Background:Absent adaptive, individualized dose-finding in early-phase oncology trials, subsequent registration trials risk suboptimal dosing that compromises statistical power and lowers the probability of technical success (PTS) for the investigational drug. While much methodological progress has been made toward adaptive dose-finding, and quantitative modeling of dose-response relationships, most such work continues to be organized around a concept of ‘the’ maximum tolerated dose (MTD). But a new methodology, Dose Titration Algorithm Tuning (DTAT), now holds forth the promise of individualized ‘MTDi’ dosing. Relative to such individualized dosing, current ‘one-size-fits-all’ dosing practices amount to a constraint that imposes costs on society. This paper estimates the magnitude of these costs.Methods:Simulated dose titration as in (Norris 2017) is extended to 1000 subjects, yielding an empirical MTDi distribution to which a gamma density is fitted. Individual-level efficacy, in terms of the probability of achieving remission, is assumed to be an Emax-type function of dose relative to MTDi, scaled (arbitrarily) to identify MTDi with the LD50 of the individual’s tumor. (Thus, a criterion 50% of the population achieve remission under individualized dosing in this analysis.) Current practice is modeled such that all patients receive a first-cycle dose at ‘the’ MTD, and those for whom MTDi < MTDthe experience a ‘dose-limiting toxicity’ (DLT) that aborts subsequent cycles. Therapy thus terminated is assumed to confer no benefit. Individuals for whom MTDi≥ MTDthe tolerate a full treatment course, and achieve remission with probability determined by the Emax curve evaluated at MTDthe/MTDi. A closed-form expression is obtained for the population remission rate, and maximized numerically over MTDthe as a free parameter, thus identifying the best result achievable under one-size-fits-all dosing. A sensitivity analysis is performed, using both a perturbation of the assumed Emax function, and an antipodal alternative specification.Results:Simulated MTDi follow a gamma distribution with shape parameter α ≈ 1.75. The population remission rate under one-size-fits-all dosing at the maximizing value of MTDthe proves to be a function of the shape parameter—and thus the coefficient of variation (CV)—of the gamma distribution of MTDi. Within a plausible range of CV(MTDi), one-size-fits-all dosing wastes approximately half of the drug’s population-level efficacy. In the sensitivity analysis, sensitivity to the perturbation proves to be of second order. The alternative exposure-efficacy specification likewise leaves all results intact.Conclusions:The CV of MTDi determines the efficacy lost under one-size-fits-all dosing at ‘the’ MTD. Within plausible ranges for this CV, failure to individualize dosing can effectively halve a drug’s value to society. In a competitive environment dominated by regulatory hurdles, this may reduce the value of shareholders’ investment in the drug to zero.Epilogue:The main result on one-size-fits-all dosing is generalized to regimens with several dose levels. Implications for the ongoing ALTA-1L trial are briefly explored; the 2 dose levels in the brigatinib arm of this trial may lend it a competitive advantage over the single-dose crizotinib arm.
Costing ‘the’ MTD
