Maximizing therapeutic potential and safety: Exploring multi/dual-payload antibody conjugates as cancer theranostics
Bibliographic record
Abstract
Tumor heterogeneity greatly contributes to the failure of traditional cancer treatments. This leads to tumor relapse, recurrence, and ultimately metastasis, presenting serious clinical challenges. In recent decades, advances in antibody-based immunotherapy have emerged as promising new pillars to combat cancers. Although single payload antibody drug conjugates (ADCs) have resulted in drastic improvements in patient outcomes compared with unconjugated antibodies, multiple de novo and acquired resistance mechanisms inherent with cancer cells have left patients with less than desired outcomes. Newer studies are exploring the use of dual and multiple payload ADCs to enhance effectiveness. These payloads include chemotherapeutic and/or radiotherapeutic agents. The approaches leverage the synergistic effects of the different payloads alongside the immunotherapeutic properties of the antibody carriers. This review presents a comprehensive overview of dual-payload monoclonal antibody conjugates for cancer therapy and diagnosis (theranostics). Additionally, it explores the use of various imageable radiometals that are conjugated to the ADCs for imaging/diagnosis. It discusses the role of radioisotope decay schemes (such as alpha emission, beta emission, or Auger electron emission) along with factors such as linker type and chelator, as well as drug-to-antibody ratio (DAR), which are aimed at enhancing the synergistic effects between the therapeutic payloads while ensuring safety. Because none of these dual-payload ADCs have reached the clinic, this review employs a predictive method to estimate human equivalent dose (HED), maximum tolerable dose (MTD), and radiotoxicity in humans based on preclinical data. Additionally, it discusses the combinatorial behavior of two cytotoxic payloads linked to a monoclonal antibody.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".