SEARCH · Search bioRxiv

Results for “clinical trials”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 recordsLinked to original sources

bioRxiv2023

Informing virtual clinical trials of hepatocellular carcinoma with spatial multi-omics analysis of a human neoadjuvant immunotherapy clinical trial

Human clinical trials are important tools to advance novel systemic therapies improve treatment outcomes for cancer patients. The few durable treatment options have led to a critical need to advance new therapeutics in hepatocellular carcinoma (HCC). Recent human clinical trials have shown that new combination immunotherapeutic regimens provide unprecedented clinical response in a subset of patients. Computational methods that can simulate tumors from mathematical equations describing cellular and molecular interactions are emerging as promising tools to simulate the impact of therapy entirely in silico. To facilitate designing dosing regimen and identifying potential biomarkers, we developed a new computational model to track tumor progression at organ scale while reflecting the spatial heterogeneity in the tumor at tissue scale in HCC. This computational model is called a spatial quantitative systems pharmacology (spQSP) platform and it is also designed to simulate the effects of combination immunotherapy. We then validate the results from the spQSP system by leveraging real-world spatial multi-omics data from a neoadjuvant HCC clinical trial combining anti-PD-1 immunotherapy and a multitargeted tyrosine kinase inhibitor (TKI) cabozantinib. The model output is compared with spatial data from Imaging Mass Cytometry (IMC). Both IMC data and simulation results suggest closer proximity between CD8 T cell and macrophages among non-responders while the reverse trend was observed for responders. The analyses also imply wider dispersion of immune cells and less scattered cancer cells in responders samples. We also compared the model output with Visium spatial transcriptomics analyses of samples from post-treatment tumor resections in the original clinical trial. Both spatial transcriptomic data and simulation results identify the role of spatial patterns of tumor vasculature and TGF{beta} in tumor and immune cell interactions. To our knowledge, this is the first spatial tumor model for virtual clinical trials at a molecular scale that is grounded in high-throughput spatial multi-omics data from a human clinical trial.

bioengineering↗

bioRxiv2017

MatchMiner: An open source computational platform for real-time matching of cancer patients to precision medicine clinical trials using genomic and clinical criteria

BackgroundMolecular profiling of cancers is now routine at many cancer centers, and the number of precision cancer medicine clinical trials, which are informed by profiling, is steadily rising. Additionally, these trials are becoming increasingly complex, often having multiple arms and many genomic eligibility criteria. Currently, it is a challenging for physicians to match patients to relevant clinical trials using the patients genomic profile, which can lead to missed opportunities. Automated matching against uniformly structured and encoded genomic eligibility criteria is essential to keep pace with the complex landscape of precision medicine clinical trials.\n\nResultsTo meet these needs, we built and deployed an automated clinical trial matching platform called MatchMiner at the Dana-Farber Cancer Institute (DFCI). The platform has been integrated with Profile, DFCIs enterprise genomic profiling project, which contains tumor profile data for >20,000 patients, and has been made available to physicians across the Institute. As no current standard exists for encoding clinical trial eligibility criteria, a new language called Clinical Trial Markup Language (CTML) was developed, and over 178 genomically-driven clinical trials were encoded using this language. The platform is open source and freely available for adoption by other institutions.\n\nConclusionMatchMiner is the first open platform developed to enable computational matching of patient-specific genomic profiles to precision cancer medicine clinical trials. Creating MatchMiner required developing clinical trial eligibility standards to support genome-driven matching and developing intuitive interfaces to support practical use-cases. Given the complexity of tumor profiling and the rapidly changing multi-site nature of genome-driven clinical trials, open source software is the most efficient, scalable, and economical option for matching cancer patients to clinical trials.

bioinformatics↗

bioRxiv2024

Virtual clinical trial reveals significant clinical potential of targeting tumour-associated macrophages and microglia to treat glioblastoma

Glioblastoma is the most aggressive primary brain tumour, with a median survival of just fifteen months with treatment. Standard-of-care (SOC) for glioblastoma consists of resection followed by radio- and chemotherapy. Clinical trials involving PD-1 inhibition with nivolumab in combination with SOC failed to increase overall survival. A quantitative understanding of the interactions between the tumour and its immune environment driving treatment outcomes is currently lacking. As such, we developed a mathematical model of tumour growth that considers cytotoxic CD8+ T cells, pro- and antitumoral tumour-associated macrophages and microglia (TAMs), SOC, and nivolumab. Our results show that PD-1 inhibition fails due to a lack of CD8+ T cell recruitment during treatment explained by TAM-driven immunosuppressive mechanisms. Using our model, we studied five TAM-targeting strategies currently under investigation for solid tumours. Our model predicts that while reducing TAM numbers does not improve prognosis, altering their functions to counter their protumoral properties has the potential to considerably reduce post-treatment tumour burden. In particular, restoring antitumoral TAM phagocytic activity through anti-CD47 treatment in combination with SOC was predicted to nearly eradicate the tumour. By studying time-varying efficacy with the same half-life as the anti-CD47 antibody Hu5F9-G4, our model predicts that repeated dosing of anti-CD47 provides sustained control of tumour growth. Thus, we propose that targeting TAMs by enhancing their antitumoral properties is a highly promising avenue to treat glioblastoma and warrants future clinical development. Together, our results provide proof-of-concept that mechanistic mathematical modelling can uncover the mechanisms driving treatment outcomes and explore the potential of novel treatment strategies for hard-to-treat tumours like glioblastoma.

immunology↗

bioRxiv2021

Should deep-sequenced amplicons become the new gold-standard for analysing malaria drug clinical trials?

BackgroundRegulatory clinical trials are required to ensure the continued supply and deployment of effective antimalarial drugs. Patient follow-up in such trials typically lasts several weeks as the drugs have long half-lives and new infections often occur during this period. "Molecular correction" is therefore used to distinguish drug failures from new infections. The current WHO-recommend method for molecular correction uses length-polymorphic alleles at highly diverse loci but is inherently poor at detecting low density clones in polyclonal infections. This likely leads to substantial underestimates of failure rates, delaying the replacement of failing drugs with potentially lethal consequences. Deep sequenced amplicons (AmpSeq) substantially increase the detectability of low-density clones and may offer a new "gold standard" for molecular correction. MethodsPharmacological simulation of clinical trials was used to evaluate the suitability of AmpSeq for molecular correction. We investigated the impact of factors such as the number of amplicon loci analysed, the informatics criteria used to distinguish genotyping noise from real low density signals, the local epidemiology of malaria transmission, and the potential impact of genetic signals from gametocytes. ResultsAmpSeq greatly improved molecular correction and provided accurate drug failure rate estimates. The use of 3 to 5 amplicons was sufficient, and simple, non-statistical, criteria could be used to classify recurrent infections as drug failures or new infections. ConclusionsThese results strongly endorse the deployment of AmpSeq as the standard for molecular correction in regulatory trials, with its potential extension into routine surveillance once the requisite technical support becomes established.

microbiology↗

bioRxiv2017

Statistical power of clinical trials has increased whilst effect size remained stable: an empirical analysis of 137 032 clinical trials between 1975-2017

BackgroundBiomedical studies with low statistical power are a major concern in the scientific community and are one of the underlying reasons for the reproducibility crisis in science. If randomized clinical trials, which are considered the backbone of evidence-based medicine, also suffer from low power, this could affect medical practice.\n\nMethodsWe analysed the statistical power in 137 032 clinical trials between 1975 and 2017 extracted from meta-analyses from the Cochrane database of systematic reviews. We determined study power to detect standardized effect sizes according to Cohen, and in meta-analysis with p-value below 0.05 we based power on the meta-analysed effect size. Average power, effect size and temporal patterns were examined.\n\nResultsThe number of trials with power [≥]80% was low but increased over time: from 9% in 1975-1979 to 15% in 2010-2014. This increase was mainly due to increasing sample sizes, whilst effect sizes remained stable with a median Cohens h of 0.21 (IQR 0.12-0.36) and a median Cohens d of 0.31 (0.19-0.51). The proportion of trials with power of at least 80% to detect a standardized effect size of 0.2 (small), 0.5 (moderate) and 0.8 (large) was 7%, 48% and 81%, respectively.\n\nConclusionsThis study demonstrates that sufficient power in clinical trials is still problematic, although the situation is slowly improving. Our data encourages further efforts to increase statistical power in clinical trials to guarantee rigorous and reproducible evidence-based medicine.

epidemiology↗

bioRxiv2019

An interactive retrieval system for clinical trial studies with context-dependent protocol elements

A clinical trial protocol defines the procedures that should be performed during a clinical trial. Every clinical trial begins with the design of its protocol. While designing the protocol, most researchers refer to electronic databases and extract protocol elements using a keyword search. However, state-of-the-art retrieval systems only offer text-based searches for user-entered keywords. In this study, we present an interactive retrieval system with a context-dependent and protocol-element-selection function for successfully designing a clinical trial protocol. To do this, we first introduce a database for a protocol retrieval system constructed from individual protocol data extracted from 184,634 clinical trials and 13,210 frame structures of clinical trial protocols. The database contains various semantic information that enables the protocols to be filtered during the search operation. Based on the database, we developed a web application called the clinical trial protocol database system (CLIPS; available at https://corus.kaist.edu/clips), which enables an interactive search by utilizing protocol elements. CLIPS provides the options to select the next element according to the previous element in the form of a connected tree, thus enabling an interactive search for combinations of protocol elements. The validation results show that our method achieves better performance than existing databases in predicting phenotypic features.

bioinformatics↗

bioRxiv2018

Genetic variability and potential effects on clinical trial outcomes: perspectives in Parkinson’s disease

BackgroundImproper randomization in clinical trials can result in the failure of the trial to meet its primary end-point. The last [~]10 years have revealed that common and rare genetic variants are an important disease factor and sometimes account for a substantial portion of disease risk variance. However, the burden of common genetic risk variants is not often considered in the randomization of clinical trials and can therefore lead to additional unwanted variance between trial arms. We simulated clinical trials to estimate false negative and false positive rates and investigated differences in single variants and mean genetic risk scores (GRS) between trial arms to investigate the potential effect of genetic variance on clinical trial outcomes at different sample sizes.\n\nMethodsSingle variant and genetic risk score analyses were conducted in a clinical trial simulation environment using data from 5851 Parkinsons Disease patients as well as two simulated virtual cohorts based on public data. The virtual cohorts included a GBA variant cohort and a two variant interaction cohort. Data was resampled at different sizes (n = 200-5000 for the Parkinsons Disease cohort) and (n = 50-800 and n = 50-2000 for virtual cohorts) for 1000 iterations and randomly assigned to the two arms of a trial. False negative and false positive rates were estimated using simulated clinical trials, and percent difference in genetic risk score and allele frequency was calculated to quantify disparity between arms.\n\nFindingsSignificant genetic differences between the two arms of a trial are found at all sample sizes. Approximately 90% of the iterations had at least one statistically significant difference in individual risk SNPs between each trial arm. Approximately 10% of iterations had a statistically significant difference between trial arms in polygenic risk score mean or variance. For significant iterations at sample size 200, the average percent difference for mean GRS between trial arms was 130.87%, decreasing to 29.87% as sample size reached 5000. In the GBA only simulations we see an average 18.86% difference in GRS scores between trial arms at n = 50, decreasing to 3.09% as sample size reaches 2000. Balancing patients by genotype reduced mean percent difference in GRS between arms to 36.71% for the main cohort and 2.00% for the GBA cohort at n = 200. When adding a drug effect to the simulations, we found that unbalanced genetics with an effect on the chosen measurable clinical outcome can result in high false negative rates among trials, especially at small sample sizes. At a sample size of n = 50 and a targeted drug effect of -0.5 points in UPDRS per year, we discovered 33.9% of trials resulted in false negatives.\n\nInterpretationsOur data support the hypothesis that within genetically unmatched clinical trials, particularly those below 1000 participants, heterogeneity could confound true therapeutic effects as expected. This is particularly important in the changing environment of drug approvals. Clinical trials should undergo pre-trial genetic adjustment or, at the minimum, post-trial adjustment and analysis for failed trials. Clinical trial arms should be balanced on genetic risk variants, as well as cumulative variant distributions represented by GRS, in order to ensure the maximum reduction in trial arm disparities. The reduction in variance after balancing allows smaller sample sizes to be utilized without risking the large disparities between trial arms witnessed in typical randomized trials. As the cost of genotyping will likely be far less than greatly increasing sample size, genetically balancing trial arms can lead to more cost-effective clinical trials as well as better outcomes.

genomics↗

bioRxiv2019

A model of Zebrafish Avatar for co-clinical trials

Animal Avatars and co-clinical trials represent an emerging concept for implementing schemes of personalized medicine in oncology. In a co-clinical trial, the cancer cells of the patient tumor are xenotransplanted in the animal Avatar for drug efficacy studies and data collected in the animal trial are used to plan the best drug treatment in the patient trial. Recently, zebrafish has been proposed for implementing Avatar models but the lack of a general criterion for chemotherapy dose conversion from humans to fishes represents a limitation for conducting co-clinical trials.\n\nHere, we validate a simple, reliant and cost-effective Avatar model based on the use of zebrafish larvae; by crossing data from safety and efficacy studies, we found a basic formula for the estimation of the dose to be used for running co-clinical trials and we validate it in a clinical study enrolling 24 adult patients with solid cancers (XenoZ, NCT03668418).

cancer biology↗

bioRxiv2018

Bridging the translational gap: Implementation of multimodal small animal imaging strategies for tumor burden assessment in a co-clinical trial

In designing co-clinical cancer studies, preclinical imaging brings unique challenges that emphasize the gap between man and mouse. Our group is developing quantitative imaging methods for the preclinical arm of a co-clinical trial studying immunotherapy and radiotherapy in a soft tissue sarcoma model. In line with treatment for patients enrolled in the clinical trial SU2C-SARC032, primary mouse sarcomas are imaged with multi-contrast micro-MRI (T1 weighted, T2 weighted, and T1 with contrast) before and after immune checkpoint inhibition and pre-operative radiation therapy. Similar to the patients, after surgery the mice will be screened for lung metastases with micro-CT using respiratory gating. A systems evaluation was undertaken to establish a quantitative baseline for both the MR and micro-CT systems against which others systems might be compared. We have constructed imaging protocols which provide clinically-relevant resolution and contrast in a genetically engineered mouse model of sarcoma. We have employed tools in 3D Slicer for semi-automated segmentation of both MR and micro-CT images to measure tumor volumes efficiently and reliably in a large number of animals. Assessment of tumor burden in the resulting images was precise, repeatable, and reproducible. Furthermore, we have implemented a publicly accessible platform for sharing imaging data collected during the study, as well as protocols, supporting information, and data analyses. In doing so, we aim to improve the clinical relevance of small animal imaging and begin establishing standards for preclinical imaging of tumors from the perspective of a co-clinical trial.

bioengineering↗

bioRxiv2018

The design, analysis and application of mouse clinical trials in oncology drug development

Mouse clinical trials (MCTs) are becoming widely used in pre-clinical oncology drug development. In this study, we provide some general guidelines on the design, analysis and application of MCTs. We first established empirical quantitative relationships between mouse number and measurement accuracy for both categorical and continuous efficacy endpoints, and showed that more mice are needed to achieve given accuracy for syngeneic models than for PDXs and CDXs. There is considerable disagreement between categorical methods on calling drug responses as objective response, indicating limitations of such approaches. We then introduced linear mixed models, or LMMs, to describe MCTs as clustered longitudinal studies, which explicitly model growth and drug response heterogeneities across mouse models and among mice within a mouse model. Several case studies were used to demonstrate the advantages of LMMs in discovering biomarkers and exploring a drugs mechanism of action. We also introduced the additive frailty models to perform survival analysis on MCTs, which more accurately estimate hazard ratios by modeling the clustered population structures in MCTs. We performed computational simulations for LMMs and frailty models to generate statistical power curves, and showed that statistical power is close for designs with similar total number of mice at given drug efficacy. Finally, we explained how MCTs can explain discrepant results in clinical trials, hence, MCTs are more than preclinical versions of clinical trials but possess their unique values. Results in the report will make MCTs a better tool for oncology drug development.

cancer biology↗

bioRxiv2015

Why clinical trials are terminated

BackgroundEvidence-based clinical practice relies on unbiased reporting of negative results. Meta-analysis of drug safety and efficacy across many clinical trials is difficult given the unconstrained nature of reasons that are provided to ClinicalTrials.gov to explain clinical trial terminations.\n\nMethods and FindingsWe scanned all trials in ClinicalTrials.gov marked with the \"terminated\" status (N=3122), meaning the trial had been stopped before the scheduled end date. Under the current reporting framework, any number of reasons may be given for termination, and these need not conform to a controlled vocabulary. Here we develop a controlled vocabulary for trial termination, and map each terminated trial to as many as three vocabulary terms. Mapping to this \"ontology of termination\" allows further analysis and conclusions. First, we identify the subset of terminated trials that ended citing safety concerns (6.2%) or failure to establish efficacy (10.8%), and were further able to stratify these rates across trials of different phases. Second, we examine termination reasons where a stricter data model could have preserved more evidentiary value, either because the data model was misused (7.6%) or because the given reason left unclear whether the decision to terminate was based on analysis of the data (74.9%, with 20.4% mentioning a decision-maker that may have had access to the data). Third, we show that imposing a controlled vocabulary of reasons for termination would avoid ambiguity and improve the evidentiary value of clinical trials.\n\nConclusionsWe encourage wider use of an \"ontology of termination\" and propose four questions that should be posed on trial termination. These simple steps would promote transparency and enable ready access to negative trial results for meta-analysis.

Scientific Communication and Education↗

bioRxiv2018

Performance of an IAVI-African Network of Clinical Research Laboratories in Standardized ELISpot and Peripheral Blood Mononuclear Cell Processing in Support of HIV Vaccine Clinical Trials

Immunological assays performed in different laboratories participating in multi-centre clinical trials must be standardized in order to generate comparable and reliable data. This entails standardized procedures for sample collection, processing, freezing and storage. The International AIDS Vaccine Initiative (IAVI) partnered with local institutions to establish Good Clinical Laboratory Practice (GCLP)-accredited laboratories to support clinical trials in Africa, Europe and Asia. Here we report on the performance of seven laboratories based in Africa and Europe in the interferon-gamma enzyme-linked immunospot (IFN-{gamma} ELISpot) assay and peripheral blood mononuclear cell (PBMC) processing over four years. Characterized frozen PBMC samples from 48 volunteer blood packs processed at a central laboratory were sent to participating laboratories. For each stimulus, there were 1751 assays performed over four years. 98% of these ELISpot data were within acceptable ranges with low responses to mock stimuli. There were no significant differences in ELISpot responses at five laboratories actively conducting immunological analyses in support of IAVI sponsored clinical trials or HIV research. In a separate study, 1,297 PBMC samples isolated from healthy HIV-1 negative participants in clinical trials of two prophylactic HIV vaccine candidates were analysed for PBMC yield from fresh blood and cell recovery and viability following freezing and thawing. 94 % and 96 % of samples had fresh PBMC viabilities and cell yields within the pre-defined acceptance criteria while for frozen PBMC, 99 % and 96 % of samples had acceptable viabilities and cell recoveries respectively, along with acceptable ELISpot responses in 95%. These findings demonstrate the competency of laboratories across different continents to generate comparable and reliable data in support of clinical trials.\n\nImportanceThere is a need for the establishment of an African network of laboratories to support large clinical trials across the continent to support and further the development of vaccine candidates against emerging infectious diseases such as Ebola, Zika and dengue viruses and the continued HIV-1 pandemic. This is particularly true in sub-Saharan Africa where the HIV-1 pandemic is most severe. In this report we have demonstrated by using standardized SOPs, training, equipment and reagents that GCLP-accredited clinical trial laboratories based in Africa and Europe can process clinical trial samples and maintain cell integrity and functionality demonstrated by IFN-{gamma} ELISpot testing, producing comparable and reliable data.

immunology↗

bioRxiv2018

Age of onset in genetic prion disease and the design of preventive clinical trials

Regulatory agencies worldwide have adopted programs to facilitate drug development for diseases where the traditional approach of a randomized trial with a clinical endpoint is expected to be prohibitively lengthy or difficult. Here we provide quantitative evidence that this criterion is met for the prevention of genetic prion disease. We assemble age of onset or death data from N=1,094 individuals with high penetrance mutations in the prion protein gene (PRNP), generate survival and hazard curves, and estimate statistical power for clinical trials. We show that, due to dramatic and unexplained variability in age of onset, randomized preventive trials would require hundreds or thousands of at-risk individuals in order to be statistically powered for an endpoint of clinical onset, posing prohibitive cost and delay and likely exceeding the number of individuals available for such trials. Instead, the characterization of biomarkers suitable to serve as surrogate endpoints will be essential for the prevention of genetic prion disease. Biomarker-based trials may require post-marketing studies to confirm clinical benefit. Parameters such as longer trial duration, increased enrollment, and the use of historical controls in a post-marketing study could provide opportunities for subsequent determination of clinical benefit.

neuroscience↗

bioRxiv2018

Professional medical writing support and the quality, ethics and timeliness of clinical trial reporting: a systematic review

BackgroundMany authors choose to work with professional medical writers when reporting the results of clinical trials. We conducted a systematic review to examine the relationship between professional medical writing support (PMWS) and the quality, ethics and timeliness of publications reporting clinical trials.\n\nMethodsUsing terms related to medical writer and observational study, we searched MEDLINE and Embase (no date limits), as well as abstracts and posters from meetings of the International Society for Medical Publication Professionals (ISMPP; 2014-2017). We also hand-searched the journals Medical Writing and The Write Stuff (2014-2017), and the bibliographies of studies identified in the electronic searches. We screened the results to identify studies that compared the quality, ethics and timeliness of clinical trial publications written with and without declared PMWS.\n\nResultsOur searches identified 97 potentially relevant studies, of which 89 were excluded during screening and full paper review. The remaining eight studies compared 849 publications with PMWS with 2073 articles developed without such support. In these eight studies, PMWS was shown to be associated with: increased adherence to Consolidated Standards of Reporting Trials (CONSORT) guidelines (in 3/3 studies in which this was assessed); publication in journals with an impact factor (one study); a higher quality of written English (one study); and a lower likelihood of reporting non-pre-specified outcomes (one study). PMWS was not associated with increased adherence to CONSORT for Abstracts guidelines (one study) or with the impact of published articles (mean number of citations per year, mean number of article views per year and Altmetric score; one study). In studies that assessed timeliness of publication, PMWS was associated with a reduced time from last patient visit in clinical trials to primary publication (one study), whereas time from submission to acceptance showed inconsistent results (two studies).\n\nConclusionsThis systematic review of eight observational studies suggests that PMWS increases the overall quality of reporting of clinical trials and may improve the timeliness of publication.

scientific communication and education↗

bioRxiv2016

Optimizing Communication of Emergency Response Adaptive Randomization Clinical Trials to Potential Participants

Introduction: Acute clinical stroke trials are challenging to communicate to patients and families considering participation. Response adaptive randomization (RAR) is a technique that alters the proportion of trial subjects receiving active treatment, based on the outcomes of previous subjects. We aimed to determine how well interactive videos would improve understanding of a simulated acute stroke trial scenario that incorporated a design with RAR. Methods: We performed a cross-sectional study of emergency department patients who were without stroke, altered mental status, or critical illness. Subjects viewed a hypothetical stroke and clinical trial scenario. They were randomized into one of four groups with either an RAR or fixed randomization clinical trial design and with either a standard consent video, or an interactive video. Results: We enrolled 720 participants. In the RAR group with interactive video, 128 out of 149 (85.9%) of the subjects were able to correctly identify the allocation method, compared to the 172 out of 285 (61.6%) in the RAR group with the uninterrupted video for an absolute increase of 25.6% (95% CI 17,33%). The RAR group with interactive video had a higher odds of correct identification of allocation method (O.R. 2.767, 95% CI [1.011,7.570] while controlling for age, sex, ethnicity, education, self-reported understanding of protocol, stroke awareness and agreement to participate in trial. Conclusions: The interactive video increased participant understanding of an RAR design in a simulated stroke scenario. Future research should focus on whether acute trial recruitment can be enhanced using similar techniques.

clinical trials↗

bioRxiv2018

FDAAA TrialsTracker: A live informatics tool to monitor compliance with FDA requirements to report clinical trial results

IntroductionNon-publication of clinical trials results is an ongoing issue. In 2016 the US government updated the results reporting requirements to ClinicalTrials.gov for trials covered under the FDA Amendments Act 2007. We set out to develop and deliver an online tool which publicly monitors compliance with these reporting requirements, facilitates open public audit, and promotes accountability. MethodsWe conducted a review of the relevant legislation to extract the requirements on reporting results. Specific areas of the statutes were operationalized in code based on the results of our policy review, publicly available data from ClinicalTrials.gov, and communications with ClinicalTrials.gov staff. We developed methods to identify trials required to report results, using publicly available registry data; to incorporate additional relevant information such as key dates and trial sponsors; and to determine when each trial became due. This data was then used to construct a live tracking website. ResultsThere were a number of administrative and technical hurdles to successful operationalization of our tracker. Decisions and assumptions related to overcoming these issues are detailed along with clarifications directly from ClinicalTrials.gov. The FDAAA TrialsTracker was successfully launched in February 2018 and provides users with an overview of results reporting compliance. DiscussionClinical trials continue to go unreported despite numerous guidelines, commitments, and legal frameworks intended to address this issue. In the absence of formal sanctions from the FDA and others, we argue tools such as ours - providing live data on trial reporting - can improve accountability and performance. In addition, our service helps sponsors identify their own individual trials that have not yet reported results: we therefore offer positive practical support for sponsors who wish to ensure that all their completed trials have reported.

clinical trials↗

bioRxiv2018

Analysis of HIV-1 latent reservoir and rebound viruses in a clinical trial of anti-HIV-1 antibody 3BNC117

A clinical trial was performed to evaluate 3BNC117, a potent anti_HIV_1 antibody, in infected individuals during suppressive antiretroviral therapy (ART) and subsequent analytical treatment interruption (ATI). The circulating reservoir was evaluated by quantitative and qualitative outgrowth assay (Q2VOA) at entry and after 6 months, prior to ATI. Although there were no significant quantitative changes in the size of the reservoir, the composition of circulating reservoir clones varied over the 6_month period before treatment interruption in a manner that did not correlate with antibody sensitivity. The neutralization profile obtained from the reservoir by Q2VOA was predictive of time to rebound after ATI, and thus of antibody efficacy. Although 3BNC117 binding site amino acid variants found in rebound viruses pre_existed in the latent reservoir, only 3 of 217 rebound viruses were identical to 868 latent viruses. Instead many of the rebound viruses appeared to be recombinants, even in individuals with resistant reservoir viruses. By incorporating the possibility of recombination, 63% of the rebound viruses could have derived from the observed latent reservoir. In conclusion, viruses emerging during ATI in individuals treated with 3BNC117 are not the dominant species found in the circulating reservoir, but instead appear to represent recombinants.\n\nSummaryIn the setting of a clinical trial evaluating the anti_HIV_1 antibody 3BNC117, Cohen et al. demonstrate that rebound viruses that emerge following interruption of antiretroviral therapy are distinct from circulating latent viruses. However, rebound viruses often appear to be recombinants between isolated latent viruses.

clinical trials↗

bioRxiv2019

Impaired Therapeutic Efficacy of Bone Marrow Cells from Post-Myocardial Infarction Patients in the TIME and LateTIME Clinical Trials

Implantation of bone marrow-derived cells (BMCs) into mouse hearts post-myocardial infarction (MI) limits cardiac functional decline. However, clinical trials of post-MI BMC therapy have yielded conflicting results. While most laboratory experiments use healthy BMC donor mice, clinical trials use post-MI autologous BMCs. Post-MI mouse BMCs are therapeutically impaired, due to inflammatory changes in BMC composition. Thus, therapeutic efficacy of the BMCs progressively worsens after MI but recovers as donor inflammatory response resolves. The availability of post-MI patient BM mononuclear cells (MNCs) from the TIME and LateTIME clinical trials enabled us to test if human post-MI MNCs undergo a similar period of impaired efficacy. We hypothesized that MNCs from TIME trial patients would be less therapeutic than healthy human donor MNCs when implanted into post-MI mouse hearts, and that therapeutic properties would be restored in MNCs from LateTIME trial patients. Post-MI SCID mice received MNCs from healthy donors, TIME patients, or LateTIME patients. Cardiac function improved considerably in the healthy donor group, but neither the TIME nor LateTIME group showed therapeutic effect. Conclusion: post-MI human MNCs lack therapeutic benefits possessed by healthy MNCs, which may partially explain why BMC clinical trials have been less successful than mouse studies.

physiology↗