Consistency between Treatment Effects on Clinical and Brain Atrophy Outcomes in Alzheimer's Disease Trials

Background: Longitudinal changes in volumetric MRI outcome measures have been shown to correlate well with longitudinal changes in clinical instruments and have been widely used as biomarker outcomes in clinical trials for Alzheimer’s disease (AD). While instances of discordant findings have been noted in some trials, especially the recent amyloid-removing therapies, the overall relationship between treatment effects on brain atrophy and clinical outcomes, and how it might depend on treatment target or mechanism, clinical instrument or imaging variable is not yet clear. / Objective: To systematically assess the consistency and therapeutic class-dependence of treatment effects on clinical outcomes and on brain atrophy in published reports of clinical trials conducted in mild cognitive impairment (MCI) and/or AD. / Design: Quantitative review of the published literature. The consistency of treatment effects on clinical and brain atrophy outcomes was assessed in terms of statistical agreement with hypothesized equal magnitude effects (e.g., 30% slowing of both) and nominal directional concordance, as a function of therapeutic class. Setting: Interventional randomized clinical trials. / Participants: MCI or AD trial participants. / Intervention: Treatments included were those that involved ingestion or injection of a putatively active substance into the body, encompassing both pharmacological and controlled dietary interventions. / Measurements: Each trial included in the analysis reported at least one of the required clinical outcomes (ADAS-Cog, CDR-SB or MMSE) and at least one of the required imaging outcomes (whole brain, ventricular or hippocampal volume). / Results: Data from 35 trials, comprising 185 pairwise comparisons, were included. Overall, the 95% confidence bounds overlapped with the line of identity for 150/185 (81%) of the imaging-clinical variable pairs. The greatest proportion of outliers was found in trials of anti-amyloid antibodies that have been shown to dramatically reduce the level of PET-detectable amyloid plaques, for which only 13/33 (39%) of observations overlapped the identity line. A Deming regression calculated using all data points yielded a slope of 0.54, whereas if data points from the amyloid remover class were excluded, the Deming regression line had a slope of 0.92. Directional discordance of treatment effects was also most pronounced for the amyloid-removing class, and for comparisons involving ventricular volume. / Conclusion: Our results provide a frame of reference for the interpretation of clinical and brain atrophy results from future clinical trials and highlight the importance of mechanism of action in the interpretation of imaging results

Modeling and Rescue of RP2 Retinitis Pigmentosa Using iPSC-Derived Retinal Organoids

RP2 mutations cause a severe form of X-linked retinitis pigmentosa (XLRP). The mechanism of RP2-associated retinal degeneration in humans is unclear, and animal models of RP2 XLRP do not recapitulate this severe phenotype. Here, we developed gene-edited isogenic RP2 knockout (RP2 KO) induced pluripotent stem cells (iPSCs) and RP2 patient-derived iPSC to produce 3D retinal organoids as a human retinal disease model. Strikingly, the RP2 KO and RP2 patient-derived organoids showed a peak in rod photoreceptor cell death at day 150 (D150) with subsequent thinning of the organoid outer nuclear layer (ONL) by D180 of culture. Adeno-associated virus-mediated gene augmentation with human RP2 rescued the degeneration phenotype of the RP2 KO organoids, to prevent ONL thinning and restore rhodopsin expression. Notably, these data show that 3D retinal organoids can be used to model photoreceptor degeneration and test potential therapies to prevent photoreceptor cell death

Evaluation of Poly-Mechanistic Antiangiogenic Combinations to Enhance Cytotoxic Therapy Response in Pancreatic Cancer

Gemcitabine (Gem) has limited clinical benefits in pancreatic ductal adenocarcinoma (PDAC). The present study investigated combinations of gemcitabine with antiangiogenic agents of various mechanisms for PDAC, including bevacizumab (Bev), sunitinib (Su) and EMAP II. Cell proliferation and protein expression were analyzed by WST-1 assay and Western blotting. In vivo experiments were performed via murine xenografts. Inhibition of in vitro proliferation of AsPC-1 PDAC cells by gemcitabine (10 µM), bevacizumab (1 mg/ml), sunitinib (10 µM) and EMAP (10 µM) was 35, 22, 81 and 6 percent; combination of gemcitabine with bevacizumab, sunitinib or EMAP had no additive effects. In endothelial HUVECs, gemcitabine, bevacizumab, sunitinib and EMAP caused 70, 41, 86 and 67 percent inhibition, while combination of gemcitabine with bevacizumab, sunitinib or EMAP had additive effects. In WI-38 fibroblasts, gemcitabine, bevacizumab, sunitinib and EMAP caused 79, 58, 80 and 29 percent inhibition, with additive effects in combination as well. Net in vivo tumor growth inhibition in gemcitabine, bevacizumab, sunitinib and EMAP monotherapy was 43, 38, 94 and 46 percent; dual combinations of Gem+Bev, Gem+Su and Gem+EMAP led to 69, 99 and 64 percent inhibition. Combinations of more than one antiangiogenic agent with gemcitabine were generally more effective but not superior to Gem+Su. Intratumoral proliferation, apoptosis and microvessel density findings correlated with tumor growth inhibition data. Median animal survival was increased by gemcitabine (26 days) but not by bevacizumab, sunitinib or EMAP monotherapy compared to controls (19 days). Gemcitabine combinations with bevacizumab, sunitinib or EMAP improved survival to similar extent (36 or 37 days). Combinations of gemcitabine with Bev+EMAP (43 days) or with Bev+Su+EMAP (46 days) led to the maximum survival benefit observed. Combination of antiangiogenic agents improves gemcitabine response, with sunitinib inducing the strongest effect. These findings demonstrate advantages of combining multi-targeting agents with standard gemcitabine therapy for PDAC

Forecasting Player Behavioral Data and Simulating in-Game Events

Understanding player behavior is fundamental in game data science. Video games evolve as players interact with the game, so being able to foresee player experience would help to ensure a successful game development. In particular, game developers need to evaluate beforehand the impact of in-game events. Simulation optimization of these events is crucial to increase player engagement and maximize monetization. We present an experimental analysis of several methods to forecast game-related variables, with two main aims: to obtain accurate predictions of in-app purchases and playtime in an operational production environment, and to perform simulations of in-game events in order to maximize sales and playtime. Our ultimate purpose is to take a step towards the data-driven development of games. The results suggest that, even though the performance of traditional approaches such as ARIMA is still better, the outcomes of state-of-the-art techniques like deep learning are promising. Deep learning comes up as a well-suited general model that could be used to forecast a variety of time series with different dynamic behaviors