Evolving use of real-world evidence in the regulatory process: a focus on immuno-oncology treatment and outcomes

Real-World Evidence to Inform Regulatory Decision Making: A Scoping Review

Real-world evidence (RWE) is increasingly considered in regulatory decision making. When, and to which extent, RWE is considered relevant by regulators likely depends on many factors. This review aimed to identify factors that make RWE necessary or desirable to inform regulatory decision making. A scoping review was conducted using literature databases (PubMed, Embase, Emcare, Web of Science, and Cochrane Library) and websites of regulatory agencies, health technology assessment agencies, research institutes, and professional organizations involved with RWE. Articles were included if: (1) they discussed factors or contexts that impact whether RWE could be necessary or desirable in regulatory decision making; (2) focused on pharmacological or biological interventions in humans; and (3) considered decision making in Europe or North America, or without a focus on a specific region. We included 118 articles in the scoping review. Two major themes and six subthemes were identified. The first theme concerns questions addressable with RWE, with subthemes epidemiology and benefit-risk assessment. The second theme concerns contextual factors, with subthemes feasibility, ethical considerations, limitations of available evidence, and disease and treatment-specific aspects. Collectively, these themes encompassed 43 factors influencing the need for RWE in regulatory decisions. Although single factors may not make RWE fully necessary, their cumulative influence could make RWE essential and pivotal in regulatory decision making. This overview contributes to ongoing discussions emphasizing the nuanced interplay of factors influencing the necessity or desirability of RWE to inform regulatory decision making.

Individualized Multimodal Immunotherapy (IMI): Scientific Rationale and Clinical Experience from a Single Institution

Oncolytic viruses and combinatorial immunotherapy for cancer (this Special Issue) are both part of cancer treatment at IOZK. This review focusses on an individual multimodal cancer immunotherapy concept developed by IOZK, Cologne, Germany. The scientific rationale for employing three main components is explained: (i) oncolytic Newcastle disease virus, (ii) modulated electrohyperthermia and (iii) individual tumor antigen and oncolytic virus modified dendritic cell vaccine (IO-VACR). The strategy involves repeated cancer-immunity cycles evoked in cancer patients by systemic oncolytic virus exposure plus hyperthermia pretreatment to induce immunogenic cell death followed by intradermal IO-VACR vaccination. As an example of the experience at IOZK, we present the latest results from combining the immunotherapy with standard treatment of patients suffering from glioblastoma multiforme. The promising clinical results in terms of overall survival benefit of additional individualized multimodal immunotherapy are presented. The cancer-immunity cycle, as introduced 10 years ago, describes key important steps occurring locally at the sites of both tumor and draining lymph nodes. This view is extended here towards systemic events occuring in blood where immunogenic cell death-induced tumor antigens are transported into the bone marrow. For 20 years it has been known that bone marrow is an antigen-responsive organ in which dendritic cells present tumor antigens to T cells leading to immunological synapse formation, tumor antigen-specific T cell activation and memory T cell formation. Bone marrow is known to be the most prominent source of de novo cellular generation in the body and to play an important role for the storage and maintenance of immunological memory. Its systemic activation is recommended to augment cancer-immunity cycles.

Just how transformative will AI/ML be for immuno-oncology?

Immuno-oncology involves the study of approaches which harness the patient's immune system to fight malignancies. Immuno-oncology, as with every other biomedical and clinical research field as well as clinical operations, is in the midst of technological revolutions, which vastly increase the amount of available data. Recent advances in artificial intelligence and machine learning (AI/ML) have received much attention in terms of their potential to harness available data to improve insights and outcomes in many areas including immuno-oncology. In this review, we discuss important aspects to consider when evaluating the potential impact of AI/ML applications in the clinic. We highlight four clinical/biomedical challenges relevant to immuno-oncology and how they may be able to be addressed by the latest advancements in AI/ML. These challenges include (1) efficiency in clinical workflows, (2) curation of high-quality image data, (3) finding, extracting and synthesizing text knowledge as well as addressing, and (4) small cohort size in immunotherapeutic evaluation cohorts. Finally, we outline how advancements in reinforcement and federated learning, as well as the development of best practices for ethical and unbiased data generation, are likely to drive future innovations.

Survival impact of post-operative immunotherapy in resected stage III cutaneous melanomas in the checkpoint era

BACKGROUND

Checkpoint inhibitors have shown improvement in recurrence-free survival in the post-operative setting for node-positive melanoma and were first approved in late 2015. However, single-agent checkpoint therapies have yet to show benefit to overall survival (OS) for lower-risk stage III cancers. We evaluated the OS benefit of post-operative immunotherapy in the National Cancer Database (NCDB).

PATIENTS AND METHODS

Patient cases were selected from the NCDB 2020 Participant Use File. Patients diagnosed with stage III cutaneous melanoma between 2016 and 2019 who underwent definitive resection for their melanoma were included. OS between those who received post-operative immunotherapy within 84 days of surgery and those who did not was analyzed by the Kaplan-Meier method. Demographic and clinical characteristics between the two groups were compared via Cox proportional hazard models.

RESULTS

14 978 patients with stage III melanoma were included. Of those, 34.9% (n = 5234) received post-operative immunotherapy and 65.1% (n = 9744) did not. Using the American Joint Committee on Cancer version 8 (AJCCv8) staging, 36-month survival was significantly higher in patients who received post-operative immunotherapy compared to no post-operative systemic therapy in those diagnosed with stage IIIB (88.0% versus 84.7%, P = 0.011), IIIC (75.6% versus 68.1%, P < 0.001), or IIID (59.2% versus 48.4%, P = 0.002). No significant improvement in 36-month survival was seen in patients who received post-operative immunotherapy in patients with stage IIIA disease (93.0% versus 92.2%, P = 0.218).

CONCLUSIONS

Post-operative immunotherapy had an OS benefit in patients with AJCCv8 stage IIIB, IIIC, and IIID disease, but had no significant survival benefit for patients with stage IIIA melanomas.

Development and validation of algorithms for identifying lines of therapy in multiple myeloma using real-world data

Aim: To validate algorithms based on electronic health data to identify composition of lines of therapy (LOT) in multiple myeloma (MM). Materials & methods: This study used available electronic health data for selected adults within Henry Ford Health (Michigan, USA) newly diagnosed with MM in 2006-2017. Algorithm performance in this population was verified via chart review. As with prior oncology studies, good performance was defined as positive predictive value (PPV) ≥75%. Results: Accuracy for identifying LOT1 (N = 133) was 85.0%. For the most frequent regimens, accuracy was 92.5-97.7%, PPV 80.6-93.8%, sensitivity 88.2-89.3% and specificity 94.3-99.1%. Algorithm performance decreased in subsequent LOTs, with decreasing sample sizes. Only 19.5% of patients received maintenance therapy during LOT1. Accuracy for identifying maintenance therapy was 85.7%; PPV for the most common maintenance therapy was 73.3%. Conclusion: Algorithms performed well in identifying LOT1 - especially more commonly used regimens - and slightly less well in identifying maintenance therapy therein.

Overall survival in the UK in mycosis fungoides or Sézary syndrome cutaneous T-cell lymphoma: comparative effectiveness of mogamulizumab versus current standard of care

Aim: Due to extensive treatment switching in the MAVORIC trial, lack of UK regulatory licence for the comparator, overall survival (OS) with mogamulizumab was compared with patients with previously treated advanced mycosis fungoides/Sézary syndrome (MF/SS) in real-world setting. Design, setting & participants: Data were from the Hospital Episode Statistics database (all patients in NHS secondary care system in 2009-2019). Patients were selected according to trial inclusion criteria, then trial and HES samples were matched on selected variables with significant imbalance. Outcomes: The analysis indicated significant improvement in OS for mogamulizumab treatment compared with UK clinical practice (hazard ratio: 0.36, 95% CI: 0.24, 0.53). Conclusion: Results suggest an OS advantage for patients with advanced MF/SS treated with mogamulizumab in MAVORIC trial compared with UK clinical practice.

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.

The Role of Real-World Data and Evidence in Oncology Medicines Approved in EU in 2018-2019

Use of Real-World data (RWD) has gained the interest of different stakeholders in cancer care. The aim of this study was to identify and describe the use of RWD/RWE during the pre-authorisation phase of products authorised by the EMA in 2018 and 2019 (n=111), with the focus on oncology medicines (n=24). Information was extracted from the European Public Assessment Report (EPAR) summaries and recorded for 5 stages (11 categories) of the drug development lifecycle (discovery, early development, clinical development, registration/market launch, lifecycle management). Specific chapters of full EPAR were reviewed to substantiate the findings on RWD/RWE use in clinical trial design, efficacy, safety, and effectiveness evaluation. RWD/RWE is present in all stages of the oncology drug development; 100.0% in discovery, 37.5% early development, 58.3% in clinical development, 62.5% in registration decision and 100.0% in post-authorization lifecycle management. Examples showed that trial design supported by RWD/RWE included use of open label/single arm studies; efficacy was about using either comparison of results to historical controls, supplying survey data obtained outside the clinical trial or utilizing expert panel advice; safety about including literature findings in evidence; and effectiveness on comparison of trial results of the given product to historical data or existing standard of care. The findings of this study provide specific insights into how RWD/RWE is used in development of cancer therapeutics, how it contributes to regulatory decision making and can guide further policy developments in this field.

Considerations for the Utility of Real-World Evidence Beyond Trial Data in Advanced NSCLC: The Case of Frontline Tyrosine Kinase Inhibitors

Background

To extend the discussion on the use of real-world evidence (RWE) in conveying the clinical value of treatment beyond trial data, the primary objective of this study was to assess if efficacy gains in progression-free survival (PFS) observed in randomized controlled trials (RCT) correlate with efficacy gains in the real-world setting. For this, we assessed the treatment benefit of three tyrosine kinase inhibitors (TKIs) in aNSCLC.

Methods

Using matched cohorts identified in the Flatiron Health database (2011-2020), we mimicked the following cohorts of TKI versus platinum-based chemotherapy (PBC) from the following trials: (1) erlotinib, EURTAC; (2) afatinib, LUX-Lung 3; and (3) crizotinib, PROFILE 1014. Time to treatment discontinuation (TTD) hazard ratio (HR) was used as a proxy for PFS HR, the primary endpoint in the selected RCTs. HRs were calculated via Cox proportional hazard models.

Results

Overall, 1,118 patients were included across the three RWE cohorts. Frontline TKI regimens had statistically significantly better real-world TTD than their matched PBC comparator group (HR 0.37, 95% confidence interval [CI] 0.30-0.44 for erlotinib; HR 0.42, 95% CI 0.32-0.55 for afatinib; HR 0.37, 95% CI 0.26-0.53 for crizotinib). The benefit in real-world OS was not different between TKIs and PBC patients, attributed to a high proportion of switching to subsequent therapy. Study findings of relative treatment benefit (HR) for real-world TTD and OS were deemed similar to those for PFS and OS from the pivotal RCTs.

Conclusion

The relative treatment effect, measured as real-world TTD HR over the long term, was similar to trial-based PFS HR, implying that the clinical benefit of aNSCLC treatments conveyed in trials translated into the clinical setting. This is important, given that OS data interpretation is limited, even with longer follow-up. Additionally, our RWE analysis endorses TTD as a relevant endpoint to measure clinical benefit.

Rationale, Strengths, and Limitations of Real-World Evidence in Oncology: A Canadian Review and Perspective

Randomized controlled trials (RCTs) continue to be the basis for essential evidence regarding the efficacy of interventions such as cancer therapies. Limitations associated with RCT designs, including selective study populations, strict treatment regimens, and being time-limited, mean they do not provide complete information about an intervention’s safety or the applicability of the trial’s results to a wider range of patients seen in real-world clinical practice. For example, recent data from Alberta showed that almost 40% of patients in the province’s cancer registry would be trial-ineligible per common exclusion criteria. Real-world evidence (RWE) offers an opportunity to complement the RCT evidence base with this kind of information about safety and about use in wider patient populations. It is also increasingly recognized for being able to provide information about an intervention’s effectiveness and is considered by regulators as an important component of the evidence base in drug approvals. Here, we examine the limitations of RCTs in oncology research, review the different types of RWE available in this area, and discuss the strengths and limitations of RWE for complementing RCT oncology data.

Real-world studies: bridging the gap between trial-assessed efficacy and routine care

Even though randomized controlled clinical trials (RCTs) have been accepted as the gold standard for official assessment of novel interventions, there is a substantial gap between the efficacy observed in RCTs and the impact on clinical practice and in terms of patient benefit. While real-world studies (RWS) are emerging to confer valuable complementing evidence in this regard and beyond, the evolving role of RWS is yet to be agreed. This article delineates an updated profile of RWS covering effectiveness verification, rare adverse effects discovery, indication repurposing, to name a few. RWS tends not only to improve the efficiency of clinical investigations for regulatory approval, but also optimizes the whole-life cycle evaluation of biomedical/pharmaceutical products.

Real World-Evidence for Regulatory Use Decision Aid: An Interactive Tool To Inform Clinical Development and Regulatory Strategies

Real-world evidence (RWE) is increasingly used to complement clinical trial data for regulatory decision-making and in certain cases utilized to establish the clinical effectiveness of a therapy. However, the use of RWE is not applicable for all regulatory submissions, and it can be challenging to identify appropriate use cases. An interactive tool was developed ("Decision Aid," https://sn.pub/TpDjZx ) to assist researchers, industry, and other stakeholders in identifying regulatory situations that can benefit from leveraging RWE by organizing precedent cases based on a given regulatory objective (new product approval, labeling expansion for new indication or additional clinical data, post-marketing requirement) and type of RWE study design (external control, observational study, pragmatic trial). Key success factors ensuring fit-for-purpose data and rigorous methods (e.g., clear endpoints, minimizing bias, data completeness) are also described. The tool allows the user to navigate through the precedent cases by selecting certain regulatory objectives and/or study designs. The Decision Aid supports regulatory activities in the RWE space and encourages further use of RWE in regulatory decision-making.

Use of Real-World Evidence to Drive Drug Development Strategy and Inform Clinical Trial Design

Interest in real-world data (RWD) and real-world evidence (RWE) to expedite and enrich the development of new biopharmaceutical products has proliferated in recent years, spurred by the 21st Century Cures Act in the United States and similar policy efforts in other countries, willingness by regulators to consider RWE in their decisions, demands from third-party payers, and growing concerns about the limitations of traditional clinical trials. Although much of the recent literature on RWE has focused on potential regulatory uses (e.g., product approvals in oncology or rare diseases based on single-arm trials with external control arms), this article reviews how biopharmaceutical companies can leverage RWE to inform internal decisions made throughout the product development process. Specifically, this article will review use of RWD to guide pipeline and portfolio strategy; use of novel sources of RWD to inform product development, use of RWD to inform clinical development, use of advanced analytics to harness "big" RWD, and considerations when using RWD to inform internal decisions. Topics discussed will include the use of molecular, clinicogenomic, medical imaging, radiomic, and patient-derived xenograft data to augment traditional sources of RWE, the use of RWD to inform clinical trial eligibility criteria, enrich trial population based on predicted response, select endpoints, estimate sample size, understand disease progression, and enhance diversity of participants, the growing use of data tokenization and advanced analytical techniques based on artificial intelligence in RWE, as well as the importance of data quality and methodological transparency in RWE.

Assessment of Alectinib vs Ceritinib in ALK-Positive Non-Small Cell Lung Cancer in Phase 2 Trials and in Real-world Data

IMPORTANCE

Quantitative assessment of bias from unmeasured confounding and missing data can help evaluate uncertainty in findings from indirect comparisons using real-world data (RWD).

OBJECTIVE

To compare the effectiveness of alectinib vs ceritinib in terms of overall survival (OS) in patients with ALK-positive, crizotinib-refractory, non-small cell lung cancer (NSCLC) and to assess the sensitivity of these findings to unmeasured confounding and missing data assumptions.

DESIGN, SETTING, AND PARTICIPANTS

This comparative effectiveness research study compared patients from 2 phase 2 alectinib trials and real-world patients. Patients were monitored from June 2013 to March 2020. Comparisons of interest were between alectinib trial data vs ceritinib RWD and alectinib RWD vs ceritinib RWD. RWD treatment groups were selected from nationally representative cancer data from US cancer clinics, the majority from community centers. Participants were ALK-positive patients aged 18 years or older with advanced NSCLC, prior exposure to crizotinib, and Eastern Cooperative Oncology Group Performance Status (PS) of 0 to 2. Data analysis was performed from October 2020 to March 2021.

EXPOSURES

Initiation of alectinib or ceritinib therapy.

MAIN OUTCOMES AND MEASURES

The main outcome was OS.

RESULTS

In total, there were 355 patients: 183 (85 men [46.4%]) in the alectinib trial, 91 (43 men [47.3%]) in the ceritinib RWD group, and 81 (38 men [46.9%]) in the alectinib RWD group. Patients in the alectinib trial were younger (mean [SD] age, 52.53 [11.18] vs 57.97 [11.71] years), more heavily pretreated (mean [SD] number of prior therapy lines, 1.95 [0.72] vs 1.47 [0.81]), and had more favorable baseline ECOG PS (ECOG PS of 0 or 1, 165 patients [90.2%] vs 37 patients [77.1%]) than those in the ceritinib RWD group. The alectinib RWD group (mean [SD] age, 58.69 [11.26] years) had more patients with favorable ECOG PS (ECOG PS of 0 or 1, 49 patients [92.4%] vs 37 patients [77.1%]) and more White patients (56 patients [72.7%] vs 53 patients [62.4%]) compared with the ceritinib group. Compared with ceritinib RWD, alectinib-exposed patients had significantly longer OS in alectinib trials (adjusted hazard ratio [HR], 0.59; 95% CI, 0.44-0.75; P < .001) and alectinib RWD (HR, 0.46; 95% CI, 0.29-0.63; P < .001) after adjustment for baseline confounders. For the worst-case HR estimate of 0.59, residual confounding by a hypothetical confounder associated with mortality and treatment by a risk ratio greater than 2.24 was required to reverse the findings. Conclusions were robust to plausible deviations from random missingness for missing ECOG PS and underrecorded comorbidities and central nervous system metastases in RWD.

CONCLUSIONS AND RELEVANCE

Alectinib exposure was associated with longer OS compared with ceritinib in patients with ALK-positive NSCLC, and only substantial levels of bias examined reversed the findings. These findings suggest that quantitative bias analysis can be a useful tool to address uncertainty of findings for decision-makers considering RWD.

Rare Diseases and Otorhinolaryngology, Head and Neck Surgery

Rare diseases pose multiple challenges for patients, relatives, physicians, nursing staff, and therapists. Their rarity impedes research and treatments due to medical and economical reasons. Many diseases in the field otorhinolaryngology, head and neck surgery are rare diseases due to their low prevalence. The initiation of the right management processes requires knowledge about diagnostics, resources like centers, networks and registries, about specifics of the physician-patient relationship, follow-up care including communication with family doctors and the role of self-help groups. Of special interest for university hospitals and our scientific society are the specific aspects of research including European networks and research funding, information management, public relations, education, training, financing, and regulations like orphan drugs and clinical trials in small populations.