Effect of Flibanserin Treatment on Body Weight in Premenopausal and Postmenopausal Women with Hypoactive Sexual Desire Disorder: A Post Hoc Analysis.

Background: Flibanserin, a 5-HT1A agonist and 5-HT2A antagonist, is indicated for the treatment of acquired, generalized hypoactive sexual desire disorder (HSDD) in premenopausal women. This post hoc analysis evaluated the effect of flibanserin treatment on body weight in premenopausal and postmenopausal women with HSDD. Materials and Methods: This analysis included three 24-week, double-blind, placebo-controlled studies of flibanserin 100 mg each bedtime (qhs) in premenopausal women, a similarly designed study in postmenopausal women, and a 52-week, open-label extension study in premenopausal women. Results: In a pooled analysis of premenopausal women, mean baseline body mass index (BMI) was 27.0 kg/m2 in the flibanserin group (n = 1227) and 26.8 kg/m2 in the placebo group (n = 1238). Among patients who completed 24 weeks of treatment, least squares (LS) mean weight change was −1.4 kg in the flibanserin group (n = 1010) and −0.1 kg in the placebo group (n = 1066; p \u3c 0.0001). Weight loss ≥5% from baseline was reported in 21.0% of patients who received flibanserin and 7.8% of patients who received placebo; weight loss ≥10% was reported in 3.8% and 2.0% of patients, respectively. In postmenopausal women, mean baseline BMI was 27.7 kg/m2 in the flibanserin group (n = 467) and 27.3 kg/m2 in the placebo group (n = 480). LS mean weight change at week 24 was −1.8 kg in the flibanserin group (n = 385) and −0.1 kg in the placebo group (n = 425; p \u3c 0.0001), with weight loss ≥5% reported in 24.7% and 7.3% of patients, respectively, and weight loss ≥10% reported in 5.2% and 1.7%, respectively. In HSDD patients with \u3e12 months (n = 880) and \u3e18 months (n = 637) of exposure to flibanserin, mean weight change was −1.0 and −1.2 kg, respectively; 25.4% and 26.9% of patients, respectively, experienced weight loss ≥5% from baseline, and 7.8% and 8.4%, respectively, experienced weight loss ≥10%. Conclusions: Women treated with flibanserin for HSDD may experience weight loss

Drops in Space: Super Oscillations and Surfactant Studies

An unprecedented microgravity observation of maximal shape oscillations of a surfactant-bearing water drop the size of a ping pong ball was observed during a mission of Space Shuttle Columbia as part of the second United States Microgravity Laboratory-USML-2 (STS-73, October 20-November 5, 1995). The observation was precipitated by the action of an intense sound field which produced a deforming force on the drop. When this deforming force was suddenly reduced, the drop executed nearly free and axisymmetric oscillations for several cycles, demonstrating a remarkable amplitude of nonlinear motion. Whether arising from the discussion of modes of oscillation of the atomic nucleus, or the explosion of stars, or how rain forms, the complex processes influencing the motion, fission, and coalescence of drops have fascinated scientists for centuries. Therefore, the axisymmetric oscillations of a maximally deformed liquid drop are noteworthy, not only for their scientific value but also for their aesthetic character. Scientists from Yale University, the Jet Propulsion Laboratory (JPL) and Vanderbilt University conducted liquid drop experiments in microgravity using the acoustic positioning/manipulation environment of the Drop Physics Module (DPM). The Yale/JPL group's objectives were to study the rheological properties of liquid drop surfaces on which are adsorbed surfactant molecules, and to infer surface properties such as surface tension, Gibb's elasticity, and surface dilatational viscosity by using a theory which relies on spherical symmetry to solve the momentum and mass transport equations

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery