistical control measures. Existing implementations have been primarily focused on detecting inlier and outlier data. Other approaches include principal component analysis and distribution of the data. Here we focus on the utilization of comparisons of centers to the Grand mean for different model types and assumptions for common data types, such as binomial, ordinal, and continuous response variables. We implement the usage of multiple comparisons of single centers to the Grand mean of all centers. This approach is also available for various non-normal data types that are abundant in clinical trials. Further, using confidence intervals, an assessment of equivalence to the Grand mean can be applied. In a Monte Carlo simulation study, the applied statistical approaches have been investigated for their ability to control type I error and the assessment of their respective power for balanced and unbalanced designs which are common in registry data and clinical trials. Data from the German Multiple Sclerosis Registry (GMSR) including proportions of missing data, adverse events and disease severity scores were used to verify the results on Real-World-Data (RWD).

Targeting Bcl-xL is a potential therapeutic strategy for extranodal NK/T cell lymphoma

Extranodal natural killer/T cell lymphoma, nasal type (ENKTL) is an aggressive lymphoid malignancy with a poor prognosis and lacks standard treatment. Targeted therapies are urgently needed. Here we systematically investigated the druggable mechanisms through chemogenomic screening and identified that Bcl-xL-specific BH3 mimetics effectively induced ENKTL cell apoptosis. Notably, the specific accumulation of Bcl-xL, but not other Bcl-2 family members, was verified in ENKTL cell lines and patient tissues. Furthermore, Bcl-xL high expression was shown to be closely associated with worse patient survival. The critical role of Bcl-xL in ENKTL cell survival was demonstrated utilizing selective inhibitors, genetic silencing, and a specific degrader. Additionally, the IL2-JAK1/3-STAT5 signaling was implicated in Bcl-xL dysregulation. Invivo, Bcl-xL inhibition reduced tumor burden, increased apoptosis, and prolonged survival in ENKTL cell line xenograft and patient-derived xenograft models. Our study indicates Bcl-xL as a promising therapeutic target for ENKTL, warranting monitoring in ongoing clinical trials by targeting Bcl-xL.

The Impact of an Enrolment in Clinical Trial on Tolerance and Pathological Response for Patients Treated by Neoadjuvant MVAC Against an Invasive Bladder Cancer. A Retrospective Comparative Study

IntroductionMVAC (Methotrexate, Vinblastine, Adriamycin, and Cisplatin) neoadjuvant chemotherapy a standard treatment for invasive bladder cancer is associated with mainly haematological toxicities. Randomized clinical trials remain a gold standard for treatment outcomes and efficacy assessment. Patients enrolled in clinical trials are selected and tend to benefit from a stricter follow-up unlike everyday clinical practice patients. Conversely, real-life observational studies better define the effectiveness of treatments in clinical routine practice. The aim of this study is to analyse the impact of clinical trial monitoring on MVAC-related toxicities. Material and MethodsPatients with an infiltrative localized bladder cancer treated by MVAC neoadjuvant chemotherapy between 2013 and 2019 were enrolled, and divided into 2 groups: patients included in a clinical trial namely “VESPER study” during their treatment and patients treated in clinical routine practice. ResultsOut of 59 patients were enrolled in this retrospective study, 13 patients were included in a clinical trial. Clinical characteristics were similar between the 2 groups. Comorbidities were more frequent in the nonclinical trial group (NCTG). Completed 6 cures treatment proportion was higher in the clinical trial group (CTG) (69.2% vs. 50%). Yet, in this group, patients had more doses reduction (38.5% vs. 19.6%). The proportion of complete pathologic response was higher in patients enrolled in clinical trial (53.8% vs. 39.1%). Statistically, the expected stricter monitoring due to clinical trial enrolment had no impact on the complete pathologic response and clinically relevant toxicities. DiscussionWhen compared to conventional clinical practice, clinical trial enrolment induced no significant difference on the pathologic complete response or toxicity rate. Further large prospective studies are needed to confirm these data.

Epistemic uncertainty in Bayesian predictive probabilities

ABSTRACT Bayesian predictive probabilities have become a ubiquitous tool for design and monitoring of clinical trials. The typical procedure is to average predictive probabilities over the prior or posterior distributions. In this paper, we highlight the limitations of relying solely on averaging, and propose the reporting of intervals or quantiles for the predictive probabilities. These intervals formalize the intuition that uncertainty decreases with more information. We present four different applications (Phase 1 dose escalation, early stopping for futility, sample size re-estimation, and assurance/probability of success) to demonstrate the practicality and generality of the proposed approach.