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. 2015 Jan;4(1):e00016.
doi: 10.1002/psp4.16. Epub 2015 Jan 21.

Developing Exposure/Response Models for Anticancer Drug Treatment: Special Considerations

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Free PMC article

Developing Exposure/Response Models for Anticancer Drug Treatment: Special Considerations

D R Mould et al. CPT Pharmacometrics Syst Pharmacol. .
Free PMC article

Abstract

Anticancer agents often have a narrow therapeutic index (TI), requiring precise dosing to ensure sufficient exposure for clinical activity while minimizing toxicity. These agents frequently have complex pharmacology, and combination therapy may cause schedule-specific effects and interactions. We review anticancer drug development, showing how integration of modeling and simulation throughout development can inform anticancer dose selection, potentially improving the late-phase success rate. This article has a companion article in Clinical Pharmacology & Therapeutics with practical examples.

Figures

Figure 1
Figure 1
Multiscale pharmacokinetic-pharmacodynamic (PK-PD) models relate quantitatively events on molecular to cellular and/or on tissue levels to support selection of biomarker to monitor drug response. These models increase the confidence in projecting translational PK-PD relationship from animal to man, as illustrated in the example. The steps include (1) establish PK-PD model to quantitatively understand the pharmacological activity of the drug in preclinical species; (2) link biomarker response to anticancer effect to define required pharmacological activity to obtain tumor regression in preclinical model; and (3) scale model to humans to select dose/dosing schedule and monitor biomarker profile in patients in early clinical trial to find the “right” dose and dosing schedule.
Figure 2
Figure 2
Response of non-small cell lung cancer during and after six cycles of treatment with gemcitabine (red bars). The predicted accumulation of the nominal amount of active substance during treatment and washout after treatment (dotted green line) determines the effect on inhibition of tumor growth. The time course of tumor size (dashed blue line) reaches a minimum some weeks after stopping treatment (adapted from ref. 45 with permission).
Figure 3
Figure 3
Simulated tumor responses using a mixture of models. In each panel, the overall range of tumor growth over time for subjects who have responded to therapy (responders), remained stable (stable disease), and not responded (progression). The solid lines represent the median tumor trajectory, the dashed lines represent the lower 2.5 percentile and the upper 97.5 percentile of expected tumor growth, and the shaded gray regions are 95% confidence intervals of the percentiles. Defining separate functions for different subpopulations can help to determine the effect of the drug in a sensitive patient population.
Figure 4
Figure 4
Hazard functions. (a) A Weibull hazard function showing the impact of varying α (the shape parameter) and λ (the scale parameter). Weibull functions can therefore account for increasing or decreasing hazards. (b) Piecewise continuous and continuously differentiable hazard functions. Although the piecewise hazard function is not easily related to physiology, the use of such functions, if appropriately supported by the data, can improve the ability of the time-to-event (TTE) model to describe the reduction in hazard because of drug exposure. In this figure, the continuous model would provide a biased estimate of hazard reduction at high first week of treatment (FWTX) if the piecewise hazard function were derived from observed data.
Figure 5
Figure 5
Example Visual Predictive Check of a time-to-event model. In the example below, the final TTE model was used to simulate multiple replicates of the study, and Kaplan–Meier plots of the simulated data (shaded area) were overlaid on the observed data (blue lines). In such evaluations, it is common to generate these figures stratified by predictive covariates; here, the data were stratified by drug exposure. In this example Visual Predictive Check, the model performs well for most exposure quantiles but slightly underestimates survival benefit in the third quantile.

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