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. 2013 Apr;15(2):542-50.
doi: 10.1208/s12248-013-9461-y. Epub 2013 Feb 13.

Modeling NSCLC progression: recent advances and opportunities available

Ahmed Abbas Suleiman  1 , Lucia Nogova, Uwe Fuhr
Affiliations

Modeling NSCLC progression: recent advances and opportunities available

Ahmed Abbas Suleiman et al. AAPS J. 2013 Apr.

Abstract

Non-small cell lung cancer (NSCLC) is one of the leading causes of death around the world with an estimated 5-year relative survival rate of 16% at diagnosis. Development of drugs treating NSCLC is not easy, and the success rate for an anticancer treatment to pass through the whole clinical development process is as low as 5%. Modeling and simulation lend themselves as tools which can potentially streamline drug development. A critical component of the models developed is a description of how the disease progresses over time and how a treatment would affect its trajectory. Our aim was to review the literature to present the models and growth functions which have been used for describing NSCLC dynamics, and how anticancer treatments can affect such dynamics, both in animals and in humans. Only a limited set of models were identified for such a purpose. Most of the models which have been used were descriptive of tumor growth, yet there were attempts to account for the underlying processes, especially in animals where it is more feasible to collect data needed for developing such models. Moreover, we discuss how modeling and simulation can aid in decision making across the different stages of drug development. Based on some encouraging results from trials of other cancer types where modeling tumor dynamics has played an important role, we propose further exploration of NSCLC using model-based techniques and further use of these techniques in designing and evaluating NSCLC trials.

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Figures

Fig. 1
Fig. 1
A scheme showing the PK/PD model developed by Simeoni et al. (39). S 1 proliferating cells, S 2, S 3, and S 4 damaged cells, PK pharmacokinetic model, c(t) plasma concentration of the anticancer agent, k 1 transit rate constant, k 2 drug potency. Adapted from (39)
Fig. 2
Fig. 2
A schematic representation of the model developed by Tham et al. (42). E max the maximum inhibition of tumor growth (fixed to 1), dose the dose of gemcitabine administered, Amt 50 the potency where 50% of the maximal drug effect has been achieved, S the tumor size, rateIN tumor growth rate at baseline, effect the inhibitory effect of the drug on the tumor growth rate, T turnover second-order time constant for the tumor turnover. Adapted from (42)
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