Allometric scaling in oncology disease progression from xenograft tumor growth to human non-small-cell lung cancer
Jeroen Elassaiss-Schaap
DMPK, Merck Research Laboratories, Merck Sharpe Dome, Oss, Netherlands
Objectives: Derive an allometric conversion between published models on tumor volume of human cell lines in xenografts and on clinically observed tumor size growth in non-small lung cancer.
Y. Wang and colleagues reported in 2009 a disease progression model of tumor size in non-small-cell lung cancer (NSCLC) [1]. Their model was developed on a clinically relevant parameter, tumor size as the sum of longest dimensions as determined by CT scans. In contrast, the majority of preclinical experiments are performed on mouse xenograft implants with tumor growth in volume. A tumor volume growth inhibition model developed by Simeoni and Rochetti [2] is commonly applied in PK-PD analysis of newly discovered compounds. These two widely recognized approaches are however challenging to integrate. And yet inter-conversion is essential if one sets out to translate preclinical data in order to predict human efficacy of new compounds.
Methods: In this presentation it is shown how geometrical algebra was applied to inter-convert between clinical 1-dimensional total tumor length and preclinical 3-dimensional tumor growth. Rate constants not affected by the dimensional differences were translated by uninformed allometric conversion. Simulations were performed using the xenograft parameters for doxacetel, present in both papers, and checked against Wang-based predictions.
Results: The Wang model predicts a disease progression that is independent of treatment effect, i.e. the model classified the treatment as symptomatic. The Simeoni model on the other hand allows full tumor regression. The outcomes therefore can only be compared up to the point where growth in the Wang model overtakes the treatment effect, about 6 months of treatment in the doxacetel case. The resulting inhibition relative to placebo at 25 weeks is 55.6 % as predicted by the Wang model and 55.0% as predicted by the scaled Rochetti model.
Conclusions: A new approach for interspecies scaling of efficacy in oncology is presented using general allometric and geometric conversions. The prediction resulting from this scaling in conjunction with clinical PK information was in excellent agreement for published data on one particular compound, doxacetal. While this is a first sign on the applicability of this approach, analysis on multiple other compounds is warranted before wider application.
References:
[1] Wang Y., Sung C., Dartois C., Ramchandani R., Booth B.P., Rock E., and Gobburu J. (2009) Elucidation of relationship between tumor size and survival in non-small-cell lung cancer patients can aid early decision making in clinical drug development. Clin. Pharmacol. Ther. 86, 167-174.
[2] Rocchetti M., Simeoni M., Pesenti E., De Nicolao G., and Poggesi I. (2007) Predicting the active doses in humans from animal studies: a novel approach in oncology. Eur. J. Cancer 43, 1862-1868.
