Opportunities for Quantitative Translational Modeling in Oncology

In silico cancer immunotherapy trials uncover the consequences of therapy-specific response patterns for clinical trial design and outcome

Late-stage cancer immunotherapy trials often lead to unusual survival curve shapes, like delayed curve separation or a plateauing curve in the treatment arm. It is critical for trial success to anticipate such effects in advance and adjust the design accordingly. Here, we use in silico cancer immunotherapy trials - simulated trials based on three different mathematical models - to assemble virtual patient cohorts undergoing late-stage immunotherapy, chemotherapy, or combination therapies. We find that all three simulation models predict the distinctive survival curve shapes commonly associated with immunotherapies. Considering four aspects of clinical trial design - sample size, endpoint, randomization rate, and interim analyses - we demonstrate how, by simulating various possible scenarios, the robustness of trial design choices can be scrutinized, and possible pitfalls can be identified in advance. We provide readily usable, web-based implementations of our three trial simulation models to facilitate their use by biomedical researchers, doctors, and trialists.

A Spatially Resolved Mechanistic Growth Law for Cancer Drug Development Predicting Tumor Growing Fractions

Mathematical models used in preclinical drug discovery tend to be empirical growth laws. Such models are well suited to fitting the data available, mostly longitudinal studies of tumor volume; however, they typically have little connection with the underlying physiologic processes. This lack of a mechanistic underpinning restricts their flexibility and potentially inhibits their translation across studies including from animal to human. Here we present a mathematical model describing tumor growth for the evaluation of single-agent cytotoxic compounds that is based on mechanistic principles. The model can predict spatial distributions of cell subpopulations and account for spatial drug distribution effects within tumors. Importantly, we demonstrate that the model can be reduced to a growth law similar in form to the ones currently implemented in pharmaceutical drug development for preclinical trials so that it can integrated into the current workflow. We validate this approach for both cell-derived xenograft and patient-derived xenograft (PDX) data. This shows that our theoretical model fits as well as the best performing and most widely used models. However, in addition, the model is also able to accurately predict the observed growing fraction of tumours. Our work opens up current preclinical modeling studies to also incorporating spatially resolved and multimodal data without significant added complexity and creates the opportunity to improve translation and tumor response predictions.

Significance

This theoretical model has the same mathematical structure as that currently used for drug development. However, its mechanistic basis enables prediction of growing fraction and spatial variations in drug distribution.

How translational modeling in oncology needs to get the mechanism just right

Translational model‐based approaches have played a role in increasing success in the development of novel anticancer treatments. However, despite this, significant translational uncertainty remains from animal models to patients. Optimization of dose and scheduling (regimen) of drugs to maximize the therapeutic utility (maximize efficacy while avoiding limiting toxicities) is still predominately driven by clinical investigations. Here, we argue that utilizing pragmatic mechanism‐based translational modeling of nonclinical data can further inform this optimization. Consequently, a prototype model is demonstrated that addresses the required fundamental mechanisms.

A meta-analysis of tumour response and relapse kinetics based on 34,881 patients: A question of cancer type, treatment and line of treatment

PURPOSE

Cancer disease burden is commonly assessed radiologically in solid tumours in support of response assessment via the RECIST criteria. These longitudinal data are amenable to mathematical modelling and these models characterise the initial tumour size, initial tumour shrinkage in responding patients and rate of regrowth as patient's disease progresses. Knowing how these parameters vary between patient populations and treatments would inform translational modelling approaches from non-clinical data as well as clinical trial design.

EXPERIMENTAL DESIGN

Here a meta-analysis of reported model parameter values is reported. Appropriate literature was identified via a PubMed search and the application of text-based clustering approaches. The resulting parameter estimates are examined graphically and with ANOVA.

RESULTS

Parameter values from a total of 80 treatment arms were identified based on 80 trial arms containing a total of 34,881 patients. Parameter estimates are generally consistent. It is found that a significant proportion of the variation in rates of tumour shrinkage and regrowth are explained by differing cancer and treatment: cancer type accounts for 66% of the variation in shrinkage rate and 71% of the variation in reported regrowth rates. Mean average parameter values by cancer and treatment are also reported.

CONCLUSIONS

Mathematical modelling of longitudinal data is most often reported on a per clinical trial basis. However, the results reported here suggest that a more integrative approach would benefit the development of new treatments as well as the further optimisation of those currently used.

Quantitative Systems Pharmacology Modeling of PBMC-Humanized Mouse to Facilitate Preclinical Immuno-oncology Drug Development

Progress in immunotherapy has resulted in explosively increased new therapeutic interventions and they have shown promising results in the treatment of cancer. Animal testing is performed to provide preliminary efficacy and safety data for drugs under development prior to clinical trials. However, translational challenges remain for preclinical studies such as study design and the relevance of animal models to humans. Hence, only a small fraction of cancer patients showed response. The explosion of drug candidates and therapies makes preclinical assessment of every plausible option impossible, but it can be easily tested using Quantitative System Pharmacology (QSP) models. Here, we developed a QSP model for humanized mice. Tumor growth dynamics, T cell dynamics, cytokine release, immune checkpoint expression, and drug administration were modeled and calibrated using experimental data. Tumor growth inhibition data were used for model validation. Pharmacokinetics of T cell engager (TCE), tumor growth profile, T cell expansion in the blood and infiltration into tumor, T cell dissemination from primary tumor, cytokine release profile, and expression of additional PD-L1 induced by IFN-γ were modeled and calibrated using a variety of experimental data and showed good consistency. Mouse-specific response to T cell engager monotherapy also showed the key features of in vivo efficacy of TCE. This novel QSP model, designed for human peripheral blood mononuclear cells (PBMC) engrafted xenograft mice, incorporating the most critical components of the mouse model with key cancer and immune cells, can become an integral part of preclinical drug development.

Clone Wars: Quantitatively Understanding Cancer Drug Resistance

A key aim of early clinical development for new cancer treatments is to detect the potential for efficacy early and to identify a safe therapeutic dose to take forward to phase II. Because of this need, researchers have sought to build mathematical models linking initial radiologic tumor response, often assessed after 6 to 8 weeks of treatment, with overall survival. However, there has been mixed success of this approach in the literature. We argue that evolutionary selection pressure should be considered to interpret these early efficacy signals and so optimize cancer therapy.