Participation in and withdrawal from cancer clinical trials: A survey of clinical research coordinators

Immunogenicity of Atezolizumab: Influence of Testing Method and Sampling Frequency on Reported Anti-drug Antibody Incidence Rates

Measurement of anti-drug antibodies (ADA) to assess the incidence of ADA in a clinical trial is a critical step in immunogenicity assessment during the development of a protein therapeutic. We developed novel graphical approaches to illustrate clinical trial ADA data for the PD-L1 inhibitor atezolizumab (Tecentriq) that included a systematic analysis of the impact of the timing of ADA sampling and ADA assay drug tolerance on reported ADA incidence. We found that approaches used across the industry for ADA incidence analysis provide a limited view of immunogenicity in oncology studies, where ADA detection may be confounded by both drug dosage and patient attrition. Moreover, these approaches can miss important temporal information about the immune response. Our results demonstrated that the methodology of ADA assessment for the atezolizumab program was specifically designed to capture most ADA responses to ensure accurate reporting of ADA incidence. We further showed that the use of sparse sampling and/or ADA test methods with insufficient drug tolerance may result in a significant underreporting of ADA incidence. We conclude that the comparison of ADA incidence between different drugs can be highly misleading and that a test method with appropriate sensitivity in the presence of the drug and a clinical sampling scheme that is aligned with ADA responses to a drug is required to accurately report ADA incidence.

New Benchmarks on Protocol Amendment Experience in Oncology Clinical Trials

BACKGROUND

The drug development industry's focus on cancer-related treatments continues to rise, with narrow patient populations and complex procedures increasing the complexity of oncology protocols at an accelerated rate compared to non-oncology drugs. Tufts Center for the Study of Drug Development utilized data from a study investigating the impact of protocol amendments to compare how oncology clinical trials differ from non-oncology and identify opportunities to optimize performance in oncology clinical trials.

METHODS

Sixteen drug development industry companies contributed data from 950 protocols and 2,188 amendments to a study conducted in 2022 investigating protocol amendments. Analysis compared differences in amendment impact and causes between 249 oncology and 701 non-oncology protocols.

RESULTS

Compared to non-oncology, oncology protocols had a significantly higher prevalence (72.1% and 91.1%, respectively) and number (3.0 and 4.0, respectively) of protocol amendments. Oncology protocols with amendments had significantly lower participant completion rates compared to oncology protocols without amendments, while no significant differences were found among non-oncology. During the COVID-19 pandemic, the study found an increased number of substantial amendments, lower completion rates, and higher dropout rates among oncology protocols compared to before the pandemic.

CONCLUSIONS

Efforts to prevent avoidable protocol amendments in the industry have not been effective in oncology, where increasingly complex designs are reflected in difficult to predict cycle times, barriers to recruitment and retention and an increase in protocol amendments.

Understanding the financial cost of cancer clinical trial participation

BACKGROUND

Though financial hardship is a well-documented adverse effect of standard-of-care cancer treatment, little is known about out-of-pocket costs and their impact on patients participating in cancer clinical trials. This study explored the financial effects of cancer clinical trial participation.

METHODS

This cross-sectional analysis used survey data collected in December 2022 and May 2023 from individuals with cancer previously served by Patient Advocate Foundation, a nonprofit organization providing social needs navigation and financial assistance to US adults with a chronic illness. Surveys included questions on cancer clinical trial participation, trial-related financial hardship, and sociodemographic data. Descriptive and bivariate analyses were conducted using Cramer's V to estimate the in-sample magnitude of association. Associations between trial-related financial hardship and sociodemographics were estimated using adjusted relative risks (aRR) and corresponding 95% confidence intervals (CI) from modified Poisson regression models with robust standard errors.

RESULTS

Of 650 survey respondents, 18% (N = 118) reported ever participating in a cancer clinical trial. Of those, 47% (n = 55) reported financial hardship as a result of their trial participation. Respondents reporting trial-related financial hardship were more often unemployed or disabled (58% vs. 43%; V = 0.15), Medicare enrolled (53% vs. 40%; V = 0.15), and traveled >1 h to their cancer provider (45% vs. 17%; V = 0.33) compared to respondents reporting no hardship. Respondents who experienced trial-related financial hardship most often reported expenses from travel (reported by 71% of respondents), medical bills (58%), dining out (40%), or housing needs (40%). Modeling results indicated that respondents traveling >1 h vs. ≤30 min to their cancer provider had a 2.2× higher risk of financial hardship, even after adjusting for respondent race, income, employment, and insurance status (aRR = 2.2, 95% CI 1.3-3.8). Most respondents (53%) reported needing $200-$1000 per month to compensate for trial-related expenses. Over half (51%) of respondents reported less willingness to participate in future clinical trials due to incurred financial hardship. Notably, of patients who did not participate in a cancer clinical trial (n = 532), 13% declined participation due to cost.

CONCLUSION

Cancer clinical trial-related financial hardship, most often stemming from travel expenses, affected almost half of trial-enrolled patients. Interventions are needed to reduce adverse financial participation effects and potentially improve cancer clinical trial participation.

Model-based prediction of progression-free survival for combination therapies in oncology

Progression-free survival (PFS) is an important clinical metric for comparing and evaluating similar treatments for the same disease within oncology. After the completion of a clinical trial, a descriptive analysis of the patients' PFS is often performed post hoc using the Kaplan-Meier estimator. However, to perform predictions, more sophisticated quantitative methods are needed. Tumor growth inhibition models are commonly used to describe and predict the dynamics of preclinical and clinical tumor size data. Moreover, frameworks also exist for describing the probability of different types of events, such as tumor metastasis or patient dropout. Combining these two types of models into a so-called joint model enables model-based prediction of PFS. In this paper, we have constructed a joint model from clinical data comparing the efficacy of FOLFOX against FOLFOX + panitumumab in patients with metastatic colorectal cancer. The nonlinear mixed effects framework was used to quantify interindividual variability (IIV). The model describes tumor size and PFS data well, and showed good predictive capabilities using truncated as well as external data. A machine-learning guided analysis was performed to reduce unexplained IIV by incorporating patient covariates. The model-based approach illustrated in this paper could be useful to help design clinical trials or to determine new promising drug candidates for combination therapy trials.

The emotional side of taking part in a cancer clinical trial

BACKGROUND

Taking part in a cancer clinical trial often represents a source of psychological distress and emotional activation among patients and their caregivers. Nowadays, social media platforms provide a space for these groups to freely express and share their emotional experiences.

AIMS

We aimed to reveal the most prevalent basic and complex emotions and sentiments in the posts of the patients and caregivers contemplating clinical trials on Reddit. Additionally, we aimed to categorize the types of users and posts.

METHODS

With the use of keywords referring to clinical trials, we searched for public posts on the subreddit 'cancer'. R studio v. 4.1.2 (2021-11-01) and NRC Emotion Lexicon was used for analysis. Following the theoretical framework of Plutchik's wheel of emotions, the analysis included: 8 basic emotions (anger, fear, anticipation, trust, surprise, sadness, joy, and disgust) and 4 types of complex emotions (primary, secondary, tertiary, and opposite dyads). We utilized the package 'PyPlutchik' to visualize the emotion wheels in Python 3.10.5.

RESULTS

A total of 241 posts were included in the final database. User types (129 patients, 112 caregivers) and post types (142 expressed shared experience, 77 expressed advice, and 85 conveyed both) were identified. Both positive (N = 2557, M = .68) and negative (N = 2154, M = .57) sentiments were high. The most prevalent basic emotions were: fear (N = 1702, M = .45), sadness (N = 1494, M = .40), trust (N = 1470, M = .44), and anticipation (N = 1376, M = .37). The prevalence of complex/dyadic emotions and their interpretation is further discussed.

CONCLUSION

In this contribution, we identified and discussed prevalent emotions such as fear, sadness, optimism, hope, despair, and outrage that mirror the psychological state of users and affect the medical choices they make. The insights gained in our study contribute to the understanding of the barriers and reinforcers to participation in trials and can improve the ability of healthcare professionals to assist patients when confronted with this choice.