ReviewFuture science OA2018
Quantitative translational modeling to facilitate preclinical to clinical efficacy & toxicity translation in oncology.
Review in Future science OA, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 31 citations in OpenAlex.
- Mathematical modeling in autoimmune diseases: from theory to clinical application.Frontiers in immunology · 2024Pooled it
- PKPD-Based Translational Modeling of Calaspargase Pegol Preclinical Activity in Hepatocellular Carcinoma.European journal of drug metabolism and pharmacokinetics · 2026Article
- Developing a melanoma-skin-on-a-chip integrated with vasculature using an edgeless skin reconstruction approach.Research square · 2026Article
- Extracellular Enolase-1 Promotes CAF-Associated Stromal Reprogramming via the Plasmin/TGF-β Axis in Multiple Myeloma.Cancers · 2026Article
- Emerging small molecule strategies target cancer stem cells through ferroptosis and metabostemness.Discover oncology · 2025Review
- Towards a platform quantitative systems pharmacology (QSP) model for preclinical to clinical translation of antibody drug conjugates (ADCs).Journal of pharmacokinetics and pharmacodynamics · 2024Article
- Retrospective Assessment of Translational Pharmacokinetic-Pharmacodynamic Modeling Performance: A Case Study with Apitolisib, a Dual PI3K/mTOR Inhibitor.Drugs in R&D · 2024Article
- Article
- A Polymer Prodrug Strategy to Switch from Intravenous to Subcutaneous Cancer Therapy for Irritant/Vesicant Drugs.Journal of the American Chemical Society · 2022Article
- Optimized scaling of translational factors in oncology: from xenografts to RECIST.Cancer chemotherapy and pharmacology · 2022Article
- Growth-rate model predicts in vivo tumor response from in vitro data.CPT: pharmacometrics & systems pharmacology · 2022Article
- Cancer patient survival can be parametrized to improve trial precision and reveal time-dependent therapeutic effects.Nature communications · 2022Article
- Quantitative modeling predicts competitive advantages of a next generation anti-NKG2A monoclonal antibody over monalizumab for the treatment of cancer.CPT: pharmacometrics & systems pharmacology · 2021Article
- Mechanistic Quantitative Pharmacology Strategies for the Early Clinical Development of Bispecific Antibodies in Oncology.Clinical pharmacology and therapeutics · 2020Review
- Quantification of Pharmacokinetic Profiles of PD-1/PD-L1 Antibodies by Validated ELISAs.Pharmaceutics · 2020Article
- Survival Prolongation Index as a Novel Metric to Assess Anti-Tumor Activity in Xenograft Models.The AAPS journal · 2019Article
- Quantitative Spatial Analysis of Metabolic Heterogeneity AcrossFrontiers in oncology · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Significant scientific advances in biomedical research have expanded our knowledge of the molecular basis of carcinogenesis, mechanisms of cancer growth, and the importance of the cancer immunity cycle. However, despite scientific advances in the understanding of cancer biology, the success rate of oncology drug development remains the lowest among all therapeutic areas. In this review, some of the key translational drug development objectives in oncology will be outlined. The literature evidence of how mathematical modeling could be used to build a unifying framework to answer these questions will be summarized with recommendations on the strategies for building such a mathematical framework to facilitate the prediction of clinical efficacy and toxicity of investigational antineoplastic agents. Together, the literature evidence suggests that a rigorous and unifying preclinical to clinical translational framework based on mathematical models is extremely valuable for making go/no-go decisions in preclinical development, and for planning early clinical studies.
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.