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. 2022 Dec;39(12):3259-3265.
doi: 10.1007/s11095-022-03380-1. Epub 2022 Sep 2.

Embracing Project Optimus: Can we Leverage Evolutionary Theory to Optimize Dosing in Oncology?

Timothy Qi #  1 , Tyler Dunlap #  1 , Yanguang Cao  2   3
Affiliations

Embracing Project Optimus: Can we Leverage Evolutionary Theory to Optimize Dosing in Oncology?

Timothy Qi et al. Pharm Res. 2022 Dec.

Abstract

Project Optimus is a US Food and Drug Administration (FDA) initiative to reform dose selection in oncology drug development. Here, we focus on tumor evolution, a broadly observed phenomenon that invariably leads to therapeutic failure and disease relapse, and its effect on the exposure-response (E-R) relationships of oncology drugs. We propose a greater emphasis on tumor evolution during clinical development to facilitate the selection of optimal doses for molecularly targeted therapies and immunotherapies in oncology.

Keywords: oncology; precision medicine; tumor evolution.

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Figures

Figure 1.
Figure 1.. Impact of intratumoral heterogeneity on exposure-response relationships
A. Anti-tumor activity at 5 exposure levels in a homogenous population of sensitive cells. Left: Normalized tumor growth over 24 weeks of therapy with 4-week intervals between radiographic assessment of tumor volume. AUC, area under the curve. Horizontal dashed lines indicate thresholds for stable disease and progressive disease per RECIST v1.1 criteria. Center: Patient RECIST v1.1 classification over 6 months of treatment. SD, stable disease; PR, partial response; PD, progressive disease. Right: responses at the 12-week landmark time across 3 exposure levels. B. (A) for a heterogenous population of sensitive (95%) and resistant (5%) cells with independent growth rates. C. (A) for a heterogenous population of sensitive (95%) and resistant (5%) cells with a shared carrying capacity. D. (A) for a heterogenous population of sensitive (95%) and resistant (5%) cells with a shared carrying capacity and treatment-mediated conversion of sensitive cells to resistant cells. E. Impact of inter-patient variability in resistant cell growth rates on rebound kinetics and landmark-based exposure response relationships. Simulations were performed as in (C), spanning 3 levels of drug exposure and 5 levels of resistant cell regrowth. Responses at each exposure level were averaged at the 12-week landmark time (left).
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