Search

Comparison of the tumor growth curves of single-drug PTX and two-drugs DDP/PTX treatments with the expected curve for the DDP/PTX combination

The margin for optimizing polychemotherapy is wide, but a quantitative comparison of current and new protocols is rare even in preclinical settings. In silico reconstruction of the proliferation process and the main perturbations induced by treatment provides insight into the complexity of drug response and grounds for a more objective rationale to treatment schemes. We analyzed 12 treatment groups in trial on an ovarian cancer xenograft, reproducing current therapeutic options for this cancer including one-, two-, and three-drug schemes of cisplatin (DDP), bevacizumab (BEV), and paclitaxel (PTX) with conventional and two levels (“equi” and “high”) of dose-dense schedules. All individual tumor growth curves were decoded via separate measurements of cell death and other antiproliferative effects, gaining fresh insight into the differences between treatment options. Single drug treatments were cytostatic, but only DDP and PTX were also cytotoxic. After treatment, regrowth stabilized with increased propensity to quiescence, particularly with BEV. More cells were killed by PTX dose-dense-equi than with PTX conventional, but with the addition of DDP, cytotoxicity was similar and considerably less than expected from that of individual drugs. In the DDP/PTX dose-dense-high scheme, both cell death and regrowth impairment were intensified enough to achieve complete remission, and addition of BEV increased cell death in all schemes. The results support the option for dose-dense PTX chemotherapy with active single doses, showing the relative additional contribution of BEV, but also indicate negative drug interactions in concomitant DDP/PTX treatments, suggesting that sequential schedules could improve antitumor efficacy. Cancer Res; 77(23); 6759–69. ©2017 AACR.

A. Cell-cycle based model of proliferation - untreated conditions. B. Modeling proliferation during/after treatment. C. Optimization and software. - References

Background Drug mass spectrometry imaging (MSI) data contain knowledge about drug and several other molecular ions present in a biological sample. However, a proper approach to fully explore the potential of such type of data is still missing. Therefore, a computational pipeline that combines different spatial and non-spatial methods is proposed to link the observed drug distribution profile with tumor heterogeneity in solid tumor. Our data analysis steps include pre-processing of MSI data, cluster analysis, drug local indicators of spatial association (LISA) map, and ions selection. Results The number of clusters identified from different tumor tissues. The spatial homogeneity of the individual cluster was measured using a modified version of our drug homogeneity method. The clustered image and drug LISA map were simultaneously analyzed to link identified clusters with observed drug distribution profile. Finally, ions selection was performed using the spatially aware method. Conclusions In this paper, we have shown an approach to correlate the drug distribution with spatial heterogeneity in untargeted MSI data. Our approach is freely available in an R package 'CorrDrugTumorMSI'.

The efficacy of therapeutic regimens incorporating weekly or every-3-weeks paclitaxel (PTX) for ovarian cancer is debated. We investigated the addition of bevacizumab in regimens of chemotherapy with different PTX doses and schedules in preclinical models. Treatments were cisplatin (DDP) with weekly PTX (conventional), or dose-dense-equi (every other day to the conventional cumulative dose), or dose-dense-high (total dose 1.5 times higher), with or without bevacizumab. Treatment efficacy was evaluated analyzing tumor growth in different time-windows in two patient-derived ovarian cancer xenografts with different sensitivity to cisplatin. Tumor progression, metastasis and survival were studied in ovarian cancer models growing orthotopically and disseminating in the mouse peritoneal cavity. Short-term effects on cell cycle, tumor cell proliferation/apoptosis and vasculature were evaluated by flow cytometry and immunohistochemistry. PTX dose-dense (with/without DDP) was superior to the conventional scheme in a dose-dependent manner; the high efficacy was confirmed by the lower ratio of tumor to normal cells. All schemes benefited from bevacizumab, which reduced tumor vessels. However, DDP/PTX dose-dense-high (only chemotherapy) was at least as active as DDP/PTX conventional plus bevacizumab. DDP/PTX dose-dense-high plus bevacizumab was the most effective in delaying tumor progression, though it did not prolong mouse survival and the continuous treatment with bevacizumab was associated with a malignant disease. These findings indicate that the effect of bevacizumab in combination with chemotherapy may depend on the schedule-dose of the treatment and help to explain the unclear benefits after bevacizumab.

The margin for optimizing polychemotherapy is wide, but a quantitative comparison of current and new protocols is rare even in preclinical settings. In silico reconstruction of the proliferation process and the main perturbations induced by treatment provides insight into the complexity of drug response and grounds for a more objective rationale to treatment schemes. We analyzed 12 treatment groups in trial on an ovarian cancer xenograft, reproducing current therapeutic options for this cancer including one-, two-, and three-drug schemes of cisplatin (DDP), bevacizumab (BEV), and paclitaxel (PTX) with conventional and two levels (“equi” and “high”) of dose-dense schedules. All individual tumor growth curves were decoded via separate measurements of cell death and other antiproliferative effects, gaining fresh insight into the differences between treatment options. Single drug treatments were cytostatic, but only DDP and PTX were also cytotoxic. After treatment, regrowth stabilized with increased propensity to quiescence, particularly with BEV. More cells were killed by PTX dose-dense-equi than with PTX conventional, but with the addition of DDP, cytotoxicity was similar and considerably less than expected from that of individual drugs. In the DDP/PTX dose-dense-high scheme, both cell death and regrowth impairment were intensified enough to achieve complete remission, and addition of BEV increased cell death in all schemes. The results support the option for dose-dense PTX chemotherapy with active single doses, showing the relative additional contribution of BEV, but also indicate negative drug interactions in concomitant DDP/PTX treatments, suggesting that sequential schedules could improve antitumor efficacy. Cancer Res; 77(23); 6759–69. ©2017 AACR.

Mass spectrometry imaging (MSI) allows visualization of endogenous and exogenous compound in tissue sections based on its molecular mass. The 3D reconstruction by MSI provides a more informative description of the tumor drug distribution compared to the high-performance liquid chromatography method, highlighting the heterogeneity of intratumor drug concentration. This additional information can be important in understanding chemoresistance to target agents. Here, we present the 3D visualization of the tyrosine kinase inhibitor (TKI), imatinib, in a xenograft model of resistant malignant pleural mesothelioma.

Mass spectrometry imaging is a valuable tool for visualizing the localization of drugs in tissues, a critical issue especially in cancer pharmacology where treatment failure may depend on poor drug distribution within the tumours. Proper preprocessing procedures are mandatory to obtain quantitative data of drug distribution in tumours, even at low intensity, through reliable ion peak identification and integration. We propose a simple preprocessing and quantification pipeline. This pipeline was designed starting from classical peak integration methods, developed when “microcomputers” became available for chromatography, now applied to MSI. This pre-processing approach is based on a novel method using the fixed mass difference between the analyte and its 5 d derivatives to set up a mass range gate. We demonstrate the use of this pipeline for the evaluating the distribution of the anticancer drug paclitaxel in tumour sections. The procedure takes advantage of a simple peak analysis and allows to quantify the drug concentration in each pixel with a limit of detection below 0.1 pmol mm-2 or 10 μg g−1. Quantitative images of paclitaxel distribution in different tumour models were obtained and average paclitaxel concentrations were compared with HPLC measures in the same specimens, showing github.com/FrancescaFalcetta/Imaging_of_drugs_distribution_and_quantifications.git .

Enhancing drug penetration in solid tumors is an interesting clinical issue of considerable importance. In preclinical research, mass-spectrometry imaging is a promising technique for visualizing drug distribution in tumors under different treatment conditions and its application in this field is rapidly increasing. However, in view of the huge variability among MSI data sets, drug homogeneity is usually manually assessed by an expert, and this approach is biased by interobserver variability and lacks reproducibility. We propose a new texture-based feature, the drug-homogeneity index (DHI), which provides an objective, automated measure of drug homogeneity in MSI data. A simulation study on synthetic data sets showed that previously known texture features do not give an accurate picture of intratumor drug-distribution patterns and are easily influenced by the tumor-tissue morphology. The DHI has been used to study the distribution profile of the anticancer drug paclitaxel in various xenograft models, which were either pretreated or not pretreated with antiangiogenesis compounds. The conclusion is that drug homogeneity is better in the pretreated condition, which is in agreement with previous experimental findings published by our group. This study shows that DHI could be useful in preclinical studies as a new parameter for the evaluation of protocols for better drug penetration.

The early metabolic signatures associated with the progression of septic shock and with responsiveness to therapy can be useful for developing target therapy. The Sequential Organ Failure Assessment (SOFA) score is used for stratifying risk and predicting mortality. This study aimed to verify whether different responses to therapy, assessed as changes in SOFA score at admission (T1, acute phase) and 48 h later (T2, post-resuscitation), are associated with different metabolite patterns. We examined the plasma metabolome of 21 septic shock patients (pts) enrolled in the Shockomics clinical trial (NCT02141607). Patients for which SOFAT2 was >8 and Δ = SOFAT1 − SOFAT2

Background: Epithelial ovarian cancer is the most lethal gynecological cancer and the high mortality is due to the frequent presentation at advanced stage, and to primary or acquired resistance to platinum-based therapy. Methods: We developed three new models of ovarian cancer patient-derived xenografts (ovarian PDXs) resistant to cisplatin (cDDP) after multiple in vivo drug treatments. By different and complementary approaches based on integrated metabolomics (both targeted and untargeted mass spectrometry-based techniques), gene expression, and functional assays (Seahorse technology) we analyzed and compared the tumor metabolic profile in each sensitive and their corresponding cDDP-resistant PDXs. Results: We found that cDDP-sensitive and -resistant PDXs have a different metabolic asset. In particular, we found, through metabolomic and gene expression approaches, that glycolysis, tricarboxylic acid cycle and urea cycle pathways were deregulated in resistant versus sensitive PDXs. In addition, we observed that oxygen consumption rate and mitochondrial respiration were higher in resistant PDXs than in sensitive PDXs under acute stress conditions. An increased oxidative phosphorylation in cDDP-resistant sublines led us to hypothesize that its interference could be of therapeutic value. Indeed, in vivo treatment of metformin and cDDP was able to partially reverse platinum resistance. Conclusions: Our data strongly reinforce the idea that the development of acquired cDDP resistance in ovarian cancer can bring about a rewiring of tumor metabolism, and that this might be exploited therapeutically.

Small interfering RNA (siRNA) is receiving increasing attention with regard to the treatment of many genetic diseases, both acquired and hereditary, such as cancer and diabetes. Being a high molecular weight (MW) polyanion, siRNA is not able to cross a cell membrane, and in addition it is unstable in physiological conditions. Accordingly, a biocompatible nanocarrier able to deliver siRNA into cells is needed. In this work, we synthesized biocompatible positively charged nanoparticles (NPs) following a two-step process that involves ring opening polymerization (ROP) and emulsion free radical polymerization (EFRP). Firstly, we proved the possibility of fine tuning the NPs' characteristics (e.g. size and surface charge) by changing the synthetic process parameters. Then the capability in loading and delivering undamaged siRNA into a cancer cell cytoplasm has been shown. This latter process occurs through the biodegradation of the polymer constituting the NPs, whose kinetics can be tuned by adjusting the polymer's MW. Finally, the ability of NPs to carry siRNA inside the cells in order to inhibit their target gene has been demonstrated using green flourescent protein positive cells.

The combination of erlotinib with gemcitabine is one of the most promising therapies for advanced pancreatic cancer. Aiming at optimizing this combination, we analyzed in detail the response to sequential treatments with erlotinib → gemcitabine and gemcitabine → erlotinib with an 18 h interval, adopting a previously established experimental/computational approach to quantify the cytostatic and cytotoxic effects at G1, S and G2M checkpoints. This assessment was achieved by contemporary fits of flow cytometric and time-lapse experiments in two human pancreatic cancer cell lines (BxPC-3 and Capan-1) with a mathematical model reproducing the fluxes of cells through the cycle during and after treatment. The S-phase checkpoint contributes in the response to erlotinib, suggesting that the G1 arrest may hamper S-phase cytotoxicity. The response to gemcitabine was driven by the dynamics of the progressive resumption from the S-phase arrest after drug washout. The effects induced by single drugs were used to simulate combined treatments, introducing changes when required. Gemcitabine → erlotinib was more than additive in both cell lines, strengthening the cytostatic effects on cells recovering from the arrest induced by gemcitabine. The interval in the erlotinib → gemcitabine sequence enabled to overcome the antagonist effect of G1 block on gemcitabine efficacy and improved the outcome in Capan-1 cells.

Background: Trabectedin is an anticancer agent registered for the second-line treatment of soft tissue sarcoma and ovarian cancer. No preclinical data are available on its tumor distribution, so a method for quantification in neoplastic tissues is required. Methods/results: We validated an LC–MS/MS assay determining the recovery, sensitivity, linearity, precision and accuracy in mouse tumor and liver samples. The limit of quantification was 0.10 ng/ml with a curve range of 0.10–3.00 ng/ml (accuracy 96.1–102.1%). Inter-day precision and accuracy of QCs were 6.0–8.2 and 97.0–102.6% respectively. The method was applied in mesothelioma xenografts treated with therapeutic doses. Conclusion: The method was validated for measuring trabectedin in tissues. In a mesothelioma xenograft model, trabectedin distributed preferentially in tumor compared with liver.

One of the major drawbacks that limits the clinical application of nanoparticles is the lack of preliminary investigations related to their biocompatibility, biodegradability and biodistribution. In this work, biodegradable PEGylated polymer nanoparticles (NPs) have been synthesized by using macromonomers based on poly(ε-caprolaconte) oligomers. More in detail, NPs have been produced by adopting a surfactant-free semibatch emulsion polymerization process using PEG chains as a stabilizing agent. The NPs were also labeled with rhodamine B covalently bound to the NPs to quantitatively study their biodistribution in vivo. NPs were investigated in both in vitro and in vivo preclinical systems to study their biodistribution in mice bearing B16/F10 melanoma, as well as their biocompatibility and biodegradability. The NP concentration was evaluated in different tissues at several times after intravenous injection. The disappearance of the NPs from the plasma was biphasic, with distribution and elimination half-lives of 30 min and 15 h, respectively. NPs were retained in tumors and in filter organs for a long time, were still detectable after 7 d and maintained a steady concentration in the tumor for 120 h. 48 h after injection, 70± 15% of the inoculated NPs were excreted in the feces. The favorable tumor uptake, fast excretion and absence of cytotoxicity foster the further development of produced NPs as drug delivery carriers. © 2014 IOP Publishing Ltd.

We reclassified 720 nonmedullary invasive thyroid carcinomas diagnosed and treated between 1975 and 1993. Twenty-seven cases met the criteria of insular carcinoma and 29 cases those of widely invasive follicular carcinoma. Comparison of these histotypes with respect to pathologic stage and overall, relative, and visceral metastasis-free survival showed a significant association between histotype and pT and pN categories. In particular, pT4 (p0.001) and pN1 (p0.001) categories were more frequent in the insular carcinoma histotype. By contrast, no significant differences in overall, relative, or visceral metastasis-free survival were observed between insular carcinoma and widely invasive follicular carcinoma. Molecular analysis by polymerase chain reaction-single-strand conformation polymorphism demonstrated RAS gene family point mutations in five of eight cases analyzed in each of the two histotypes, with a high proportion of CAA--AAA transversion at codon 61 of the N-RAS gene in insular carcinoma. These findings suggest that insular carcinoma represents a de novo entity distinct from widely invasive follicular carcinoma, that widely invasive follicular carcinoma has biologic characteristics more consistent with poorly differentiated than well-differentiated carcinomas, and that both insular carcinoma and widely invasive follicular carcinoma share similar molecular alterations.

A delivery system based on polymer nanoparticles (NPs) is developed and tested in relevant biological conditions for breast cancer treatment. ɛ-Caprolactone (CL) and polyethylene glycol (PEG) copolymers have been used for the one pot synthesis of surfactant free PEGylated NPs which are monodispersed, stable in physiological conditions and have size in the range 90-250 nm. The degradation behavior of these NPs has been investigated in cell medium and a relation between degradation time and molecular weight of the starting CL-based material has been established. This allows producing NPs with controlled degradation kinetics. Finally, selected NPs have been tested in 4T1 breast cancer cells to check their toxicity and to investigate the uptake process, in order to validate their use as targeted vectors for breast cancer treatment.

The antiproliferative response to anticancer treatment is the result of concurrent responses in all cell cycle phases, extending over several cell generations, whose complexity is not captured by current methods. In the proposed experimental/computational approach, the contemporary use of time-lapse live cell microscopy and flow cytometric data supported the computer rendering of the proliferative process through the cell cycle and subsequent generations during/after treatment. The effects of treatments were modelled with modules describing the functional activity of the main pathways causing arrest, repair and cell death in each phase. A framework modelling environment was created, enabling us to apply different types of modules in each phase and test models at the complexity level justified by the available data. We challenged the method with time-course measures taken in parallel with flow cytometry and time-lapse live cell microscopy in X-ray-treated human ovarian cancer cells, spanning a wide range of doses. The most suitable model of the treatment, including the dose-response of each effect, was progressively built, combining modules with a rational strategy and fitting simultaneously all data of different doses and platforms. The final model gave for the first time the complete rendering in silico of the cycling process following X-ray exposure, providing separate and quantitative measures of the dose-dependence of G1, S and G2M checkpoint activities in subsequent generations, reconciling known effects of ionizing radiations and new insights in a unique scenario., Author Summary The antiproliferative response to anticancer treatment is the result of concurrent effects in all cell cycle phases, where molecular control pathways (checkpoints) are activated and cells may be arrested to repair DNA damage or killed if not able to succeed in the repair process. The complexity and inter-cell variability of these phenomena are not captured by the available methods, and the origin of the dose-dependence of the response remains elusive. In this work, we present an experimental-computational method that discloses and measures the individual responses of cell cycle controls in each phase and generation. We demonstrate that the method, exploiting jointly data sets obtained by flow cytometry and time-lapse in vivo imaging with a suitable experimental design, is able to achieve a full reconstruction in silico of the actual movement of cell cohorts following X-ray exposure, providing separate and quantitative measures of the dose-dependence of G1, S and G2M checkpoint activities in subsequent generations. Best fit parameters values are actual measures of the probability of activation of the specific pathways of arrest, repair or death within the cell population, linking the molecular scale to the “macroscopic” response, with full appreciation of its dynamics and inter-cell heterogeneity.

Purpose MicroRNAs (miRNAs) are small, noncoding RNA molecules that can be down- or upregulated in colorectal cancer and have been associated to prognosis and response to treatment. We studied miRNA expression in tumor biopsies of patients with rectal cancer to identify a specific “signature” correlating with pathological complete response (pCR) after neoadjuvant chemoradiotherapy. Methods and Materials A total of 38 T3–4/N+ rectal cancer patients received capecitabine-oxaliplatin and radiotherapy followed by surgery. Pathologic response was scored according to the Mandard TRG scale. MiRNA expression was analyzed by microarray and confirmed by real-time Reverse Transcription Polymerase Chain Reaction (qRT-PCR) on frozen biopsies obtained before treatment. The correlation between miRNA expression and TRG, coded as TRG1 (pCR) vs. TRG >1 (no pCR), was assessed by methods specifically designed for this study. Results Microarray analysis selected 14 miRNAs as being differentially expressed in TRG1 patients, and 13 were confirmed by qRT-PCR: 11 miRNAs (miR-1183, miR-483-5p, miR-622, miR-125a-3p, miR-1224-5p, miR-188-5p, miR-1471, miR-671-5p, miR-1909∗, miR-630, miR-765) were significantly upregulated in TRG1 patients, 2 (miR-1274b, miR-720) were downexpressed. MiR-622 and miR-630 had a 100% sensitivity and specificity in selecting TRG1 cases. Conclusions A set of 13 miRNAs is strongly associated with pCR and may represent a specific predictor of response to chemoradiotherapy in rectal cancer patients.

Multicellular systems are currently studied both in vitro and in vivo using different platforms, providing high throughput data of different types. Mathematical modelling is now called to interpret this reality and has to face more and more with quantitative data. This requires a connection between the basic theoretical model and the data structures, taking account of the processes of measure. Working on the response to anticancer treatment, we considered the data provided by flow cytometry (FC) and time-lapse live cell imaging (TL) in time-course experiments in vitro with untreated and treated cell populations. We created a flexible cell cycle simulator including subsequent cell generations to achieve a full reconstruction in silico of the cell cycle progression under a variety of treatment effects. Unperturbed growth was modelled taking into account intercellular variability of G1,S and G2M transit times, quiescent cells and natural cell loss. The effect of treatment was modelled by “perturbation modules” associated to each cell cycle phase and cell generation, containing a submodel of the checkpoint activity in that phase. Upon input of a set of parameters associated to unperturbed growth and perturbation modules, the program reproduced the time course of cell cycling through subsequent generations, providing outputs comparable with both TL and FC measures. The challenges to fit the data of specific experiments were discussed, indicating a feasible procedure for model building and identification. This lead to a dynamic rendering of proliferation midway between the macroscopic data level and the underlying molecular processes.

The milk thistle extract silymarin, alone or in combined chemotherapy, is now under investigation in anticancer research, with particular interest for its possible employ in the treatment of chemoresistant tumours. So far, the consequences of a silymarin pre-treatment have not been thoroughly investigated. We studied whether silymarin pre-treatment synergized with chemotherapy, exploring the dose-dependence of the interaction in sensitive and multidrug-resistant cells.We studied cell cycle perturbations induced by silymarin in two colon carcinoma cell lines, LoVo and the multidrug-resistant isogenic LoVo/DX. Synergism/additivity/antagonism of silymarin-doxorubicin silymarin-paclitaxel combined treatments were evaluated by isobologram/combination index analysis, in the whole spectrum of active and sub-active concentrations of all drugs. The mechanisms of silymarin interaction with the other drugs were investigated by measuring drug uptake and cell cycle perturbations.Silymarin had similar antiproliferative activity against both cell lines. Pre-treatment with low silymarin concentrations synergised with both doxorubicin and paclitaxel in LoVo but not in LoVo/DX. Higher silymarin concentrations were additive with doxorubicin and paclitaxel in both cell lines. Silymarin favourably interfered with uptake and cell cycle effects of the chemotherapeutics in LoVo but not in LoVo/DX.These findings confirm activity of silymarin against colon carcinoma, including multidrug-resistant types, at relatively high but clinically achievable concentrations. In view of its low toxicity, two schedules based on low- and high-dose silymarin pre-treatment might offer a valuable option for combined treatment.

The association of erlotinib (ERL) with gemcitabine (GEM) is a promising combined therapy for advanced pancreatic cancer, but current protocols have only a modest impact on the overall survival of patients respect to GEM alone. Sequential treatment can improve the efficacy of this drug combination. Towards optimization of the drug combination, we simulated cell cycle progression during/after treatment, based on the dynamics of checkpoint activation, with the probabilities of cell arrest or death in each cell cycle phase, applying a previously established experimental/computational approach (Lupi et al. Cancer Res. 64: 2825; Ubezio et al Cancer Res. 69: 5234; Falcetta et al. PLoS Comput Biol. e1003293). The simulation gives the temporal evolution of cell flow through G1, S and G2M over subsequent division cycles, from which the time courses of cell cycle percentages and the number of cells in each cell generation were derived enabling a joint validation of the model with independent flow cytometric and time-lapse live-imaging experimental data, collected in time course experiments up to 96h with different concentrations of GEM (6h treatment) and ERL (48h treatment) with two pancreatic cell lines (BxPC-3 and capan-1). Analysis of the response to ERL treatment reveals that at cytostatic concentrations a partial G1 arrest mainly occurs after the first division following the start of treatment (gen 1 cells) while a stronger delay of the progression in S phase is active also before division (gen 0). The activity of G1 and S phase checkpoints were dose dependent and at higher cytotoxic concentrations we observed a near complete arrest in both G1 and S phases while most cell deaths were in phase S in gen 0. After treatment discontinuation, while G1 blocked cells re-enter in cycle, S phase progression in gen 0 and gen 1 remained slow but without additional death events. The model of GEM demonstrated the activation of a strong delay in phase S shortly after the start of treatment, both in gen 0 and gen 1, already at non-cytotoxic concentrations, lasting several hours after treatment discontinuation, and thereafter progressively reduced, resulting in a wave of broadly semisynchronised cells traversing S and G2M phases in gen 0. At cytotoxic concentrations, part of the cells remained arrested in S phase and eventually died. A temporary delay in G1 was also demonstrated already at fairly low concentrations, acting only during exposure to the drug. Based on these models of GEM and ERL treatment, studies are ongoing to simulate the expected outcomes of sequential treatments. Preliminary analysis suggests that the GEM = > ERL sequence is particularly favourable in both cell lines when a cytostatic ERL concentration was given after a fairly cytototoxic GEM concentration when S phase progression resumed, by strengthening the long term cytostatic effects in S phase. Citation Format: Monica Lupi, Francesca Falcetta, Maurizio D'Incalci, Paolo Ubezio. In silico rendering of cell cycle progression of erlotinib and gemcitabine treatment in pancreatic cancer cells. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 3777. doi:10.1158/1538-7445.AM2015-3777

Studies of cellular internalization of nanoparticles (NPs) play a paramount role for the design of efficient drug delivery systems, but so far they lack a robust experimental technique able to quantify the NP uptake in terms of number of NPs internalized in each cell. In this work we propose a novel method which provides a quantitative evaluation of fluorescent NP uptake by combining flow cytometry and plate fluorimetry with measurements of number of cells. Single cell fluorescence signals measured by flow cytometry were associated with the number of internalized NPs, exploiting the observed linearity between average flow cytometric fluorescence and overall plate fluorimeter measures, and previous calibration of the microplate reader with serial dilutions of NPs. This precise calibration has been made possible by using biocompatible fluorescent NPs in the range of 20-300 nm with a narrow particle size distribution, functionalized with a covalently bonded dye, Rhodamine B, and synthesized via emulsion free-radical polymerization. We report the absolute number of NPs internalized in mouse mammary tumor cells (4T1) as a function of time for different NP dimensions and surface charges and at several exposure concentrations. The obtained results indicate that 4T1 cells incorporated 10(3)-10(4) polymer NPs in a short time, reaching an intracellular concentration 15 times higher than the external one.