Optimizing Adaptive Therapy Based on the Reachability to Tumor Resistant Subpopulation

Abstract

Adaptive therapy exploits the self-organization of tumor cells to delay the outgrowth of resistant subpopulations successfully. When the tumor has aggressive resistant subpopulations, the outcome of adaptive therapy was not superior to maximum tolerated dose therapy (MTD). To explore methods to improve the adaptive therapy's performance of this case, the tumor system was constructed by osimertinib-sensitive and resistant cell lines and illustrated by the Lotka-Volterra model in this study. Restore index proposed to assess the system reachability can predict the duration of each treatment cycle. Then the threshold of the restore index was estimated to evaluate the timing of interrupting the treatment cycle and switching to high-frequency administration. The introduced reachability-based adaptive therapy and classic adaptive therapy were compared through simulation and animal experiments. The results suggested that reachability-based adaptive therapy showed advantages when the tumor has an aggressive resistant subpopulation. This study provides a feasible method for evaluating whether to continue the adaptive therapy treatment cycle or switch to high-frequency administration. This method improves the gain of adaptive therapy by taking into account the benefits of tumor intra-competition and the tumor control of killing sensitive subpopulation.

Is the Fixed Periodic Treatment Effective for the Tumor System without Complete Information?

“…Then the cell number can be converted to the A549 volume of the mixed tumor according to the cell volume of A549 from our previous study. 28 The cell volume of A549 in our previous study was obtained from the regression of cell number and tumor volume.…”

“…The model of mixed tumor administered with osimertinib or FK866 was as following Equations 2 and 3, the equation of osimertinib administration was the same as our previous study. 28 In equations, H and A represent the volume of H1975 and A549, C OSI and C FK866 represent the concentration of osimertinib and FK866, E OSI represents the efficacy of osimertinib on H1975, E FK866 represents the efficacy of FK866 on A549. When C OSI and C FK866 equal zero, Equations 2 and 3 illustrate the normal growth of the mixed tumor.…”

“…In addition, the second term in Equation 2 describes the survival support of A549 to H1975 when osimertinib was administered. Because the inhibition of osimertinib on H1975 was significantly reduced in the mixed tumor, 28 we suspected that A549 might produce some secretions to support the survival of H1975, which is also present in other TKIs. 31 The second term in Equation 3 indicates the impact of actual carrying capacity on the efficacy of FK866.…”

“…The parameters about tumor normal growth, cell competition and efficacy of osimertinib were obtained from the previous study. 28 The efficacy of FK866 was estimated based on the volume of single A549, total volume of the mixed tumors and the A549 volume of the mixed tumors. The goodness-of-fit plots and residual plots evaluated the model.…”

“…Equation 2 and the parameters contained in these equations were adopted from our previous study. 28 The pharmacodynamic model of FK866 and related parameters were explored in this study. The experimental data used for the model establishment and parameter estimation consisted of two parts.…”