Effects of ponesimod,a selective S1P1 receptor modulator,on the pharmacokinetics of a hormonal combination contraceptive

Purpose

To determine the effects of steady-state concentrations of the selective S1P₁ receptor modulator ponesimod on the pharmacokinetics (PK) of a single dose of a combined oral contraceptive, containing 1 mg norethisterone (NET) and 35 μg ethinyl estradiol (EE) and to investigate the effects on heart rate at different ponesimod doses within an up-titration regimen prior to co-administration of the contraceptive.

Methods

Twenty-two healthy women (age: 29-60 years) received twice a single oral dose of the combined oral contraceptive, alone or in combination with multiple doses of 40 mg ponesimod attained by an up-titration regimen. Heart rate (HR) effects were assessed on the first day of each up-titration level. PK parameters of NET and EE were determined by non-compartmental analysis.

Results

Geometric mean ratios (ponesimod and contraceptive / contraceptive alone) of C_max and AUC_0-24 of NET were 0.87 (90 % CI: 0.80, 0.94) and 0.84 (90 % CI: 0.76, 0.93), respectively. Geometric mean ratios of C_max and AUC_0-24 of EE were 0.94 (90 % CI: 0.86, 1.03) and 0.95 (90 % CI: 0.89, 1.01), respectively. The maximum mean HR reduction after the first dose of 10 mg ponesimod was 12.4 bpm (SD?±?6.2) at 2.5 h post-dose. On Day 4 (first dose of 20 mg) and Day 7 (first dose of 40 mg) the maximum mean HR reduction was 4.3 bpm (SD?±?5.7) and 1.4 (SD?±?6.4), respectively, at 2.5 h post-dose compared to baseline.

Conclusion

No clinically relevant PK interactions between ponesimod and the combined oral contraceptive were observed, therefore, efficacy of hormonal contraceptives is not expected to be affected by concomitant administration of ponesimod. The up-titration regimen showed that HR reductions are diminished upon repeated ponesimod administration.     

Targeted disruption of the mouse beta1-adrenergic receptor gene: developmental and cardiovascular effects.

At least three distinct beta-adrenergic receptor (beta-AR) subtypes exist in mammals. These receptors modulate a wide variety of processes, from development and behavior, to cardiac function, metabolism, and smooth muscle tone. To understand the roles that individual beta-AR subtypes play in these processes, we have used the technique of gene targeting to create homozygous beta 1-AR null mutants (beta 1-AR -/-) in mice. The majority of beta 1-AR -/- mice die prenatally, and the penetrance of lethality shows strain dependence. Beta l-AR -/- mice that do survive to adulthood appear normal, but lack the chronotropic and inotropic responses seen in wild-type mice when beta-AR agonists such as isoproterenol are administered. Moreover, this lack of responsiveness is accompanied by markedly reduced stimulation of adenylate cyclase in cardiac membranes from beta 1-AR -/- mice. These findings occur despite persistent cardiac beta 2-AR expression, demonstrating the importance of beta 1-ARs for proper mouse development and cardiac function, while highlighting functional differences between beta-AR subtypes.     

Simultaneous selection by object-based attention in visual and frontal cortex

Models of visual attention hold that top-down signals from frontal cortex influence information processing in visual cortex. It is unknown whether situations exist in which visual cortex actively participates in attentional selection. To investigate this question, we simultaneously recorded neuronal activity in the frontal eye fields (FEF) and primary visual cortex (V1) during a curve-tracing task in which attention shifts are object-based. We found that accurate performance was associated with similar latencies of attentional selection in both areas and that the latency in both areas increased if the task was made more difficult. The amplitude of the attentional signals in V1 saturated early during a trial, whereas these selection signals kept increasing for a longer time in FEF, until the moment of an eye movement, as if FEF integrated attentional signals present in early visual cortex. In erroneous trials, we observed an interareal latency difference because FEF selected the wrong curve before V1 and imposed its erroneous decision onto visual cortex. The neuronal activity in visual and frontal cortices was correlated across trials, and this trial-to-trial coupling was strongest for the attended curve. These results imply that selective attention relies on reciprocal interactions within a large network of areas that includes V1 and FEF.Visual scenes are usually too complex for all information to be analyzed at once. Selective attention selects a subset of the objects in the visual scene for detailed analysis at the expense of other items. Visual objects compete for selection, and the outcome of this competition depends on bottom-up cues such as saliency and perceptual organization and top-down cues that signal the objects’ behavioral relevance (1). It is not well understood how these different cues interact and which brain areas take the lead in visual selection.The top-down mechanisms for attentional selection are tightly linked to those for the selection of actions (2), and accordingly, cortical areas related to action planning influence the deployment of visual attention. The frontal eye fields (FEF) is one such area that is involved in visual processing, shifts of visual attention (2, 3), and also in the control of eye movements (4, 5). Area FEF contains different types of cells. Visual processing relies on visual and visuomovement cells, whereas the programming of eye movements relies on the activity of visuomovement and movement cells (6, 7). There are several lines of evidence that also implicate FEF in attentional control. First, FEF inactivation impairs attention shifts toward the contralateral visual field (8, 9). Second, subthreshold FEF microstimulation enhances neuronal activity in visual cortex in a manner that is reminiscent of selective attention (10, 11). Third, a role of FEF in the top-down guidance of attention is supported by studies on visual search. In search, selection signals in frontal cortex precede those in area V4 by 50 ms, suggesting that the frontal cortex determines selection and then provides feedback to visual cortex (12, 13). A comparable interareal delay in attentional effects was observed between the lateral intraparietal area and the motion sensitive middle temporal area (14). Thus, the parietal and frontal cortices appear to take the lead in attentional selection and to provide top-down signals to visual cortex. Within the visual cortex, such a reverse hierarchy (15) of attentional effects was observed in a task that required shifts of spatial attention (16) and also in a task demanding shifts between visual and auditory attention (17). Attentional signals in area V4 preceded signals in V2 by 50–250 ms, which in turn preceded attentional effects in the primary visual cortex (V1) by 50–400 ms.However, top-down factors are not the only ones that guide attention. Attention can be object-based, implying that the visual stimulus itself influences the distribution of attention too. If attention is directed to a feature, attention tends to coselect visually related features on the basis of perceptual grouping cues (18) so that entire objects rather than isolated features are attended (19, 20). The influence of perceptual grouping on attentional selection can be investigated with a curve-tracing task that requires grouping of the contour elements of a single curve (21, 22). Attention in this task is directed to the entire curve, implying that the curve’s shape itself influences the distribution of attention (22). Indeed, a traced curve evokes stronger activity in primary visual cortex than an irrelevant curve, revealing a neuronal correlate of object-based attention (23). However, it is not known if the coselection of all image elements of a single object is determined within early visual cortex or is guided by the frontal cortex, just as was shown for other tasks.Here we compare selection signals in areas FEF and V1 in the curve-tracing task with simultaneous recordings in the two areas. A priori, several possibilities exist for the interaction between V1 and FEF. First, the frontal cortex might select the relevant curve and then feed a guiding signal back to visual cortex (24, 25) as in the other tasks described above. If so, attentional selection signals in V1 might arise tens to hundreds of milliseconds later than in FEF. However, the chain of events in the curve-tracing task might differ because visual shape has a profound influence on the distribution of attention (26). Thus, a second possibility is that the visual cortex determines selection so that the attentional modulation in visual cortex precedes that in frontal cortex. A third possibility is that visual and frontal areas jointly determine what is relevant and what is not. In this situation, the selection signals are expected to occur in both areas at approximately the same time. It is also possible that the order of selection in different areas depends on the difficulty of the task. For example, the reverse hierarchy theory of visual perception (15) proposed that easy tasks are usually solved by higher visual areas, whereas lower visual areas are recruited when the picture has to be scrutinized. We therefore varied the difficulty of the curve-tracing task.     

Computer-assisted surgery in orthopedic oncology: Technique,indications, and a descriptive study of 130 cases

Background and purpose —

In orthopedic oncology, computer-assisted surgery (CAS) can be considered an alternative to fluoroscopy and direct measurement for orientation, planning, and margin control. However, only small case series reporting specific applications have been published. We therefore describe possible applications of CAS and report preliminary results in 130 procedures.

Patients and methods —

We conducted a retrospective cohort study of all oncological CAS procedures in a single institution from November 2006 to March 2013. Mean follow-up time was 32 months. We categorized and analyzed 130 procedures for clinical parameters. The categories were image-based intralesional treatment, image-based resection, image-based resection and reconstruction, and imageless resection and reconstruction.

Results —

Application to intralesional treatment showed 1 inadequate curettage and 1 (other) recurrence in 63 cases. Image-based resections in 42 cases showed 40 R0 margins; 16 in 17 pelvic resections. Image-based reconstruction facilitated graft creation with a mean reconstruction accuracy of 0.9 mm in one case. Imageless CAS was helpful in resection planning and length- and joint line reconstruction for tumor prostheses.

Interpretation —

CAS is a promising new development. Preliminary results show a high number of R0 resections and low short-term recurrence rates for curettage.Oncological surgical treatment can be considered to be a trade-off between margins and function, with margins being the most important factor to consider. Accuracy is needed to achieve an efficient but oncologically safe result. To assist in this, most procedures in bone tumor surgery require intraoperative imaging with fluoroscopy and/or measurements with rulers for anatomical orientation and margin control. The best examples of this are pelvic resections. Cartiaux et al. (2008) demonstrated that 4 experienced surgeons could achieve a 10-mm resection margin, with 5-mm tolerance, on pelvic sawbones in only half of the resections. The supportive imaging and measuring modalities have, however, remained more or less unchanged for many years. In a 2-dimensional (2D) workflow such as fluoroscopy, there is still the requirement for an accurate frame of reference based on anatomical landmarks for adequate 3-dimensional (3D) margin control.In recent years, the use of computer-assisted surgery (CAS) in orthopedic surgery has become more common as an alternative for intraoperative imaging and measurements, providing the necessary precision in bone tumor surgery. The technique that is mostly used in orthopedic oncology is image-based navigation. The patient’s own anatomy (MRI and/or CT) is entered into the system and used during surgery. This provides real-time, continuous, 3D imaging feedback and may lead to more precise margin control, better tissue preservation, and new approaches to reconstruction while remaining oncologically safe. Several publications have supported CAS as being a safe navigation platform for planning and performing resections (Wong et al. 2007, So et al. 2010, Cho et al. 2012). A recent publication describes lessons in the technological approach and offers comments on CAS workflow (Wong 2010). However, to date the largest case series have involved only 20 and 31 cases (Cheong and Letson 2011, Jeys et al. 2013). The reported use has mostly been limited to complex tumor resections (e.g. pelvic), and due to the novelty of the technique, applications, approaches, and set-up times differ greatly (Saidi 2012). Here we describe possible applications of CAS in bone tumor surgery (also outside of complex resections), consider their usefulness, and report preliminary results from 130 CAS procedures performed at a single institution.     

Motor unit number estimation: A technology and literature review

Introduction: Numerous methods for motor unit number estimation (MUNE) have been developed. The objective of this article is to summarize and compare the major methods and the available data regarding their reproducibility, validity, application, refinement, and utility. Methods: Using specified search criteria, a systematic review of the literature was performed. Reproducibility, normative data, application to specific diseases and conditions, technical refinements, and practicality were compiled into a comprehensive database and analyzed. Results: The most commonly reported MUNE methods are the incremental, multiple‐point stimulation, spike‐triggered averaging, and statistical methods. All have established normative data sets and high reproducibility. MUNE provides quantitative assessments of motor neuron loss and has been applied successfully to the study of many clinical conditions, including amyotrophic lateral sclerosis and normal aging. Conclusions: MUNE is an important research technique in human subjects, providing important data regarding motor unit populations and motor unit loss over time. Muscle Nerve 50 : 884–893, 2014