Prediction of Response to Temozolomide in Low-Grade Glioma Patients Based on Tumor Size Dynamics and Genetic Characteristics

International audienceBoth molecular profiling of tumors and longitudinal tumor size data modeling are relevant strategies to predict cancer patients' response to treatment. Herein we propose a model of tumor growth inhibition integrating a tumor's genetic characteristics (p53 mutation and 1p/19q codeletion) that successfully describes the time course of tumor size in patients with low-grade gliomas treated with first-line temozolomide chemotherapy. The model captures potential tumor progression under chemotherapy by accounting for the emergence of tissue resistance to treatment following prolonged exposure to temozolomide. Using information on individual tumors' genetic characteristics, in addition to early tumor size measurements, the model was able to predict the duration and magnitude of response, especially in those patients in whom repeated assessment of tumor response was obtained during the first 3 months of treatment. Combining longitudinal tumor size quantitative modeling with a tumor''s genetic characterization appears as a promising strategy to personalize treatments in patients with low-grade gliomas. WHAT IS THE CURRENT KNOWLEDGE ON THE TOPIC? þ First-line temozolomide is frequently used to treat low-grade gliomas (LGG), which are slow-growing brain tumors. The duration of response depends on genetic characteristics such as 1p/19q chromosomal codeletion, p53 mutation, and IDH mutations. However, up to now there are no means of predicting, at the individual level, the duration of the response to TMZ and its potential benefit for a given patient. • WHAT QUESTION DID THIS STUDY ADDRESS? þ The present study assessed whether combining longitudinal tumor size quantitative modeling with a tumor's genetic characterization could be an effective means of predicting the response to temozolomide at the individual level in LGG patients. • WHAT THIS STUDY ADDS TO OUR KNOWLEDGE þ For the first time, we developed a model of tumor growth inhibition integrating a tumor's genetic characteristics which successfully describes the time course of tumor size and captures potential tumor progression under chemotherapy in LGG patients treated with first-line temozolomide. The present study shows that using information on individual tumors' genetic characteristics, in addition to early tumor size measurements, it is possible to predict the duration and magnitude of response to temozolomide. • HOW THIS MIGHT CHANGE CLINICAL PHARMACOLOGY AND THERAPEUTICS þ Our model constitutes a rational tool to identify patients most likely to benefit from temozolomide and to optimize in these patients the duration of temozolomide therapy in order to ensure the longest duration of response to treatment. Response evaluation criteria such as RECIST—or RANO for brain tumors—are commonly used to assess response to anticancer treatments in clinical trials. 1,2 They assign a patient's response to one of four categories, ranging from " complete response " to " disease progression. " Yet, criticisms have been raised regarding the use of such categorical criteria in the drug development process, 3,4 and regulatory agencies have promoted the additional analysis of longitudinal tumor size measurements through the use of quantitative modeling. 5 Several mathematical models of tumor growth and response to treatment have been developed for this purpose. 6,7 These analyses have led to th

Net displacement and temporal scaling: Model fitting, interpretation and implementation

Net displacement is an integral component of numerous ecological processes and is critically dependent on the tortuosity of a movement trajectory and hence on the temporal scale of observation. Numerous attempts have been made to quantitatively describe net displacement while accommodating tortuosity, typically evoking a power law, but scale‐dependency in tortuosity limits the utility of approaches based on power law relationships that must assume scale‐invariant tortuosity. We describe a phenomenological model of net displacement that permits both scale‐variant and scale‐invariant movement. Movement trajectories are divided into pairs of relocations specifying start‐ and end‐points, and net displacements between points are calculated across a vector of time intervals. A bootstrap is implemented to create new datasets that are independent both across and within time intervals, and the model is fitted to the bootstrapped dataset using log–log regression. We apply this model to simulated trajectories and both fine‐grain and coarse‐grain trajectories obtained from an Aldabra giant tortoise Aldabrachelys gigantea, African elephants Loxodonta africana, black‐backed jackals Canis mesomelas and Northern elephant seals Mirounga angustirostris. The model was able to quantify the characteristics of net displacement from simulated movement trajectories corresponding to both scale‐variant (e.g. correlated random walks) and scale‐invariant (e.g. random walk) movement models. Furthermore, the model produced identical outputs across time vectors corresponding to different intervals and absolute ranges of time for scale‐invariant models. The model characterized the tortoise as generally exhibiting long scale‐invariant steps, which was corroborated by visual comparison of model outputs to observed trajectories. Elephants, jackals and seals exhibited movement parameters consistent with their known movement behaviours (nomadism, territoriality and widely ranging searching). We describe how the model may be used to compare movements within and between species, for example by partitioning movement into scale‐variant and scale‐invariant components, and by calculating a unitless net displacement scaled to the basal movement capacities of an animal. We also identify several useful derived quantities and realistic parameter ranges and discuss how the model may be implemented in a variety of ecological studies

The use of the SAEM algorithm in MONOLIX software for estimation of population pharmacokinetic-pharmacodynamic-viral dynamics parameters of maraviroc in asymptomatic HIV subjects

Using simulated viral load data for a given maraviroc monotherapy study design, the feasibility of different algorithms to perform parameter estimation for a pharmacokinetic-pharmacodynamic-viral dynamics (PKPD-VD) model was assessed. The assessed algorithms are the first-order conditional estimation method with interaction (FOCEI) implemented in NONMEM VI and the SAEM algorithm implemented in MONOLIX version 2.4. Simulated data were also used to test if an effect compartment and/or a lag time could be distinguished to describe an observed delay in onset of viral inhibition using SAEM. The preferred model was then used to describe the observed maraviroc monotherapy plasma concentration and viral load data using SAEM. In this last step, three modelling approaches were compared; (i) sequential PKPD-VD with fixed individual Empirical Bayesian Estimates (EBE) for PK, (ii) sequential PKPD-VD with fixed population PK parameters and including concentrations, and (iii) simultaneous PKPD-VD. Using FOCEI, many convergence problems (56%) were experienced with fitting the sequential PKPD-VD model to the simulated data. For the sequential modelling approach, SAEM (with default settings) took less time to generate population and individual estimates including diagnostics than with FOCEI without diagnostics. For the given maraviroc monotherapy sampling design, it was difficult to separate the viral dynamics system delay from a pharmacokinetic distributional delay or delay due to receptor binding and subsequent cellular signalling. The preferred model included a viral load lag time without inter-individual variability. Parameter estimates from the SAEM analysis of observed data were comparable among the three modelling approaches. For the sequential methods, computation time is approximately 25% less when fixing individual EBE of PK parameters with omission of the concentration data compared with fixed population PK parameters and retention of concentration data in the PD-VD estimation step. Computation times were similar for the sequential method with fixed population PK parameters and the simultaneous PKPD-VD modelling approach. The current analysis demonstrated that the SAEM algorithm in MONOLIX is useful for fitting complex mechanistic models requiring multiple differential equations. The SAEM algorithm allowed simultaneous estimation of PKPD and viral dynamics parameters, as well as investigation of different model sub-components during the model building process. This was not possible with the FOCEI method (NONMEM version VI or below). SAEM provides a more feasible alternative to FOCEI when facing lengthy computation times and convergence problems with complex models

The LHC test string: first operational experience

CERN operates the first version of the LHC Test String which consists of one quadrupole and three 10-m twin aperture dipole magnets. An experimental programme aiming at the validation of the LHC systems started in February 1995. During this programme the string has been powered 100 times 35 of which at 12.4 kA or above. The experiments have yielded a number of results some of which, like quench recovery for cryogenics, have modified the design of subsystems of LHC. Others, like controlled helium leaks in the cold bore and quench propagation bewteen magnets, have given a better understanding on the evolution of the phenomena inside a string of superconducting magnets cooled at superfluid helium temperatures. Following the experimental programme, the string will be powered up and powered down in one hour cycles as a fatigue test of the structure thus simulating 20 years of operation of LHC

Cardiovasc Diabetol

BACKGROUND: Advanced glycation end-products play a role in diabetic vascular complications. Their optical properties allow to estimate their accumulation in tissues by measuring the skin autofluorescence (SAF). We searched for an association between SAF and major adverse cardiovascular events (MACE) incidence in subjects with Type 1 Diabetes (T1D) during a 7 year follow-up. METHODS: During year 2009, 232 subjects with T1D were included. SAF measurement, clinical [age, sex, body mass index (BMI), comorbidities] and biological data (HbA1C, blood lipids, renal parameters) were recorded. MACE (myocardial infarction, stroke, lower extremity amputation or a revascularization procedure) were registered at visits in the center or by phone call to general practitioners until 2016. RESULTS: The participants were mainly men (59.5%), 51.5 +/- 16.7 years old, with BMI 25.0 +/- 4.1 kg/m(2), diabetes duration 21.5 +/- 13.6 years, HbA1C 7.6 +/- 1.1%. LDL cholesterol was 1.04 +/- 0.29 g/L, estimated Glomerular Filtration Rates (CKD-EPI): 86.3 +/- 26.6 ml/min/1.73 m(2). Among these subjects, 25.1% were smokers, 45.3% had arterial hypertension, 15.9% had elevated AER (>/= 30 mg/24 h), and 9.9% subjects had a history of previous MACE. From 2009 to 2016, 22 patients had at least one new MACE: 6 myocardial infarctions, 1 lower limb amputation, 15 revascularization procedures. Their SAF was 2.63 +/- 0.73 arbitrary units (AU) vs 2.08 +/- 0.54 for other patients (p = 0.002). Using Cox-model, after adjustment for age (as the scale time), sex, diabetes duration, BMI, hypertension, smoking status, albumin excretion rates, statin treatment and a previous history of MACE, higher baseline levels of SAF were significantly associated with an increased risk of MACE during follow-up (HR = 4.13 [1.30-13.07]; p = 0.02 for 1 AU of SAF) and Kaplan-Meier curve follow-up showed significantly more frequent MACE in group with SAF upper the median (p = 0.001). CONCLUSION: A high SAF predicts MACE in patients with T1D