DDX5 mRNA-targeting antisense oligonucleotide as a new promising therapeutic in combating castration-resistant prostate cancer.

The heat shock protein 27 (Hsp27) has emerged as a principal factor of the castration-resistant prostate cancer (CRPC) progression. Also, an antisense oligonucleotide (ASO) against Hsp27 (OGX-427 or apatorsen) has been assessed in different clinical trials. Here, we illustrate that Hsp27 highly regulates the expression of the human DEAD-box protein 5 (DDX5), and we define DDX5 as a novel therapeutic target for CRPC treatment. DDX5 overexpression is strongly correlated with aggressive tumor features, notably with CRPC. DDX5 downregulation using a specific ASO-based inhibitor that acts on DDX5 mRNAs inhibits cell proliferation in preclinical models, and it particularly restores the treatment sensitivity of CRPC. Interestingly, through the identification and analysis of DDX5 protein interaction networks, we have identified some specific functions of DDX5 in CRPC that could contribute actively to tumor progression and therapeutic resistance. We first present the interactions of DDX5 and the Ku70/80 heterodimer and the transcription factor IIH, thereby uncovering DDX5 roles in different DNA repair pathways. Collectively, our study highlights critical functions of DDX5 contributing to CRPC progression and provides preclinical proof of concept that a combination of ASO-directed DDX5 inhibition with a DNA damage-inducing therapy can serve as a highly potential novel strategy to treat CRPC.

Metronomic reloaded: Theoretical models bringing chemotherapy into the era of precision medicine.

Oncology has benefited from an increasingly growing number of groundbreaking innovations over the last decade. Targeted therapies, biotherapies, and the most recent immunotherapies all contribute to increase the number of therapeutic options for cancer patients. Consequently, substantial improvements in clinical outcomes for some disease with dismal prognosis such as lung carcinoma or melanoma have been achieved. Of note, the latest innovations in targeted therapies or biotherapies do not preclude the use of standard cytotoxic agents, mostly used in combination. Importantly, and despite the rise of bioguided (a.k.a. precision) medicine, the administration of chemotherapeutic agents still relies on the maximum tolerated drug (MTD) paradigm, a concept inherited from theories conceptualized nearly half a century ago. Alternative dosing schedules such as metronomic regimens, based upon the repeated and regular administration of low doses of chemotherapeutic drugs, and adaptive therapy (i.e. modulating the dose and frequency of cytotoxics administration to control disease progression rather than eradicate it at all cost) have emerged as possible strategies to improve response rates while reducing toxicities. The recent changes in paradigm in the way we theorize cancer biology and evolution, metastatic spreading and tumor ecology, alongside the recent advances in the field of immunotherapy, have considerably strengthened the interest for these alternative approaches. This paper aims at reviewing the recent evolutions in the field of theoretical biology of cancer and computational oncology, with a focus on the consequences these changes have on the way we administer chemotherapy. Here, we advocate for the development of model-guided strategies to refine doses and schedules of chemotherapy administration in order to achieve precision medicine in oncology.

Revisiting dosing regimen using PK/PD modeling: the MODEL1 phase I/II trial of docetaxel plus epirubicin in metastatic breast cancer patients.

The MODEL1 trial is the first model-driven phase I/II dose-escalation study of densified docetaxel plus epirubicin administration in metastatic breast cancer patients, a regimen previously known to induce unacceptable life-threatening toxicities. The primary objective was to determine the maximum tolerated dose of this densified regimen. Study of the efficacy was a secondary objective. Her2-negative, hormone-resistant metastatic breast cancer patients were treated with escalating doses of docetaxel plus epirubicin every 2 weeks for six cycles with granulocyte colony stimulating factor support. A total of 16 patients were treated with total doses ranging from 85 to 110 mg of docetaxel plus epirubicin per cycle. Dose escalation was controlled by a non-hematological toxicity model. Dose densification was guided by a model of neutrophil kinetics, able to optimize docetaxel plus epirubicin dosing with respect to pre-defined acceptable levels of hematological toxicity while ensuring maximal efficacy. The densified treatment was safe since hematological toxicity was much lower compared to previous findings, and other adverse events were consistent with those observed with this regimen. The maximal tolerated dose was 100 mg given every 2 weeks. The response rate was 45 %; median progression-free survival was 10.4 months, whereas 54.6 months of median overall survival was achieved. The optimized docetaxel plus epirubicin dosing regimen led to fewer toxicities associated with higher efficacy as compared with standard or empirical densified dosing. This study suggests that model-driven dosage adjustment can lead to improved efficacy-toxicity balance in patients with cancer when several anticancer drugs are combined.

A phase Ia/Ib clinical trial of metronomic chemotherapy based on a mathematical model of oral vinorelbine in metastatic non-small cell lung cancer and malignant pleural mesothelioma: rationale and study protocol.

BACKGROUND: Metronomic oral vinorelbine is effective in metastatic NSCLC and malignant pleural mesothelioma, but all the studies published thus far were based upon a variety of empirical and possibly suboptimal schedules, with inconsistent results. Mathematical modelling showed by simulation that a new metronomic protocol could lead to a better safety and efficacy profile. DESIGN: This phase Ia/Ib trial was designed to confirm safety (phase Ia) and evaluate efficacy (phase Ib) of a new metronomic oral vinorelbine schedule. Patients with metastatic NSCLC or malignant pleural mesothelioma in whom standard treatments failed and who exhibited ECOG performance status 0-2 and adequate organ function will be eligible. Our mathematical PK-PD model suggested an alternative weekly D1, D2 and D4 schedule (named Vinorelbine Theoretical Protocol) with a respective dose of 60, 30 and 60 mg. Trial recruitment will be two-staged, as 12 patients are planned to participate in phase Ia to confirm safety and consolidate the calibration of the model parameters. Depending on the phase Ia results and after a favourable decision from a consultative committee, the extension phase (phase Ib) will be an efficacy study including 20 patients who will receive the Optimal Vinorelbine Theoretical Protocol. The primary endpoint is the tolerance (assessed by CTC v4.0) for the phase Ia and the objective response according to RECIST 1.1 for phase Ib. An ancillary study on circulating angiogenesis biomarkers will be a subproject of the trial. DISCUSSION: This ongoing trial is the first to prospectively test a mathematically optimized schedule in metronomic chemotherapy. As such, this trial can be considered as a proof-of-concept study demonstrating the feasibility to run a computational-driven protocol to ensure an optimal efficacy/toxicity balance in patients with cancer. TRIAL REGISTRATION: EudraCT N°: 2015-000138-31.

Revisiting metronomic vinorelbine with mathematical modelling: a Phase I trial in lung cancer.

BACKGROUND: A phase Ia/Ib trial of metronomic oral vinorelbine (MOV) driven by a mathematical model was performed in heavily pretreated metastatic Non-Small Cell Lung Cancer or Pleural Mesothelioma patients. Disease Control Rate, progression free survival, toxicity and PK/PD were the main endpoints. METHODS: Best MOV scheduling was selected using a simplified phenomenological, semi-mechanistic model with a total weekly dose of 150-mg vinorelbine. Computation of individual PK parameters was performed using population approach. RESULTS: The mathematical model proposed the following metronomic schedule for a 150-mg weekly dose of vinorelbine: 60 mg D1, 30 mg D2, 60 mg D4. A total of 37 heavily pre-treated patients (30 evaluable) were enrolled. Grade III/IV neutropenia was observed in 30% patients. Median PFS was 11 weeks. Disease Control Rate was 73% (i.e.; 13% partial response and 60% stable disease). A large variability in drug exposure (AUC0-24 h: 53%) and PK parameters (Cl: 83%) were observed among patients. Simulated trough levels after D2 and D4 showed similarly 56-73% variability among patients. Drug exposure was not associated with efficacy, but neutropenia was more frequent in patients with AUC > 250 ng/ml.h. Tumor burden, performance status and neutrophils-to-lymphocyte ratio (NLR) were associated with PFS, suggesting that MOV would be indicated in selected patients. We built a composite score to predict efficacy, mixing baseline tumor size and NLR showing 84% selectivity and 75% specificity. CONCLUSIONS: MOV was characterized by important variability in drug exposure among patients. However, and despite being all heavily pre-treated, 73% of disease control rate and 11 weeks PFS were achieved with manageable toxicities. PK/PD relationships yielded conflicting results depending on the initial tumor burden and BSA, suggesting that patients should be carefully selected prior to be scheduled for metronomic regimen. Possible role NLR could play as a predictive marker suggests immunomodulating features with MOV.

A prospective behavioral and imaging study exploring the impact on long-term memory of radiotherapy delivered for a brain tumor in childhood and adolescence.

BACKGROUND: Posterior fossa tumors represent two thirds of brain tumors in children. Although progress in treatment has improved survival rates over the past few years, long-term memory impairments in survivors are frequent and have an impact on academic achievement. The hippocampi, cerebellum and cerebellar-cortical networks play a role in several memory systems. They are affected not only by the location of the tumor itself and its surgical removal, but also by the supratentorial effects of complementary treatments, particularly radiotherapy. The IMPALA study will investigate the impact of irradiation doses on brain structures involved in memory, especially the hippocampi and cerebellum. METHODS/DESIGN: In this single-center prospective behavioral and neuro-imaging study, 90 participants will be enrolled in three groups. The first two groups will include patients who underwent surgery for a posterior fossa brain tumor in childhood, who are considered to be cured, and who completed treatment at least 5 years earlier, either with radiotherapy (aggressive brain tumor; Group 1) or without (low-grade brain tumor; Group 2). Group 3 will include control participants matched with Group 1 for age, sex, and handedness. All participants will perform an extensive battery of neuropsychological tests, including an assessment of the main memory systems, and undergo multimodal 3 T MRI. The irradiation dose to the different brain structures involved in memory will be collected from the initial radiotherapy dosimetry. DISCUSSION: This study will provide long-term neuropsychological data about four different memory systems (working memory, episodic memory, semantic memory, and procedural memory) and the cognitive functions (attention, language, executive functions) that can interfere with them, in order to better characterize memory deficits among the survivors of brain tumors. We will investigate the correlations between neuropsychological and neuroimaging data on the structural (3DT1), microstructural (DTI), functional (rs-fMRI), vascular (ASL) and metabolic (spectroscopy) impact of the tumor and irradiation dose. This study will thus inform the setting of dose constraints to spare regions linked to the development of cognitive and memory functions. TRIAL REGISTRATION: ClinicalTrials.gov: NCT04324450, registered March 27, 2020, updated January 25th, 2021. Retrospectively registered, https://www.clinicaltrials.gov/ct2/show/NCT04324450.

Machine Learning for Prediction of Immunotherapy Efficacy in Non-Small Cell Lung Cancer from Simple Clinical and Biological Data.

BACKGROUND: Immune checkpoint inhibitors (ICIs) are now a therapeutic standard in advanced non-small cell lung cancer (NSCLC), but strong predictive markers for ICIs efficacy are still lacking. We evaluated machine learning models built on simple clinical and biological data to individually predict response to ICIs. METHODS: Patients with metastatic NSCLC who received ICI in second line or later were included. We collected clinical and hematological data and studied the association of this data with disease control rate (DCR), progression free survival (PFS) and overall survival (OS). Multiple machine learning (ML) algorithms were assessed for their ability to predict response. RESULTS: Overall, 298 patients were enrolled. The overall response rate and DCR were 15.3% and 53%, respectively. Median PFS and OS were 3.3 and 11.4 months, respectively. In multivariable analysis, DCR was significantly associated with performance status (PS) and hemoglobin level (OR 0.58, p < 0.0001; OR 1.8, p < 0.001). These variables were also associated with PFS and OS and ranked top in random forest-based feature importance. Neutrophil-to-lymphocyte ratio was also associated with DCR, PFS and OS. The best ML algorithm was a random forest. It could predict DCR with satisfactory efficacy based on these three variables. Ten-fold cross-validated performances were: accuracy 0.68 ± 0.04, sensitivity 0.58 ± 0.08; specificity 0.78 ± 0.06; positive predictive value 0.70 ± 0.08; negative predictive value 0.68 ± 0.06; AUC 0.74 ± 0.03. CONCLUSION: Combination of simple clinical and biological data could accurately predict disease control rate at the individual level.

Population pharmacokinetic model of irinotecan and its four main metabolites in patients treated with FOLFIRI or FOLFIRINOX regimen.

PURPOSE: The aim of the present study was to characterize the pharmacokinetics of irinotecan and its four main metabolites (SN-38, SN-38G, APC and NPC) in metastatic colorectal cancer patients treated with FOLFIRI and FOLFIRINOX regimens and to quantify and explain the inter-individual pharmacokinetic variability in this context. METHODS: A multicenter study including 109 metastatic colorectal cancer patients treated with FOLFIRI or FOLFIRINOX regimen, associated or not with a monoclonal antibody, was conducted. Concentrations of irinotecan and its four main metabolites were measured in 506 blood samples during the first cycle of treatment. Collected data were analyzed using the population approach. First, fixed and random effects models were selected using statistical and graphical methods; second, the impact of covariates on pharmacokinetic parameters was evaluated to explain the inter-individual variability in pharmacokinetic parameters. RESULTS: A seven-compartment model best described the pharmacokinetics of irinotecan and its four main metabolites. First-order rates were assigned to distribution, elimination, and metabolism processes, except for the transformation of irinotecan to NPC which was nonlinear. Addition of a direct conversion of NPC into SN-38 significantly improved the model. Co-administration of oxaliplatin significantly modified the distribution of SN-38. CONCLUSION: To our knowledge, the present model is the first to allow a simultaneous description of irinotecan pharmacokinetics and of its four main metabolites. Moreover, a direct conversion of NPC into SN-38 had never been described before in a population pharmacokinetic model of irinotecan. The model will be useful to develop pharmacokinetic-pharmacodynamic models relating SN-38 concentrations to efficacy and digestive toxicities. CLINICAL TRIALS REGISTRATION NUMBER: ClinicalTrials.gov identifier: NCT00559676.

Mathematical modeling of peripheral blood neutrophil kinetics to predict CLAD after lung transplantation.

BACKGROUND: The presence of neutrophils in the lung was identified as a factor associated with CLAD but requires invasive samples. The aim of this study was to assess the kinetics of peripheral blood neutrophils after lung transplantation as early predictor of CLAD. METHODS: We retrospectively included all recipients transplanted in our center between 2009 and 2014. Kinetics of blood neutrophils were evaluated to predict early CLAD by mathematical modeling using unadjusted and adjusted analyses. RESULTS: 103 patients were included, 80 in the stable group and 23 in the CLAD group. Bacterial infections at 1 year were associated with CLAD occurrence. Neutrophils demonstrated a high increase postoperatively and then a progressive decrease until normal range. Recipients with CLAD had higher neutrophil counts (mixed effect coefficient beta over 3 years = +1.36 G/L, 95% Confidence Interval [0.99-1.92], p < .001). A coefficient of celerity (S for speed) was calculated to model the kinetics of return to the norm before CLAD occurrence. After adjustment, lower values of S (slower decrease of neutrophils) were associated with CLAD (Odds Ratio = 0.26, 95% Confidence Interval [0.08-0.66], p = .01). CONCLUSION: A slower return to the normal range of blood neutrophils was early associated with CLAD occurrence.

Mathematical and numerical analysis for a model of growing metastatic tumors.

In cancer diseases, the appearance of metastases is a very pejorative forecast. Chemotherapies are systemic treatments which aim at the elimination of the micrometastases produced by a primitive tumour. The efficiency of chemotherapies closely depends on the protocols of administration. Mathematical modeling is an invaluable tool to help in evaluating the best treatment strategy. Iwata et al. [K. Iwata, K. Kawasaki, N. Shigesad, A dynamical model for the growth and size distribution of multiple metastatic tumors, J. Theor. Biol. 203 (2000) 177.] proposed a partial differential equation (PDE) that describes the metastatic evolution of an untreated tumour. In this article, we conducted a thorough mathematical analysis of this model. Particularly, we provide an explicit formula for the growth rate parameter, as well as a numerical resolution of this PDE. By increasing our understanding of the existing model, this work is crucial for further extension and refinement of the model. It settles down the framework necessary for the consideration of drugs administration effects on tumour development.

Population pharmacokinetics of FOLFIRINOX: a review of studies and parameters.

PURPOSE: FOLFIRINOX regimen is commonly used in colorectal and more recently pancreatic cancer. However, FOLFIRINOX induces significant and dose-limiting toxic effects leading to empirical dose reduction and sometimes treatment discontinuation. Model-based FOLFIRINOX regimen optimization might help improving patients' outcome. As a first step, the current review aims at bringing together all published population pharmacokinetics models for FOLFIRINOX anticancer drugs. METHODS: A literature search was conducted in the PubMed database from inception to February 2018, using the following terms: population pharmacokinetic(s), irinotecan, oxaliplatin, fluorouracil, FOLFIRI, FOLFOX, FOLFIRINOX. Only articles displaying nonlinear mixed effect models were included. Study description, pharmacokinetic parameter values and influential covariates are reported. For each model, the typical pharmacokinetic profile was simulated for the standard FOLFIRINOX protocol. RESULTS: The FOLFIRINOX compounds have been studied only separately so far. A total of six articles were retained for 5-fluorouracil, 6 for oxaliplatin and 5 for irinotecan (also including metabolites). Either one- or two-compartment models have been described for 5-fluorouracil, while two- or three-compartment models were reported for oxaliplatin and irinotecan pharmacokinetics. Non-linear elimination was sometimes reported for 5-fluorouracil. Sex and body size were found as influential covariates for all molecules in some publications. Despite some differences in model structures and parameter values, the simulated profiles and subsequent exposure were consistent between studies. CONCLUSIONS: The current review allows for a global understanding of FOLFIRINOX pharmacokinetics, and will provide a basis for further development of pharmacokinetics-pharmacodynamics-toxicity models for model-driven FOLFIRINOX protocol optimization to reach the best benefit-to-risk ratio.

Validation of Neutrophil Count as An Algorithm-Based Predictive Factor of Progression-Free Survival in Patients with Metastatic Soft Tissue Sarcomas Treated with Trabectedin.

Introduction: Based on a mathematical model of trabectedin-induced neutropenia, we assessed the predictive value of absolute neutrophil count (ANC) on progression-free survival (PFS) in an independent validation cohort of patients treated with trabectedin. Methods: We collected data from 87 patients in two expert centers who received at least two cycles of trabectedin for soft tissue sarcomas (STS) treatment. Correlations between ANC, patients' characteristics, and survival were assessed, and a multivariate model including tumor grade, performance status, ANC, and hemoglobin level was developed. Results: Therapeutic ANC ≥ 7.5 G/L level was associated with shorter PFS: 3.22 months (95% confidence interval (CI), 1.57⁻4.87) in patients with ANC ≥ 7.5 G/L vs. 5.78 months (95% CI, 3.95⁻7.61) in patients with ANC < 7.5 G/L (p = 0.009). Age, primary localization, lung metastases, dose reduction, hemoglobin, and albumin rates were also associated with PFS. In multivariate analysis, ANC ≥ 7.5 G/L was independently associated with poor PFS and overall survival. Conclusion: We validated increased pre-therapeutic ANC as a predictive factor of short PFS in patients starting trabectedin for STS. ANC appears to have an impact on survival rates and may be used as a decision-making tool for personalizing second-line strategies in patients with metastatic STS.

Immunologically effective dose: a practical model for immuno-radiotherapy.

OBJECTIVES: Concomitant radiotherapy with immune checkpoint blockade could be synergistic. Out-of-field effects could improve survival by slowing or blocking metastatic spreading. However, not much is known about the optimal size per fraction and inter-fraction time in that new context. METHODS: The new concept of Immunologically Effective Dose (IED) is proposed: it models an intrinsic immunogenicity of radiotherapy schedules, i.e. the fraction of immunogenicity that results from the choice of the dosing regimen. The IED is defined as the single dose, given in infinitely low dose rate, that produces the same amount of abscopal response as the radiation schedule being considered. The IED uses the classic parameters of the BED formula and adds two parameters for immunogenicity that describe the local availability of immune effectors within the tumor micro-environment. Fundamentally, the IED adds a time dimension in the BED formula and describes an intrinsic immunogenicity level for radiotherapy. RESULTS: The IED is positively related to the intensity of the out-of-field, radiotherapy-mediated, immune effects described in some preclinical data. Examples of numerical simulations are given for various schedules. A web-based calculator is freely available. CONCLUSIONS: Out-of-field effects of radiotherapy with immune checkpoint blockers might be better predicted and eventually, radiotherapy schedules with better local and systemic immunogenicity could be proposed. ADVANCES IN KNOWLEDGE: A model for the intrinsic level of immunogenicity of radiotherapy schedules, referred to as the Immunologically Effective Dose (IED), that is independent of the type of immunotherapy.

Mathematical modeling of disease dynamics in SDHB- and SDHD-related paraganglioma: Further step in understanding hereditary tumor differences and future therapeutic strategies.

Succinate dehydrogenase subunit B and D (SDHB and SDHD) mutations represent the most frequent cause of hereditary pheochromocytoma and paraganglioma (PPGL). Although truncation of the succinate dehydrogenase complex is thought to be the disease causing mechanism in both disorders, SDHB and SDHD patients exihibit different phenotypes. These phenotypic differences are currently unexplained by molecular genetics. The aim of this study is to compare disease dynamics in these two conditions via a Markov chain model based on 4 clinically-defined steady states. Our model corroborates at the population level phenotypic observations in SDHB and SDHD carriers and suggests potential explanations associated with the probabilities of disease maintenance and regression. In SDHB-related syndrome, PPGL maintenance seems to be reduced compared to SDHD (p = 0.04 vs 0.95) due to higher probability of tumor cell regression in SDHB vs SDHD (p = 0.87 vs 0.00). However, when SDHB-tumors give rise to metastases, metastatic cells are able to thrive with decreased probability of regression compared with SDHD counterparts (p = 0.17 vs 0.89). By constrast, almost all SDHD patients develop PGL (mainly head and neck) that persist throughout their lifetime. However, compared to SDHB, maintenance of metastatic lesions seems to be less effective for SDHD (p = 0.83 vs 0.11). These findings align with data suggesting that SDHD-related PPGL require less genetic events for tumor initiation and maintenance compared to those related to SDHB, but fail to initiate biology that promotes metastatic spread and metastatic cell survival in host tissues. By contrast, the higher number of genetic abnormalities required for tumor initiation and maintenance in SDHB PPGL result in a lower penetrance of PGL, but when cells give rise to metastases they are assumed to be better adapted to sustain survival. These proposed differences in disease progression dynamics between SDHB and SDHD diseases provide new cues for future exploration of SDHx PPGL behavior, offering considerations for future specific therapeutic and prevention strategies.

Revisiting Bevacizumab + Cytotoxics Scheduling Using Mathematical Modeling: Proof of Concept Study in Experimental Non-Small Cell Lung Carcinoma.

Concomitant administration of bevacizumab and pemetrexed-cisplatin is a common treatment for advanced nonsquamous non-small cell lung cancer (NSCLC). Vascular normalization following bevacizumab administration may transiently enhance drug delivery, suggesting improved efficacy with sequential administration. To investigate optimal scheduling, we conducted a study in NSCLC-bearing mice. First, experiments demonstrated improved efficacy when using sequential vs. concomitant scheduling of bevacizumab and chemotherapy. Combining this data with a mathematical model of tumor growth under therapy accounting for the normalization effect, we predicted an optimal delay of 2.8 days between bevacizumab and chemotherapy. This prediction was confirmed experimentally, with reduced tumor growth of 38% as compared to concomitant scheduling, and prolonged survival (74 vs. 70 days). Alternate sequencing of 8 days failed in achieving a similar increase in efficacy, thus emphasizing the utility of modeling support to identify optimal scheduling. The model could also be a useful tool in the clinic to personally tailor regimen sequences.

A new methodology to derive 3D kinetic parametric FDG PET images based on a mathematical approach integrating an error model of measurement.

BACKGROUND: In FDG-PET, SUV images are hampered by large potential biases. Our aim was to develop an alternative method (ParaPET) to generate 3D kinetic parametric FDG-PET images easy to perform in clinical oncology. METHODS: The key points of our method are the use of a new error model of PET measurement extracted from a late dynamic PET acquisition of 15 min, centered over the lesion and an image-derived input function (IDIF). The 15-min acquisition is reconstructed to obtain five images of FDG mean activity concentration and images of its variance to model errors of PET measurement. Our approach is carried out on each voxel to derive 3D kinetic parameter images. ParaPET was evaluated and compared to Patlak analysis as a reference. Hunter and Barbolosi methods (Barbolosi-Bl: with blood samples or Barbolosi-Im: with IDIF) were also investigated and compared to Patlak. Our evaluation was carried on Ki index, the net influx rate and its maximum value in the lesion (Ki,max). RESULTS: This parameter was obtained from 41 non-small cell lung cancer lesions associated with 4 to 5 blood samples per patient, required for the Patlak analysis. Compare to Patlak, the median relative difference and associated range (median; [min;max]) in Ki,max estimates were not statistically significant (Wilcoxon test) for ParaPET (- 3.0%; [- 31.9%; 47.3%]; p = 0.08) but statistically significant for Barbolosi-Bl (- 8.0%; [- 30.8%; 53.7%]; p = 0.001), Barbolosi-Im (- 7.9%; [- 38.4%; 30.6%]; p = 0.007) or Hunter (32.8%; [- 14.6%; 132.2%]; p < 10- 5). In the Bland-Altman plots, the ratios between the four methods and Patlak are not dependent of the Ki magnitude, except for Hunter. The 95% limits of agreement are comparable for ParaPET (34.7%), Barbolosi-Bl (30.1%) and Barbolosi-Im (30.8%), lower to Hunter (81.1%). In the 25 lesions imaged before and during the radio-chemotherapy, the decrease in the FDG uptake (ΔSUVmax or ΔKi,max) is statistically more important (p < 0.02, Wilcoxon one-tailed test) when estimated from the Ki images than from the SUV images (additional median variation of - 2.3% [- 52.6%; + 19.1%] for ΔKi,max compared to ΔSUVmax). CONCLUSION: None of the four methodologies is yet ready to replace the Patlak approach, and further improvements are still required. Nevertheless, ParaPET remains a promising approach, offering a non-invasive alternative to methods based on multiple blood samples and only requiring a late PET acquisition. It allows deriving Ki values, highly correlated and presenting the lowest relative bias with Patlak estimates, in comparison to the other methods we evaluated. Moreover, ParaPET gives access to quantitative information at the pixel level, which needs to be evaluated in the perspective of radiomic and tumour response. TRIAL REGISTRATION: NCT 02821936 ; May 2016.

Mathematical Modeling of Tumor-Tumor Distant Interactions Supports a Systemic Control of Tumor Growth.

Interactions between different tumors within the same organism have major clinical implications, especially in the context of surgery and metastatic disease. Three main explanatory theories (competition, angiogenesis inhibition, and proliferation inhibition) have been proposed, but precise determinants of the phenomenon remain poorly understood. Here, we formalized these theories into mathematical models and performed biological experiments to test them with empirical data. In syngeneic mice bearing two simultaneously implanted tumors, growth of only one of the tumors was significantly suppressed (61% size reduction at day 15, P < 0.05). The competition model had to be rejected, whereas the angiogenesis inhibition and proliferation inhibition models were able to describe the data. Additional models including a theory based on distant cytotoxic log-kill effects were unable to fit the data. The proliferation inhibition model was identifiable and minimal (four parameters), and its descriptive power was validated against the data, including consistency in predictions of single tumor growth when no secondary tumor was present. This theory may also shed new light on single cancer growth insofar as it offers a biologically translatable picture of how local and global action may combine to control local tumor growth and, in particular, the role of tumor-tumor inhibition. This model offers a depiction of concomitant resistance that provides an improved theoretical basis for tumor growth control and may also find utility in therapeutic planning to avoid postsurgery metastatic acceleration. Cancer Res; 77(18); 5183-93. ©2017 AACR.

Pharmacokinetics and Pharmacodynamics-Based Mathematical Modeling Identifies an Optimal Protocol for Metronomic Chemotherapy.

Metronomic chemotherapy is usually associated with better tolerance than conventional chemotherapy, and encouraging response rates have been reported in various settings. However, clinical development of metronomic chemotherapy has been hampered by a number of limitations, including the vagueness of its definition and the resulting empiricism in protocol design. In this study, we developed a pharmacokinetic/pharmacodynamic mathematical model that identifies in silico the most effective administration schedule for gemcitabine monotherapy. This model is based upon four biological assumptions regarding the mechanisms of action of metronomic chemotherapy, resulting in a set of 6 minimally parameterized differential equations. Simulations identified daily 0.5-1 mg/kg gemcitabine as an optimal protocol to maximize antitumor efficacy. Both metronomic protocols (0.5 and 1 mg/kg/day for 28 days) were evaluated in chemoresistant neuroblastoma-bearing mice and compared with the standard MTD protocol (100 mg/kg once a week for 4 weeks). Systemic exposure to gemcitabine was 14 times lower in the metronomic groups compared with the standard group. Despite this, metronomic gemcitabine significantly inhibited tumor angiogenesis and reduced tumor perfusion and inflammation in vivo, while standard gemcitabine did not. Furthermore, metronomic gemcitabine yielded a 40%-50% decrease in tumor mass at the end of treatment as compared with control mice (P = 0.002; ANOVA on ranks with Dunn test), while standard gemcitabine failed to significantly reduce tumor growth. Stable disease was maintained in the metronomic groups for up to 2 months after treatment completion (67%-72% reduction in tumor growth at study conclusion, P < 0.001; ANOVA on ranks with Dunn test). Collectively, our results confirmed the superiority of metronomic protocols in chemoresistant tumors in vivoCancer Res; 77(17); 4723-33. ©2017 AACR.

Model driven optimization of antiangiogenics + cytotoxics combination: application to breast cancer mice treated with bevacizumab + paclitaxel doublet leads to reduced tumor growth and fewer metastasis.

Bevacizumab is the first-in-class antiangiogenic drug and is almost always administrated in combination with cytotoxics. Reports have shown that bevacizumab could induce a transient phase of vascular normalization, thus ensuring a better drug delivery when cytotoxics administration is adjuvant. However, determining the best sequence remains challenging. We have developed a mathematical model describing the impact of antiangiogenics on tumor vasculature. A 3.4 days gap between bevacizumab and paclitaxel was first proposed by our model. To test its relevance, 84 mice were orthotopically xenografted with human MDA-231Luc+ refractory breast cancer cells. Two sets of experiments were performed, based upon different bevacizumab dosing (10 or 20 mg/kg) and inter-cycle intervals (7 or 10 days), comprising several combinations with paclitaxel. Results showed that scheduling bevacizumab 3 days before paclitaxel improved antitumor efficacy (48% reduction in tumor size compared with concomitant dosing, p < 0.05) and reduced metastatic spreading. Additionally, bevacizumab alone could lead to more aggressive metastatic disease with shorter survival in animals. Our model was able to fit the experimental data and provided insights on the underlying dynamics of the vasculature's ability to deliver the cytotoxic agent. Final simulations suggested a new, data-informed optimal gap of 2.2 days. Our experimental data suggest that current concomitant dosing between bevacizumab and paclitaxel could be a sub-optimal strategy at bedside. In addition, this proof of concept study suggests that mathematical modelling could help to identify the optimal interval among a variety of possible alternate treatment modalities, thus refining the way experimental or clinical studies are conducted.