ReviewClinical and translational science2026
Harnessing Quantitative Medicine to Advance Oncology Drug Development.
Review in Clinical and translational science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Oncology drug development continues to have a high attrition rate despite major advances in therapeutic modalities such as antibody-drug conjugates, bispecific antibodies, cell therapies, and cancer vaccines. Critical development decisions are often made under substantial uncertainty, creating a need for quantitative approaches that integrate diverse sources of evidence. Quantitative medicine (QM) and model-informed drug development (MIDD) provide a framework to support decision-making throughout the oncology drug development lifecycle by leveraging pharmacology, disease biology, clinical data, biomarkers, and computational modeling. This review highlights three potential applications of QM that address drug development challenges. First, tumor growth inhibition-overall survival (TGI-OS) modeling links longitudinal tumor dynamics with survival outcomes, enabling earlier assessment of treatment benefit and supporting Phase III go/no-go decisions using Phase Ib/II data. Second, pan-molecule modeling across multiple antibody-drug conjugates that share a common linker-payload construct. This QM approach characterizes the class-specific exposure-toxicity relationships and informs dose optimization strategies, as illustrated by peripheral neuropathy risk modeling for vc-MMAE-containing agents. Third, population pharmacokinetic modeling and clinical trial simulation can facilitate intravenous-to-subcutaneous bridging by predicting pharmacokinetic non-inferiority, optimizing dose selection, and reducing development risk, as demonstrated for pertuzumab/trastuzumab and atezolizumab. Collectively, these examples demonstrate how QM can improve confidence in critical development decisions, optimize benefit-risk assessment, and enhance development efficiency. Continued integration of quantitative approaches, including emerging artificial intelligence and mechanistic modeling methodologies, has the potential to improve the probability of success and accelerate the delivery of effective oncology therapies to patients.
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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.