A non-intrusive evaluation method for tumor-targeting characteristics of nanomedicines based on in vivo near-infrared fluorescence imaging

Abstract

We developed a novel evaluation method for tumor-targeting characteristics of nanomedicines, average tumor-targeting index (average TTI) and “area under the tumor-targeting index-time curve” (AUTC) were established as the indicators for tumor targeting of nanomedicines based on NIR fluorescence imaging, which helps real-time monitoring of targeting ability and tumor changes in vivo without culling animals.

Graphical Abstract

A novel fluorescence imaging-based method for non-intrusive evaluation of in vivo tumor targeting was established, in which average tumor-targeting index (average TTI) and “area under the tumor-targeting index-time curve” (AUTC) were established as the indicators for tumor targeting of nanomedicines based on NIR fluorescence imaging, which helps real-time monitoring of targeting ability and tumor changes in vivo without culling animals.

1. Introduction

Nanomedicines are widely used for tumor-targeted cancer therapy because of their excellent in vivo properties, such as good biocompatibility, passive and/or active tumor-targeting ability, as well as bioavailability enhancement. Verifying the tumor-targeting ability of nanomedicines usually depends on in vivo imaging techniques, such as magnetic resonance imaging, photoacoustic imaging, ultrasonic imaging, single-photon emission computed tomography, near-infrared (NIR) fluorescence imaging, and positron emission tomography. Some of these imaging techniques utilize radionuclide contrast agents for signal detection that present safety concerns, and/or do not produce clear in vivo images. Recently NIR fluorescence imaging has been noticed for its substantial sensitivity and resolution for in vivo studies.^1,2 This non-invasive method is a fast and efficient way to study the pharmacokinetics and to observe the biodistribution of nanocarriers for anticancer drugs. It has demonstrated excellent potential for clinical applications. To date, in vivo targeting abilities of all kinds of nanomedicines have been investigated by NIR fluorescence imaging.³

Although in vivo tumor-targeting ability is an important indicator in assessing targeted anticancer efficacy of nanomedicines, the development of suitable methods for quantitative analysis of in vivo fluorescence intensity has not been the focus of research. This is mainly because NIR fluorescence imaging results are different in various samples and measurement conditions, and thus cannot be directly compared. Additionally, real-time monitor of dynamic biodistribution (particularly in tumors) and targeting ability of nanomedicines is difficult to perform. At present, tumor-targeted characteristics or drug concentration in tumor at a certain point must be measured with tumor tissues of culled animals, which is time-consuming and expensive. Therefore, a novel method to evaluate tumor-targeting characteristics of nanomedicines based on in vivo NIR fluorescence imaging should be urgently developed. A novel method can provide an accurate assessment of the ability of a nanomedicine to specifically target tumors, as well as real-time monitoring of the biodistribution of nanomedicine in the body.

The tumor-targeting ability of a nanomedicine can be reflected by the percentage of drug amount in the tumor in relation to the amount of drug in the whole body, which is usually indicated by the drug concentration-time curve (AUC) in biopharmaceutical studies. We proposed the “area under the tumor-targeting index-time curve” (AUTC) as indicator for the tumor-targeting of nanomedicines. AUTC is calculated based on the fluorescence intensity obtained from in vivo fluorescence imaging, similarly with AUC in biopharmaceutical studies (Fig. 1), which demonstrates the overall effects of tumor-targeting. However, the calculation of AUTC is a complicated process. We further proposed the average tumor-targeting index (average TTI) as an alternative. NIR fluorescence imaging experiments using different animal tumor models were conducted to validate these novel parameters. The average TTI values were consistent with AUTC values, and drug formulations with high average TTI were more likely to exhibit good antitumor efficacy. The lack of real-time monitor-based guidance for nanomedicine design is still present. Given the variations in the experimental conditions and environment, comparing different nanomedicines quantitatively is difficult. By contrast, our proposed method for the quantification is based on the normalized fluorescence intensity, which offsets some of the variations among different experimental conditions. Therefore, the average TTI provides an important way for the design and optimization of novel nanomedicines for tumor-targeted cancer therapy. The use of average TTI can avoid culling of animals and can produce results with the same consistency as ex vivo imaging. This method provides a valuable reference for non-invasively speculating the antitumor effects of nanomedicines, thereby indicating its great potential for the translation of fluorescence imaging into clinical applications.

Fig.1.

Calculation of the tumor-targeting index (TTI), the area under the tumor-targeting index-time curve (AUTC), and average TTI. The AUTC and average TTI are derived from in vivo NIR fluorescence imaging, and are excellent indicators for tumor targeting ability of nanomedicines.

2. Results and discussion

2.1. Quantify evaluation methods for tumor-targeting characteristics of nanomedicines via in vivo NIR fluorescence imaging

In this study, all animal experiments were performed in compliance with the Guidelines for Use and Care of Animals in the Xi’an Jiaotong University and approved by the animal ethics committee of Xi’an Jiaotong University. DiR is one of the widely used dyes for fluorescence imaging.⁴ This fluorescent dye has excellent photo stability for repeated and long-term imaging in vivo, and has been reported not to significantly affect the viability and activity of cells in vitro. Therefore, DiR was chosen for the fluorescence imaging study. In this study, NIR fluorescence imaging was done using different nanoparticles in different tumor models to introduce the evaluation method for tumor-targeting characteristics of nanomedicines, which is widely used for tumor imaging. We have used this technique for many years, and have summarized the characters of in vivo NIR fluorescence tumor imaging,⁴ therefore we selected NIR fluorescence imaging for investigation of tumor targeting.

The preparation of different nanomedicines and the NIR fluorescence imaging process are as presented in the electronic supplementary information (ESI). PgSHA was a double-layered nanoparticle consisting of hyaluronic acid (HA) and stearic acid-grafted polyethyleneimine (PgS). In the aqueous environment, PgS as an amphiphilic polymer formed micelles with positive charges due to the primary amino groups of polyethyleneimine. PgSHA nanoparticles with an average particle size of around 130 nm were formed when PgS micelles were covered with negatively charged HA via electrostatic interactions. It should be noted that, tumor-targeted delivery of nanocarriers with about 100 nm diameters can occur through the enhanced permeability and retention (EPR) effect, which exploits the abnormalities of vasculature in solid tumors.^5,6 On the other hand, HA-based nanoparticles as potential carriers of anticancer drugs can specifically bind to various cancer cells that overexpress CD44, an HA receptor with high affinity for HA.⁷ CL-1 is a subpopulation of PC-3 that was developed in the lab of Dr. Xu in University of Kansas. PC-3 is a CD44⁺ prostate cancer cell line.^8,9 Like PC-3, CL-1 cells also express CD44, which is favorable for the active tumor-targeting of the PgSHA nanoparticles. Therefore, both the EPR effect and the incorporation of HA may have contributed to the tumor targeting capacity of PgSHA, although it is yet to be investigated which factor has contributed more. Therefore, PgSHA is a typical tumor targeting drug carrier.

PEG-EB₂ and PEG-VE₂ micelles were formed using PEG-EB₂ and PEG-VE₂, respectively. Since the particle size of both PEG-EB₂ and PEG-VE₂ micelles were around 40 nm on average, and the micelles were not incorporated with tumor-targeting components of any kind reported, the EPR effect was probably the main factor that has contributed to the tumor targeting capacity of both micelles, although HCT 116 used for the tumor model is a CD44⁺ colon cancer cell line.¹⁰ Moreover, the conjugates PEG-EB₂ and PEG-VE₂ are both derivatives of PEG and exhibit similar physicochemical characters, therefore PEG-EB₂ and PEG-VE₂ micelles were assumed to exhibit similar tumor-targeting abilities, and they are typical passive tumor targeting drug carriers.

CSaStHA was a double-layered nanoparticle consisting of HA and cationized stearic acid-grafted starch (CSaSt). In the aqueous environment, CSaSt as an amphiphilic polymer formed micelles with positive charges due to the ammonium of the cationized starch. CSaStHA nanoparticles with an average particle size of around 80 nm were formed when CSaSt micelles were covered with negatively charged HA via electrostatic interactions. Similar to PgSHA nanoparticles, both the EPR effect and the incorporation of HA may have contributed to the tumor targeting capacity of CSaStHA nanoparticles, therefore it is also a typical tumor targeting drug carrier.

Fig. 2A shows that considerable fluorescence signal was observed in some tumors of the mice injected with DiR-PgSHA at 6 h post-injection. The signal in these tumors remained strong for several days, demonstrating the considerable accumulation of DiR-PgSHA in these tumors. For the mice injected with free DiR, the fluorescence signal decreased quickly within the first 0.5 h post-injection due to rapid clearance and non tumor-targeting of free DiR. Fig. 2B shows the quantified fluorescent intensity of tumor regions in each group (n=4) at different time post-injection. Fluorescence intensity of tumor regions in the DiR-PgSHA group was significantly stronger than in the free DiR group at 6 h post-injection (***P < 0.001). The comparison is based on the absolute fluorescent intensity of the tumor regions. However, a strong fluorescent intensity in the tumor can be caused not only by good tumor-targeting of nanomedicines but also by a strong fluorescent intensity in the whole body. For this reason, the TTI-based quantification was proposed to evaluate the tumor-targeting ability of nanomedicines more accurately. TTI is the percentage of the tumor region fluorescence intensity with relation to the whole-body fluorescence intensity, which was calculated using the following formula:

$$\text{TTI}\left(\%\right)=\frac{I_{tumor}}{I_{whole body}}\times 100\%$$ (1)

where I_tumor and I_{whole body} are the absolute fluorescence intensities of each tumor region, and of the whole body of the same mouse, respectively. The effects of different measurement can be counteracted due to the relative ratio of TTI value.

Fig.2.

In vivo imaging of CL-1 tumor-bearing SCID mice. A) In vivo NIR fluorescence imaging of mice injected with free DiR or DiR-loaded PgSHAs (DiR-PgSHA) at different time post-injection. The loading quantity of DiR in DiR-PgSHA was 1.2 nmol mg⁻¹. Each mouse was i.v. injected DiR-PgSHA loading 10 nmol DiR or ethanol/PBS (1:4, v/v) containing 10 nmol DiR. Tumor regions are indicated by white arrows. B) DiR fluorescence intensity curve of tumor regions. Data are shown as mean ± SD (n = 4), ***P < 0.001 at all time points after 6 h, as compared with free DiR group. C) Tumor-targeting index (TTI) curve of different formulations. Data are shown as mean ± SD (n = 4), *P < 0.05 at 24 h, as compared with free DiR group.

The calculation of TTI at certain time points presents the tumor accumulation of a formulation as function of time, which works similarly with the drug concentration as function of time. This calculation can help real-time monitoring of tumor accumulation of the drug in the same formulation, thereby assisting the determination of dose schedule. Based on TTI values, AUTC is the area under TTI-time curve, which demonstrates the overall effect of tumor-targeting. The AUTC is calculated as follows:

$${\text{AUTC}}_{0\to t_n}={\int }_0^{t_n}TTIdt\approx {\sum }_{i=1}^n\frac{\left(TT{I}_i+TT{I}_{i-1}\right)}{2}\left(t_i-t_{i-1}\right)$$ (2)

where t_n is the time of the final imaging after injection, n is the total number of times of imaging, and TTI_i is the TTI value at each time point. Theoretically, larger n and t_n values bring the calculated AUTC closer to reality. For multiple dosing, the t_n must be equivalent to or larger than the time interval of drug administration.

AUTC is similar to the traditional concept of the AUC in biopharmaceutical studies and may predict the overall antitumor effects of drug-loaded nanocarriers. However, given that the calculation of AUTC is a complicated process, we further raised the average TTI as an alternative. The average TTI is the mean of TTI values obtained at different time points:

$$\text{Average TTI}=\frac{{\sum }_{i=1}^{n}TT{I}_i}{n}$$ (3)

where n is the total number of times of imaging and TTI_i is the TTI value at each time point.

As shown in Fig. 2C, although the TTI values in the DiR-PgSHA group were higher than those in the free DiR group, only the one at 24 h post-injection was significantly higher (*P < 0.05), thereby indicating that the tumor-targeting effect of PgSHA nanoparticle reached climax at 24 h post-injection. This result can be a helpful reference for the determination of administration frequency of drug-loaded PgSHA nanoparticle. As presented in Table 1, the AUTCs of DiR-PgSHA and free DiR were 691.27 h% and 389.05 h%, respectively. Moreover, the average TTI of DiR-PgSHA and free DiR were 6.73 and 3.91, respectively. The ratio of AUTC of the two groups was 1.78, which was very close to the ratio of their average TTI values (i.e., 1.72) which indicated that average TTI can be a useful parameter for the comparison of overall targeting effects as AUTC. It is also noteworthy that, PgS has notable cytotoxicity due to the positive charge of the PEI portion that led to the intensive interaction with cell membranes.^11,12 The i.v. injected PgS micelles can result in severe weakness or even death of the animals. PgSHA nanoparticles were formed by covering the positively charged PgS micelles with negatively charged hyaluronic acid via electrostatic interactions. With the biocompatible hyaluronic acid and the negative charges on the surface, PgSHA nanoparticles have shown better biocompatibility towards normal cells in vitro and have not shown notable toxicity towards normal tissues in vivo at the investigated dose levels.¹¹ Therefore in this study, the DiR-loaded PgSHA nanoparticle group was not compared with the DiR-loaded PgS micelle group.

Table 1.

AUTC, average of TTIs, and IR of different formulations.

Formulations	AUTC (h%)	Average TTI(%)	IR(%)	AUTC ratio	Average TTI ratio	IR ratio	Drugs	Reference
PgSHA NPs	691.27	6.73	56.36	1.78	1.72	1.78	(−)G	Ref. 11
Free dye/drug	389.05	3.91	31.73
PEG5k-Fmoc-VE2 micelles	702.25	7.72	71.11	2.68	2.67	1.68	DOX	Ref. 20
Free dye/drug	261.89	2.89	42.22
MLDC NCs	618.41	5.97	86.59	1.66	1.53	1.78	ApoG2/DOX	Ref. 21
Free dye/drug	372.19	3.89	48.78

A comparison may occur between different nanomedicines for choice. PEG-EB₂ and PEG-VE₂ micelles loaded with 10 nmol DiR were injected via the tail vein into female mice bearing HCT 116 colon cancer xenografts. The mice were imaged at 0.5, 6, 24, 48, 72, and 96 h post-injection (Fig. S4A). The DiR signal intensity in the tumor regions is maximal in both mice at 24 h. The PEG-EB₂ micelle showed a stronger DiR signal in the tumor regions compared with PEG-VE₂ micelle, which was demonstrated by both the in vivo images and the fluorescent intensity quantifications (Fig. S4B). According to the TTI values in Fig. S4C, the PEG-EB₂ micelle demonstrated a greater tumor-targeting ability than PEG-VE₂ throughout the investigation period. Therefore, PEG-EB₂ micelle exhibited better tumor-targeting ability than PEG-VE₂ micelle in general. This result somewhat disagreed with the assumption that PEG-EB₂ and PEG-VE₂ micelles may exhibit similar tumor-targeting abilities. It should be noted that, the PEG-vitamin E conjugates have dramatic P-gp inhibition activity, whereas the PEG-embelin conjugates showed comparable antitumor activity to free embelin in vitro.^13,14 Therefore, vitamin E and embelin may have also affected the tumor targeting capacity of the micelles, and it is yet to be investigated why PEG-EB₂ micelle performed better tumor targeting ability.

Imaging experiments can also be designed to measure tumor vasculature penetration of nanomedicines based on tumors with different volumes. To avoid individual differences during imaging, tumors in one animal were designed to possess different sizes or other biological characteristics.^15,16 Variations among different tumors in one single mouse are quiet common for tumor growth, because different tumors are grown from different amounts of tumor cells and are in diverse biological environments within the body. Therefore, finding that the fluorescence in one tumor is stronger than that in another tumor on the same mouse is not unusual. By taking advantage of the variations in this study, we wanted to determine the magnitude of difference between two tumors in one mouse. Thus, we especially prepared some mice bearing two MDA-MB-231 tumors with evidently different sizes, by injecting the cells at different times. MDA-MB-231 is a CD44⁺ breast cancer cell line.^17,18 The small tumor was approximately 35 mm³. The big tumor was approximately 200 mm³. DiR-encapsulated CSaStHA nanoparticles were then injected in one of the mice for in vivo imaging. The images of the mice and the quantifications are shown in Fig. S5. The fluorescence in the large tumor was much stronger than in the small tumor. The TTI of the DiR-loaded nanocarriers differed evidently in different tumors of the same animal. This difference was probably due to immature vasculature in small tumor, making it difficult for sub-100 nm nanocarriers to enter the tumor region.

2.2. Quantify evaluation methods for tumor-targeting characteristics of nanomedicines via ex vivo NIR fluorescence imaging

At present, the planar (2-dimensional) fluorescence imaging is still more commonly used than 3-dimensional NIR fluorescence imaging. In clinical practices however, in vivo tumor imaging must be more practical than excised tumor imaging, especially for diagnostic purpose. For planar fluorescence imaging, the in vivo quantification methods are only suitable for subcutaneous tumors and shallow organs/tissues in the body because the fluorescence originating from deep organs/tissues in the body attenuate a lot when it reaches the camera. This phenomenon is why ex vivo imaging is still needed for more comprehensive observation. To further verify the results of NIR fluorescence imaging of nanomedicines in vivo, we performed ex vivo imaging to investigate the biodistribution of nanomedicines in organs and tumors more clearly, thereby avoiding the masking effects of the skin and hair on fluorescence. We excised and measured fluorescent intensity of the tumor, liver, spleen, lung, kidney, heart, and front leg muscle. Nevertheless, for an increasingly comprehensive investigation, the skin, bone, intestine, bladder, brain, and any other organs of interests may also be measured for fluorescent intensity, depending on the type of nanomedicine and the targeted tissue. DiR signal from each tissue was overlaid on the shadow image of the same tissue. Fig. 3A shows that the tumor fluorescence in DiR-PgSHA group is stronger than that in the free DiR group, consistent with the in vivo imaging results. Strong fluorescence was also observed in livers of DiR-PgSHA group due to the effect of HA receptor expressed in the liver. By contrast, the lungs in the free DiR group showed stronger fluorescence signal compared with DiR-PgSHA group, which can hardly be found through the in vivo imaging, because this organ was far from the camera when the mice were placed in their abdominal position during the imaging.

Fig.3.

Ex vivo imaging of tumors and organs. A) Ex vivo NIR fluorescence imaging of tumors and organs from each mouse at 96 h after injection of free DiR or DiR-loaded PgSHAs (DiR-PgSHA). B) The absolute DiR signal intensity of tumors and organs from each mouse. C) Ratios of DiR signal intensity of tumors to liver, spleen and lung. D) DiR %ID/g of tissues of each mouse. To obtain the DiR %ID/g of each tissue, ROI was set as part of each tissue on the images. The weight of each part of tissue was then measured right after the imaging. The %ID/g of each tissue was then calculated according to the standard curve of each formulation.

The absolute fluorescence intensity of each tumor and excised organ was quantified, as shown in Fig. 3B, which demonstrated more accumulation of DiR-PgSHA in the tumor and liver and less accumulation in the lung and spleen, as compared with free DiR. Quantification of the fluorescence intensity can provide an intuitive comparison among different tissues. However, the quantification of absolute intensity is insufficient for cross-mouse comparison, because when the fluorescence intensity is strong in the tumor of a mouse, it may also be strong in other organs, e.g., the liver. To perform cross-mouse comparison of the tumor-targeting ability, we used the ratio of fluorescence intensity of tumor to particular organs, such as liver, spleen, and lung. The ratios of ex vivo fluorescence intensity of tumors to that of liver, spleen, or lung were calculated using the following formula:

$$\text{Intensity ratio}=\frac{I_{ROI}}{I_x}$$ (4)

where I_ROI and I_x are the absolute fluorescence intensities of each tumor and of the liver, spleen, or lung in the same mouse, respectively. For DiR-PgSHA, the ratios of tumor fluorescence intensity of the liver, spleen, and lung were all significantly higher as compared with free DiR group, thereby indicating an improved tumor-specificity of DiR-PgSHA (Fig. 3C). Since fluorescence intensity reflects the biodistribution of the dye-labeled nanocarriers, a high ratio indicates a better accumulation of nanocarriers within the tumor rather than in normal organs. These results went well with the in vivo evaluation results as described above.

Another important ex vivo parameter to compare the biodistribution of nanocarriers is the fluorescence percentage of injected dose per gram (%ID/g) of tissue. Quantification in %ID/g is commonly used for radiolabeled probes and is less accurate for NIR fluorescent probes due to the quenching of fluorescence and scattering of photons by tissue components.¹⁹ However, the quantification in %ID/g can be a semiquantitative method that provides deep insights into the in vivo fate of a formulation, as it avoids the interference of tissue weight. The %ID/g values for DiR-PgSHA and free DiR groups are shown in Fig. 3D. The %ID/g values of spleen were relatively high for both groups, whereas the %ID/g values of liver were relatively low. These results were inconsistent with the quantification of fluorescence intensity (Fig. 3B), which indicated that the formulations also resulted in considerable accumulation of DiR in the spleen other than the liver and lung.

As with all the previous approach, the ex vivo imaging of HCT 116 tumor-bearing mice injected with DiR-encapsulated PEG-EB₂ and PEG-VE₂ micelles was also conducted. Fig. S6 shows that the fluorescence in PEG-EB₂-delivered tumors is stronger than in PEG-VE₂-delivered tumors, which was consistent with the in vivo imaging results. In other tissues, however, the fluorescent intensity of both micelles appears comparable and generally much lower than the intensity measured in the tumors. Consistent with previously presented in vivo and ex vivo results, quantifications using %ID/g also demonstrated that the tumor-targeting ability of PEG-EB₂ micelle was greater than that of PEG-VE₂ micelle (Fig. S9).

2.3. In vivo tumor inhibition of (−)-G-PgSHA

To finally verify the quantitative evaluation methods for tumor-targeting characteristics of nanomedicines, we took the in vivo tumor inhibition of (−)-G-PgSHA as an example. Fig. S10A shows the PBS control group exhibited rapid tumor growth as a function of time, whereas the free (−)-gossypol at the dose of 10 mg/kg exhibited a modest effect in inhibiting the growth of tumor, with a tumor growth IR of 31.73%. By contrast, (−)-G-PgSHA at the same dosage was significantly more effective in the tumor growth inhibition (P < 0.001, compared with both PBS and (−)-gossypol group after day 22), with the tumor growth IR of 56.36%. The pathological section images of the tumor tissues from each group are shown in Fig. S10B. The tumor tissue from PBS control group was generally intact, whereas those in the free (−)-gossypol and (−)-G-PgSHA groups exhibited different degrees of necrosis. The least intact tumor cells were observed in the (−)-G-PgSHA group, thereby indicating the enhanced antitumor effect of (−)-G-PgSHA. The results were consistent with the targeted delivery of PgSHA nanoparticle as demonstrated by NIR fluorescence imaging. The IR, AUTC, and average TTI values of DiR-PgSHA and free DiR groups are summarized in Table 1. The ratios of each parameter of the two groups were very close to each other, which suggested that both the AUTC and average TTI values can be good indicators for the prediction of the antitumor efficacy of drug-loaded nanomedicines. These results showed that our quantitative evaluation method for nanomedicines is a realistic evaluation tool to assist determining tumor-targeting characteristics.

We also calculated the IR, AUTC, and average TTI value for two other nanomedicines, as reported elsewhere,^20,21 and compared them with those of the solvent-based formulations (i.e., free dye/drug) used in the same studies. The calculated results are summarized in Table 1. The ratios of the IR, AUTC, and average TTI value of a nanomedicine and its corresponding solvent-based formulation were similar to each other. These result means that the IR, AUTC, and average TTI value are positively related, namely tumors with high AUTC and average TTI during in vivo imaging are likely to exhibit a strong response to anticancer drugs that are in the same formulation. The average TTI values were consistent with AUTC values, and drug formulations with high average TTI or AUTC were more likely to result in good antitumor efficacy. For the moment, there is still lack of real-time monitor-based guidance for nanomedicine design. Comparing different nanomedicines quantitatively is difficult, due to unavoidable variations in the experimental conditions, such as the individual differences among different mice. Our proposed method for the quantification is based on the normalized fluorescence intensity, which offsets some of the variations among different experimental conditions. Therefore, TTI and AUTC values can assist with non-invasive prediction of the antitumor effects of drug-loaded nanomedicines and can help determine whether or not a drug-loaded formulation is proper for a patient with certain kind of solid tumor. The results from this study indicated that this method is practicable for a variety of nanomedicines and tumor models. Moreover, given that TTI and AUTC can be obtained without culling animals, they can hopefully help developing in vivo fluorescence imaging-guided cancer nanomedicine for clinical applications.

Conclusions

A novel evaluation method to quantify tumor-targeting characteristics of nanomedicines was developed via in vivo and ex vivo NIR fluorescence imaging. Average TTI and AUTC were used as in vivo indicators for tumor-targeting characteristics of nanomedicines. These indicators well represent the tumor-targeting ability, which can help predicting the in vivo tumor inhibition of nanomedicines. This non-intrusive method is useful for not only the optimization of nanomedicines, but also real-time monitoring of the targeting ability and tumor changes in vivo without culling the animal, indicating its great potential for clinical application.