ReviewCancer biology & medicine2014
Mechanistic considerations for the use of monoclonal antibodies for cancer therapy.
Review in Cancer biology & medicine, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
61 citing papers in PubMed, 134 citations in OpenAlex.
- Advancements in Drug Discovery Techniques for Cancer Treatment: Clinical Progress and Future Directions.Current drug discovery technologies · 2026Review
- Pharmacological considerations for next-generation protein therapeutics in cardiovascular disease.The Journal of pharmacology and experimental therapeutics · 2025Review
- Targeting acute myeloid leukemia through antibody engineering: innovations in immunotherapy and combination regimens.Clinical and experimental medicine · 2025Review
- Accelerating Biologics PBPK Modelling with Automated Model Building: A Tutorial.Pharmaceutics · 2025Review
- A Comprehensive Review of the Mechanisms and Clinical Development of Monoclonal Antibodies in Cancer Therapy.Advances in experimental medicine and biology · 2025Review
- Cyclophilin A: promising target in cancer therapy.Cancer biology & therapy · 2024Review
- Protein ubiquitination in ovarian cancer immunotherapy: The progress and therapeutic strategy.Genes & diseases · 2024Review
- Monoclonal antibodies: From magic bullet to precision weapon.Molecular biomedicine · 2024Review
- GD2-targeting therapy: a comparative analysis of approaches and promising directions.Frontiers in immunology · 2024Review
- Unrevealed roles of extracellular enolase‑1 (ENO1) in promoting glycolysis and pro‑cancer activities in multiple myeloma via hypoxia‑inducible factor 1α.Oncology reports · 2023Article
- Development of an Antibody Delivery Method for Cancer Treatment by Combining Ultrasound with Therapeutic Antibody-Modified Nanobubbles Using Fc-Binding Polypeptide.Pharmaceutics · 2022Article
- Mixed-dimensional multi-scale poroelastic modeling of adipose tissue for subcutaneous injection.Biomechanics and modeling in mechanobiology · 2022Article
- Review of the Existing Translational Pharmacokinetics Modeling Approaches Specific to Monoclonal Antibodies (mAbs) to Support the First-In-Human (FIH) Dose Selection.International journal of molecular sciences · 2022Review
- Evaluating Antibody Pharmacokinetics as Prerequisite for Determining True Efficacy as Shown by Dual Targeting of PD-1 and CD96.Biomedicines · 2022Article
- L-5-hydroxytryptophan promotes antitumor immunity by inhibiting PD-L1 inducible expression.Journal for immunotherapy of cancer · 2022Article
- Pharmacokinetics and pharmacodynamics of MEDI0680, a fully human anti-PD-1 monoclonal antibody, in patients with advanced malignancies.Cancer chemotherapy and pharmacology · 2022Article
- Development of anti-rituximab antibodies in rituximab-treated patients: Related parameters & consequences.The Indian journal of medical research · 2022Review
- A nano-innate immune system activator for cancer therapy in a 4T1 tumor-bearing mouse model.Journal of nanobiotechnology · 2022Article
- Immune checkpoint inhibitor use in patients with end-stage kidney disease: an analysis of reported cases and literature review.Clinical kidney journal · 2021Review
- Bi-specific and Tri-specific NK Cell Engagers: The New Avenue of Targeted NK Cell Immunotherapy.Molecular diagnosis & therapy · 2021Review
1 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Since the approval of rituximab in 1997, monoclonal antibodies (mAbs) have become an increasingly important component of therapeutic regimens in oncology. The success of mAbs as a therapeutic class is a result of great strides that have been made in molecular biology and in biotechnology over the past several decades. Currently, there are 14 approved mAb products for oncology indications, and there are ten additional mAbs in late stages of clinical trials. Compared to traditional chemotherapeutic agents, mAbs have several advantages, including a long circulating half-life and high target specificity. Antibodies can serve as cytotoxic agents when administered alone, exerting a pharmacologic effect through several mechanisms involving the antigen binding (Fab) and/or Fc domains of the molecule, and mAbs may also be utilized as drug carriers, targeting a toxic payload to cancer cells. The extremely high affinity of mAbs for their targets, which is desirable with respect to pharmacodynamics (i.e., contributing to the high therapeutic selectivity of mAb), often leads to complex, non-linear, target-mediated pharmacokinetics. In this report, we summarize the pharmacokinetic and pharmacodynamics of mAbs that have been approved and of mAbs that are near approval for oncology indications, with particular focus on the molecular and cellular mechanisms responsible for their disposition and efficacy.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.