Investigator initiated trials versus industry sponsored trials - translation of randomized controlled trials into clinical practice (IMPACT)

Multicentre phase II trial of cabozantinib in patients with hepatocellular carcinoma after immune checkpoint inhibitor treatment

BACKGROUND & AIMS

Prospective data on treatment after immune checkpoint inhibitor (ICI) therapy in hepatocellular carcinoma (HCC) are lacking. We conducted a phase II multicentre study on cabozantinib after ICI treatment in HCC.

METHODS

This is an investigator-initiated, single-arm, clinical trial involving academic centres in Hong Kong and Korea. Key eligibility criteria included diagnosis of HCC, refractoriness to prior ICI-based treatment, and Child-Pugh A liver function. A maximum of two prior lines of therapy were allowed. All patients were commenced on cabozantinib at 60 mg/day. The primary endpoint was progression-free survival (PFS).

RESULTS

Forty-seven patients were recruited from Oct 2020 to May 2022; 27 and 20 patients had received one and two prior therapies, respectively. Median follow-up was 11.2 months. The median PFS was 4.1 months (95% CI 3.3-5.3). The median overall survival (OS) was 9.9 months (95% CI 7.3-14.4), and the 1-year OS rate was 45.3%. Partial response and stable disease occurred in 3 (6.4%) and 36 (76.6%) patients, respectively. When used as a second-line treatment (n = 27), cabozantinib was associated with a median PFS and OS of 4.3 (95% CI 3.3-6.7) and 14.3 (95% CI 8.9-NR) months, respectively. The corresponding median PFS and OS were 4.3 (95% CI 3.3-11.0) and 14.3 (95% CI 9.0-NR) months, respectively, for those receiving ICI-based regimens with proven benefits (n = 17). The most common grade 3-4 treatment-related adverse event was thrombocytopenia (6.4%). The median dose of cabozantinib was 40 mg/day. The number of prior therapies was an independent prognosticator (one vs. two; hazard ratio = 0.37; p = 0.03).

CONCLUSIONS

Cabozantinib demonstrated efficacy in patients who had received prior ICI regimens; survival data for second-line cabozantinib following first-line ICI regimens provide a reference for future clinical trial design. The number of prior lines of treatment may be considered a stratification factor in randomised studies.

IMPACT AND IMPLICATIONS

Prospective data on systemic treatment following prior immune checkpoint inhibitor (ICI) therapy for hepatocellular carcinoma (HCC) are lacking. This phase II clinical trial provides efficacy and safety data on cabozantinib in patients who had received prior ICI-based treatment. Exploratory analyses showed that the performance of cabozantinib differed significantly when used as a second- or third-line treatment. The above data could be used as a reference for clinical practice and the design of future clinical trials on subsequent treatment lines following ICIs.

CLINICALTRIALS

GOV IDENTIFIER

NCT04588051.

Analysis of animal-to-human translation shows that only 5% of animal-tested therapeutic interventions obtain regulatory approval for human applications

There is an ongoing debate about the value of animal experiments to inform medical practice, yet there are limited data on how well therapies developed in animal studies translate to humans. We aimed to assess 2 measures of translation across various biomedical fields: (1) The proportion of therapies which transition from animal studies to human application, including involved timeframes; and (2) the consistency between animal and human study results. Thus, we conducted an umbrella review, including English systematic reviews that evaluated the translation of therapies from animals to humans. Medline, Embase, and Web of Science Core Collection were searched from inception until August 1, 2023. We assessed the proportion of therapeutic interventions advancing to any human study, a randomized controlled trial (RCT), and regulatory approval. We meta-analyzed the concordance between animal and human studies. The risk of bias was probed using a 10-item checklist for systematic reviews. We included 122 articles, describing 54 distinct human diseases and 367 therapeutic interventions. Neurological diseases were the focus of 32% of reviews. The overall proportion of therapies progressing from animal studies was 50% to human studies, 40% to RCTs, and 5% to regulatory approval. Notably, our meta-analysis showed an 86% concordance between positive results in animal and clinical studies. The median transition times from animal studies were 5, 7, and 10 years to reach any human study, an RCT, and regulatory approval, respectively. We conclude that, contrary to widespread assertions, the rate of successful animal-to-human translation may be higher than previously reported. Nonetheless, the low rate of final approval indicates potential deficiencies in the design of both animal studies and early clinical trials. To ameliorate the efficacy of translating therapies from bench to bedside, we advocate for enhanced study design robustness and the reinforcement of generalizability.

Evaluation of factors that act as barriers in conducting academic trials - An investigator's perception

Background

Academic trials are essential in investigating health research questions relevant to the society. Only a few leading research institutions in India have been engaged in academic trials. Thus, there is a need to understand what factors dampen the spirit of the academician in conducting academic clinical trials.

Aims and Objectives

The aim of the study is to evaluate the investigator's perception of obstacles to carrying out academic trials and to identify factors that will motivate investigators in conducting academic trials.

Materials and Methods

We conducted a prospective observational study in a tertiary care hospital for 6 months. Faculty members working in academic institutes were selected. A structured questionnaire was designed for the study and administered using google forms. Responses were taken on a Likert scale. Validity and reliability assessments were carried out. Mann-Whitney test was applied to assess differences between demographic groups. P <0.05 was considered significant.

Results

Most of the participants rated applying for research grants (76%), obtaining funding for the study and making arrangements for compensation for trial-related events (75%) as extremely challenging. We found that the degree of challenge is significantly lower in the faculty members who conducted clinical trials in the past as against those who did not (P = 0.00069). We also found that the degree of challenge is significantly higher in the faculty members with <10 years of experience than those with >10 years of experience (P = 0.00001).

Conclusion

Thus, to conclude the challenges faced by investigators were at multiple levels, most common being applying for research grants and making arrangements for the funds for payment towards participation or study-related injury. Faculty members with exposure to conducting clinical trials and with experience of more than 10 years had perceived a reduced degree of challenges.

The Development of Investigator-Initiated Clinical Trials in Surgical Oncology

Investigator-initiated trials (IITs) are designed by principal investigators who identify important, unaddressed clinical gaps and opportunities to answer these questions through clinical trials. Surgical oncologists are poised to lead IITs due to their multidisciplinary clinical practice and substantial research background. The process of developing, organizing, and implementing IITs is multifaceted and involves important steps including (but not limited to) navigating regulatory requirements, obtaining funding, and meeting enrollment targets. Here, the authors explore the steps, methodology, and barriers of IIT development by surgical oncologists and highlight the importance of IITs in oncology.

Predicting Publication of Clinical Trials Using Structured and Unstructured Data: Model Development and Validation Study

BACKGROUND

Publication of registered clinical trials is a critical step in the timely dissemination of trial findings. However, a significant proportion of completed clinical trials are never published, motivating the need to analyze the factors behind success or failure to publish. This could inform study design, help regulatory decision-making, and improve resource allocation. It could also enhance our understanding of bias in the publication of trials and publication trends based on the research direction or strength of the findings. Although the publication of clinical trials has been addressed in several descriptive studies at an aggregate level, there is a lack of research on the predictive analysis of a trial's publishability given an individual (planned) clinical trial description.

OBJECTIVE

We aimed to conduct a study that combined structured and unstructured features relevant to publication status in a single predictive approach. Established natural language processing techniques as well as recent pretrained language models enabled us to incorporate information from the textual descriptions of clinical trials into a machine learning approach. We were particularly interested in whether and which textual features could improve the classification accuracy for publication outcomes.

METHODS

In this study, we used metadata from ClinicalTrials.gov (a registry of clinical trials) and MEDLINE (a database of academic journal articles) to build a data set of clinical trials (N=76,950) that contained the description of a registered trial and its publication outcome (27,702/76,950, 36% published and 49,248/76,950, 64% unpublished). This is the largest data set of its kind, which we released as part of this work. The publication outcome in the data set was identified from MEDLINE based on clinical trial identifiers. We carried out a descriptive analysis and predicted the publication outcome using 2 approaches: a neural network with a large domain-specific language model and a random forest classifier using a weighted bag-of-words representation of text.

RESULTS

First, our analysis of the newly created data set corroborates several findings from the existing literature regarding attributes associated with a higher publication rate. Second, a crucial observation from our predictive modeling was that the addition of textual features (eg, eligibility criteria) offers consistent improvements over using only structured data (F₁-score=0.62-0.64 vs F₁-score=0.61 without textual features). Both pretrained language models and more basic word-based representations provide high-utility text representations, with no significant empirical difference between the two.

CONCLUSIONS

Different factors affect the publication of a registered clinical trial. Our approach to predictive modeling combines heterogeneous features, both structured and unstructured. We show that methods from natural language processing can provide effective textual features to enable more accurate prediction of publication success, which has not been explored for this task previously.

Panoramic quality assessment tool for investigator initiated trials

Objectives

Quality can be a challenge for Investigator initiated trials (IITs) since these trials are scarcely overseen by a sponsor or monitoring team. Therefore, quality assessment for departments managing clinical research grants program is important and urgently needed. Our study aims at developing a handy quality assessment tool for IITs that can be applied by both departments and project teams.

Methods

The framework of the quality assessment tool was developed based on the literature studies, accepted guidelines and the Delphi method. A total of 272 ongoing IITs funded by Shanghai non-profit organizations in 2015 and 2016 were used to extract quality indexes. Confirmatory factor analysis (CFA) was used to further evaluate the validity and feasibility of the conceptual quality assessment tool.

Results

The tool consisted of 4 critical quality attributes, including progress, quality, regulation, scientificity, and 13 observed quality indexes. A total of 257 IITs were included in the validity and feasibility assessment. The majority (60.29%) were Randomized Controlled Trial (RCT), and 41.18% were multi-center studies. In order to test the validity and feasibility of IITs quality assessment tool, CFA showed that the model fit the data adequately. (CMIN/DF = 1.868, GFI = 0.916; CFI = 0.936; TLI = 0.919; RMSEA = 0.063; SRMR = 0.076). Different types of clinical studies fit well in the tool. However, RCT scored lower than prospective cohort and retrospective study in enrollment progress (7.02 vs. 7.43, 9.63, respectively).

Conclusion

This study established a panoramic quality assessment tool based on the Delphi method and CFA, and the feasibility and effectiveness of the tool were verified through clinical research examples. The use of this tool can help project management departments effectively and dynamically manage research projects, rationally allocate resources, and ensure the quality of IITs.

Methodological Quality and Risk of Bias Assessment of Cardiovascular Disease Research: Analysis of Randomized Controlled Trials Published in 2017

Background

All randomized-controlled trials (RCTs) are required to follow high methodological standards. In this study, we aimed to assess the methodological quality of published cardiovascular clinical research trials in a representative sample of RCTs published in 2017.

Methods

Cochrane Central Register of Controlled Trials was used to identify cardiovascular clinical research trials with adult participants published in 2017. Overall, 250 (10%) RCTs were randomly selected from a total of 2,419 studies. Data on general trial characteristics were extracted and the risk of bias (RoB) was determined.

Results

Overall, 86% of RCTs have reported at least one statistically significant result, with the primary outcome significant in 69%, treatment favored in 55%, and adverse events reported in 68%. Less than one-third (29%) of trials were overall low RoB, while the other two-thirds were rated unclear (40%) or with high RoB (31%). Sequence generation, allocation concealment, and selective reporting were the domains most often rated with high RoB. Drug trials were more likely to have low RoB than non-drug trials. Significant differences were found in RoB for the allocation concealment and blinding of participants and personnel between industry-funded and non-industry-funded trials, with industry-funded trials more often rated at low RoB.

Conclusion

Almost two-thirds of RCTs in the field of cardiovascular disease (CVD) research, were at high or unclear RoB, indicating a need for more rigorous trial planning and conduct. Prospective trial registration is a factor predicting a lower risk of bias.