Innovation in oncology clinical trial design

Standardizing eligibility and patient selection for Pressurized Intraperitoneal Aerosol Chemotherapy: A Delphi consensus statement

INTRODUCTION

Pressurized Intraperitoneal Aerosol Chemotherapy (PIPAC) is a procedure for minimally invasive drug administration in patients with peritoneal metastasis. Previous studies have emphasized the importance of uniformity in treatment protocols and standardization of this practice. This study aimed to reach a consensus on eligibility, patient selection, and choice of chemotherapy for PIPAC.

METHODS

A three-round modified Delphi study was conducted. A steering group formulated a list of baseline statements, addressing the objectives. The steering group consisted of seven expert surgical and medical oncologists. Available evidence and published key opinions were critically reviewed. An international expert panel scored those statements on a 4-point Likert scale. The statements were submitted electronically and anonymously. Consensus was reached if the agreement rate was ≥75%. A minimum Cronbach's alpha of >0.8 was set.

RESULTS

Forty-five (45/58; 77.6%) experts participated and completed all rounds. Experts were digestive surgeons (n = 28), surgical oncologists (n = 7), gynecologists (n = 5), medical oncologists (n = 4), and one clinical researcher. Their assessment of 81 preliminary statements in the first round resulted in 41 consolidated statements. In round two, consensus was reached on 40 statements (40/41; 97.6%) with a consensus of ≥80% for each individual statement. In the third round, 40 statements were unanimously approved as definitive. The choice of first- and second-line chemotherapy remained controversial and could not reach consensus.

CONCLUSIONS

This International Delphi study provides practical guidance on eligibility and patient selection for PIPAC. Ongoing trial data and long-term results that could contribute to the further standardization of PIPAC are eagerly awaited.

Factors Associated with Inclusion of Japan in Phase I Multiregional Clinical Trials in Oncology

BACKGROUND

Early inclusion of Japan in the global development program could be a key factor in reducing the drug lag, making participation in phase I multiregional clinical trials (Ph. I MRCTs) an important consideration for oncology drug development in Japan. We aimed to investigate the factors associated with the inclusion of Japan in Ph. I MRCTs in oncology.

METHODS

We compared the trial design, target population, type of primary tested drug, trial conduct profile, and sponsor profile for Ph. I MRCTs with or without Japan conducted by the top 20 companies in more than two countries and started between January 1, 2011, and December 31, 2020.

RESULTS

One hundred and ninety-seven Ph. I MRCTs included Japan, and 697 did not. Detailed features of the Ph. I MRCTs in oncology were summarized, and several factors (trial design, target population, trial conduct profile, and sponsor profile) associated with inclusion of Japan in the Ph. I MRCTs were identified.

CONCLUSIONS

It is important for Japanese subsidiaries within global pharmaceutical companies to closely communicate with the headquarters based on medical practice and unmet needs in Japan to join global development from an early stage. In addition, further efforts to attract emerging biopharmaceutical companies to Japan from the regulatory and/or political perspectives would be needed, thereby preventing drug lag in Japan.

Evaluation of an artificial intelligence-based clinical trial matching system in Chinese patients with hepatocellular carcinoma: a retrospective study

BACKGROUND

Artificial intelligence (AI)-assisted clinical trial screening is a promising prospect, although previous matching systems were developed in English, and relevant studies have only been conducted in Western countries. Therefore, we evaluated an AI-based clinical trial matching system (CTMS) that extracts medical data from the electronic health record system and matches them to clinical trials automatically.

METHODS

This study included 1,053 consecutive inpatients primarily diagnosed with hepatocellular carcinoma who were referred to the liver tumor center of an academic medical center in China between January and December 2019. The eligibility criteria extracted from two clinical trials, patient attributes, and gold standard were decided manually. We evaluated the performance of the CTMS against the established gold standard by measuring the accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and run time required.

RESULTS

The manual reviewers demonstrated acceptable interrater reliability (Cohen's kappa 0.65-0.88). The performance results for the CTMS were as follows: accuracy, 92.9-98.0%; sensitivity, 51.9-83.5%; specificity, 99.0-99.1%; PPV, 75.7-85.1%; and NPV, 97.4-98.9%. The time required for eligibility determination by the CTMS and manual reviewers was 2 and 150 h, respectively.

CONCLUSIONS

We found that the CTMS is particularly reliable in excluding ineligible patients in a significantly reduced amount of time. The CTMS excluded ineligible patients for clinical trials with good performance, reducing 98.7% of the work time. Thus, such AI-based systems with natural language processing and machine learning have potential utility in Chinese clinical trials.

Clinical data mining: challenges, opportunities, and recommendations for translational applications

Clinical data mining of predictive models offers significant advantages for re-evaluating and leveraging large amounts of complex clinical real-world data and experimental comparison data for tasks such as risk stratification, diagnosis, classification, and survival prediction. However, its translational application is still limited. One challenge is that the proposed clinical requirements and data mining are not synchronized. Additionally, the exotic predictions of data mining are difficult to apply directly in local medical institutions. Hence, it is necessary to incisively review the translational application of clinical data mining, providing an analytical workflow for developing and validating prediction models to ensure the scientific validity of analytic workflows in response to clinical questions. This review systematically revisits the purpose, process, and principles of clinical data mining and discusses the key causes contributing to the detachment from practice and the misuse of model verification in developing predictive models for research. Based on this, we propose a niche-targeting framework of four principles: Clinical Contextual, Subgroup-Oriented, Confounder- and False Positive-Controlled (CSCF), to provide guidance for clinical data mining prior to the model's development in clinical settings. Eventually, it is hoped that this review can help guide future research and develop personalized predictive models to achieve the goal of discovering subgroups with varied remedial benefits or risks and ensuring that precision medicine can deliver its full potential.

Can precision medicine be integrated into routine therapeutic decisions at the bedside of patients?

Therapeutic strategies are shifting from a "one-size-fits-all" population-based approach to a stratified approach targeting groups with similar characteristics, or even individuals, tailoring treatments to the unique characteristics of each patient. Since such strategies rely on increasingly complex knowledge and healthcare technologies, along with an understanding of the tools of precision medicine, the appropriate dissemination and use of these strategies involves a number of challenges for the medical community. Having evaluation methodologies that have been jointly designed with the institutional, industrial, academic stakeholders, and also patients, like streamlining the processes and externally validating performances, could enhance the relevance of the "evaluation" aspect of precision medicine. Creating a network of expert precision-medicine centers and ensuring that precision-medicine procedures are reimbursed by social security would guarantee fair and sustainable access. Finally, training healthcare professionals, creating interfaces between precision-medicine expert centers and primary care professionals as well as patients, and integrating individual patient data into medical records are all key drivers that will enable information from precision-medicine to be made available and guarantee the proper use of these approaches.

Implementation of inclusion and exclusion criteria in clinical studies in OHDSI ATLAS software

Clinical trials are essential parts of a medical study process, but studies are often cancelled due to a lack of participants. Clinical Trial Recruitment Support Systems are systems that help to increase the number of participants by seeking more suitable subjects. The software ATLAS (developed by Observational Health Data Sciences and Informatics) can support the launch of a clinical trial by building cohorts of patients who fulfill certain criteria. The correct use of medical classification systems aiming at clearly defined inclusion and exclusion criteria in the studies is an important pillar of this software. The aim of this investigation was to determine whether ATLAS can be used in a Clinical Trial Recruitment Support System to portray the eligibility criteria of clinical studies. Our analysis considered the number of criteria feasible for integration with ATLAS and identified its strengths and weaknesses. Additionally, we investigated whether nonrepresentable criteria were associated with the utilized terminology systems. We analyzed ATLAS using 223 objective eligibility criteria from 30 randomly selected trials conducted in the last 10 years. In the next step, we selected appropriate ICD, OPS, LOINC, or ATC codes to feed the software. We classified each criterion and study based on its implementation capability in the software, ensuring a clear and logical progression of information. Based on our observations, 51% of the analyzed inclusion criteria were fully implemented in ATLAS. Within our selected example set, 10% of the studies were classified as fully portrayable, and 73% were portrayed to some extent. Additionally, we conducted an evaluation of the software regarding its technical limitations and interaction with medical classification systems. To improve and expand the scope of criteria within a cohort definition in a practical setting, it is recommended to work closely with personnel involved in the study to define the criteria precisely and to carefully select terminology systems. The chosen criteria should be combined according to the specific setting. Additional work is needed to specify the significance and amount of the extracted criteria.

RESTART trial design: two-stage seamless transition design with operational considerations

Oncology/hematology is a competitive therapeutic area where the landscape is constantly evolving. With regulatory support, many drug developers have spent a lot of resources on the operationalization of innovative clinical trial designs, for example, adaptive Bayesian designs in confirmatory clinical trial settings. While overall survival is considered the gold standard in these designs, it is often not a viable choice in identifying treatment efficacy at a reasonable pace, especially for early-stage therapies. In recent years, several binary response surrogate endpoints have been used for accelerated or conditional approval of novel cancer therapies. Utilizing surrogate endpoints in the study design to predict objective clinical outcomes, such as overall survival, is particularly fundamental in cancer treatment clinical development. This manuscript will investigate logistic and statistical considerations of our proposed RESTART design, a new two-stage, seamless, single- to double-arm Bayesian design. This design could be used for single-arm dose expansion to a randomized confirmatory study. The operating characteristics of the RESTART design are evaluated based on simulations. Future directions and further modifications of this design will also be elaborated.

Exploratory Analysis of Drug Lag in New Oncology Drugs Between Japan and the US

BACKGROUND

Drug lag in Japan has greatly decreased over the past decades; however, new instances of drug lag have appeared along with changes in the circumstances of oncology drug development. We aimed to investigate the factors associated with the approval lag for new oncology drugs between Japan and the United States (US) over the past decade by comparing approval dates and modalities, lead indications, approval types, and phase I strategies for earlier approval in Japan.

METHOD

We descriptively evaluated the characteristics of 117 new oncology drugs approved in either Japan or the US from January 1, 2011, to December 31, 2020.

RESULTS

Seventy-one drugs were approved in Japan, 112 in the US, five only in Japan, and 46 only in the US. Interestingly, new oncology drugs were predominantly developed by the top 20 pharmaceutical companies in Japan; however, the opposite was true for drugs that were not yet approved in Japan. However, no clear trend was observed in the relationship between drug lag and the studied factors, except for the phase I strategy. There was a numerical but clear trend in which a higher percentage of phase I multiregional clinical trials (MRCTs) in the drug development strategy was observed for drugs with earlier approval in Japan.

CONCLUSION

Participation in global drug development during the early stages, such as during phase I MRCTs, is one of the keys to successfully minimizing this new instance of drug lag in Japan.

The Application of Evidence-Based Medicine in Individualized Medicine

The fundamental aim of healthcare is to improve overall health of the population by providing state-of-the-art healthcare for individuals at an affordable cost. The foundation for this system is largely referred to as "evidence-based medicine". Too often, evidence-based medicine is based solely on so-called "best research evidence", collected through randomized controlled trials while disregarding clinical expertise and patient expectations. As healthcare gravitates towards personalized and individualized medicine, such external clinical (research) evidence can inform, but never replace, individual clinical expertise. This applies in particular to orphan diseases, for which clinical trials are methodologically particularly problematic, and evidence derived from them is often questionable. Evidence-based medicine constitutes a complex process to allow doctors and patients to select the best possible solutions for each individual based on rapidly developing new therapeutic directions. This requires a revisit of the foundations of evidence-based medicine. A proposition as to how to manage evidence-based data in individualized immune-oncology is presented here.

Oncology Clinical Trials in Greece: Progress in the Past Decade

Cancer is established as a major contributor to global burden as millions of deaths are reported every year. Advances in molecular, epidemiologic and clinical research have led to significant improvements in prevention, screening and treatment of tumors. The purpose of the study is to describe the progress of oncology clinical trials performed in Greece during the past decade and the obstacles that still need to be addressed in cancer research. A search was conducted in the public database EU Clinical Trials Register using the algorithm 'cancer AND Greece'. Results included relevant trials approved between 2010 and 2020. A total of 480 trials were approved for conduct in Greece from 2010 to 2020. The majority are multinational, phase III trials, exploring the efficacy and safety of agents in the management of lung cancer and multiple myeloma. A variety of small-molecules and monoclonal antibodies has and is being tested against key binding targets. Based on their promising effects on patients' responses and outcomes, many have been marketed for the treatment of several cancer types and are considered milestones in cancer discovery. It goes without saying that oncology research has made tremendous steps towards the development of potent and tolerable anticancer agents, with Greece having an active role. Current efforts focus on the use of alternative designs and tools aiming at further improving patients' survival and quality of life, while globalization of clinical research is also a matter of high importance.

Current Technology Developments Can Improve the Quality of Research and Level of Evidence for Rehabilitation Interventions: A Narrative Review

The current important limitations to the implementation of Evidence-Based Practice (EBP) in the rehabilitation field are related to the validation process of interventions. Indeed, most of the strict guidelines that have been developed for the validation of new drugs (i.e., double or triple blinded, strict control of the doses and intensity) cannot-or can only partially-be applied in rehabilitation. Well-powered, high-quality randomized controlled trials are more difficult to organize in rehabilitation (e.g., longer duration of the intervention in rehabilitation, more difficult to standardize the intervention compared to drug validation studies, limited funding since not sponsored by big pharma companies), which reduces the possibility of conducting systematic reviews and meta-analyses, as currently high levels of evidence are sparse. The current limitations of EBP in rehabilitation are presented in this narrative review, and innovative solutions are suggested, such as technology-supported rehabilitation systems, continuous assessment, pragmatic trials, rehabilitation treatment specification systems, and advanced statistical methods, to tackle the current limitations. The development and implementation of new technologies can increase the quality of research and the level of evidence supporting rehabilitation, provided some adaptations are made to our research methodology.

Exploring uncertainty and use of real-world data in the National Institute for Health and Care Excellence single technology appraisals of targeted cancer therapy

OBJECTIVES

Dealing with uncertainty is one of the critical topics in health technology assessment. The greater decision uncertainty in appraisals, the less clear the clinical- and cost-effectiveness of the health technology. Although the development of targeted cancer therapies (TCTs) has improved patient health care, additional complexity has been introduced in drug appraisals due to targeting more specific populations. Real-world data (RWD) are expected to provide helpful information to fill the evidence gaps in appraisals. This study compared appraisals of TCTs with those of non-targeted cancer therapies (non-TCTs) regarding sources of uncertainty and reviewed how RWD have been used to supplement the information in these appraisals.

METHODS

This study reviews single technology appraisals (STAs) of oncology medicines performed by the National Institute for Health and Care Excellence (NICE) over 11 years up to December 2021. Three key sources of uncertainty were identified for comparison (generalisability of clinical trials, availability of direct treatment comparison, maturity of survival data in clinical trials). To measure the intensity of use of RWD in appraisals, three components were identified (overall survival, volume of treatment, and choice of comparators).

RESULTS

TCTs received more recommendations for provision through the Cancer Drugs Fund (27.7, 23.6% for non-TCT), whereas similar proportions were recommended for routine commissioning. With respect to sources of uncertainty, the external validity of clinical trials was greater in TCT appraisals (p = 0.026), whereas mature survival data were available in fewer TCT appraisals (p = 0.027). Both groups showed similar patterns of use of RWD. There was no clear evidence that RWD have been used more intensively in appraisals of TCT.

CONCLUSIONS

Some differences in uncertainty were found between TCT and non-TCT appraisals. The appraisal of TCT is generally challenging, but these challenges are neither new nor distinctive. The same sources of uncertainty were often found in the non-TCT appraisals. The uncertainty when appraising TCT stems from insufficient data rather than the characteristics of the drugs. Although RWD might be expected to play a more active role in appraisals of TCT, the use of RWD has generally been limited.

Estimands and Complex Innovative Designs

Since the release of the ICH E9(R1) (International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use Addendum on Estimands and Sensitivity Analysis in Clinical Trials to the Guideline on Statistical Principles for Clinical Trials) document in 2019, the estimand framework has become a fundamental part of clinical trial protocols. In parallel, complex innovative designs have gained increased popularity in drug development, in particular in early development phases or in difficult experimental situations. While the estimand framework is relevant to any study in which a treatment effect is estimated, experience is lacking as regards its application to these designs. In a basket trial for example, should a different estimand be specified for each subpopulation of interest, defined, for example, by cancer site? Or can a single estimand focusing on the general population (defined, for example, by the positivity to a certain biomarker) be used? In the case of platform trials, should a different estimand be proposed for each drug investigated? In this work we discuss possible ways of implementing the estimand framework for different types of complex innovative designs. We consider trials that allow adding or selecting experimental treatment arms, modifying the control arm or the standard of care, and selecting or pooling populations. We also address the potentially data-driven, adaptive selection of estimands in an ongoing trial and disentangle certain statistical issues that pertain to estimation rather than to estimands, such as the borrowing of nonconcurrent information. We hope this discussion will facilitate the implementation of the estimand framework and its description in the study protocol when the objectives of the trial require complex innovative designs.

Risk and benefit for umbrella trials in oncology: a systematic review and meta-analysis

BACKGROUND

Umbrella clinical trials in precision oncology are designed to tailor therapies to the specific genetic changes within a tumor. Little is known about the risk/benefit ratio for umbrella clinical trials. The aim of our systematic review with meta-analysis was to evaluate the efficacy and safety profiles in cancer umbrella trials testing targeted drugs or a combination of targeted therapy with chemotherapy.

METHODS

Our study was prospectively registered in PROSPERO (CRD42020171494). We searched Embase and PubMed for cancer umbrella trials testing targeted agents or a combination of targeted therapies with chemotherapy. We included solid tumor studies published between 1 January 2006 and 7 October 2019. We measured the risk using drug-related grade 3 or higher adverse events (AEs), and the benefit by objective response rate (ORR), progression-free survival (PFS), and overall survival (OS). When possible, data were meta-analyzed.

RESULTS

Of the 6207 records identified, we included 31 sub-trials or arms of nine umbrella trials (N = 1637). The pooled overall ORR was 17.7% (95% confidence interval [CI] 9.5-25.9). The ORR for targeted therapies in the experimental arms was significantly lower than the ORR for a combination of targeted therapy drugs with chemotherapy: 13.3% vs 39.0%; p = 0.005. The median PFS was 2.4 months (95% CI 1.9-2.9), and the median OS was 7.1 months (95% CI 6.1-8.4). The overall drug-related death rate (drug-related grade 5 AEs rate) was 0.8% (95% CI 0.3-1.4), and the average drug-related grade 3/4 AE rate per person was 0.45 (95% CI 0.40-0.50).

CONCLUSIONS

Our findings suggest that, on average, one in five cancer patients in umbrella trials published between 1 January 2006 and 7 October 2019 responded to a given therapy, while one in 125 died due to drug toxicity. Our findings do not support the expectation of increased patient benefit in cancer umbrella trials. Further studies should investigate whether umbrella trial design and the precision oncology approach improve patient outcomes.

Reported Challenges in Health Technology Assessment of Complex Health Technologies

OBJECTIVES

With complex health technologies entering the market, methods for health technology assessment (HTA) may require changes. This study aimed to identify challenges in HTA of complex health technologies.

METHODS

A survey was sent to European HTA organizations participating in European Network for HTA (EUnetHTA). The survey contained open questions and used predefined potentially complex health technologies and 7 case studies to identify types of complex health technologies and challenges faced during HTA. The survey was validated, tested for reliability by an expert panel, and pilot tested before dissemination.

RESULTS

A total of 22 HTA organizations completed the survey (67%). Advanced therapeutic medicinal products (ATMPs) and histology-independent therapies were considered most challenging based on the predefined complex health technologies and case studies. For the case studies, more than half of the reported challenges were "methodological," equal in relative effectiveness assessments as in cost-effectiveness assessments. Through the open questions, we found that most of these challenges actually rooted in data unavailability. Data were reported as "absent," "insufficient," "immature," or "low quality" by 18 of 20 organizations (90%), in particular data on quality of life. Policy and organizational challenges and challenges because of societal or political pressure were reported by 8 (40%) and 4 organizations (20%), respectively. Modeling issues were reported least often (n = 2, 4%).

CONCLUSIONS

Most challenges in HTA of complex health technologies root in data insufficiencies rather than in the complexity of health technologies itself. As the number of complex technologies grows, the urgency for new methods and policies to guide HTA decision making increases.

Clinical development of anticancer drugs can be enhanced using efficacy data of small population clinical trials

WHAT IS KNOWN AND OBJECTIVE

Although there are accelerated approval pathways based on data of small populations and surrogate endpoints, the concern that these pathways authorize the use of inefficacious drugs based on limited data from earlier phase clinical trials remains. We retrospectively investigated the efficacy of anticancer drugs, which were approved or whose development was terminated in small and large clinical trials, and verified whether small clinical trials could reflect the results for efficacy in large clinical trials.

METHODS

All anticancer drugs approved in Japan or whose development was terminated from 2015 to 2019 were searched. The median overall survival (OS), median progression-free survival (PFS), and overall response rates (ORR) between small clinical trials (sample size ≤100) and large clinical trials (sample size >100) with identical target populations and treatment settings were compared. Simple linear regression analysis, Spearman's correlation analysis, and paired sample t-test were performed.

RESULTS AND DISCUSSION

A total of 61 comparable small and large clinical trials were identified. For all endpoints, statistically significant linear trends and correlation were detected (p < 0.001). There were no statistically significant differences in the median PFS and ORR between small and large clinical trials. The mean differences of both clinical trials were -0.102 months and -1.531%, respectively.

WHAT IS NEW AND CONCLUSION

Even when the sample size of the clinical trial was increased, the efficacy data of anticancer drugs could not be changed significantly. These results supported the accelerated approval pathway based on the promising efficacy data of small populations in anticancer drug development.

Real-World Metastatic Renal Cell Carcinoma Treatment Patterns and Clinical Outcomes in The Netherlands

The number of treatment options for patients with metastatic renal cell carcinoma (mRCC) has significantly grown in the last 15 years. Although randomized controlled trials are fundamental in investigating mRCC treatment efficacy, their external validity can be limited. Therefore, the efficacy of the different treatment options should also be evaluated in clinical practice. We performed a chart review of electronic health records using text mining software to study the current treatment patterns and outcomes. mRCC patients from two large hospitals in the Netherlands, starting treatment between January 2015 and May 2020, were included. Data were collected from electronic health records using a validated text mining tool. Primary endpoints were progression-free survival (PFS) and overall survival (OS). Statistical analyses were performed using the Kaplan-Meier method. Most frequent first-line treatments were pazopanib (n = 70), sunitinib (n = 34), and nivolumab with ipilimumab (n = 28). The overall median PFS values for first-line treatment were 15.7 months (95% confidence interval [95%CI], 8.8-20.7), 16.3 months (95%CI, 9.3-not estimable [NE]) for pazopanib, and 6.9 months (95% CI, 4.4-NE) for sunitinib. The overall median OS values were 33.4 months (95%CI, 28.1-50.9 months), 39.3 months (95%CI, 29.5-NE) for pazopanib, and 28.1 months (95%CI, 7.0-NE) for sunitinib. For nivolumab with ipilimumab, median PFS and median OS were not reached. Of the patients who finished first- and second-line treatments, 64 and 62% received follow-up treatments, respectively. With most patients starting on pazopanib and sunitinib, these real-world treatment outcomes were most likely better than in pivotal trials, which may be due to extensive follow-up treatments.

Consensus statement for treatment protocols in pressurized intraperitoneal aerosol chemotherapy (PIPAC)

Objectives

Safe implementation and thorough evaluation of new treatments require prospective data monitoring and standardization of treatments. Pressurized intraperitoneal aerosol chemotherapy (PIPAC) is a promising alternative for the treatment of patients with peritoneal disease with an increasing number of suggested drug regimens. The aim was to reach expert consensus on current PIPAC treatment protocols and to define the most important research topics.

Methods

The expert panel included the most active PIPAC centers, organizers of PIPAC courses and principal investigators of prospective studies on PIPAC. A comprehensive literature review served as base for a two-day hybrid consensus meeting which was accompanied by a modified three-round Delphi process. Consensus bar was set at 70% for combined (strong and weak) positive or negative votes according to GRADE. Research questions were prioritized from 0 to 10 (highest importance).

Results

Twenty-two out of 26 invited experts completed the entire consensus process. Consensus was reached for 10/10 final questions. The combination of doxorubicin (2.1 mg/m²) and cisplatin (10.5 mg/m²) was endorsed by 20/22 experts (90.9%). 16/22 (72.7%) supported oxaliplatin at 120 with potential reduction to 90 mg/m² (frail patients), and 77.2% suggested PIPAC-Ox in combination with 5-FU. Mitomycin-C and Nab-paclitaxel were favoured as alternative regimens. The most important research questions concerned PIPAC conditions (n=3), standard (n=4) and alternative regimens (n=5) and efficacy of PIPAC treatment (n=2); 8/14 were given a priority of ≥8/10.

Conclusions

The current consensus should help to limit heterogeneity of treatment protocols but underlines the utmost importance of further research.

Impact of limited sample size and follow-up on single event survival extrapolation for health technology assessment: a simulation study

INTRODUCTION

Extrapolation of time-to-event data from clinical trials is commonly used in decision models for health technology assessment (HTA). The objective of this study was to assess performance of standard parametric survival analysis techniques for extrapolation of time-to-event data for a single event from clinical trials with limited data due to small samples or short follow-up.

METHODS

Simulated populations with 50,000 individuals were generated with an exponential hazard rate for the event of interest. A scenario consisted of 5000 repetitions with six sample size groups (30-500 patients) artificially censored after every 10% of events observed. Goodness-of-fit statistics (AIC, BIC) were used to determine the best-fitting among standard parametric distributions (exponential, Weibull, log-normal, log-logistic, generalized gamma, Gompertz). Median survival, one-year survival probability, time horizon (1% survival time, or 99th percentile of survival distribution) and restricted mean survival time (RMST) were compared to population values to assess coverage and error (e.g., mean absolute percentage error).

RESULTS

The true exponential distribution was correctly identified using goodness-of-fit according to BIC more frequently compared to AIC (average 92% vs 68%). Under-coverage and large errors were observed for all outcomes when distributions were specified by AIC and for time horizon and RMST with BIC. Error in point estimates were found to be strongly associated with sample size and completeness of follow-up. Small samples produced larger average error, even with complete follow-up, than large samples with short follow-up. Correctly specifying the event distribution reduced magnitude of error in larger samples but not in smaller samples.

CONCLUSIONS

Limited clinical data from small samples, or short follow-up of large samples, produce large error in estimates relevant to HTA regardless of whether the correct distribution is specified. The associated uncertainty in estimated parameters may not capture the true population values. Decision models that base lifetime time horizon on the model's extrapolated output are not likely to reliably estimate mean survival or its uncertainty. For data with an exponential event distribution, BIC more reliably identified the true distribution than AIC. These findings have important implications for health decision modelling and HTA of novel therapies seeking approval with limited evidence.

A New Comprehensive Approach to Assess the Probability of Success of Development Programs Before Pivotal Trials

The point at which clinical development programs transition from early phase to pivotal trials is a critical milestone. Substantial uncertainty about the outcome of pivotal trials may remain even after seeing positive early phase data, and companies may need to make difficult prioritization decisions for their portfolio. The probability of success (PoS) of a program, a single number expressed as a percentage reflecting the multitude of risks that may influence the final program outcome, is a key decision-making tool. Despite its importance, companies often rely on crude industry benchmarks that may be "adjusted" by experts based on undocumented criteria and which are typically misaligned with the definition of success used to drive commercial forecasts, leading to overly optimistic expected net present value calculations. We developed a new framework to assess the PoS of a program before pivotal trials begin. Our definition of success encompasses the successful outcome of pivotal trials, regulatory approval and meeting the requirements for market access as outlined in the target product profile. The proposed approach is organized in four steps and uses an innovative Bayesian approach to synthesize all relevant evidence. The new PoS framework is systematic and transparent. It will help organizations to make more informed decisions. In this paper, we outline the rationale and elaborate on the structure of the proposed framework, provide examples, and discuss the benefits and challenges associated with its adoption.

An insight into the risk factors of brain tumors and their therapeutic interventions

Brain tumors are an abnormal growth of cells in the brain, also known as multifactorial groups of neoplasm. Incidence rates of brain tumors increase rapidly, and it has become a leading cause of tumor related deaths globally. Several factors have potential risks for intracranial neoplasm. To date, the International Agency for Research on Cancer has classified the ionizing radiation and the N-nitroso compounds as established carcinogens and probable carcinogens respectively. Diagnosis of brain tumors is based on histopathology and suitable imaging techniques. Labeled amino acids and fluorodeoxyglucose with or without contrast-enhanced MRI are used for the evaluation of tumor traces. T2-weighted MRI is an advanced diagnostic implementation, used for the detection of low-grade gliomas. Treatment decisions are based on tumor size, location, type, patient's age and health status. Conventional therapeutic approaches for tumor treatment are surgery, radiotherapy and chemotherapy. While the novel strategies may include targeted therapy, electric field treatments and vaccine therapy. Inhibition of cyclin-dependent kinase inhibitors is an attractive tumor mitigation strategy for advanced-stage cancers; in the future, it may prove to be a useful targeted therapy. The blood-brain barrier poses a major hurdle in the transport of therapeutics towards brain tissues. Moreover, nanomedicine has gained a vital role in cancer therapy. Nano drug delivery system such as liposomal drug delivery has been widely used in the cancer treatment. Liposome encapsulated drugs have improved therapeutic efficacy than free drugs. Numerous treatment therapies for brain tumors are in advanced clinical research.

Pragmatic patient engagement in designing pragmatic oncology clinical trials

Oncology trials are the cornerstone of effective and safe therapeutic discoveries. However, there is increasing demand for pragmatism and patient engagement in the design, implementation and dissemination of oncology trials. Many researchers are uncertain about making trials more practical and even less knowledgeable about how to meaningfully engage patients without compromising scientific rigor to meet regulatory requirements. The present work provides practical guidance for addressing both pragmaticism and meaningful patient engagement. Applying evidence-based approaches like PRECIS-2-tool and the 10-Step Engagement Framework offer practical guidance to make future trials in oncology truly pragmatic and patient-centered. Consequently, such patient-centered trials have improved participation, faster recruitment and greater retention, and uptake of innovative technologies in community-based care.

Artificial intelligence in cancer research: learning at different levels of data granularity

From genome-scale experimental studies to imaging data, behavioral footprints, and longitudinal healthcare records, the convergence of big data in cancer research and the advances in Artificial Intelligence (AI) is paving the way to develop a systems view of cancer. Nevertheless, this biomedical area is largely characterized by the co-existence of big data and small data resources, highlighting the need for a deeper investigation about the crosstalk between different levels of data granularity, including varied sample sizes, labels, data types, and other data descriptors. This review introduces the current challenges, limitations, and solutions of AI in the heterogeneous landscape of data granularity in cancer research. Such a variety of cancer molecular and clinical data calls for advancing the interoperability among AI approaches, with particular emphasis on the synergy between discriminative and generative models that we discuss in this work with several examples of techniques and applications.

Publication Bias in Gastrointestinal Oncology Trials Performed over the Past Decade

BACKGROUND

Randomized controlled trials (RCTs) are the gold standard for evidence-based practice, but their development and implementation is resource intensive. We aimed to describe modern RCTs in gastrointestinal (GI) cancer and identify predictors of successful accrual and publication.

MATERIALS AND METHODS

ClinicalTrials.gov was queried for phase III GI cancer RCTs opened between 2010 and 2019 and divided into two cohorts: past and recruiting. Past trials were analyzed for predictors of successful accrual and the subset with ≥3 years follow-up were analyzed for predictors of publication. Univariate and multivariable (MVA) logistic regression were used to identify covariates associated with complete accrual and publication status.

RESULTS

A total of 533 GI RCTs were opened from 2010 to 2019, 244 of which are still recruiting. In the "past" trials cohort (235/533) MVA, Asian continent of enrollment was a predictor for successful accrual, whereas trials with prolonged enrollment (duration longer than median of 960 days) trended to failed accrual. Predictors for publication on MVA included international enrollment and accrual completion. Sponsorship was not associated with accrual or publication. Notably, 33% of past trials remain unpublished, and 60% of trials that were closed early remain unpublished.

CONCLUSION

Accrual rate and the primary continent of enrollment drive both trial completion and publication in GI oncology. Accrual must be streamlined to enhance the impact of RCTs on clinical management. A large portion of trials remain unpublished, underscoring the need to encourage dissemination of all trials to, at a minimum, inform future trial design.

IMPLICATIONS FOR PRACTICE

Two-thirds of gastrointestinal (GI) oncology phase III randomized controlled trials successfully accrue; however, one third of these trials are unpublished and more than half of trials that close early are unpublished. The strongest predictors for publication are successful accrual and international collaborations. Initiatives to optimize the trial enrollment process need to be explored to maximize the potential for trials to engender progress in clinical practice. Moreover, this study identified a significant publication bias in the realm of GI oncology, and the field should promote reporting of all trials in order to better inform future trial questions and design.

Statistical fundamentals on cancer research for clinicians: Working with your statisticians

PURPOSE

To facilitate understanding statistical principles and methods for clinicians involved in cancer research.

METHODS

An overview of study design is provided on cancer research for both observational and clinical trials addressing study objectives and endpoints, superiority tests, non-inferiority and equivalence design, and sample size calculation. The principles of statistical models and tests including contemporary standard methods of analysis and evaluation are discussed. Finally, some statistical pitfalls frequently evident in clinical and translational studies in cancer are discussed.

RESULTS

We emphasize the practical aspects of study design (superiority vs non-inferiority vs equivalence study) and assumptions underpinning power calculations and sample size estimation. The differences between relative risk, odds ratio, and hazard ratio, understanding outcome endpoints, purposes of interim analysis, and statistical modeling to minimize confounding effects and bias are also discussed.

CONCLUSION

Proper design and correctly constructed statistical models are critical for the success of cancer research studies. Most statistical inaccuracies can be minimized by following essential statistical principles and guidelines to improve quality in research studies.

Implementing Historical Controls in Oncology Trials

Drug development in oncology has broadened from mainly considering randomized clinical trials to also including single-arm trials tailored for very specific subtypes of cancer. They often use historical controls, and this article discusses benefits and risks of this paradigm and provide various regulatory and statistical considerations. While leveraging the information brought by historical controls could potentially shorten development time and reduce the number of patients enrolled, a careful selection of the past studies, a prespecified statistical analysis accounting for the heterogeneity between studies, and early engagement with regulators will be key to success. Although both the European Medicines Agency and the U.S. Food and Drug Administration have already approved medicines based on nonrandomized experiments, the evidentiary package can be perceived as less comprehensive than randomized experiments. Use of historical controls, therefore, is better suited for cases of high unmet clinical need, where the disease course is well characterized and the primary endpoint is objective. IMPLICATIONS FOR PRACTICE: Incorporating historical data in single-arm oncology trials has the potential to accelerate drug development and to reduce the number of patients enrolled, compared with standard randomized controlled clinical trials. Given the lack of blinding and randomization, such an approach is better suited for cases of high unmet clinical need and/or difficult experimental situations, in which the trajectory of the disease is well characterized and the endpoint can be measured objectively. Careful pre-specification and selection of the historical data, matching of the patient characteristics with the concurrent trial data, and innovative statistical methodologies accounting for between-study variation will be needed. Early engagement with regulators (e.g., via Scientific Advice) is highly recommended.

Combining Isotopic Tracer Techniques to Increase Efficiency of Clinical Pharmacokinetic Trials in Oncology

With increasing numbers of drugs tested in oncology for smaller patient populations, fewer patients are available to answer important clinical pharmacological questions in the timeframe of clinical drug development. The quality and efficiency of trials to assess the pharmacokinetics of new drugs can be improved by making better use of available resources. One approach to do this is by making more effective use of isotopic tracer techniques. With increasing sensitivity of liquid chromatography-tandem mass spectrometry analyzing equipment over the years, it has now become possible to generate much more rich, high-quality pharmacokinetic data than before. In particular we want to make a plea here for a hybrid trial approach, where both radiolabeled drug and stable isotopically labeled drug are administered to patients to assess both the absolute bioavailability and absorption, distribution, metabolism and excretion in a single clinical trial experiment.

Engineering microrobots for targeted cancer therapies from a medical perspective

Systemic chemotherapy remains the backbone of many cancer treatments. Due to its untargeted nature and the severe side effects it can cause, numerous nanomedicine approaches have been developed to overcome these issues. However, targeted delivery of therapeutics remains challenging. Engineering microrobots is increasingly receiving attention in this regard. Their functionalities, particularly their motility, allow microrobots to penetrate tissues and reach cancers more efficiently. Here, we highlight how different microrobots, ranging from tailor-made motile bacteria and tiny bubble-propelled microengines to hybrid spermbots, can be engineered to integrate sophisticated features optimised for precision-targeting of a wide range of cancers. Towards this, we highlight the importance of integrating clinicians, the public and cancer patients early on in the development of these novel technologies.

Analysis of the WHO ICTRP for novel coronavirus clinical trial registrations

Up-to-date information on the current progress made in the research and development to control the global COVID-19 pandemic is important. The study aimed to analyze the clinical trial characteristics and vaccine development progress of the new Coronavirus Disease 2019 (COVID-19) registered with the World Health Organization International Clinical Trial Registry Platform (WHO ICTRP).A comprehensive search of COVID-19 clinical trials since the establishment of the ICTRP to June 11, 2020, was conducted to record and analyze relevant characteristics. Chi-Squared test was used to compare the statistical differences between different research types, interventions, and sources.A total of 3282 COVID-19 clinical trials in 17 clinical trial registration centers were registered with the WHO ICTRP. The main research sources for the present study were ClinicalTrials.gov and ChiCTR. There were significant differences in the parameters of study location (P = .000), number of participants (P = .000), study duration (P = .001), research stage (P = .000), randomization procedure (P = .000), and blinding method (P = .000) between the 2 registration sources. There were significant differences in all the parameters between different kinds of intervention methods. Hydroxychloroquine, plasma therapy, and Xiyanping injection were the high-frequency research drugs used. Ten different vaccine studies were registered under phases I-II.Amongst the studies researched, heterogeneity existed for various parameters. Differences in the type of study, interventions, and registration sources of the studies led to significant differences in certain parameters of the COVID-19 clinical trials. The statistics of high-frequency drugs and the progress of vaccine trials may provide an informative reference for the prevention and control of COVID-19.

Guidelines of the International Headache Society for controlled trials of preventive treatment of migraine attacks in episodic migraine in adults

Clinical trials are a key component of the evidence base for the treatment of headache disorders. In 1991, the International Headache Society Clinical Trials Standing Committee developed and published the first edition of the Guidelines for Controlled Trials of Drugs in Migraine. Advances in drugs, devices, and biologicals, as well as novel trial designs, have prompted several updates over the nearly 30 years since, including most recently the Guidelines for controlled trials of preventive treatment of chronic migraine (2018), the Guidelines for controlled trials of acute treatment of migraine attacks in adults (2019), and Guidelines for controlled trials of preventive treatment of migraine in children and adolescents (2019). The present update incorporates findings from new research and is intended to optimize the design of controlled trials of preventive pharmacological treatment of episodic migraine in adults. A guideline for clinical trials with devices will be published separately.

An Electronic Health Record Text Mining Tool to Collect Real-World Drug Treatment Outcomes: A Validation Study in Patients With Metastatic Renal Cell Carcinoma

Real‐world evidence can close the inferential gap between marketing authorization studies and clinical practice. However, the current standard for real‐world data extraction from electronic health records (EHRs) for treatment evaluation is manual review (MR), which is time‐consuming and laborious. Clinical Data Collector (CDC) is a novel natural language processing and text mining software tool for both structured and unstructured EHR data and only shows relevant EHR sections improving efficiency. We investigated CDC as a real‐world data (RWD) collection method, through application of CDC queries for patient inclusion and information extraction on a cohort of patients with metastatic renal cell carcinoma (RCC) receiving systemic drug treatment. Baseline patient characteristics, disease characteristics, and treatment outcomes were extracted and these were compared with MR for validation. One hundred patients receiving 175 treatments were included using CDC, which corresponded to 99% with MR. Calculated median overall survival was 21.7 months (95% confidence interval (CI) 18.7–24.8) vs. 21.7 months (95% CI 18.6–24.8) and progression‐free survival 8.9 months (95% CI 5.4–12.4) vs. 7.6 months (95% CI 5.7–9.4) for CDC vs. MR, respectively. Highest F1‐score was found for cancer‐related variables (88.1–100), followed by comorbidities (71.5–90.4) and adverse drug events (53.3–74.5), with most diverse scores on international metastatic RCC database criteria (51.4–100). Mean data collection time was 12 minutes (CDC) vs. 86 minutes (MR). In conclusion, CDC is a promising tool for retrieving RWD from EHRs because the correct patient population can be identified as well as relevant outcome data, such as overall survival and progression‐free survival.

On strategic choices faced by large pharmaceutical laboratories and their effect on innovation risk under fuzzy conditions

OBJECTIVES

We develop a fuzzy evaluation model that provides managers at different responsibility levels in pharmaceutical laboratories with a rich picture of their innovation risk as well as that of competitors. This would help them take better strategic decisions around the management of their present and future portfolio of clinical trials in an uncertain environment. Through three structured fuzzy inference systems (FISs), the model evaluates the overall innovation risk of the laboratories by capturing the financial and pipeline sides of the risk.

METHODS AND MATERIALS

Three FISs, based on the Mamdani model, determine the level of innovation risk of large pharmaceutical laboratories according to their strategic choices. Two subsystems measure different aspects of innovation risk while the third one builds on the results of the previous two. In all of them, both the partitions of the variables and the rules of the knowledge base are agreed through an innovative 2-tuple-based method. With the aid of experts, we have embedded knowledge into the FIS and later validated the model.

RESULTS

In an empirical application of the proposed methodology, we evaluate a sample of 31 large pharmaceutical laboratories in the period 2008-2013. Depending on the relative weight of the two subsystems in the first layer (capturing the financial and the pipeline sides of innovation risk), we estimate the overall risk. Comparisons across laboratories are made and graphical surfaces are analyzed in order to interpret our results. We have also run regressions to better understand the implications of our results.

CONCLUSIONS

The main contribution of this work is the development of an innovative fuzzy evaluation model that is useful for analyzing the innovation risk characteristics of large pharmaceutical laboratories given their strategic choices. The methodology is valid for carrying out a systematic analysis of the potential for developing new drugs over time and in a stable manner while managing the risks involved. We provide all the necessary tools and datasets to facilitate the replication of our system, which also may be easily applied to other settings.

Pharmacogenomic Biomarkers in Docetaxel Treatment of Prostate Cancer: From Discovery to Implementation

Prostate cancer is the fifth leading cause of male cancer death worldwide. Although docetaxel chemotherapy has been used for more than fifteen years to treat metastatic castration resistant prostate cancer, the high inter-individual variability of treatment efficacy and toxicity is still not well understood. Since prostate cancer has a high heritability, inherited biomarkers of the genomic signature may be appropriate tools to guide treatment. In this review, we provide an extensive overview and discuss the current state of the art of pharmacogenomic biomarkers modulating docetaxel treatment of prostate cancer. This includes (1) research studies with a focus on germline genomic biomarkers, (2) clinical trials including a range of genetic signatures, and (3) their implementation in treatment guidelines. Based on this work, we suggest that one of the most promising approaches to improve clinical predictive capacity of pharmacogenomic biomarkers in docetaxel treatment of prostate cancer is the use of compound, multigene pharmacogenomic panels defined by specific clinical outcome measures. In conclusion, we discuss the challenges of integrating prostate cancer pharmacogenomic biomarkers into the clinic and the strategies that can be employed to allow a more comprehensive, evidence-based approach to facilitate their clinical integration. Expanding the integration of pharmacogenetic markers in prostate cancer treatment procedures will enhance precision medicine and ultimately improve patient outcomes.