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Record W4399040613 · doi:10.1080/14737140.2024.2344647

Antibody-drug conjugates, bispecific antibodies and CAR-T cells therapy in multiple myeloma

2024· article· en· W4399040613 on OpenAlexfundno aff
Paola Tacchetti, Marco Talarico, Simona Barbato, Lucia Pantani, Katia Mancuso, Ilaria Rizzello, Elena Zamagni, Michèle Cavo

Bibliographic record

VenueExpert Review of Anticancer Therapy · 2024
Typearticle
Languageen
FieldMedicine
TopicCAR-T cell therapy research
Canadian institutionsnot available
FundersMinistry of Health, British Columbia
KeywordsMedicineMultiple myelomaAntibody-drug conjugateBispecific antibodyAntibodyDrugImmunotherapyOncologyRefractory (planetary science)Antibody therapyInternal medicineImmunologyCancer researchMonoclonal antibodyPharmacologyCancer

Abstract

fetched live from OpenAlex

INTRODUCTION: Modern immunotherapy approaches are revolutionizing the treatment scenario of relapsed/refractory (RR) multiple myeloma (MM) patients, providing an opportunity to reach deep level of responses and extend survival outcomes. AREAS COVERED: Antibody-drug conjugates (ADCs) and T-cell redirecting treatments, including bispecific antibodies (BsAbs) and chimeric antigen receptor (CAR) T cells therapy, have been recently introduced in the treatment of RRMM. Some agents have already received regulatory approval, while newer constructs, novel combinations, and applications in earlier lines of therapy are currently being explored. This review discusses the current landscape and possible development of ADCs, BsAbs and CAR-T cells immunotherapies. EXPERT OPINION: ADCs, BsAbs, and CAR-T therapy have demonstrated substantial activity in heavily pretreated, triple-class exposed (TCE) MM patients, and T-cell redirecting treatments represent new standards of care after third (European Medicines Agency, EMA), or fourth (Food and Drug Administration, FDA), line of therapy. All these three immunotherapies carry advantages and disadvantages, with different accessibility and new toxicities that require appropriate management and guidelines. Multiple on-going programs include combinations therapies and applications in earlier lines of treatment, as well as the development of novel agents or construct to enhance potency, reduce toxicity and facilitate administration. Sequencing is a challenge, with few data available and mechanisms of resistance still to be unraveled.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.702
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.040
GPT teacher head0.378
Teacher spread0.338 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreReview

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".

Quick stats

Citations12
Published2024
Admission routes1
Has abstractyes

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