Tumor Dynamics in Oncology Drug Development

Tumour dynamics refers to the quantitative description of changes in tumour burden over time in response to therapeutic intervention. In oncology drug development, robust characterisation of tumour growth and regression is fundamental to optimise dosing strategies, predict clinical outcomes and support regulatory decision-making. Model-informed approaches integrate pharmacokinetics, pharmacodynamics and disease progression to simulate treatment effects and refine trial design, helping to identify patient subgroups most likely to benefit from new agents. Recent advances have seen the incorporation of longitudinal biomarkers, treatment-resistance mechanisms and machine-learning frameworks to capture the complexity of tumour evolution and host interactions. These developments aim to increase the precision of early efficacy metrics, such as response rates or progression-free survival, and to guide adaptive treatment schedules that mitigate resistance. By bridging preclinical experiments with clinical data, tumour dynamic models enhance the interpretability of phase I–III trials, facilitate personalised dosing regimens and accelerate the translation of novel therapies into practice.

Research from Nature Portfolio

A mathematical framework considering clonal diversity and evolving resistance under targeted therapy has been developed to evaluate alternative dosing schedules. Simulations of continuous, intermittent and circulating tumour DNA-guided adaptive regimens demonstrated that integrating treatment holidays or biomarker-driven dosing can substantially prolong progression-free survival and sustain tumour size below baseline. This approach provides a rationale for prospective trials to validate adaptive schedules and optimise long-term control of resistant subclones.

In advanced melanoma, a semi-mechanistic model linking longitudinal serological biomarkers to survival outcomes has been proposed. The relative change in lactate dehydrogenase levels emerged as the most significant predictor of overall survival, while concurrent modelling of treatment toxicity enabled simultaneous assessment of benefit and risk. This framework exemplifies the integration of tumour dynamics with biomarker trajectories to identify early indicators of therapeutic efficacy and tailor adjuvant strategies.

Tumor Dynamics in Oncology Drug Development publication trend

The graph below shows the total number of articles in tumor dynamics in oncology drug development across all publications each year (not limited to Nature Index journals).

Technical terms

Tumour dynamics: Quantitative characterisation of tumour growth and shrinkage over time under therapy.

Pharmacokinetic–pharmacodynamic (PK–PD) modelling: Mathematical modelling of drug absorption, distribution, action and its effect on tumour metrics.

Response Evaluation Criteria in Solid Tumours (RECIST): Standardised guidelines for assessing changes in tumour size via imaging.

Progression-free survival (PFS): Interval during which a patient’s disease does not worsen following treatment.

Clonal heterogeneity: Coexistence of genetically distinct tumour cell populations influencing resistance evolution.

Neural ordinary differential equations (Neural-ODE): Machine-learning framework that infers dynamical systems from time-series data.

References

1. Integrated modeling of biomarkers, survival and safety in clinical oncology drug development. Advanced Drug Delivery Reviews (2024).
2. Anti-cancer treatment schedule optimization based on tumor dynamics modelling incorporating evolving resistance. Scientific Reports (2022).
3. Predicting circulating biomarker response and its impact on the survival of advanced melanoma patients treated with adjuvant therapy. Scientific Reports (2020).
4. Explainable deep learning for tumor dynamic modeling and overall survival prediction using Neural-ODE. npj Systems Biology and Applications (2023).
5. C-Reactive Protein as an Early Predictor of Efficacy in Advanced Non-Small-Cell Lung Cancer Patients: A Tumor Dynamics-Biomarker Modeling Framework. Cancers (2023).