Demonstrating a powerful scale-up strategy for Biosimilar mAb in single use systems via physicochemical and functional characterization

Biosimilars have received a remarkable attention in the recent years. Due to the heterogeneity of biosimilar mAbs, they need to be well-characterized by various orthogonal techniques in order to identify their physicochemical and functional characteristics. Characterization of the post translational modifications, especially, glycosylation is vital to define the critical quality attributes (CQAs) which affect safety, efficacy and quality of drugs. In this study, we were able to manipulate the quality of the drug by using scale-up strategies for single use systems. By using ultra-performance liquid chromatography (UPLC) coupled to mass spectrometry (MS), we were able to demonstrate physicochemical similarities between innovator and its biosimilar candidate. Even the PTM (N-terminal pyroglutamic acid formation, C-terminal lysine truncation, methionine and tryptophan oxidation, asparagine deamidation, N-glycosylation and glycation) levels of two products from 3 and 200-liter single-use bioreactors were highly similar compared to the innovator. The mass spectrometry studies showed that the scale-up strategy from 3 liter to 200 liter was successful. Deconvoluted mass spectrum for intact and reduced masses (heavy and light chain) of innovator and its biosimilar candidates from different production scales were significantly similar. Oxidation was observed to be lower in 200 liter bioreactor compared to the 3 liter. The N-glycan profiles for the major and minor glycan species were highly similar compared to the originator. Aggregation level in 200 liter was slightly lower than that of the small scale production. Mass spectrometry becomes an important tool to enhance the biosimilarity to the originator in order to decrease the clinical efforts to be able to provide affordable drugs to the patients

Olfactory Neuroblastomas: An Experience of 24 Years

Objective. The aim of this study was to evaluate clinicopathological findings and the efficacy of the treatment modalities used in patients with olfactory neuroblastomas. Study Design. Retrospective record review. Setting. Istanbul University, Cerrahpasa Medical Faculty, medical oncology outpatient clinic. Subjects and Methods. There were 3 stage A tumors, 5 stage B and 11 stage C according to the Kadish staging system. There were 5 grade I/II and 12 grade III/IV according to the Hyams' histopathologic system. Involvement to orbita was detected in eight patients at the time of diagnosis. Results. The median follow-up period was 23.7 months. The 5-year survival rate for the whole group was 26%. The stage A/B groups exhibited a better survival rate than the C group with 2-year survival rates being 25 versus 71% respectively (P = .008). The grade I/II groups exhibited a better survival rate than the grade III/IV groups with 2-year survival rates being 50 versus 16% respectively (P = .001). The group who had orbital involvement exhibited a poor survival rate than the group of patients who had no involvement of the orbital. Conclusion. In our study, tumor stage, histopathologic grading, involvement of the orbita, brain and bone marow metastases were the statistically significant prognostic factors

Cognitive Trajectories in Preclinical and Prodromal Alzheimer's Disease Related to Amyloid Status and Brain Atrophy:A Bayesian Approach

Background: Cognitive decline is a key outcome of clinical studies in Alzheimer’s disease (AD). Objective: To determine effects of global amyloid load as well as hippocampus and basal forebrain volumes on longitudinal rates and practice effects from repeated testing of domain specific cognitive change in the AD spectrum, considering non-linear effects and heterogeneity across cohorts. Methods: We included 1,514 cases from three cohorts, ADNI, AIBL, and DELCODE, spanning the range from cognitively normal people to people with subjective cognitive decline and mild cognitive impairment (MCI). We used generalized Bayesian mixed effects analysis of linear and polynomial models of amyloid and volume effects in time. Robustness of effects across cohorts was determined using Bayesian random effects meta-analysis. Results: We found a consistent effect of amyloid and hippocampus volume, but not of basal forebrain volume, on rates of memory change across the three cohorts in the meta-analysis. Effects for amyloid and volumetric markers on executive function were more heterogeneous. We found practice effects in memory and executive performance in amyloid negative cognitively normal controls and MCI cases, but only to a smaller degree in amyloid positive controls and not at all in amyloid positive MCI cases. Conclusions: We found heterogeneity between cohorts, particularly in effects on executive functions. Initial increases in cognitive performance in amyloid negative, but not in amyloid positive MCI cases and controls may reflect practice effects from repeated testing that are lost with higher levels of cerebral amyloid

Cognitive Trajectories in Preclinical and Prodromal Alzheimer's Disease Related to Amyloid Status and Brain Atrophy: A Bayesian Approach

Background: Cognitive decline is a key outcome of clinical studies in Alzheimer's disease (AD). Objective: To determine effects of global amyloid load as well as hippocampus and basal forebrain volumes on longitudinal rates and practice effects from repeated testing of domain specific cognitive change in the AD spectrum, considering non-linear effects and heterogeneity across cohorts. Methods: We included 1,514 cases from three cohorts, ADNI, AIBL, and DELCODE, spanning the range from cognitively normal people to people with subjective cognitive decline and mild cognitive impairment (MCI). We used generalized Bayesian mixed effects analysis of linear and polynomial models of amyloid and volume effects in time. Robustness of effects across cohorts was determined using Bayesian random effects meta-analysis. Results: We found a consistent effect of amyloid and hippocampus volume, but not of basal forebrain volume, on rates of memory change across the three cohorts in the meta-analysis. Effects for amyloid and volumetric markers on executive function were more heterogeneous. We found practice effects in memory and executive performance in amyloid negative cognitively normal controls and MCI cases, but only to a smaller degree in amyloid positive controls and not at all in amyloid positive MCI cases. Conclusions: We found heterogeneity between cohorts, particularly in effects on executive functions. Initial increases in cognitive performance in amyloid negative, but not in amyloid positive MCI cases and controls may reflect practice effects from repeated testing that are lost with higher levels of cerebral amyloid

Improving 3D convolutional neural network comprehensibility via interactive visualization of relevance maps: Evaluation in Alzheimer's disease

Background: Although convolutional neural networks (CNN) achieve high diagnostic accuracy for detecting Alzheimer's disease (AD) dementia based on magnetic resonance imaging (MRI) scans, they are not yet applied in clinical routine. One important reason for this is a lack of model comprehensibility. Recently developed visualization methods for deriving CNN relevance maps may help to fill this gap. We investigated whether models with higher accuracy also rely more on discriminative brain regions predefined by prior knowledge. Methods: We trained a CNN for the detection of AD in N=663 T1-weighted MRI scans of patients with dementia and amnestic mild cognitive impairment (MCI) and verified the accuracy of the models via cross-validation and in three independent samples including N=1655 cases. We evaluated the association of relevance scores and hippocampus volume to validate the clinical utility of this approach. To improve model comprehensibility, we implemented an interactive visualization of 3D CNN relevance maps. Results: Across three independent datasets, group separation showed high accuracy for AD dementia vs. controls (AUC$\geq$0.92) and moderate accuracy for MCI vs. controls (AUC$\approx$0.75). Relevance maps indicated that hippocampal atrophy was considered as the most informative factor for AD detection, with additional contributions from atrophy in other cortical and subcortical regions. Relevance scores within the hippocampus were highly correlated with hippocampal volumes (Pearson's r$\approx$-0.86, p<0.001). Conclusion: The relevance maps highlighted atrophy in regions that we had hypothesized a priori. This strengthens the comprehensibility of the CNN models, which were trained in a purely data-driven manner based on the scans and diagnosis labels.Comment: 24 pages, 9 figures/tables, supplementary material, source code available on GitHu

Improving 3D convolutional neural network comprehensibility via interactive visualization of relevance maps: evaluation in Alzheimer’s disease

Background: Although convolutional neural networks (CNNs) achieve high diagnostic accuracy for detecting Alzheimer’s disease (AD) dementia based on magnetic resonance imaging (MRI) scans, they are not yet applied in clinical routine. One important reason for this is a lack of model comprehensibility. Recently developed visualization methods for deriving CNN relevance maps may help to fill this gap as they allow the visualization of key input image features that drive the decision of the model. We investigated whether models with higher accuracy also rely more on discriminative brain regions predefined by prior knowledge. Methods: We trained a CNN for the detection of AD in N = 663 T1-weighted MRI scans of patients with dementia and amnestic mild cognitive impairment (MCI) and verified the accuracy of the models via cross-validation and in three independent samples including in total N = 1655 cases. We evaluated the association of relevance scores and hippocampus volume to validate the clinical utility of this approach. To improve model comprehensibility, we implemented an interactive visualization of 3D CNN relevance maps, thereby allowing intuitive model inspection. Results: Across the three independent datasets, group separation showed high accuracy for AD dementia versus controls (AUC ≥ 0.91) and moderate accuracy for amnestic MCI versus controls (AUC ≈ 0.74). Relevance maps indicated that hippocampal atrophy was considered the most informative factor for AD detection, with additional contributions from atrophy in other cortical and subcortical regions. Relevance scores within the hippocampus were highly correlated with hippocampal volumes (Pearson’s r ≈ −0.86, p < 0.001). Conclusion: The relevance maps highlighted atrophy in regions that we had hypothesized a priori. This strengthens the comprehensibility of the CNN models, which were trained in a purely data-driven manner based on the scans and diagnosis labels. The high hippocampus relevance scores as well as the high performance achieved in independent samples support the validity of the CNN models in the detection of AD-related MRI abnormalities. The presented data-driven and hypothesis-free CNN modeling approach might provide a useful tool to automatically derive discriminative features for complex diagnostic tasks where clear clinical criteria are still missing, for instance for the differential diagnosis between various types of dementia

Assessment of musculoskeletal pain, fatigue and grip strength in hospitalized patients with COVID-19

BACKGROUND: Although there are some retrospective studies to present musculoskeletal findings of the COVID-19, still the muscle strength and fatigue has not been studied in detail