Quantitative Translational Modeling to Facilitate Preclinical to Clinical Efficacy & Toxicity Translation in Oncology

Figures & data

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.

Table 1.  Commonly used pharmacokinetic/pharmacodynamics models to describe tumor growth kinetics.

Study (year)	Tumor growth term	Tumor kill term	Transit compartments	Comments	Ref.
Phase nonspecific models
Kogame et al. (2013)			0	This model was developed for a potent, selective hedgehog signaling pathway inhibitor. It was used to identify the degree of PD inhibition required to inhibit tumor growth.	[26]
Yamazaki (2008)			0	This model was developed to understand the PK/PD relationship of a small molecule cMet inhibitor. The model structure closely resembles an indirect response model.	[28]
Wong et al. (2009)			0	This model was developed to understand the PK/PD relationship of a B-Raf inhibitor. No transit compartments were used.	[30]
Salphati (2010)			0	This model was developed understand the PK/PD relationship of a PI3K inhibitor. This model used a hill coefficient on the effect term which is very unique.	[27]
Phase-specific models
Lobo et al. (2002)			3	One of the first PK/PD models utilizing transit compartment to describe the antitumor. It was initially developed for methotrexate.	[22]
Simeoni et al. (2004)			3	This is one of the most commonly used model structure for modeling preclinical antitumor activity. It was initially validated against a few cytotoxic agents, but the structure has been subsequently applied to many other types of compounds.	[23]
Bueno et al. (2008)		Effect = biomarker	2	It was developed for LY2157299, a new type 1 receptor TGF-β antagonist. Tumor growth inhibition was linked to PD biomarker level.	[24]
Jumbe et al. (2010)			2	This model was developed for trastuzumab-DM1 (an ADC) in order to determine the optimal dose and dosing schedule for antitumor activity.	[25]

ADC: Antibody–drug conjugate; PK/PD: Pharmacokinetic/pharmacodynamics.

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.

Table 2.  The key preclinical to clinical translational differences between cytotoxic antineoplastic agents and immuno-oncology agents.

Key translational differences	Cytotoxic agents	Immuno-oncology agents
Preclinical model	Xenograft	Syngeneic
Target	Tumor	Host immune system
Benchmark for clinical efficacy	At least 60% GRI	–^†
Efficacy response	Continuous and show dose response	Sometimes binary
Translation	Exposure-based translation	Biomarker-based translation/–^†
FIH dose	1/6 of HNSTD	MABEL

^†Unknown.

FIH: First in human; GRI: Growth rate inhibition; HNSTD: Highest nonseverely toxic dose; MABEL: Minimal anticipated biological effect levels.

Kogame A, Tagawa Y, Shibata S et al. Pharmacokinetic and pharmacodynamic modeling of hedgehog inhibitor TAK-441 for the inhibition of Gli1 messenger RNA expression and antitumor efficacy in xenografted tumor model mice. Drug Metab. Dispos. 41(4), 727–734 (2013).

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Simeoni M, Magni P, Cammia C et al. Predictive pharmacokinetic–pharmacodynamic modeling of tumor growth kinetics in xenograft models after administration of anticancer agents. Cancer Res. 64(3), 1094–1101 (2004).

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Bueno L, De Alwis DP, Pitou C et al. Semi-mechanistic modelling of the tumour growth inhibitory effects of LY2157299, a new type I receptor TGF-β kinase antagonist, in mice. Eur. J. Cancer 44(1), 142–150 (2008).

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Jumbe NL, Xin Y, Leipold DD et al. Modeling the efficacy of trastuzumab-DM1, an antibody drug conjugate, in mice. J. Pharmacokinet. Pharmacodyn. 37(3), 221–242 (2010).

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Wong H, Choo EF, Alicke B et al. Antitumor activity of targeted and cytotoxic agents in murine subcutaneous tumor models correlates with clinical response. Clin. Cancer Res. 18(14), 3846–3855 (2012).

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