Use of Real-World Evidence to Support FDA Approval of Oncology Drugs

Response- and Progression-Based End Points in Trial and Observational Cohorts of Patients With NSCLC

Importance

Response Evaluation Criteria in Solid Tumors (RECIST) are commonly used to assess therapeutic response in clinical trials but not in routine care; thus, RECIST-based end points are difficult to include in observational studies. Clinician-anchored approaches for measuring clinical response have been validated but not widely compared with clinical trial data, limiting their use as evidence for clinical decision-making.

Objective

To compare response- and progression-based end points in clinical trial and observational cohorts of patients with non-small cell lung cancer (NSCLC).

Design, Setting, and Participants

This retrospective cohort study used patient-level data from the IMpower132 trial (conducted April 7, 2016, to May 31, 2017) and a nationwide electronic health record (EHR)-derived deidentified database (data collected January 1, 2011, to March 31, 2022). Patients in the observational cohort were selected according to the inclusion and exclusion criteria of the IMpower132 trial. All patients in the observational cohort had stage IV NSCLC.

Exposure

All patients were randomized to or received first-line carboplatin or cisplatin plus pemetrexed.

Main Outcomes and Measures

End points included response rates, duration of response, and progression-free survival, compared between the trial and observational cohorts before and after weighting. Response rates for the observational cohort were derived from the EHR.

Results

A total of 769 patients met inclusion criteria, 494 in the observational cohort (median [IQR] age, 67 [60-74] years; 228 [46.2%] female; 45 [9.1%] Black or African American; 352 [71.3%] White; 53 [10.7%] American Indian or Alaska Native, Asian, Hawaiian or Pacific Islander, or multiracial) and 275 in the trial cohort (median [IQR] age, 63 [56-68] years; 90 [32.7%] female; 4 [1.5%] Black or African American; 194 [70.5%] White; 65 [23.6%] American Indian or Alaska Native, Asian, Hawaiian or Pacific Islander, or multiracial). All 3 end points were comparable between the study cohorts. Trial patients had a higher number of response assessments compared with patients in the weighted observational cohort. The EHR-derived response rate was numerically higher than the objective response rate after weighting (100.3 of 249.3 [40.2%] vs 105 of 275 [38.2%]) due to higher rates of observed partial response than RECIST-based partial response. Among patients with at least 1 response assessment, the EHR-derived response rate remained higher than the objective response rate (100.3 of 193.4 [51.9%] vs 105 of 256 [41.0%]) due to a higher proportion of patients in the observational cohort with no response assessment.

Conclusions and Relevance

In this study, response- and progression-based end points were similar between clinical trial and weighted observational cohorts, which increases confidence in the reliability of observational end points and can inform their interpretation in relation to trial end points. Additionally, the difference observed in response rates (including vs excluding patients with no response assessment) highlights the importance of future research adopting this 2-way approach when evaluating the relationship of EHR-derived and objective response rates.

Utility of real-world evidence in biosimilar development

Biosimilar development refers to the process of creating a biologic drug that is similar to an existing approved biologic drug, also known as a reference drug. Due to the complex nature of biologics drugs and the inherent variability in their manufacturing process biosimilars are not identical but highly similar to the reference drug in terms of quality, safety, and efficacy. Efficacy and safety trials for biosimilars involve large numbers of patients to confirm comparable clinical performance of the biosimilar and the reference product in appropriately sensitive clinical indications and for appropriate sensitive endpoints. The objective of a biosimilar clinical data is to address slight differences observed at previous steps and to confirm comparable clinical performance of the biosimilar and the reference product. In recent years with advances in big data computing, there has been increasing interest to incorporate the totality of information from different data sources (e.g. Real World data and published literature) in design and conduct of clinical trial to support regulatory objectives. The biosimilar development is an ideal framework for utilization of Real-World Evidence in design of trials as potentially large amount of data are available for the reference dug. Hence there may be an opportunity to use RWD in establishing, improving or validating equivalence margins (EQM) for biosimilar designs, specifically in the case there is no historical published data in the intended sensitive population. In this article, we propose a variation of matching method that seems promising to identify the matched set from a real-world data for which the effect size of targeted endpoint would be comparable to historical data. We believe this is a reasonable approach because in design stage, we can view covariates and secondary endpoints as data feature that can be used in a matching method. This approach was illustrated through a case study which indicated the estimate of the primary endpoint is within 1% of published results and thus RWD may be used to justify or estimate the equivalence margin. To ensure consistent results we recommend using this approach in different indications and endpoint scenarios. Thus utilization of RWD/RWE can provide an important opportunity to increase access to biologic therapies, reducing cost by repurposing existing data.

The Use of Real-World Evidence for Regulatory Decisions in China

There is a growing demand for the use of high-quality real-world evidence (RWE) to support regulatory decision-making worldwide and in China, which highlights the need for conducting literature reviews to evaluate the available data and evidence. This study aims to review the use of RWE in Chinese regulatory decisions and to summarize relevant regulatory and methodological considerations to inform the future use of RWE in China. We identified policy documents, technical guidance documents, and cases on official Chinese government websites and extracted their contents separately. We consulted experts from the National Medical Products Administration (NMPA) and academic institutes and searched case-related articles for enrichment. We also searched and included articles related to the use of RWE/Real-world data in Chinese regulatory decisions. Six trial versions of technical guidance documents, 7 case studies, and 40 articles related to the Chinese regulatory decisions were included in this study. Based on the technical guidance, data quality, and appropriate study design and statistical analysis are the main concerns for RWE generation. The cases and articles related to regulatory decisions revealed 9 main concerns, including data sources and applicability, data quality, strength of existing evidence, appropriate study design and statistical analysis, regulated and transparent process for analysis and evidence generation, product safety and efficacy, product characteristics and clinical needs, ethical considerations and data security, and communicate adequately with regulatory authorities. Among these concerns, data issues are central. Preliminary attempts have been made by the NMPA to promote the use of RWE, but substantial challenges still remain.

Tumor-agnostic baskets to N-of-1 platform trials and real-world data: Transforming precision oncology clinical trial design

Choosing the right drug(s) for the right patient via advanced genomic sequencing and multi-omic interrogation is the sine qua non of precision cancer medicine. Traditional cancer clinical trial designs follow well-defined protocols to evaluate the efficacy of new therapies in patient groups, usually identified by their histology/tissue of origin of their malignancy. In contrast, precision medicine seeks to optimize benefit in individual patients, i.e., to define who benefits rather than determine whether the overall group benefits. Since cancer is a disease driven by molecular alterations, innovative trial designs, including biomarker-defined tumor-agnostic basket trials, are driving ground-breaking regulatory approvals and deployment of gene- and immune-targeted drugs. Molecular interrogation further reveals the disruptive reality that advanced cancers are extraordinarily complex and individually distinct. Therefore, optimized treatment often requires drug combinations and N-of-1 customization, addressed by a new generation of N-of-1 trials. Real-world data and structured master registry trials are also providing massive datasets that are further fueling a transformation in oncology. Finally, machine learning is facilitating rapid discovery, and it is plausible that high-throughput computing, in silico modeling, and 3-dimensional printing may be exploitable in the near future to discover and design customized drugs in real time.

Systematic analysis of off-label and off-guideline cancer therapy usage in a real-world cohort of 165,912 US patients

Patients with cancer may be given treatments that are not officially approved (off-label) or recommended by guidelines (off-guideline). Here we present a data science framework to systematically characterize off-label and off-guideline usages using real-world data from de-identified electronic health records (EHR). We analyze treatment patterns in 165,912 US patients with 14 common cancer types. We find that 18.6% and 4.4% of patients have received at least one line of off-label and off-guideline cancer drugs, respectively. Patients with worse performance status, in later lines, or treated at academic hospitals are significantly more likely to receive off-label and off-guideline drugs. To quantify how predictable off-guideline usage is, we developed machine learning models to predict which drug a patient is likely to receive based on their clinical characteristics and previous treatments. Finally, we demonstrate that our systematic analyses generate hypotheses about patients' response to treatments.

Analysis of research topics and trends in investigator-initiated research/trials (IIRs/IITs): A topic modeling study

BACKGROUND

With the exponential growth of publications in the field of investigator-initiated research/trials (IIRs/IITs), it has become necessary to employ text mining and bibliometric analysis as tools for gaining deeper insights into this area of study. By using these methods, researchers can effectively identify and analyze research topics within the field.

METHODS

This study retrieved relevant publications from the Web of Science Core Collection and conducted bioinformatics analysis. The latent Dirichlet allocation model, which is based on machine learning, was utilized to identify subfield research topics.

RESULTS

A total of 4315 articles related to IIRs/IITs were obtained from the Web of Science Core Collection. After excluding duplicates and articles with missing abstracts, a final dataset of 3333 articles was included for bibliometric analysis. The number of publications showed a steady increase over time, particularly since 2000. The United States, Germany, the United Kingdom, the Netherlands, Canada, Denmark, Japan, Switzerland, and France emerged as the most productive countries in terms of IIRs/IITs. The citation analysis revealed intriguing trends, with certain highly cited articles showing a significant increase in citation frequency in recent years. A model with 45 topics was deemed the best fit for characterizing the extensively researched fields within IIRs/IITs. Our analysis revealed 10 top topics that have garnered significant attention, spanning domains such as community health, cancer treatment, brain development and disease mechanisms, nursing research, and stem cell therapy. These top topics offer researchers valuable directions for further investigation and innovation. Additionally, we identified 12 hot topics, which represent the most cutting-edge and highly regarded research areas within the field.

CONCLUSION

This study contributes to a comprehensive understanding of the current research landscape and provides valuable insights for researchers working in this domain.

Lessons learned from postmarketing withdrawals of expedited approvals for oncology drug indications

In the past decade, there have been a record number of oncology therapy approvals by the US Food and Drug Administration (FDA) and the European Medicines Agency (EMA). Besides the EMA's conditional marketing authorisation programme and the FDA's Accelerated Approval Program, we observe a tendency towards fast approval for exploratory studies with non-randomised, uncontrolled designs and surrogate endpoints. This issue raises concerns about the robustness and effectiveness of accepted treatments, leaving patients and health-care professionals in a state of uncertainty. A substantial number of accelerated approvals have recently been withdrawn in the USA, with some still authorised in Europe, emphasising discrepancies in regulatory standards that affect both patients and society as a whole. We highlight examples of drugs, authorised on the basis of surrogate endpoints, that were later withdrawn due to an absence of overall survival benefit. Our findings address the challenges and consequences of accelerated approval pathways in oncology. In conclusion, this Policy Review calls for regulatory bodies to better align their procedures and insist on robust evidence, preferably through unbiased randomised controlled trials. Drug approval processes should prioritise patient benefit, overall survival, and quality of life to minimise risks and uncertainties for patients.

Treatment Outcomes with Standard of Care in Relapsed/Refractory Diffuse Large B-Cell Lymphoma: Real-World Data Analysis

INTRODUCTION

Despite new therapies for relapsed or refractory (R/R) diffuse large B-cell lymphoma (DLBCL), treatments with chemotherapy, single-agent rituximab/obinutuzumab, single-agent lenalidomide, or combinations of these agents continue to be commonly used.

METHODS

This retrospective study utilized longitudinal data from 4226 real-world electronic health records to characterize outcomes in patients with R/R DLBCL. Eligible patients were diagnosed with DLBCL between January 2010 and March 2022 and had R/R disease treated with ≥ 1 prior systemic line of therapy (LOT), including ≥ 1 anti-CD20-containing regimen.

RESULTS

A total of 573 patients treated with ≥ 1 prior LOT were included (31.2% and 13.4% with ≥ 2 and ≥ 3 prior LOTs, respectively). Median duration of follow-up was 7.7 months. Most patients (57.1%) were male; mean standard deviation (SD) age was 63 (14.7) years. Overall and complete response rates (95% confidence interval (CI) were 52% (48-56) and 23% (19-27). Median duration of response and duration of complete response were 3.5 and 18.4 months. Median progression-free and overall survival (95% CI) was 3.0 (2.8-3.3) and 12.9 (10.1-16.9) months, respectively. Patients with a higher number of prior LOTs, primary refractoriness, refractoriness to last LOT, refractoriness to last anti-CD20-containing regimen, and prior CAR T exposure had worse outcomes (i.e., challenging-to-treat R/R DLBCL) compared with those without these characteristics.

CONCLUSIONS

Outcomes in patients with R/R DLBCL treated with chemotherapy, single-agent rituximab/obinutuzumab, single-agent lenalidomide, or combinations of these agents remain poor, especially for those with challenging-to-treat R/R DLBCL. These findings underscore the unmet need for new, safe, and effective therapies, especially for challenging-to-treat R/R DLBCL populations.

Raising the Bar for Real-World Data in Oncology: Approaches to Quality Across Multiple Dimensions

PURPOSE

Electronic health record (EHR)-based real-world data (RWD) are integral to oncology research, and understanding fitness for use is critical for data users. Complexity of data sources and curation methods necessitate transparency into how quality is approached. We describe the application of data quality dimensions in curating EHR-derived oncology RWD.

METHODS

A targeted review was conducted to summarize data quality dimensions in frameworks published by the European Medicines Agency, The National Institute for Healthcare and Excellence, US Food and Drug Administration, Duke-Margolis Center for Health Policy, and Patient-Centered Outcomes Research Institute. We then characterized quality processes applied to curation of Flatiron Health RWD, which originate from EHRs of a nationwide network of academic and community cancer clinics, across the summarized quality dimensions.

RESULTS

The primary quality dimensions across frameworks were relevance (including subdimensions of availability, sufficiency, and representativeness) and reliability (including subdimensions of accuracy, completeness, provenance, and timeliness). Flatiron Health RWD quality processes were aligned to each dimension. Relevancy to broad or specific use cases is optimized through data set size and variable breadth and depth. Accuracy is addressed using validation approaches, such as comparison with external or internal reference standards or indirect benchmarking, and verification checks for conformance, consistency, and plausibility, selected on the basis of feasibility and criticality of the variable to the intended use case. Completeness is assessed against expected source documentation; provenance by recording data transformation, management procedures, and auditable metadata; and timeliness by setting refresh frequency to minimize data lags.

CONCLUSION

Development of high-quality, scaled, EHR-based RWD requires integration of systematic processes across the data lifecycle. Approaches to quality are optimized through knowledge of data sources, curation processes, and use case needs. By addressing quality dimensions from published frameworks, Flatiron Health RWD enable transparency in determining fitness for real-world evidence generation.

External control arms for rare diseases: building a body of supporting evidence

Comparator arms in randomized clinical trials may be impractical and/or unethical to assemble in rare diseases. In the absence of comparator arms, evidence generated from external control studies has been used to support successful regulatory submissions and health technology assessments (HTA). However, conducting robust and rigorous external control arm studies is challenging and despite all efforts, residual biases may remain. As a result, regulatory and HTA agencies may request additional external control analyses so that decisions may be made based upon a body of supporting evidence.This paper introduces external control studies and provides an overview of the key methodological issues to be considered in the design of these studies. A series of case studies are presented in which evidence derived from one or more external controls was submitted to regulatory and HTA agencies to provide support for the consistency of findings.

Real-World Evidence Studies in Oncology Therapeutics: Hope or Hype?

Randomized controlled trial (RCT) remains a gold standard in evidence-based medicine for assessing the efficacy and safety of cancer therapies. However, due to some inherent methodological limitations of RCT, such as stringent inclusion criteria, highly specific treatment, ethical and scientific compromise in rare cancer, and inability to adequately assess safety, real-world evidence (RWE) has been adjudged as a suitable option to complement data obtained from RCT. Moreover, in the context of cancer therapeutics, few notable merits pertain to developing a novel product for rare cancer subtypes, establishing new indications for already approved drugs, optimization of treatment regimen and sequence, a better description of long-term safety, and supporting the reimbursement-related decision. However, the implementation of RWE for the aforementioned purposes will be limited by various challenges, especially in the context of developing economies such as India. Special attention should be given to the availability of data, maintaining the quality standard, and establishing stringent regulations for privacy and security along with active regulatory engagement with relevant stakeholders. Such activities will be key to facilitating the use of RWE in cancer therapeutics.

Combined Results of Two Cross-Sectional Surveys on the Participation in Clinical Trials and the e-Consent Procedure in the Landscape of Haematology

Despite the motivation of oncology patients to take part in clinical trials, only a minority of them are enrolled in clinical trials. Implementation of new practical procedures can become a barrier that withholds patients from participating in clinical trials. Treating physicians are crucial in augmenting trial accrual. The drivers that promote physicians to allocate patients for clinical trials need further assessment. We conducted two separate cross-sectional surveys, addressing patients with a haematological disease in one survey and haematologists in another survey. The patient survey was filled out by 420 patients. Significant relationships between the willingness to participate in a trial and trial knowledge (p < 0.001) and between doctor-patient relationship and participation willingness (p = 0.007) were noted. Patients above 60 years were less willing to use an electronic consent procedure vs. patients younger than 60 (p < 0.001). The physician questionnaire was completed by 42 participants of whom most (83%) were active in and (94%) motivated for clinical trials. Apart from the patient benefit and scientific interest, prestige was an equal motivator closely followed by financial remunerations. First goal was not to harm the patient. Our study confirms the high willingness of patients for trial participation and the need to rethink the structure of trial organisation. The e-consent procedure is not the method preferred by most patients above 60 years old.

Real-world effectiveness of ribociclib in metastatic breast cancer patients: Does dose affect survival?

Introduction: Real-world data are critical to demonstrate the reproducibility of evidence and external generalizability of randomized clinical trials. The purpose of this study was to assess real-world security profile and management of adverse events (AEs) presented with ribociclib for the treatment of HR + /HER2- metastatic breast cancer (MBC). Our secondary objective was to provide real-world effectiveness of this treatment (measured with progression-free survival (PFS)) and to confirm the hypothesis that dose reductions are not related with disease progression. Material and methods: Observational retrospective study evaluating all females with MBC treated with ribociclib. Study period: January 2017 to September 2019. Follow-up was done until November 2021. Response was assessed through the PFS according to RECIST1.1 and National Cancer Institute Common Terminology Criteria for Adverse Events (CTCAE) was used to classify AEs. Results: The most common AE was any grade neutropenia, documented in 37 of 53 patients (69.8%) during the course of treatment. By the end of the follow-up period, overall median PFS with ribociclib therapy was 27.3 months (95% confidence interval (CI) 20.8-71.8 months). In total, 50 patients (94.4%) initiated ribociclib at 600 mg dose, 28 patients (58%) required dose reductions. PFS of patients receiving ribociclib as first-line treatment was 28 (95% CI 15-41 months). Conclusions: Our results from patients treated in real-world clinical settings indicate that ribociclib is safe and their AEs are manageable with active monitoring, temporal suspension of treatment and dose reduction. Furthermore, our results indicate that dose reduction of ribociclib is not associated with a loss of efficacy.

Paving the way for patient centricity in real-world evidence (RWE): Qualitative interviews to identify considerations for wider implementation of patient-reported outcomes in RWE generation

Objectives

Real-world evidence (RWE) generation can be enhanced by including patient-reported outcomes (PROs). Methods for collecting and using PRO data in the real-world setting are currently underdeveloped and there is no international guidance specific to its use in this context. This study explored stakeholders' perspectives and needs for using PROs in RWE generation. Barriers, facilitators, and opportunities for wider use of PROs in real-world studies were also investigated.

Methods

Online semi-structured interviews were conducted with international stakeholders: patients, patient advocates, regulators, payers, clinicians, academic researchers, and industry experts. Interviews were recorded, transcribed verbatim and analysed using NVivo 20. Thematic analysis was conducted based on the updated Consolidated Framework for Implementation Research (CFIR).

Results

Twenty-three interviews were conducted. Participants confirmed that the use of PROs in RWE generation is not yet well established. Participants expressed a mixed level of confidence in the value of PROs collected in a real-world setting. Operational challenges associated with collecting routine PRO data to inform care delivery at the individual level (e.g., setting up infrastructure) need to be addressed. Methodological and other challenges (e.g., financing research) associated with collecting prospective de novo data in a real-world setting should be considered to facilitate PRO utilisation in real-world studies.

Conclusions

Several opportunities and challenges were identified regarding the broader use of PROs in RWE research. Joint efforts from different stakeholders are needed to maximise PRO implementation, with consideration given to each stakeholders' specific needs (e.g., by developing good practices).

External control arms: COVID-19 reveals the merits of using real world evidence in real-time for clinical and public health investigations

Randomized controlled trials are considered the 'gold standard' to reduce bias by randomizing patients to an experimental intervention, versus placebo or standard of care cohort. There are inherent challenges to enrolling a standard of care or cohorts: costs, site engagement logistics, socioeconomic variability, patient willingness, ethics of placebo interventions, cannibalizing the treatment arm population, and extending study duration. The COVID-19 pandemic has magnified aspects of constraints in trial recruitment and logistics, spurring innovative approaches to reducing trial sizes, accelerating trial accrual while preserving statistical rigor. Using data from medical records and databases allows for construction of external control arms that reduce the costs of an external control arm (ECA) randomized to standard of care. Simultaneously examining covariates of the clinical outcomes in ECAs that are being measured in the interventional arm can be particularly useful in phase 2 trials to better understand social and genetic determinants of clinical outcomes that might inform pivotal trial design. The FDA and EMA have promulgated a number of publicly available guidance documents and qualification reports that inform the use of this regulatory science tool to streamline clinical development, of phase 4 surveillance, and policy aspects of clinical outcomes research. Availability and quality of real-world data (RWD) are a prevalent impediment to the use of ECAs given such data is not collected with the rigor and deliberateness that characterizes prospective interventional control arm data. Conversely, in the case of contemporary control arms, a clinical trial outcome can be compared to a contemporary standard of care in cases where the standard of care is evolving at a fast pace, such as the use of checkpoint inhibitors in cancer care. Innovative statistical methods are an essential aspect of an ECA strategy and regulatory paths for these innovative approaches have been navigated, qualified, and in some cases published.

Commercializing Personal Health Information: A Critical Qualitative Content Analysis of Documents Describing Proprietary Primary Care Databases in Canada

BACKGROUND

Commercial data brokers have amassed large collections of primary care patient data in proprietary databases. Our study objective was to critically analyze how entities involved in the collection and use of these records construct the value of these proprietary databases. We also discuss the implications of the collection and use of these databases.

METHODS

We conducted a critical qualitative content analysis using publicly available documents describing the creation and use of proprietary databases containing Canadian primary care patient data. We identified relevant commercial data brokers, as well as entities involved in collecting data or in using data from these databases. We sampled documents associated with these entities that described any aspect of the collection, processing, and use of the proprietary databases. We extracted data from each document using a structured data tool. We conducted an interpretive thematic content analysis by inductively coding documents and the extracted data.

RESULTS

We analyzed 25 documents produced between 2013 and 2021. These documents were largely directed at the pharmaceutical industry, as well as shareholders, academics, and governments. The documents constructed the value of the proprietary databases by describing extensive, intimate, detailed patient-level data holdings. They provided examples of how the databases could be used by pharmaceutical companies for regulatory approval, marketing and understanding physician behaviour. The documents constructed the value of these data more broadly by claiming to improve health for patients, while also addressing risks to privacy. Some documents referred to the trade-offs between patient privacy and data utility, which suggests these considerations may be in tension.

CONCLUSION

Documents in our analysis positioned the proprietary databases as socially legitimate and valuable, particularly to pharmaceutical companies. The databases, however, may pose risks to patient privacy and contribute to problematic drug promotion. Solutions include expanding public data repositories with appropriate governance and external regulatory oversight.

Randomized controlled trial data for successful new drug application for rare diseases in the United States

BACKGROUND

Randomized controlled trial (RCT) data have important implications in drug development. However, the feasibility and cost of conducting RCTs lower the motivation for drug development, especially for rare diseases. We investigated the potential factors associated with the need for RCTs in the clinical data package for new drug applications for rare diseases in the United States (US). This study focused on 233 drugs with orphan drug designations approved in the US between April 2001 and March 2021. Univariable and multivariable logistic regression analyses were conducted to investigate the association between the presence or absence of RCTs in the clinical data package for new drug applications.

RESULTS

Multivariable logistic regression analysis showed that the severity of the disease outcome (odds ratio [OR] 5.63, 95% confidence interval [CI] 2.64-12.00), type of drug usage (odds ratio [OR] 2.95, 95% confidence interval [CI] 1.80-18.57), and type of primary endpoint (OR 5.57, 95% CI 2.57-12.06) were associated with the presence or absence of RCTs.

CONCLUSIONS

Our results indicated that the presence or absence of RCT data in the clinical data package for successful new drug application in the US was associated with three factors: severity of disease outcome, type of drug usage, and type of primary endpoint. These results highlight the importance of selecting target diseases and potential efficacy variables to optimize orphan drug development.

Development of an External Control Arm Using Electronic Health Record-Based Real-World Data to Evaluate the Efficacy of COVID-19 Treatment

To protect people from severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) infection, tremendous research efforts have been made toward coronavirus disease 19 (COVID-19) treatment development. Externally controlled trials (ECTs) may help reduce their development time. To evaluate whether ECT using real-world data (RWD) of patients with COVID-19 is feasible enough to be used for regulatory decision making, we built an external control arm (ECA) based on RWD as a control arm of a previously conducted randomized controlled trial (RCT), and compared it to the control arm of the RCT. The electronic health record (EHR)-based COVID-19 cohort dataset was used as RWD, and three Adaptive COVID-19 Treatment Trial (ACTT) datasets were used as RCTs. Among the RWD datasets, eligible patients were evaluated as a pool of external control subjects of the ACTT-1, ACTT-2, and ACTT-3 trials, respectively. The ECAs were built using propensity score matching, and the balance of age, sex, and baseline clinical status ordinal scale as covariates between the treatment arms of Asian patients in each ACTT and the pools of external control subjects was assessed before and after 1:1 matching. There was no statistically significant difference in time to recovery between ECAs and the control arms of each ACTT. Among the covariates, the baseline status ordinal score had the greatest influence on the building of ECA. This study demonstrates that ECA based on EHR data of COVID-19 patients could sufficiently replace the control arm of an RCT, and it is expected to help develop new treatments faster in emergency situations, such as the COVID-19 pandemic.

Selection Bias in Real-World Data Studies Used to Support Health Technology Assessments: A Case Study in Metastatic Cancer

Real-world evidence has been increasingly used to support evaluations of emerging therapies. These investigations are often conducted in settings that may not be representative of the underlying population. The purpose of this investigation was to empirically quantify the magnitude of this selection bias. Individuals diagnosed with solid metastatic cancer in Alberta, Canada, between 2010-2019 were identified using the provincial cancer registry for 13 common metastatic sites. Two outcomes used to support oncology reimbursement decisions were examined: the proportion of individuals who initiated systemic therapy and median overall survival (OS). These outcomes were assessed in the entire population and in a subset of individuals who were referred to a medical oncologist. Among the 23,152 individuals in the entire population, 40.8% (95% CI: 40.2-41.4) initiated systemic therapy, and the median OS from diagnosis was 5.4 months (95% CI: 5.3-5.6). Among those who were referred to a medical oncologist (n = 13,372; 57.8%), 67.4% (95% CI: 66.6-68.2) initiated systemic therapy, and the median OS from diagnosis was 11.2 months (95% CI: 10.9-11.5). The magnitude of bias varied by cancer site where lower referral rates were associated with greater bias. Non-referral is an important source of selection bias in real-world investigations. Studies that rely on limited-catchment real-world data should be interpreted with caution, particularly in metastatic cancer settings.

Augmenting randomized clinical trial data with historical control data: Precision medicine applications

As precision medicine becomes more precise, the sizes of the molecularly targeted subpopulations become increasingly smaller. This can make it challenging to conduct randomized clinical trials of the targeted therapies in a timely manner. To help with this problem of a small patient subpopulation, a study design that is frequently proposed is to conduct a small randomized clinical trial (RCT) with the intent of augmenting the RCT control arm data with historical data from a set of patients who have received the control treatment outside the RCT (historical control data). In particular, strategies have been developed that compare the treatment outcomes across the cohorts of patients treated with the standard (control) treatment to guide the use of the historical data in the analysis; this can lessen the potential well-known biases of using historical controls without any randomization. Using some simple examples and completed studies, we demonstrate in this commentary that these strategies are unlikely to be useful in precision medicine applications.

RWD-derived response in multiple myeloma

Real-world data (RWD) are important for understanding the treatment course and response patterns of patients with multiple myeloma. This exploratory pilot study establishes a way to reliably assess response from incomplete laboratory measurements captured in RWD. A rule-based algorithm, adapted from International Myeloma Working Group response criteria, was used to derive response using RWD. This derived response (dR) algorithm was assessed using data from the phase III BELLINI trial, comparing the number of responders and non-responders assigned by independent review committee (IRC) versus the dR algorithm. To simulate a real-world scenario with missing data, a sensitivity analysis was conducted whereby available laboratory measurements in the dataset were artificially reduced. Associations between dR and overall survival were evaluated at 1) individual level and 2) treatment level in a real-world patient cohort obtained from a nationwide electronic health record-derived de-identified database. The algorithm's assignment of responders was highly concordant with that of the IRC (Cohen's Kappa 0.83) using the BELLINI data. The dR replicated the differences in overall response rate between the intervention and placebo arms reported in the trial (odds ratio 2.1 vs. 2.3 for IRC vs. dR assessment, respectively). Simulation of missing data in the sensitivity analysis (-50% of available laboratory measurements and -75% of urine monoclonal protein measurements) resulted in a minor reduction in the algorithm's accuracy (Cohen's Kappa 0.75). In the RWD cohort, dR was significantly associated with overall survival at all landmark times (hazard ratios 0.80-0.81, p<0.001) at the individual level, while the overall association was R2 = 0.67 (p<0.001) at the treatment level. This exploratory pilot study demonstrates the feasibility of deriving accurate response from RWD. With further confirmation in independent cohorts, the dR has the potential to be used as an endpoint in real-world studies and as a comparator in single-arm clinical trials.

The Midazolam RAMPART Study Medical Records Project: A Unique Use of Real-World Data in a Complex Collaborative Partnership to Support a New Drug Application

INTRODUCTION

This project aimed to retrospectively obtain, review, and extract key safety data from medical records of participants enrolled in RAMPART, the NIH-supported Rapid Anticonvulsant Medication Prior to ARrival Trial of intramuscular midazolam versus intravenous lorazepam for pre-hospital treatment of status epilepticus, to support a US new drug application (NDA) for intramuscular midazolam.

METHODS

A collaborative partnership was established between the NDA sponsor, the RAMPART trial lead academic institution, US government agencies, and contract research organizations to retrieve, review, and extract relevant safety data from the medical records of RAMPART participants and summarize those data to include in an NDA submitted to the US Food and Drug Administration (FDA).

RESULTS

Key data in the medical records of 890 RAMPART trial participants (1020 enrollments, including 130 repeat enrollments) were reviewed and extracted into a project database. Safety events occurred in 771 (86.6%) participants, and included additional information not collected in the RAMPART trial. This database also enabled subgroup analyses based on medical history and prior/concurrent medications, building upon previous analyses according to age, sex, and race. No previously unrecognized safety patterns were identified, and no association was observed between efficacy and medical history or medication usage.

CONCLUSIONS

The use of unstructured real-world retrospective medical record data can effectively support an NDA submission in place of conducting another interventional clinical trial. This retrospective medical records review and extraction of additional safety data contributed to the FDA approval of intramuscular midazolam for the pre-hospital treatment of status epilepticus in 2018.

CLINICALTRIALS

GOV: NCT00809146.

Reverse Engineering of Digital Measures: Inviting Patients to the Conversation

Background

Digital measures offer an unparalleled opportunity to create a more holistic picture of how people who are patients behave in their real-world environments, thereby establishing a better connection between patients, caregivers, and the clinical evidence used to drive drug development and disease management. Reaching this vision will require achieving a new level of co-creation between the stakeholders who design, develop, use, and make decisions using evidence from digital measures.

Summary

In September 2022, the second in a series of meetings hosted by the Swiss Federal Institute of Technology in Zürich, the Foundation for the National Institutes of Health Biomarkers Consortium, and sponsored by Wellcome Trust, entitled "Reverse Engineering of Digital Measures," was held in Zurich, Switzerland, with a broad range of stakeholders sharing their experience across four case studies to examine how patient centricity is essential in shaping development and validation of digital evidence generation tools.

Key Messages

In this paper, we discuss progress and the remaining barriers to widespread use of digital measures for evidence generation in clinical development and care delivery. We also present key discussion points and takeaways in order to continue discourse and provide a basis for dissemination and outreach to the wider community and other stakeholders. The work presented here shows us a blueprint for how and why the patient voice can be thoughtfully integrated into digital measure development and that continued multistakeholder engagement is critical for further progress.

A meta-analytic framework to adjust for bias in external control studies

While randomized controlled trials (RCTs) are the gold standard for estimating treatment effects in medical research, there is increasing use of and interest in using real-world data for drug development. One such use case is the construction of external control arms for evaluation of efficacy in single-arm trials, particularly in cases where randomization is either infeasible or unethical. However, it is well known that treated patients in non-randomized studies may not be comparable to control patients-on either measured or unmeasured variables-and that the underlying population differences between the two groups may result in biased treatment effect estimates as well as increased variability in estimation. To address these challenges for analyses of time-to-event outcomes, we developed a meta-analytic framework that uses historical reference studies to adjust a log hazard ratio estimate in a new external control study for its additional bias and variability. The set of historical studies is formed by constructing external control arms for historical RCTs, and a meta-analysis compares the trial controls to the external control arms. Importantly, a prospective external control study can be performed independently of the meta-analysis using standard causal inference techniques for observational data. We illustrate our approach with a simulation study and an empirical example based on reference studies for advanced non-small cell lung cancer. In our empirical analysis, external control patients had lower survival than trial controls (hazard ratio: 0.907), but our methodology is able to correct for this bias. An implementation of our approach is available in the R package ecmeta.

Real-World Evidence: A Primer

Real-world evidence (RWE) is clinical evidence on a medical product's safety and efficacy that is generated using real-world data (RWD) resulting from routine healthcare delivery. There are several sources of RWD, including electronic health records (EHRs), registries, claims/billing data, and patient-generated data, as well as those from mobile health applications and wearable devices. Real-world data from these sources can be collected and analysed through different study designs such as prospective and retrospective cohort studies, case-control studies, and pragmatic clinical trials. Real-world evidence in the form of post-marketing surveillance has been extensively used to generate pharmacovigilance data. Of late, it has been realised that, apart from safety, RWE has additional applications in different stages of the drug approval cycle, and can be used to optimize the design of randomised controlled trials (RCTs). There has been an increasing awareness and acceptance of RWE from different stakeholders, including physicians, pharmaceutical companies, payers, regulators, and patients. Several regulatory authorities have also created frameworks and guidelines for efficient harnessing of RWE while acknowledging several challenges in RWD collection and analysis. The purpose of this review is to offer an outline of the current information on RWE, its advantages and disadvantages, as well as the associated challenges and ways to overcome them, while also throwing some light on the future of RWE.

For insights into the real world, consider real-world data

Randomized control trials (RCTs) are required before drug and device approvals and have contributed to patient safety, but they have also increased the cost and time of regulatory assessments. We propose that using real-world evidence to complement or, in some settings, to replace RCTs will accelerate delivery of new drugs to patients.

Real-world assessment of attenuated dosing anti-PD1 therapy as an alternative dosing strategy in a high-income country (as defined by World Bank)

The rising cost of oncological drugs poses a global challenge to patients, insurers, and policy makers, with the leading drugs worldwide by revenue from immune checkpoint inhibitors (ICIs). Despite its cost, ICI is marked as a paradigm shift, offering the potential of a long-term cure. To reduce cost, an attenuated dose of ICI based on pharmacological principles can be used while maintaining efficacy. This real-world study aims to examine the prescribing patterns, the effect of financial constraints, and the outcomes in non-small cell lung cancer (NSCLC). All patients receiving palliative intent ICI treatment for advanced NSCLC between January 2014 and April 2021 in National University Hospital, Singapore were recruited. Demographics, prescription trends, factors affecting the prescription of attenuated dose ICI (AD ICI) versus standard dose ICI (SD ICI), and the effect of dose on survival outcomes, toxicities, and costs were examined. Two hundred seventy-four received ICI. The majority of them were treated in first-line setting. One hundred sixty-two (59%) of patients received AD ICI, whereas 112 (41%) received SD ICI. Patients who did not have a supplemental private as-charged health insurance plan were more likely to have received AD ICI (OR: 4.53 [2.69–7.61] p &lt; 0.001). There was no difference in progression-free survival (PFS) and overall survival (OS)—adjusted HR 1.07 CI [0.76, 1.50] p = 0.697 and HR 0.95 CI [0.67, 1.34] p = 0.773, respectively, between patients who received AD versus SD ICI. A cost minimization analysis evaluating the degree of cost savings related to drug costs estimated a within study cost saving of USD 7,939,059 over 7 years. Our study provides evidence for AD-ICI as a promising strategy to maximize the number of patients who can be treated with ICI. This has the potential to make significant economic impact and allow more patients to benefit from novel therapies.

Transferability of real-world data across borders for regulatory and health technology assessment decision-making

Recently, there has been increased consideration of real-world data (RWD) and real-world evidence (RWE) in regulatory and health technology assessment (HTA) decision-making. Due to challenges in identifying high-quality and relevant RWD sources, researchers and regulatory/HTA bodies may turn to RWD generated in locales outside of the locale of interest (referred to as "transferring RWD"). We therefore performed a review of stakeholder guidance as well as selected case studies to identify themes for researchers to consider when transferring RWD from one jurisdiction to another. Our review highlighted that there is limited consensus on defining decision-grade, transferred RWD; certain stakeholders have issued relevant guidance, but the recommendations are high-level and additional effort is needed to generate comprehensive guidance. Additionally, the case studies revealed that RWD transferability has not been a consistent concern for regulatory/HTA bodies and that more focus has been put on the evaluation of internal validity. To help develop transferability best practices (alongside internal validity best practices), we suggest that researchers address the following considerations in their justification for transferring RWD: treatment pathways, nature of the healthcare system, incidence/prevalence of indication, and patient demographics. We also recommend that RWD transferability should garner more attention as the use of imported RWD could open doors to high-quality data sources and potentially reduce methodological issues that often arise in the use of local RWD; we thus hope this review provides a foundation for further dialogue around the suitability and utility of transferred RWD in the regulatory/HTA decision-making space.

Do Early Phase Oncology Trials Predict Clinical Efficacy in Subsequent Biomarker-Enriched Phase III Randomized Trials?

BACKGROUND

Promising early phase trial results of biomarker-targeted therapies have occasionally led to regulatory approval.

OBJECTIVE

We examined if early phase trials were predictive of efficacy in randomized controlled trials (RCTs) with matching treatment settings.

PATIENTS AND METHODS

Cancer drug RCTs conducted between January 2006 and March 2021 were identified through Clinicaltrials.gov. Biomarker-enriched RCTs and associated matching early phase trials were included. Trial pairs were compared using objective response rate (ORR) and progression-free survival (PFS). We examined whether early phase trials results were associated with RCT results using logistic regression.

RESULTS

The search yielded 2157 unique RCTs and 27 RCTs pairing with early phase trials were included. Based on average difference of trial pairs, ORR was similar (1.6%; 95% confidence interval (CI) - 2.5 to 5.6, p = 0.50) and median PFS was higher in early phase trials (2.0 months; 95% CI 0.9-3.0, p < 0.05). On an individual pair basis, there was large variability in difference for ORR (range - 23.9 to 20.2%) and median PFS (range - 0.8 to 7.4 months). The probability of the RCT meeting its primary endpoint is 95% (95% prediction interval (PI) 72.8-99.3%) when the early phase trial ORR is 77.7%.

CONCLUSIONS

Overall, in early phase trials, ORR has minimal bias and median PFS appears to be slightly overestimated. Substantial variability between trials suggests early phase trial results may be inconsistent with subsequent RCT. Early phase trial results may be associated with RCTs meeting their primary endpoint when ORR is very high; however, caution must be exercised when using early phase trials as representative of RCTs.

A proper statistical inference framework to compare clinical trial and real-world progression-free survival data

The past decade has witnessed an increasing trend in utilizing external control data in clinical trials, especially in the form of synthetic control arms (SCA) derived from real-world or historical trial data. Including such data in clinical trial analysis can improve trial feasibility and efficiency, provided the issues caused by non-randomization and systematic differences are appropriately addressed. Current methodology development in this area focuses on establishing the comparability of patient baseline characteristics between arms, and more research is needed to ensure comparability of other elements such as endpoints. Motivated by the comparative analysis of SCA progression-free survival (PFS) and trial arm PFS, we aim to address another important but little discussed issue for external time-to-event (TTE) data that depend on disease assessment schedules (DAS). Since DAS are generally inconsistent across different data sources, we propose a proper statistical inference framework that harmonizes the DAS through data augmentation by multiple imputation. We demonstrate through extensive simulations that the proposed framework is unbiased in estimating median TTE and hazard ratio, well controls the type I error and achieves desirable power for log-rank test, while the unadjusted analysis can be biased and suffer from severe type I error inflation or power loss depending on the direction of the bias. Given the desirable performance, we recommend the proposed framework for comparative analysis using external DAS-based TTE data in clinical trials.

The relevance of real-world data for the evaluation of neuropathic pain treatments

Treatment of neuropathic pain (NP) is challenging. Interest in real-world evidence (RWE) for benefit-risk assessments of NP treatments increases given the paucity of drugs showing efficacy in randomized controlled trials and restricted labels of available medicines. To provide further context, a literature review regarding regulatory use of RWE and a clinical trial registry search for randomized controlled trials over the last 10 years was carried out. Taken together, and especially for available NP treatments, there is increasing support to consider RWE when evaluating their benefit-risk profile. Examples are provided in which RWE could be used effectively for updating the product label and informing treatment recommendations. Collected and analyzed according to state-of-the-art standards, RWE can inform treatment recommendations and product label decisions.

Comparing Survival Outcomes for Advanced Cancer Patients Who Received Complex Genomic Profiling Using a Synthetic Control Arm

BACKGROUND

Complex genomic profiling (CGP) has transformed cancer treatment decision making, yet there is a lack of robust and quantifiable evidence for how utilisation of CGP improves patient outcomes.

OBJECTIVE

This study evaluated cohort level clinical effectiveness of CGP to improve overall survival (OS) in real-world advanced cancer patients using a registry-based matched control population.

PATIENTS AND METHODS

Two cohorts of advanced and refractory cancer patients were seen in consecutive series for early phase trial enrolment consideration. The first cohort (CGP group) accessed tumour profiling via a research study; while the second cohort that followed was not profiled. Overall survival between cohorts was compared using Kaplan-Meier curves and Cox proportional hazard models. Potential confounding was analysed and adjusted for using stabilised weights based on propensity scores.

RESULTS

Within the CGP group, 25 (17.6%) patients received treatment informed by CGP results and this subgroup had significantly improved survival compared with CGP patients in whom results did not impact their treatment (unadjusted HR = 0.44, (0.22-0.88), p = 0.02). However, when comparing the entire CGP cohort with the No CGP cohort, no significant survival benefit was evident with adjusted median OS for CGP of 13.5 months (9.2-17.0) compared with 11.0 (9.2-17.4) for No CGP (adjusted HR = 0.92, (0.65-1.30), p = 0.63).

CONCLUSIONS

This study utilised real-world data to simulate a control arm and quantify the clinical effectiveness of genomic testing. The magnitude of survival benefit for patients who had CGP result-led treatments was insufficient to drive an overall survival gain for the entire tested population. Translation of CGP into clinics requires strategies to ensure higher rates of tested patients obtain clinical benefit to deliver on the value proposition of CGP in an advanced cancer population.

Clinical trial design in the era of precision medicine

Recent rapid biotechnological breakthroughs have led to the identification of complex and unique molecular features that drive malignancies. Precision medicine has exploited next-generation sequencing and matched targeted therapy/immunotherapy deployment to successfully transform the outlook for several fatal cancers. Tumor and liquid biopsy genomic profiling and transcriptomic, immunomic, and proteomic interrogation can now all be leveraged to optimize therapy. Multiple new trial designs, including basket and umbrella trials, master platform trials, and N-of-1 patient-centric studies, are beginning to supplant standard phase I, II, and III protocols, allowing for accelerated drug evaluation and approval and molecular-based individualized treatment. Furthermore, real-world data, as well as exploitation of digital apps and structured observational registries, and the utilization of machine learning and/or artificial intelligence, may further accelerate knowledge acquisition. Overall, clinical trials have evolved, shifting from tumor type-centered to gene-directed and histology-agnostic trials, with innovative adaptive designs and personalized combination treatment strategies tailored to individual biomarker profiles. Some, but not all, novel trials now demonstrate that matched therapy correlates with superior outcomes compared to non-matched therapy across tumor types and in specific cancers. To further improve the precision medicine paradigm, the strategy of matching drugs to patients based on molecular features should be implemented earlier in the disease course, and cancers should have comprehensive multi-omic (genomics, transcriptomics, proteomics, immunomic) tumor profiling. To overcome cancer complexity, moving from drug-centric to patient-centric individualized combination therapy is critical. This review focuses on the design, advantages, limitations, and challenges of a spectrum of clinical trial designs in the era of precision oncology.

Contribution of patient registries to regulatory decision making on rare diseases medicinal products in Europe

Between 2000 and 2021, the European Medicines Agency (EMA) assigned the orphan designation to over 1,900 medicines. Due to their small target populations, leading to challenges regarding clinical trial recruitment, study design and little knowledge on the natural history of the disease, the overall clinical evidence submitted at the time of marketing authorisation application for these medicines is often limited. Patient registries have been recognised as important sources of data on healthcare practices, drug utilisation and clinical outcomes. They may help address these challenges by providing information on epidemiology, standards of care and treatment patterns of rare diseases. In this review, we illustrate the utility of patient registries across the different stages of development of medicinal products, including orphans, to provide evidence in the context of clinical studies and to generate post-authorisation long term data on their effectiveness and safety profiles. We present important initiatives leveraging the role of registries for orphan medicinal products' development and monitoring to ultimately improve patients' lives.

Analysis of Factors Hindering the Dissemination of Medical Information Standards

Many medical information standards are not widely used in Japan, and this hinders the promotion of the use of real-world data. However, the complex intertwining of many factors hindering the dissemination of medical information standards makes it difficult to solve this problem. This study analyzed and visualized relationships among factors that inhibit the dissemination of medical information standards. Five medical informatics experts affiliated with universities and hospitals were interviewed about the factors that hinder the dissemination of medical information standards in Japan. The presented factors were analyzed using the interpretive structural modeling (ISM) method and the decision-making trial and evaluation laboratory (DEMATEL) method. We found that “legislation” and “reliability” were important inhibiting factors for the dissemination of medical information standards in Japan. We also found a six-layered structure in which “reliability” was satisfied when “legislation” was in place and “expectations” and “personal information” were resolved. The DEMATEL analysis indicated the relationships and classifications of factors hindering the dissemination of medical information standards. Since the adoption of medical information standards does not directly lead to revenue for medical institutions, it is possible to meet the “expectation” of improving the quality of medical care by ensuring “legislation” and “reliability”, that is, ensuring the dependability of medical treatment. The results of this study visually show the structure of the factors and will help solve the problems that hinder the effective and efficient spread of standards. Solving these problems may support the efficient use of real-world data.

Randomized Controlled Trial Data for New Drug Application for Rare Diseases in Japan

BACKGROUND

High-quality evidence is often not obtained in the clinical trials of rare diseases because these trials tend to be smaller in size and non-controlled. We investigated the potential factors associated with the need for randomized controlled trials (RCTs) in the clinical data package for new drug applications for rare diseases in Japan.

METHODS

This study focused on 130 drugs with orphan drug designation approved in Japan between April 2004 and March 2020.

RESULTS

Multivariable regression analysis showed that the prevalence (odds ratio [OR] 3.21, 95% confidence interval [CI] 1.18-8.6) and the type of primary endpoint (OR 6.66, 95% CI 2.41-18.37) were associated with the need for RCTs in the clinical data package in Japan.

CONCLUSIONS

Our findings highlight the importance of adequate understanding of the target disease in new drug development for rare diseases.

The use of real-world evidence to support FDA post-approval study requirements for oncology drugs

BACKGROUND

: The 21^st Century Cures Act of 2016 included provisions for the Food and Drug Administration (FDA) to evaluate the potential for real-world evidence (RWE) to support or fulfill post-approval study requirements. This study reviewed post-marketing requirement (PMR) and post-marketing commitment (PMC) obligations for oncology drugs approved by the FDA post-Cures Act to identify those with RWE components.

METHODS

: Approval letters issued by the FDA between 2017-2020 for oncology drugs were systematically analyzed for PMRs or PMCs with requests for RWE. For each PMR/PMC identified, the characteristics of the approvals, the PMRs/PMCs, and the RWE requested were reviewed.

RESULTS

: Of 189 oncology drug approvals with 456 associated PMRs/PMCs, a total of 15 PMRs/PMCs specified RWE. Compared with all oncology drug approvals, the 14 approvals with PMRs/PMCs requesting RWE were more frequently accelerated approvals, for new therapies, with orphan indications. All 15 PMRs/PMCs requested real-world safety data, with 3 also requesting real-world effectiveness data. RWE requested included post-marketing safety reports, prospective observational studies, expanded access study data, and registry data.

CONCLUSION

: As a greater proportion of safety and efficacy data generation for oncology drugs shifts to the post-marketing setting, RWE has the potential to become an integral component of PMR/PMC fulfillment.

Regulatory Approval With Real-World Data From Regulatory Science Perspective in Japan

Recently, there has been a growing trend in clinical development to utilize real-world data (RWD) to improve the efficiency of drug/medical device development. Especially, the use of RWD to generate real-world evidence (RWE) in regulatory approval is currently undergoing a period of great change with an increasing degree of active discussion. In Japan, RWE has been used in the control arms of clinical trials, observational studies, post-marketing surveillance, and public knowledge-based applications for regulatory approval. However, the exclusive use of RWE applications has still not been applied. In this paper, we summarize the history and the current situation of RWE and focus on the utilization for the purpose of regulatory approval. In addition, we will discuss the issues and perspectives for registry research in the utilization for regulatory approval in Japan.

Real world data and data science in medical research: present and future

Real world data (RWD) are generating greater interest in recent times despite being not new. There are various purposes of the RWD analytics in medical research as follows: effectiveness and safety of medical treatment, epidemiology such as incidence and prevalence of disease, burden of disease, quality of life and activity of daily living, medical costs, etc. The RWD research in medicine is a mixture of digital transformation, statistics or data science, public health, and regulatory science. Most of the articles describing the RWD or real-world evidence (RWE) in medical research cover only a portion of these specializations, which might lead to an incomplete understanding of the RWD. This article summarizes the overview and challenges of the RWD analysis in medical fields from methodological perspectives. As the first step for the RWD analysis, data source of the RWD should be comprehended. The progress of the RWD is closely related to the digitization, especially of medical administrative data and medical records. Second, the selection of appropriate statistical and epidemiological methods is highly critical for an RWD analysis than those for randomized clinical trials. This is because it contains greater varieties of bias, which should be controlled by balancing the underlying risk between treatment groups. Last, the future of the RWD is discussed in terms of overcoming limited data by proxy confounders, using unstructured text data, linking of multiple databases, using the RWD or RWE for a regulatory purpose, and evaluating values and new aspects in medical research brought by the RWD.

Delivering precision oncology to patients with cancer

With the increasing use of genomic profiling for diagnosis and therapy guidance in many tumor types, precision oncology is rapidly reshaping cancer care. However, the current trajectory of drug development in oncology results in a paradox: if patients cannot access advanced diagnostics, we may be developing drugs that will reach few patients. In this Perspective, we outline the major challenges to the implementation of precision oncology and discuss critical steps toward resolving these, including facilitation of equal access to genomics tests, ensuring that clinical studies provide robust evidence for new drugs and technologies, enabling physicians to interpret genomics data, and empowering patients toward shared decision-making. A multi-stakeholder approach to evidence generation, value assessment, and healthcare delivery is necessary to translate advances in precision oncology into benefits for patients with cancer globally.

Real-world data in drug development strategies for orphan drugs: tafasitamab in B cell lymphoma, a case study for approval based on a single-arm combination trial

Tafasitamab (TAF) plus lenalidomide (LEN) is a novel treatment option for patients with relapsed/refractory diffuse large B cell lymphoma (rrDLBCL) who are not eligible for autologous stem cell transplantation. The initial US/EU approvals for TAF represent precedents because this is the first time that approval of a novel combination therapy was granted based on a pivotal single-arm trial (SAT). Matching real world-data (RWD) helped to disentangle the contribution of individual agents. In this review, we present the TAF development strategy, the prospective incorporation of RWD within the clinical development plan, the corresponding regulatory hurdles of this strategy, and the prior regulatory actions for other cancer drugs that previously incorporated RWD and propensity score matching in EU and US regulatory submissions. We also outline how RWD could further advance and impact orphan drug development.

Factors Affecting Citizen Trust and Public Engagement Relating to the Generation and Use of Real-World Evidence in Healthcare

The potential for the use of real-world data (RWD) to generate real-world evidence (RWE) that can inform clinical decision-making and health policy is increasingly recognized, albeit with hesitancy in some circles. If used appropriately, the rapidly expanding wealth of health data could improve healthcare research, delivery of care, and patient outcomes. However, this depends on two key factors: (1) building structures that increase the confidence and willingness of European Union (EU) citizens to permit the collection and use of their data, and (2) development of EU health policy to support and shape data collection infrastructures, methodologies, transmission, and use. The great potential for use of RWE in healthcare improvement merits careful exploration of the drivers of, and challenges preventing, efficient RWD curation. Literature-based research was performed to identify relevant themes and discussion topics for two sets of expert panels, organized by the European Alliance for Personalised Medicine. These expert panels discussed steps that would enable a gradual but steady growth in the quantity, quality, and beneficial deployment of RWE. Participants were selected to provide insight based on their professional medical, economic, patient, industry, or governmental experience. Here, we propose a framework that addresses public trust and access to data, cross-border governance, alignment of evidence frameworks, and demonstrable improvements in healthcare decisions. We also discuss key case studies that support these recommendations, in accordance with the discussions at the expert panels.

Identifying the Steps Required to Effectively Implement Next-Generation Sequencing in Oncology at a National Level in Europe

Next-generation sequencing (NGS) may enable more focused and highly personalized cancer treatment, with the National Comprehensive Cancer Network and European Society for Medical Oncology guidelines now recommending NGS for daily clinical practice for several tumor types. However, NGS implementation, and therefore patient access, varies across Europe; a multi-stakeholder collaboration is needed to establish the conditions required to improve this discrepancy. In that regard, we set up European Alliance for Personalised Medicine (EAPM)-led expert panels during the first half of 2021, including key stakeholders from across 10 European countries covering medical, economic, patient, industry, and governmental expertise. We describe the outcomes of these panels in order to define and explore the necessary conditions for NGS implementation into routine clinical care to enable patient access, identify specific challenges in achieving them, and make short- and long-term recommendations. The main challenges identified relate to the demand for NGS tests (governance, clinical standardization, and awareness and education) and supply of tests (equitable reimbursement, infrastructure for conducting and validating tests, and testing access driven by evidence generation). Recommendations made to resolve each of these challenges should aid multi-stakeholder collaboration between national and European initiatives, to complement, support, and mutually reinforce efforts to improve patient care.

Application of Real-World Data to External Control Groups in Oncology Clinical Trial Drug Development

Randomized controlled trials (RCTs) that assess overall survival are considered the "gold standard" when evaluating the efficacy and safety of a new oncology intervention. However, single-arm trials that use surrogate endpoints (e.g., objective response rate or duration of response) to evaluate clinical benefit have become the basis for accelerated or breakthrough regulatory approval of precision oncology drugs for cases where the target and research populations are relatively small. Interpretation of efficacy in single-arm trials can be challenging because such studies lack a standard-of-care comparator arm. Although an external control group can be based on data from other clinical trials, using an external control group based on data collected outside of a trial may not only offer an alternative to both RCTs and uncontrolled single-arm trials, but it may also help improve decision-making by study sponsors or regulatory authorities. Hence, leveraging real-world data (RWD) to construct external control arms in clinical trials that investigate the efficacy and safety of drug interventions in oncology has become a topic of interest. Herein, we review the benefits and challenges associated with the use of RWD to construct external control groups, and the relevance of RWD to early oncology drug development.

Data collection framework for electronic medical record-based real-world data to evaluate the effectiveness and safety of cancer drugs: a nationwide real-world study of the Korean Cancer Study Group

Background:

Electronic medical records (EMRs) have the highest value among real-world data (RWD). The aim of the present study was to propose a data collection framework of EMR-based RWD to evaluate the effectiveness and safety of cancer drugs by conducting a nationwide real-world study based on the Korean Cancer Study Group.

Methods:

We considered all patients who received ramucirumab plus paclitaxel (RAM/PTX) for gastric cancer and trastuzumab emtansine (T-DM1) for breast cancer at relevant institutions in South Korea. Standard operating procedures for systematic data collection were prospectively developed. Investigator reliability was evaluated using the concordance rate between the recommended input value for representative fictional cases and the input value of each investigator. Reliability of collected data was evaluated twice during the study period at three institutions randomly selected using the concordance rate between the previously collected data and data collected by an independent investigator. The reliability results of the investigators and collected data were used for revision of the electronic data capture system and site training.

Results:

Between the starting date of medical insurance coverage and December 2018, a total of 1063 patients at 56 institutions in the RAM/PTX cohort and 824 patients at 60 institutions in the T-DM1 cohort were included. Mean investigator reliability in the RAM/PTX and T-DM1 cohorts was 73.5% and 71.9%, respectively. Mean reliability of collected data in the RAM/PTX and T-DM1 cohort was 90.0% for both cohorts in the first analysis and 89.0% and 84.0% in the second analysis, respectively. Mean missing values of the RAM/PTX and T-DM1 cohorts at the time of simulation of fictional cases and final data analysis decreased from 20.7% to 0.46% and from 18.5% to 0.76%, respectively.

Conclusion:

This real-world study provides a framework that ensures relevance and reliability of EMR-based RWD for evaluating the effectiveness and safety of cancer drugs.

Clinical trials in amyotrophic lateral sclerosis: a systematic review and perspective

Amyotrophic lateral sclerosis is a progressive and devastating neurodegenerative disease. Despite decades of clinical trials, effective disease-modifying drugs remain scarce. To understand the challenges of trial design and delivery, we performed a systematic review of Phase II, Phase II/III and Phase III amyotrophic lateral sclerosis clinical drug trials on trial registries and PubMed between 2008 and 2019. We identified 125 trials, investigating 76 drugs and recruiting more than 15 000 people with amyotrophic lateral sclerosis. About 90% of trials used traditional fixed designs. The limitations in understanding of disease biology, outcome measures, resources and barriers to trial participation in a rapidly progressive, disabling and heterogenous disease hindered timely and definitive evaluation of drugs in two-arm trials. Innovative trial designs, especially adaptive platform trials may offer significant efficiency gains to this end. We propose a flexible and scalable multi-arm, multi-stage trial platform where opportunities to participate in a clinical trial can become the default for people with amyotrophic lateral sclerosis.