Abstract
Recent drug discovery and development efforts have created a large arsenal of targeted and chemotherapeutic drugs for precision medicine. However, drug resistance remains a major challenge as minor pre-existing resistant subpopulations are often found to be enriched at relapse. Current drug design has been heavily focused on initial efficacy, and we do not fully understand the effects of drug selective pressure on long-term drug resistance potential. Using a minimal two-population model, taking into account subpopulation proportions and growth/kill rates, we modeled long-term drug treatment and performed parameter sweeps to analyze the effects of each parameter on therapeutic efficacy. We found that drugs with the same overall initial kill may exert differential selective pressures, affecting long-term therapeutic outcome. We validated our conclusions experimentally using a preclinical model of Burkitt’s lymphoma. Furthermore, we highlighted an intrinsic tradeoff between drug-imposed overall selective pressure and rate of adaptation. A principled approach in understanding the effects of distinct drug selective pressures on short-term and long-term tumor response enables better design of therapeutics that ultimately minimize relapse.
Highlights
Pressures on the individual subpopulations may lead to significantly different drug sensitivities in the long-term
We can assume a sufficient initial subpopulation size such that tumor dynamics can be modeled as deterministic processes, using ordinary differential equations (ODEs)
Using the experimentally determined growth rates and killing rates (Supplementary Table S2), we can further parameterize our mathematical model to visualize where our experimentally validated parameter set lies using the same schematic of differential growth versus differential kill that we have introduced earlier
Summary
We focused our analysis in the regime of large population size (e.g. at times of diagnosis), and assumed that the population is well-mixed and contains pre-existing resistant subpopulations. We observed that the region of high rate of adaptation (i.e. more negative rate of change in tumor sensitivity, colored blue in the heatmap in Fig. 3a) broadens with increased overall kill, along the differential growth rate axis. This suggests that when we increase the overall selective pressure on the tumor population, the rate of adaptation becomes less dependent on the differences in growth rates between subpopulations. While drugs with intrinsic asymmetric selective pressure on individual subpopulations can achieve effective initial response compared to symmetric regimens, they can amplify rate of adaptation and eventual resistance acquisition
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