Real world data on Anti BCMA bispecific antibody therapy in relapsed refractory multiple myeloma - treading on a razor edge between efficacy and safety

Objective: Patients with triple-class refractory (TCR) multiple myeloma (MM) have limited treatment options and poor prognoses. This high unmet need has prompted the development of new therapies allowing for improved outcomes for these patients. Recently, new targeted therapies for the treatment of patients with relapsed or refractory MM have been approved based on single-arm clinical trial results. Real-world (RW) data enable a better understanding of the effectiveness of new therapies in clinical practice and provide external controls for single-arm studies. However, using RW data to identify patients with TCR MM is challenging and subject to limitations. Methods: In this retrospective cohort study of an analysis of the COTA electronic health record (EHR) database, we used four algorithms to define refractory status and created four groups of patients with TCR MM initiating post-TCR therapy. Each algorithm relied on slightly different criteria to identify TCR patients, but all were based on the International Myeloma Working Group (IMWG)-derived and/or healthcare provider (HCP)-reported progressions within the database. Results: A total of 3815 patients with newly diagnosed MM met the eligibility criteria for this study. The choice of the algorithm did not impact the characteristics of identified patients with TCR MM (Algorithm 1 [n = 404], Algorithm 2 [n = 123], Algorithm 3 [n = 404], and Algorithm 4 [n = 375]), including their demographic and disease characteristics, MM treatment history, or treatment patterns received after becoming TCR. However, identifying TCR MM using a combination of IMWG-derived and HCP-reported progressions allowed up to a 70% increase in the size of the identified group of patients compared with using only IMWG-derived progressions. Conclusion: In RW settings, progressions from both IMWG-derived data and physician reports may be used to identify patients with TCR MM.

Real-World and Clinical Trial Data in Relapsed/Refractory Multiple Myeloma (RRMM): Evaluating Treatment Duration and Comparing Effectiveness and Efficacy

Real World Data – Does it Cut the Mustard or Should We Take it With a Pinch of Salt?

Augmentation of Real-World Mortality Data in the Electronic Medical Record: Assessing the Impact on Overall Survival Estimates in Multiple Myeloma

<div><p>Pneumonitis is a potentially life-threatening complication of anticancer therapy, and future treatment decisions may be informed by characterizing patients receiving therapies in the real-world setting. In this study, the incidence of treatment-associated pneumonitis (TAP) was compared among patients with advanced non–small cell lung cancer receiving immune checkpoint inhibitors (ICI) or chemotherapies in either of two settings: randomized clinical trials (RCT) or real world data (RWD)-based clinical practice. Pneumonitis cases were identified using International Classification of Diseases codes (for RWD), or the Medical Dictionary for Regulatory Activities preferred terms (for RCTs). TAP was defined as pneumonitis diagnosed during treatment or within 30 days of the last treatment administration. Overall TAP rates in the RWD cohort were lower [ICI: 1.9%; 95% confidence interval (CI), 1.2–3.2; chemotherapy: 0.8%; 95% CI, 0.4–1.6] than overall rates in the RCT cohort (ICI: 5.6%; 95% CI, 5.0–6.2; chemotherapy: 1.2%; 95% CI, 0.9–1.5). Overall RWD TAP rates were similar to grade 3+ RCT TAP rates (ICI: 2.0%; 95% CI, 1.6–2.3; chemotherapy: 0.6%; 95% CI, 0.4–0.9). In both cohorts, higher TAP incidence was observed among patients with a past medical history of pneumonitis than those without, regardless of treatment group. On the basis of this sizable study leveraging RWD, TAP incidence was low in the RWD cohort, likely in part due to methodology used for RWD focusing on clinically significant cases. Past medical history of pneumonitis was associated with TAP in both cohorts.</p>Significance:<p>Pneumonitis is a potentially life-threatening complication of anticancer treatment. As treatment options expand, management decisions become increasingly complex, and there is a greater need to understand the safety profiles of the treatment options in the real-world setting. Real-world data serve as an additional source of valuable information to complement clinical trial data and inform understanding of toxicity in patients with non–small cell lung cancer receiving ICIs or chemotherapies.</p></div>

<div><p>Pneumonitis is a potentially life-threatening complication of anticancer therapy, and future treatment decisions may be informed by characterizing patients receiving therapies in the real-world setting. In this study, the incidence of treatment-associated pneumonitis (TAP) was compared among patients with advanced non–small cell lung cancer receiving immune checkpoint inhibitors (ICI) or chemotherapies in either of two settings: randomized clinical trials (RCT) or real world data (RWD)-based clinical practice. Pneumonitis cases were identified using International Classification of Diseases codes (for RWD), or the Medical Dictionary for Regulatory Activities preferred terms (for RCTs). TAP was defined as pneumonitis diagnosed during treatment or within 30 days of the last treatment administration. Overall TAP rates in the RWD cohort were lower [ICI: 1.9%; 95% confidence interval (CI), 1.2–3.2; chemotherapy: 0.8%; 95% CI, 0.4–1.6] than overall rates in the RCT cohort (ICI: 5.6%; 95% CI, 5.0–6.2; chemotherapy: 1.2%; 95% CI, 0.9–1.5). Overall RWD TAP rates were similar to grade 3+ RCT TAP rates (ICI: 2.0%; 95% CI, 1.6–2.3; chemotherapy: 0.6%; 95% CI, 0.4–0.9). In both cohorts, higher TAP incidence was observed among patients with a past medical history of pneumonitis than those without, regardless of treatment group. On the basis of this sizable study leveraging RWD, TAP incidence was low in the RWD cohort, likely in part due to methodology used for RWD focusing on clinically significant cases. Past medical history of pneumonitis was associated with TAP in both cohorts.</p>Significance:<p>Pneumonitis is a potentially life-threatening complication of anticancer treatment. As treatment options expand, management decisions become increasingly complex, and there is a greater need to understand the safety profiles of the treatment options in the real-world setting. Real-world data serve as an additional source of valuable information to complement clinical trial data and inform understanding of toxicity in patients with non–small cell lung cancer receiving ICIs or chemotherapies.</p></div>

650 Background: Randomized controlled trials (RCTs) are the basis of approval for medical interventions, but may not fully reflect populations seen in clinical practice. Sunitinib is a widely used 1st-line treatment for patients (pts) with metastatic renal cell carcinoma (mRCC). This is the first large-scale meta-analysis to evaluate the efficacy of sunitinib using the novel approach of combining RCTs and real-world data (RWD). Methods: PubMed, Ovid, MEDLINE and EMBASE were searched from 2000-2017 for RCTs and RWD studies of sunitinib as 1st-line treatment in pts with mRCC. Eligible studies contained a cohort of ≥50 adult pts per study arm. The meta-analysis combined RWD and RCT study arms, adjusting for data type (RCT or RWD). Recorded outcomes were: median progression-free survival (mPFS), median overall survival (mOS) and objective response rate (ORR). A random effects model to account for study heterogeneity was applied to each endpoint. Sensitivity analyses evaluated the robustness of the overall estimate. Results: Of the studies that met eligibility criteria, mPFS, mOS and ORR were reported by 18, 19 and 15 studies, respectively. Combined RWD and RCT analyses are presented in the Table. Reported mPFS (RWD, 7.5–11.0; RCTs, 5.6–15.1 months) and ORR data (RWD, 14.0–34.6%; RCTs, 18.8–46.9%) were consistent with the overall estimates. Reported mOS showed greater variation in RWD (6.8–33.2 months) compared with RCTs (21.8–31.5 months). Sensitivity analyses showed no evidence of lack of robustness for mPFS, mOS or ORR. Interpretation of these results is limited by differences in trial design and cohort characteristics. Conclusions: This novel, large-scale meta-analysis validates sunitinib as an effective 1st-line treatment for pts with mRCC in both RCTs and everyday clinical practice. [Table: see text]

Immunotherapies have significantly improved outcomes in patients with multiple myeloma yet maintaining a durable response in heavily pretreated patients remains challenging. Therapies that target B cell maturation antigen (BCMA) provide additional treatment options in patients whose disease becomes refractory to several drug classes in early lines of therapy. Clinical trial data from selected patient populations and controlled settings are complemented by real-world data (RWD) from actual clinical practice. In this podcast, the authors reviewed and discussed seven abstracts presented at the 65th Annual Meeting of the American Society of Hematology, focusing on BCMA-directed therapies, emphasizing the value of RWD in treatment decision-making, and suggesting how RWD can help advance multiple myeloma research. These abstracts include real-world outcome studies in patients with relapsed or refractory multiple myeloma with triple-class exposed or refractory disease (abstracts 542, 3358, and 6727); an analysis on disease burden associated with delayed diagnosis (abstract 3771); comparability of real-world outcomes vs clinical trial data (abstracts 91 and 545); and outcomes in patients with multiple myeloma who experienced early treatment failure after upfront quadruplet therapy (abstract 1989).Podcast available for this article.Supplementary InformationThe online version contains supplementary material available at 10.1007/s12325-024-02842-9.

<p>Supplementary Table 1. Pneumonitis defined using A) ICD-9 and B) ICD-10 codes that included ‘pneumonitis’ or associated concepts in the Unified Medical Language System, with Bidirectional General Equivalence Mappings used to identify equivalent terms between ICD-9 and ICD-10 in the Real World Data (RWD) cohort</p><p>Supplementary Table 2: ICD codes used to identify history of chronic lung condition in the Real World Data (RWD) cohort</p><p>Supplementary Table 3. Selection of clinical trial (RCT) population</p><p>Supplementary Table 4. Incidence of Treatment-Associated Pneumonitis in the Randomized Clinical Trial (RCT) Cohort and the Real World Data (RWD) Cohort based on sensitivity analyses</p><p>Supplementary Table 5. Treatment-associated pneumonitis counts by grade in the Randomized Clinical Trial (RCT) cohort</p><p>Supplementary Table 6. Demographic and Clinical Characteristics of Patients With and Without Radiation Therapy (RT) status available in the Real World Data (RWD) Cohort</p>

<p>Supplementary Table 1. Pneumonitis defined using A) ICD-9 and B) ICD-10 codes that included ‘pneumonitis’ or associated concepts in the Unified Medical Language System, with Bidirectional General Equivalence Mappings used to identify equivalent terms between ICD-9 and ICD-10 in the Real World Data (RWD) cohort</p><p>Supplementary Table 2: ICD codes used to identify history of chronic lung condition in the Real World Data (RWD) cohort</p><p>Supplementary Table 3. Selection of clinical trial (RCT) population</p><p>Supplementary Table 4. Incidence of Treatment-Associated Pneumonitis in the Randomized Clinical Trial (RCT) Cohort and the Real World Data (RWD) Cohort based on sensitivity analyses</p><p>Supplementary Table 5. Treatment-associated pneumonitis counts by grade in the Randomized Clinical Trial (RCT) cohort</p><p>Supplementary Table 6. Demographic and Clinical Characteristics of Patients With and Without Radiation Therapy (RT) status available in the Real World Data (RWD) Cohort</p>

One of the major impediments in deployment of Autonomous Driving Systems (ADS) is their safety and reliability. The primary reason for the complexity of testing ADS is that it operates in an open world characterized by its non-deterministic, high-dimensional and non-stationary nature where the actions of other actors in the environment are uncontrollable from the ADS's perspective. This leads to a state space explosion problem and one way of mitigating this problem is by concretizing the scope for the system under test (SUT) by testing for a set of behavioral competencies which an ADS must demonstrate. A popular approach to testing ADS is scenario-based testing where the ADS is presented with driving scenarios from real world (and synthetically generated) data and expected to meet defined safety criteria while navigating through the scenario. We present SAFR-AV, an end-to-end ADS testing platform to enable scenario-based ADS testing. Our work addresses key real-world challenges of building an efficient large scale data ingestion pipeline and search capability to identify scenarios of interest from real world data, creating digital twins of the real-world scenarios to enable Software-in-the-Loop (SIL) testing in ADS simulators and, identifying key scenario parameter distributions to enable optimization of scenario coverage. These along with other modules of SAFR-AV would allow the platform to provide ADS pre-certifications.

FDA Oncology Center of Excellence Review of Real World Data Submissions Supporting Drug Development in Hematologic Malignancies

e18787 Background: Aligning with 21st Century Cures legislation, the FDA is exploring trial design modernization and methodology to advance appropriate uses of Real World Data (RWD) to generate Real World Evidence (RWE). The Oncology Center of Excellence RWE Program was established in 2020 to advance RWE efforts specific to oncology drug development. Inclusion of RWD to support regulatory decision making has increased in oncology, and a landscape analysis was conducted to characterize the RWD included in submissions. Methods: A systematic search was conducted using internal FDA databases to identify RWD submissions from 2010 to 2020. Search terms included: real world evidence, real world data, electronic health record, cancer registry, administrative claims, external control arm, observational cohort, historical control arm, rwOS, rwRR, rwCR, and rwORR. Relevant regulatory submissions were reviewed, and pre-defined common data elements were extracted. A team of FDA reviewers assessed agreement through subset validation (20%). Descriptive statistics were calculated. Results: A total of 142 regulatory submissions included RWD from 2011 to 2020. A subset of 94 submissions met the criteria for evaluation, consisting of 78 unique studies evaluating 56 molecular entities. RWD submissions increased substantially over time, with 28 submissions in 2020. Nearly half of the RWD submissions were for solid tumor indications (68%), with lung cancer being the most predominant site. More than one third of the RWD submissions (37%) were for rare indications. The most common primary RWD study objective was effectiveness (62%) and the most commonly referenced RWD source was EHR/clinical data (54%). The most frequently used primary RWD endpoints were survival (rwOS, 35%) and response (rwORR/PR/BTR, 31%) outcomes (Table). Conclusions: Our review demonstrates a dramatic increase in RWD submissions to support FDA oncology drug development programs. Submissions included a variety of study objectives, data sources, and endpoints. While this landscape analysis provides a picture of potential regulatory objectives, the adequacy of each proposal to support regulatory decision making was not evaluated. Establishing a set of clear regulatory objectives can help advance the development of metrics for robust data characterization and outcome validation to ensure that RWD can be appropriately evaluated and provide the rigor necessary to be considered adequate RWE.[Table: see text]

Third dose of COVID-19 vaccine leads to seroconversion in 56% cancer patients that are seronegative after primary vaccination and a fourth can further boost immune response in patients with hematologic malignancies, which can be predicted by IgM and CD19 levels.

Third dose of COVID-19 vaccine leads to seroconversion in 56% cancer patients that are seronegative after primary vaccination and a fourth can further boost immune response in patients with hematologic malignancies, which can be predicted by IgM and CD19 levels.