ArticleClinical and translational science2021
Mechanistic Modeling of Intra-Tumor Spatial Distribution of Antibody-Drug Conjugates: Insights into Dosing Strategies in Oncology.
Article in Clinical and translational science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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
18 citing papers in PubMed, 25 citations in OpenAlex.
- Preclinical Pharmacokinetic-Pharmacodynamic Modeling of Antibody Nanoconjugates for Breast Cancer Treatment.CPT: pharmacometrics & systems pharmacology · 2026Article
- Antibody-drug conjugate engineering: from design to efficacy and safety.Signal transduction and targeted therapy · 2026Review
- Review
- Antibody-Drug Conjugates in Breast Cancer: Navigating Innovations, Overcoming Resistance, and Shaping Future Therapies.Biomedicines · 2025Review
- Exploring Experimental and In Silico Approaches for Antibody-Drug Conjugates in Oncology Therapies.Pharmaceuticals (Basel, Switzerland) · 2025Review
- Reprogramming the tumor microenvironment: synergistic mechanisms of antibody-drug conjugates and immune checkpoint inhibitors.Antibody therapeutics · 2025Review
- Antibody-drug conjugates in breast cancer treatment: resistance mechanisms and the role of therapeutic sequencing.Cancer drug resistance (Alhambra, Calif.) · 2025Review
- QSP modeling of a transiently inactivating antibody-drug conjugate highlights benefit of short antibody half life.Journal of pharmacokinetics and pharmacodynamics · 2024Article
- Advancing cancer drug development with mechanistic mathematical modeling: bridging the gap between theory and practice.Journal of pharmacokinetics and pharmacodynamics · 2024Review
- Evolution of Pyrrolysyl-tRNA Synthetase: From Methanogenesis to Genetic Code Expansion.Chemical reviews · 2024Review
- Single-Domain Antibodies as Antibody-Drug Conjugates: From Promise to Practice-A Systematic Review.Cancers · 2024Review
- Moving the needle for oncology dose optimization: A call for action.CPT: pharmacometrics & systems pharmacology · 2024Article
- Moving the needle for oncology dose optimization: A call for action.Clinical and translational science · 2024Article
- Modeling antibody drug conjugate potential using a granzyme B antibody fusion protein.BMC biology · 2024Article
- Improving Intracellular Delivery of an Antibody-Drug Conjugate Targeting Carcinoembryonic Antigen Increases Efficacy at Clinically Relevant Doses In Vivo.Molecular cancer therapeutics · 2024Article
- Engineered Ultrasmall Nanoparticle Drug-Immune Conjugates with "Hit and Run" Tumor Delivery to Eradicate Gastric Cancer.Advanced therapeutics · 2023Article
- Pharmacokinetics and Pharmacodynamics of TAK-164 Antibody Drug Conjugate Coadministered with Unconjugated Antibody.The AAPS journal · 2022Article
- Clinical Pharmacology of Antibody-Drug Conjugates.Antibodies (Basel, Switzerland) · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Antibody drug conjugates (ADCs) provide targeted delivery of cytotoxic agents directly inside tumor cells. However, many ADCs targeting solid tumors have exhibited limited clinical efficacy, in part, due to insufficient penetration within tumors. To better understand the relationship between ADC tumor penetration and efficacy, previously applied Krogh cylinder models that explore tumor growth dynamics following ADC administration in preclinical species were expanded to a clinical framework by integrating clinical pharmacokinetics, tumor penetration, and tumor growth inhibition. The objective of this framework is to link ADC tumor penetration and distribution to clinical efficacy. The model was validated by comparing virtual patient population simulations to observed overall response rates from trastuzumab-DM1 treated patients with metastatic breast cancer. To capture clinical outcomes, we expanded upon previous Krogh cylinder models to include the additional mechanism of heterogeneous tumor growth inhibition spatially across the tumor. This expansion mechanistically captures clinical response rates by describing heterogeneous ADC binding and tumor cell killing; high binding and tumor cell death close to capillaries vs. low binding, and high tumor cell proliferation far from capillaries. Sensitivity analyses suggest that clinical efficacy could be optimized through dose fractionation, and that clinical efficacy is primarily dependent on the ADC-target affinity, payload potency, and tumor growth rate. This work offers a mechanistic basis to predict and optimize ADC clinical efficacy for solid tumors, allowing dosing strategy optimization to improve patient outcomes.
Indexed as
Identifiers
What OpenQuestion holds
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.