Unmet Needs in Multiple Myeloma and Potential Solutions: A Systemic Literature Review
Bibliographic record
Abstract
Introduction: Unmet needs refer to unaddressed resources, unachieved health/survival outcomes or needs necessary for optimal well-being of patients. There has been no systematic reporting of the unmet needs in multiple myeloma (MM). We conducted this systematic review to highlight the unmet needs in MM and their proposed solutions in the literature and provide direction for future research. Methods: PubMed, Embase, and ClinicalTrials.gov were searched following the PRISMA guidelines using the relevant keywords and MeSH terms related to “multiple myeloma” and “unmet” needs without restrictions capturing studies from 2004 to Jan 2024. They were further screened to select the studies reporting unmet needs to develop a timeline of their reporting, identify the article types and the proposed solutions. Results: Out of 1214 articles, 668 (58% abstracts and 42% full manuscripts) were included. The use of the term “unmet” increased through the years [2004-2009 (1%), 2010-2017 (27%) and 2018-2024 (72%)]. None of the articles defined the term “unmet” or “unmet need”. Twenty-three articles (3%) specified the unmet needs of a particular country/region (China 7, Canada 3, Brazil 2, India 2, US 2 and 1 each for Italy, Korea, Lebanon, Portugal, Ukraine, Turkey, and Latin America). Narrative reviews (24%) were the most common article types followed by retrospective studies (21%), and clinical trials (19%). Among various aspects of MM, “treatment” was consistently the most common (73%) reported unmet need which included treatment of relapsed/refractory MM (RRMM) (38%). Treatment of bone disease (2%) and treatment cost (1%) were also consistently reported treatment-related unmet needs through this period. Other unmet needs reported were patient and family experience/supportive care (11%), pathophysiology (4%), diagnosis/response assessment (4%), toxicity management/prevention (3%) and poor prognosis (4%). Unmet needs that were reported starting from 2010 [and their proposed solutions] include treatment of high-risk cytogenetic features (4%) [elotuzumab/isatuximab/melflufen/selinexor/anti-BCMA conjugates], treatment of plasma cell leukemia/extramedullary disease (3%) [utilizing MM drugs (daratumumab/boretezemib/melfufen/frontline ASCT etc.)], prevention/management of toxicities (mucositis, neuropathy, nephropathy and GVHD) (1%) [CR4056/amifostine/cannabidiol], treatment/prevention of thromboembolism (1%) [risk assessment model for thromboembolism/studies on anticoagulation], treatment of frail/elderly (1%) [SEA BCMA/melflufen], immune response in MM (<1%) [immunological studies], and measures to assess response to treatment (<1%) [heavy light chain ratio/DW imaging/BH3 profiling]. Unmet needs [and proposed solutions] reported specifically after 2018 include CAR-T refractory MM (<1%) [bispecific antibodies/antibody-drug conjugates/combination therapies/allogeneic HCT], steroid-free myeloma regimens (<1%) [vactosertib], MM with high baseline LDH (<1%) [alternative therapy to AHCT], MM in young (<1%) [standardized definition for young and studies focusing this age], tools to evaluate the risk of death at first relapse (<1%) [an algorithm to stratify patients by defining patient-specific risk and combine both frailty and disease aggressiveness into a single tool], noninvasive blood-based MRD assays (<1%) [ctDNA/NGF/EasyM assay], COVID-19 in MM (<1%) [investigating humoral/T-cell response to SARS-CoV-2 vaccination in MM patients] etc. Talquetamab (2022), ciltacaptagene (2021), elranatamab (2021), teclistamab (2020), idecabtagene (2020), isatuximab (2017), melflufen (2017) etc. were some of the most recent options reported to address the unmet need for treatment of RRMM. Conclusion: Our study identified the use of term “unmet” to indicate diverse needs related to MM and a broad range of solutions reported for these needs inferring a need to standardize the use of the term “unmet need”. Treatment of RRMM remains the most reported unmet need with a satisfactory solution yet to be uncovered with future research. With advancements, more unmet needs are being uncovered/reported with time. The dynamic nature of these needs should be considered while addressing gaps with future research. Our study only captured studies that used the term “unmet” and might have missed unmet needs reported by articles that didn't specifically use this term.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.027 | 0.020 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".