Quantitative translational modeling to facilitate preclinical to clinical efficacy & toxicity translation in oncology

Abstract

Significant scientific advances in biomedical research have expanded our knowledge of the molecular basis of carcinogenesis, mechanisms of cancer growth, and the importance of the cancer immunity cycle. However, despite scientific advances in the understanding of cancer biology, the success rate of oncology drug development remains the lowest among all therapeutic areas. In this review, some of the key translational drug development objectives in oncology will be outlined. The literature evidence of how mathematical modeling could be used to build a unifying framework to answer these questions will be summarized with recommendations on the strategies for building such a mathematical framework to facilitate the prediction of clinical efficacy and toxicity of investigational antineoplastic agents. Together, the literature evidence suggests that a rigorous and unifying preclinical to clinical translational framework based on mathematical models is extremely valuable for making go/no-go decisions in preclinical development, and for planning early clinical studies.

Keywords: GRI; PK/PD; QSP; cancer growth modeling; drug combination; myelosuppression; pharmacokinetics; toxicity; translational; xenograft.

Figure 1.. Commonly used antitumor efficacy metrics.

Top panel: T/C ratio. Middle panel: TGI. Bottom panel: GRI. T/C overestimates the antitumor activity of the fast-growing tumors compared with the slow-growing tumors, which significantly limits its value for predicting clinical efficacy. TGI is generally less dependent on the tumor growth rate than T/C. GRI, which is calculated by fitting all available tumor volume data first to an exponential growth function, is the least dependent on the intrinsic growth rate of the tumor. GRI: Growth rate inhibition; T/C: Tumor volume over control volume; TGI: Tumor growth inhibition.

Figure 2.. Correlation between tumor growth inhibition and growth rate inhibition for xenograft tumors of different growth rates.

Tumor growth inhibition and growth rate inhibition show good correlations for slow-growing xenograft tumors. For fast-growing xenograft tumors, growth rate inhibition has a much more dynamic range compared with the tumor growth inhibition, which saturates at around 100%. GRI: Growth rate inhibition; TGI: Tumor growth inhibition.

Figure 3.. Common translational modeling approach for predicting the efficacy potential of an investigational antineoplastic agent.

Firstly, a pharmacokinetic mathematical model needs to be constructed as a foundation, based on pharmacokinetic measurements in mice. Secondly, xenograft efficacy studies are used to establish an exposure–response relationship. Thirdly, the xenograft exposure–response relationship is translated into humans based on human data. Lastly, a translational exposure-tolerability model based on animal and human toxicity data is used to predict whether the drug would have a meaningful tumor regression in humans at a tolerable dose. PK: Pharmacokinetic.

Figure 4.. Antitumor activity in xenograft correlates with clinical response.

The data were collected from the literature and percentage growth rate inhibition was calculated using digitalized data [12]. Antitumor activity in xenograft correlates with clinical response for a spectrum of molecularly targeted and cytotoxic agents at clinical maximum tolerable exposure. GRI: Growth rate inhibition; RCC: Renal cell carcinoma.