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Editorials
have resolved, because the protocol called for restarting therapy if MMR was lost. Thus, to implement their recom- mendation in a subsequent clinical trial, clinicians would have to resist the current dogma to resume the higher dose or to switch to another TKI should the levels increase.
How then should the model’s enticing predictions be interpreted? Similar to in vitro models or animal models which aim to recapitulate key features of human disease, the primary role of mathematical models is to generate a hypothesis for a definitive test of concept studies in humans, rather than providing “proof” of the concept. Accordingly, mathematical model simulations of clinical tri- als can critically inform the design of clinical trials them- selves, but should never replace them. Indeed, the fact that Fassoni et al.’s model neglects several potential reasons for treatment failure in patients following dose reduction, such as de novo loss of immunological control or TKI resistance, highlights the point that the model’s novel hypotheses need to be tested.
Fassoni et al.’s simulations validate the concept that effec- tive TKI dose reduction is biologically plausible. Moreover, the output of the model provides useful information for TKI dose selection and sampling frequency in subsequent clini- cal trials. Model output suggests that the amount of TKI dose reduction may be individualized, based partly on observed BCR/ABL kinetics during the initial phase of ther- apy, which can then be used to derive two key parameters: 1) leukemic stem cell activation; and 2) TKI efficacy. Estimation of these parameters will require frequent BCR/ABL measurements during both primary and second phases of TKI therapy, an important consideration for trial design.
Most importantly, the modeling provides informed crite- ria for assessing treatment failure after dose reduction, and specifically recommends that investigators should not immediately classify increases in BCR-ABL transcripts as treatment failures. Rather, the model suggests that increases in BCR-ABL ratio could be permitted for as long as a year, at which point second phase decay would be expected to occur. Again, sampling following dose reduction must be frequent enough to allow model fitting that precisely char- acterizes this phenomenon. A third critical parameter, the LSC proliferation rate, can only be estimated following TKI dose reduction and could theoretically be leveraged to
make dose adjustments in real time. Finally, because the model predicts effect size, it could also theoretically be used to inform power calculations to project an adequate sample size for ‘proof of concept’ trials.
To understand the importance of these findings, one only need consider the situation in which a priori mathematical modeling is not performed, and TKI dose reduction is for- mally tested and labeled a failure based on what could have been only a temporary increase in BCR/ABL transcript lev- els. This would represent a spurious rejection of an impor- tant and valid scientific hypothesis, as well as a waste of resources. It is likely that these types of errors are not uncommon in clinical trial design, and that many could be predicted a priori with the use of strategic mathematical modeling.
References
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8. Fassoni AC, Baldow C, Roeder I, Glauche I. Reduced tyrosine kinase inhibitor dose is predicted to be as effective as standard dose in chronic myeloid leukemia: a simulation study based on phase III trial data. Hematologica. 2018 Jun 28. [Epub ahead of print]
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