Cardiovascular/anti-inflammatory drugs repurposed for treating or preventing cancer: A systematic review and meta-analysis of randomized trials

BACKGROUND

Due to encouraging pre-clinical data and supportive observational studies, there has been growing interest in applying cardiovascular drugs (including aspirin, angiotensin-converting enzyme [ACE] inhibitors, statins, and metformin) approved to treat diseases such as hypertension, hyperlipidemia, and diabetes mellitus to the field of oncology. Moreover, given growing costs with cancer care, these medications have offered a potentially more affordable avenue to treat or prevent recurrence of cancer. We sought to investigate the anti-cancer effects of drugs repurposed from cardiology or anti-inflammatories to treat cancer. We specifically evaluated the following drug classes: HMG-CoA reductase inhibitors (statins), cyclo-oxygenase inhibitors, aspirin, metformin, and both angiotensin receptor blockers (ARBs) and angiotensin-converting enzyme inhibitors. We also included non-steroidal anti-inflammatory drugs (NSAIDs) because they exert a similar mechanism to aspirin by blocking prostaglandins and reducing inflammation that is thought to promote the development of cancer.

METHODS

We performed a systematic literature review using PubMed and Web of Science with search terms including "aspirin," "NSAID," "statin" (including specific statin drug names), "metformin," "ACE inhibitors," and "ARBs" (including specific anti-hypertensive drug names) in combination with "cancer." Searches were limited to human studies published between 2000 and 2023.

MAIN OUTCOMES AND MEASURES

The number and percentage of studies reported positive results and pooled estimates of overall survival, progression-free survival, response, and disease-free survival.

RESULTS

We reviewed 3094 titles and included 67 randomized clinical trials. The most common drugs that were tested were metformin (n = 21; 30.9%), celecoxib (n = 20; 29.4%), and simvastatin (n = 8; 11.8%). There was only one study that tested cardiac glycosides and none that studied ACE inhibitors. The most common tumor types were non-small-cell lung cancer (n = 19; 27.9%); breast (n = 8; 20.6%), colorectal (n = 7; 10.3%), and hepatocellular (n = 6; 8.8%). Most studies were conducted in a phase II trial (n = 38; 55.9%). Most studies were tested in metastatic cancers (n = 49; 72.1%) and in the first-line setting (n = 36; 521.9%). Four studies (5.9%) were stopped early because of difficulty with accrual. The majority of studies did not demonstrate an improvement in either progression-free survival (86.1% of studies testing progression-free survival) or in overall survival (94.3% of studies testing overall survival). Progression-free survival was improved in five studies (7.4%), and overall survival was improved in three studies (4.4%). Overall survival was significantly worse in two studies (3.8% of studies testing overall survival), and progression-free survival was worse in one study (2.8% of studies testing progression-free survival).

CONCLUSIONS AND RELEVANCE

Despite promising pre-clinical and population-based data, cardiovascular drugs and anti-inflammatory medications have overall not demonstrated benefit in the treatment or preventing recurrence of cancer. These findings may help guide future potential clinical trials involving these medications when applied in oncology.

Type 1 and type 2 cytokine-mediated immune orchestration in the tumour microenvironment and their therapeutic potential

Cancer remains the second leading cause of death worldwide despite modern breakthroughs in medicine, and novel treatments are urgently needed. The revolutionary success of immune checkpoint inhibitors in the past decade serves as proof of concept that the immune system can be effectively harnessed to treat cancer. Cytokines are small signalling proteins with critical roles in orchestrating the immune response and have become an attractive target for immunotherapy. Type 1 immune cytokines, including interferon γ (IFNγ), interleukin-12 (IL-12), and tumour necrosis factor α (TNFα), have been shown to have largely tumour suppressive roles in part through orchestrating anti-tumour immune responses mediated by natural killer (NK) cells, CD8⁺ T cells and T helper 1 (Th1) cells. Conversely, type 2 immunity involving group 2 innate lymphoid cells (ILC2s) and Th2 cells are involved in tissue regeneration and wound repair and are traditionally thought to have pro-tumoural effects. However, it is found that the classical type 2 immune cytokines IL-4, IL-5, IL-9, and IL-13 may have conflicting roles in cancer. Similarly, type 2 immunity-related cytokines IL-25 and IL-33 with recently characterised roles in cancer may either promote or suppress tumorigenesis in a context-dependent manner. Furthermore, type 1 cytokines IFNγ and TNFα have also been found to have pro-tumoural effects under certain circumstances, further complicating the overall picture. Therefore, the dichotomy of type 1 and type 2 cytokines inhibiting and promoting tumours respectively is not concrete, and attempts of utilising these for cancer immunotherapy must take into account all available evidence. This review provides an overview summarising the current understanding of type 1 and type 2 cytokines in tumour immunity and discusses the prospects of harnessing these for immunotherapy in light of previous and ongoing clinical trials.

Approximate Bayesian computation design for phase I clinical trials

In the development of new cancer treatment, an essential step is to determine the maximum tolerated dose in a phase I clinical trial. In general, phase I trial designs can be classified as either model-based or algorithm-based approaches. Model-based phase I designs are typically more efficient by using all observed data, while there is a potential risk of model misspecification that may lead to unreliable dose assignment and incorrect maximum tolerated dose identification. In contrast, most of the algorithm-based designs are less efficient in using cumulative information, because they tend to focus on the observed data in the neighborhood of the current dose level for dose movement. To use the data more efficiently yet without any model assumption, we propose a novel approximate Bayesian computation approach to phase I trial design. Not only is the approximate Bayesian computation design free of any dose-toxicity curve assumption, but it can also aggregate all the available information accrued in the trial for dose assignment. Extensive simulation studies demonstrate its robustness and efficiency compared with other phase I trial designs. We apply the approximate Bayesian computation design to the MEK inhibitor selumetinib trial to demonstrate its satisfactory performance. The proposed design can be a useful addition to the family of phase I clinical trial designs due to its simplicity, efficiency and robustness.

A Comprehensive Comparison of Additional Benefit Assessment Methods Applied by Institute for Quality and Efficiency in Health Care and European Society for Medical Oncology for Time-to-Event Endpoints After Significant Phase III Trials-A Simulation Study

OBJECTIVES

After a successful Marketing Authorization Application for clinical trials with time-to-event endpoints, the degree of the added benefit from new treatments remains unknown and needs to be assessed. Unfortunately, until now no clear definition for added benefit determination of a treatment exists. Nevertheless, European authorities / societies have developed 2 "additional benefit assessment" methods, which have up to now not been compared: the European Society for Medical Oncology (ESMO) developed a dual rule considering relative and absolute benefit. The German Institute for Quality and Efficiency in Health Care (IQWiG) developed a method using upper 95% hazard ratio confidence interval.

METHODS

We evaluate and compare both methods in an extensive simulation study including different censoring rates, failure time distributions, and treatment effects for sample size calculation. The methods' performance is assessed via Receiver Operating Characteristic curves, Spearman correlation, and percentage of achieved maximal scores.

RESULTS

The results show that IQWiG's method has in many situations a lower maximal scoring proportion than ESMO's rule, that is, up to 28.5% versus 94.7%. Various failure time distributions lead to strongly changed maximal scoring percentages for ESMO. High positive correlation between the methods is present for moderate treatment effects.

CONCLUSIONS

IQWiG's method is usually more conservative than ESMO's. ESMO's rule tends to be more susceptible for various failure time distributions. Using the lower confidence interval limit seems to be a better solution resulting in a higher true-positive rate without increasing the false-positive rate. Thus, IQWiG's method might need to be adapted accordingly to achieve a better overall classification.

Gatekeeping in cancer clinical trials in Canada: The ethics of recruiting the "ideal" patient

Background

Perspectives of clinical trial (CT) personnel on accrual to oncology CTs are relatively absent from the literature. This study explores CT personnel's experience recruiting patients to oncology CTs.

Methods

A qualitative study design was utilized. In‐depth, individual interviews with 12 oncology CT personnel were conducted, including six CT nurses and six physician‐investigators. Interviews were digitally recorded and transcribed verbatim. Data were subjected to thematic and ethical analysis to identify key concepts and themes.

Results

CT personnel reported considering two ethical commitments in CT recruitment: maintaining trial integrity and ensuring patient autonomy through obtaining informed consent. The process of gatekeeping emerged as a way to navigate these ethical commitments during CT accrual. Gatekeeping was influenced by: (a) perceptions of patients’ personal suitability for a trial, and (b) healthcare resources and infrastructure. CT personnel's discernment of personal suitability was influenced by patients’ cognitive and mental health status, language and cultural background, geographic location, family support, and disease status. Three structural factors impacted gatekeeping: complexity of CTs, consent process, and time limitations in the healthcare system. CT personnel experienced most factors as constraints to accrual and gaining patients’ informed consent.

Conclusion

CT personnel discussed navigating ethical challenges in CT recruitment by offering enrollment to specific patient populations, exacerbating other ethical tensions. Systems‐level strategies are needed to address barriers to ethical CT recruitment. Future research should investigate the role of policies and/or tools (eg, decision aids) to support patients and CT personnel's discussions about CT participation, promote more ethical recruitment, and potentially increase accrual.

Quality-of-Life (QOL) during Screening for Phase 1 Trial Studies in Patients with Advanced Solid Tumors and Its Impact on Risk for Serious Adverse Events

Background: Serious adverse events (SAEs) and subject replacements occur frequently in phase 1 oncology clinical trials. Whether baseline quality-of-life (QOL) or social support can predict risk for SAEs or subject replacement among these patients is not known. Methods: Between 2011-2013, 92 patients undergoing screening for enrollment into one of 22 phase 1 solid tumor clinical trials at Roswell Park Cancer Institute were included in this study. QOL Questionnaires (EORTC QLQ-C30 and FACT-G), Medical Outcomes Study Social Support Survey (MOSSSS), Charlson comorbidity scores (CCS) and Royal Marsden scores (RMS) were obtained at baseline. Frequency of dose limiting toxicities (DLTs), subject replacement and SAEs that occurred within the first 4 cycles of treatment were recorded. Fisher's exact test and Mann-Whitney-Wilcoxon test were used to study the association between categorical and continuous variables, respectively. A linear transformation was used to standardize QOL scores. p-value ≤ 0.05 was considered statistically significant. Results: Baseline QOL, MOSSSS, CCS and RMS were not associated with subject replacement nor DLTs. Baseline EORTC QLQ-C30 scores were significantly lower among patients who encountered SAEs within the first 4 cycles (p = 0.04). Conclusions: Lower (worse) EORTC QLQ-C30 score at baseline is associated with SAE occurrence during phase 1 oncology trials.

Lugano 2014 criteria for assessing FDG-PET/CT in lymphoma: an operational approach for clinical trials

An operationalized workflow paradigm is presented and validated with pilot subject data. This approach is reproducible with a high concordance rate between individual readers (kappa 0.73 [confidence interval 0.59-0.87; P=<0.0001]) using a 5-point scale to assess [18F] labeled fluorodeoxyglucose metabolic activity in lymphomatous lesions. These results suggest an operationally practical 5-point scale workflow paradigm for potential use in larger clinical trials evaluating lymphoma therapeutics.

Companion diagnostics and molecular imaging-enhanced approaches for oncology clinical trials

In the era of personalized medicine, diagnostic approaches are helping pharmaceutical and biotechnology sponsors streamline the clinical trial process. Molecular assays and diagnostic imaging are routinely being used to stratify patients for treatment, monitor disease, and provide reliable early clinical phase assessments. The importance of diagnostic approaches in drug development is highlighted by the rapidly expanding global cancer diagnostics market and the emergent attention of regulatory agencies worldwide, who are beginning to offer more structured platforms and guidance for this area. In this paper, we highlight the key benefits of using companion diagnostics and diagnostic imaging with a focus on oncology clinical trials. Nuclear imaging using widely available radiopharmaceuticals in conjunction with molecular imaging of oncology targets has opened the door to more accurate disease assessment and the modernization of standard criteria for the evaluation, staging, and treatment responses of cancer patients. Furthermore, the introduction and validation of quantitative molecular imaging continues to drive and optimize the field of oncology diagnostics. Given their pivotal role in disease assessment and treatment, the validation and commercialization of diagnostic tools will continue to advance oncology clinical trials, support new oncology drugs, and promote better patient outcomes.

A new statistical decision rule for single-arm phase II oncology trials

Most single-arm phase II clinical trials compare the efficacy of a new treatment with historical controls through statistical hypothesis testing. One major problem with such a comparison is that the efficacy of the historical control is treated as a known constant, whereas in reality, it is never precisely known. This partially explains why many "Go" decisions made in single-arm phase II trials are shown to be incorrect in phase III trials. In this paper, we propose a new decision rule for an improved transitional decision for single-arm phase II oncology clinical trials with binary endpoints. This new decision rule is jointly based on the p value and a new statistical index named the testing confidence value. The testing confidence value reflects the uncertainty associated with the null value in the hypothesis testing of single-arm trials. Simulations are used to evaluate the operating characteristics of the new decision rule in comparison with the traditional decision rule and a widely used Bayesian decision rule. The application of the new decision rule is illustrated using a clinical trial on marginally resectable pancreatic cancer. A webpage http://www.yiyichenbiostatistics.com/TCV.html is available for readers to interactively compute the testing confidence value and to find the suggested decision based on the new decision rule.

Phase I oncology trials: why the therapeutic misconception will not go away

In many cases, the “therapeutic misconception” may be an unavoidable part of the imperfect process of recruitment and consent in medical research