The link between cortical precursors G1 duration (TG1) and their mode of division remains a major unresolved issue of potential importance for regulating corticogenesis. Here, we induced a 25% reduction in TG1 in mouse cortical precursors via forced expression of cyclin D1 and cyclin E1. We found that in utero electroporationmediated gene transfer transfects a cohort of synchronously cycling precursors, necessitating alternative methods of measuring cell-cycle phases to those classical used. TG1 reduction promotes cell-cycle reentry at the expense of differentiation and increases the self-renewal capacities of Pax6 precursors as well as of Tbr2 basal precursors (BPs). A population level analysis reveals sequential and lineage-specific effects, showing that TG1 reduction: (i) promotes Pax6 self-renewing proliferative divisions before promoting divisions wherein Pax6 precursors generate Tbr2 BPs and (ii) promotes self-renewing proliferative divisions of Tbr2 precursors at the expense of neurogenesis, thus leading to an amplification of the BPs pool in the subventricular zone and the dispersed mitotic compartment of the intermediate zone. These results point to the G1 mode of division relationship as an essential control mechanism of corticogenesis. This is further supported by longterm studies showing that TG1 reduction results in cytoarchitectural modifications including supernumerary supragranular neuron production. Modeling confirms that the TG1-induced changes in neuron production and laminar fate are mediated via the changes in the mode of division. These findings also have implications for understanding the mechanisms that have contributed to brain enlargement and complexity during evolution.basal progenitor ͉ cell-cycle ͉ corticogenesis C ortical areas are characterized by their cytoarchitecture, an expression of the morphology and density of their constituent neurons. Areal differences in neuron number and phenotype are distinguishing features both within and across species (1, 2). The developmental processes that specify the number of neurons and their laminar fate are therefore instrumental in specifying cortical cytoarchitecture. Neuron number in layers and areas correlate with changes in the rate of neuron production, largely determined by the balance between cell-cycle reentry and exit (3, 4). Proliferative division generates two progenitors that re-enter the cell-cycle, whereas differentiative division gives rise to at least one daughter cell that undergoes differentiation. An open question is how the decision between proliferative versus differentiative division is made (5).Key observations suggest a concerted regulation of TG1 and mode of division. During mouse corticogenesis, a progressive increase in rates of neuron production, is accompanied by increasing frequencies of differentiative divisions, and a slowing down of TG1 (6). Proliferative divisions are characterized by short TG1 and differentiative divisions by long TG1 (3, 7-9). G1 represents a critical phase during cell-cycle progression, wh...

AIMThe aim of this study was to develop a PK/PD model to assess drug-drug interactions between dabigatran and P-gp modulators, using the example of clarithromycin, a strong inhibitor of P-gp. METHODSTen healthy male volunteers were randomized to receive in the first treatment period a single 300 mg dose of dabigatran etexilate (DE) and in the second treatment period 500 mg clarithromycin twice daily during 3 days and then 300 mg DE plus 500 mg clarithromycin on the fourth day, or the same treatments in the reverse sequence. Dabigatran plasma concentration and ecarin clotting time (ECT) were measured on 11 blood samples. Models were built using a non-linear mixed effect modelling approach. RESULTSThe best PK model was based on an inverse Gaussian absorption process with two compartments. The relationship between dabigatran concentration and ECT was implemented as a linear function. No continuous covariate was associated with a significant decrease in the objective function. The concomitant administration of clarithromycin induced a significant change only in DE bioavailability, which increased from 6.5% to 10.1% in the presence of clarithromycin. Clarithromycin increased peak concentration and AUC by 60.2% and 49.1% respectively. CONCLUSIONThe model proposed effectively describes the complex PK of dabigatran and takes into account drug-drug interactions with P-gp activity modulators, such as clarithromycin. WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT• Dabigatran etexilate has a bioavailability of 6.5% due to a complex absorption process.• Dabigatran etexilate is a substrate for P-gp and its reflux can be modulated by other drugs.• P-gp inhibitors increase the AUC of dabigatran from about 50% to over 200%. WHAT THIS STUDY ADDS• The pharmacokinetics and pharmacodynamics of dabigatran were described by a two compartment model with an absorption following an inverse Gaussian law, associated with a linear effect model. • We showed that this phenomenon is explained solely by an increase in bioavailability from 6.5 to 10%. • Exposure to dabigatran is increased by 50% in the presence of clarithromycin and is characterized by substantial variability.

Understanding how tumors develop resistance to chemotherapy is a major issue in oncology. When treated with temozolomide (TMZ), an oral alkylating chemotherapy drug, most low-grade gliomas (LGG) show an initial volume decrease but this effect is rarely long lasting. In addition, it has been suggested that TMZ may drive tumor progression in a subset of patients as a result of acquired resistance. Using longitudinal tumor size measurements from 121 patients, the aim of this study was to develop a semi-mechanistic mathematical model to determine whether resistance of LGG to TMZ was more likely to result from primary and/or from chemotherapy-induced acquired resistance that may contribute to tumor progression. We applied the model to a series of patients treated upfront with TMZ (n = 109) or PCV (procarbazine, CCNU, vincristine) chemotherapy (n = 12) and used a population mixture approach to classify patients according to the mechanism of resistance most likely to explain individual tumor growth dynamics. Our modeling results predicted acquired resistance in 51% of LGG treated with TMZ. In agreement with the different biological effects of nitrosoureas, none of the patients treated with PCV were classified in the acquired resistance group. Consistent with the mutational analysis of recurrent LGG, analysis of growth dynamics using mathematical modeling suggested that in a subset of patients, TMZ might paradoxically contribute to tumor progression as a result of chemotherapy-induced resistance. Identification of patients at risk of developing acquired resistance is warranted to better define the role of TMZ in LGG.