Leveraging Hypoxia-Activated Prodrugs to Prevent Drug Resistance in Solid Tumors

Abstract

Experimental studies have shown that one key factor in driving the emergence of drug resistance in solid tumors is tumor hypoxia, which leads to the formation of localized environmental niches where drug-resistant cell populations can evolve and survive. Hypoxia-activated prodrugs (HAPs) are compounds designed to penetrate to hypoxic regions of a tumor and release cytotoxic or cytostatic agents; several of these HAPs are currently in clinical trial. However, preliminary results have not shown a survival benefit in several of these trials. We hypothesize that the efficacy of treatments involving these prodrugs depends heavily on identifying the correct treatment schedule, and that mathematical modeling can be used to help design potential therapeutic strategies combining HAPs with standard therapies to achieve long-term tumor control or eradication. We develop this framework in the specific context of EGFR-driven non-small cell lung cancer, which is commonly treated with the tyrosine kinase inhibitor erlotinib. We develop a stochastic mathematical model, parametrized using clinical and experimental data, to explore a spectrum of treatment regimens combining a HAP, evofosfamide, with erlotinib. We design combination toxicity constraint models and optimize treatment strategies over the space of tolerated schedules to identify specific combination schedules that lead to optimal tumor control. We find that (i) combining these therapies delays resistance longer than any monotherapy schedule with either evofosfamide or erlotinib alone, (ii) sequentially alternating single doses of each drug leads to minimal tumor burden and maximal reduction in probability of developing resistance, and (iii) strategies minimizing the length of time after an evofosfamide dose and before erlotinib confer further benefits in reduction of tumor burden. These results provide insights into how hypoxia-activated prodrugs may be used to enhance therapeutic effectiveness in the clinic.

A Spatio-Temporal Model of Macrophage-Mediated Drug Resistance in Glioma Immunotherapy

“…Moreover, some efforts using modeling approaches to investigate the role of the microenvironment in drug resistance have also been conducted recently. For instance, Lindsay and colleagues (22) developed a stochastic model to simulate the effect of oxygen on the growth kinetics of cancer cells and to investigate the potential benefits of combining hypoxia-activated prodrugs with standard targeted therapy to prevent drug resistance in non-small cell lung cancer. In addition, our previous study (23) designed a modeling framework using stochastic differential equations to simulate the therapy-induced microenvironmental adaptation that promotes the growth and metastasis of resistant tumor cells, and therefore contributes to the development of the acquired drug resistance.…”

Targeting hypoxic habitats with hypoxia pro-drug evofosfamide in preclinical models of sarcoma

“…A different study reached a similar conclusion with an EGFR targeted agent with a mathematical model showing that longer time under TH-302 therapy without the targeted inhibitor erlotinib allowing the EGFR sensitive cell population to expand drastically due to TH-302 resistance. Optimal schedule of an agent affecting both the hypoxic and normoxic populations may allow for the longest duration of control of the two populations (11).…”

Combining hypoxia-activated prodrugs and radiotherapy in silico: Impact of treatment scheduling and the intra-tumoural oxygen landscape

“…Another concurrent article used similar mathematical concepts to compare Class I HAPs to Class II HAPs and, furthermore, to determine optimal properties for Class II HAPs [23]. Lindsay et al [32] developed a stochastic model to study monotherapies and combination therapies involving HAPs, specifically TH-302, and erlotinib. Amongst other findings, they concluded that a combination therapy of the two drugs impedes the uprising of drug resistance.…”

“…Lindsay et al . [ 32 ] developed a stochastic model to study monotherapies and combination therapies involving HAPs, specifically TH-302, and erlotinib. Amongst other findings, they concluded that a combination therapy of the two drugs impedes the uprising of drug resistance.…”