Exposure-Response Relationship between Von Willebrand Factor (VWF) Activity and Bleeding Events Following Prophylaxis with Recombinant VWF (rVWF, vonicog alfa) in Patients with Severe Von Willebrand Disease (VWD)

Background: rVWF (vonicog alfa; Vonvendi ®,Baxalta US Inc, a Takeda company, Lexington, MA, USA) is a purified recombinant VWF concentrate approved for on-demand (OD) treatment of hemorrhage and management of surgical bleeding in adults with VWD. The efficacy and safety of rVWF prophylaxis has also been evaluated in a recent open-label phase 3 trial in adults with severe VWD (NCT02973087). Aims: To evaluate the exposure-response (ER) relationship between VWF activity (measured by VWF:ristocetin cofactor [RCo]), endogenous factor VIII (FVIII) activity (measured by FVIII:C), and spontaneous bleeding events (sBEs) in patients with severe VWD receiving rVWF prophylaxis for up to 1 year. Methods: The modeling framework involved developing population pharmacokinetic (PK) and PK/pharmacodynamic (PD) models and conducting simulations to characterize VWF activity/PK and FVIII activity/PD, then developing an ER model for VWF and FVIII activities in association with sBEs. The population PK and PK/PD models were developed using data from 100 unique patients receiving intravenous rVWF in 4 completed clinical studies (NCT00816660; NCT01410227; NCT02283268; NCT02973087). The PK and PK/PD analyses were used to generate model parameters and evaluate predictors of heterogeneity for PK/VWF:RCo and PD/FVIII:C time profiles. The ER relationship was evaluated using sBEs from the phase 3 rVWF prophylaxis trial (NCT02973087) in 23 adults with severe VWD (VWF:RCo <20 IU/dL) requiring VWF therapy to manage BEs during the year before study entry: 13/23 patients were previously treated OD with a VWF (Prior OD group) and 10/23 had received plasma-derived VWF (pdVWF) prophylaxis (Switch group). For this ER evaluation, a repeated time-to-event (RTTE) model was used, including a piecewise exponential additive model, and the covariate effect of previous treatment (OD with a VWF or prophylaxis with pdVWF) was explored. Model selection was performed by comparing the goodness of fit of linear and nonlinear ER models based on the PK and PK/PD models' predicted values for 1) VWF:RCo and FVIII:C trough levels; 2) average VWF:RCo and FVIII:C levels in dosing interval; and 3) average VWF:RCo and FVIII:C levels over 24 h following rVWF treatment, with comparisons of these levels before sBE onset in patients with and without sBEs. The PK and PK/PD models were also used to derive the VWF:RCo and FVIII:C levels for pdVWF, and these were applied to the ER model. The impact of the dosing regimens (twice weekly [BIW] or once weekly [QW]) on the ER for rVWF and pdVWF were investigated based on population simulations. Hazard ratios (HRs) for the probability of bleeding were generated as a function of median VWF activity at steady state for patients with type 3 VWD. Results: The RTTE model with a linear ER function linking the average levels of VWF:RCo or FVIII:C over 24 h before sBE onset was selected as the best model. A statistically significant ER relationship was observed (p<0.05) for the ER model with VWF:RCo, in which higher exposure to VWF:RCo was associated with a lower risk of sBE occurrence. The covariate effect of previous treatment (OD with a VWF or prophylaxis with pdVWF) was not statistically significant (p=0.6732). Simulations suggested that the HR per 10 IU/dL increment in the average exposure of VWF:RCo 24 h before an sBE was 0.673 (95% CI: 0.454-0.999). The HR per 20 IU/dL increment in the average exposure of VWF:RCo 24 h before an sBE was 0.453 (95% CI: 0.206-0.998). In addition, the predicted risk of a sBE for the 50 IU/kg QW regimen of rVWF and pdVWF was 30% and 43% higher, respectively, compared with the 50 IU/kg BIW regimen of rVWF (ie, reference regimen). The predicted risk of bleeding with the 50 IU/kg BIW regimen of pdVWF was 20% higher compared with the 50 IU/kg BIW regimen of rVWF. A trend was observed for the ER relationship based on FVIII:C (average levels of FVIII over 24 h before the sBE) suggesting a lower risk of sBEs with increased FVIII:C, which was however not statistically significant. Conclusions: Analysis of exposure to VWF:RCo or FVIII:C vs sBE occurrence indicated a causal association between VWF:RCo and sBEs; higher VWF:RCo was associated with a lower sBE risk. This relationship was independent of the patients' previous treatment (OD with a VWF or prophylaxis with pdVWF). Once further supported with additional data, this ER model could be utilized for individualized dosing strategies to optimize patient outcomes with rVWF prophylaxis. Disclosures Wang: Takeda Development Center Americas, Inc.: Current Employment; Takeda: Current equity holder in publicly-traded company. Özen: Takeda Development Center Americas, Inc.: Current Employment; Takeda: Current equity holder in publicly-traded company. Mellgård: Takeda Development Center Americas, Inc.: Current Employment; Takeda: Current equity holder in publicly-traded company. Marier: Certara Strategic Consulting: Current Employment. Barriere: Certara Strategic Consulting: Current Employment. Vasilinin: Certara Strategic Consulting: Current Employment. Bhattacharya: Takeda: Current equity holder in publicly-traded company; Takeda Development Center Americas, Inc.: Current Employment. OffLabel Disclosure: Abstract reports results from a population ER analysis using data from a clinical trial investigating the efficacy and safety of rVWF prophylaxis. rVWF is not currently authorized for use as a prophylactic treatment.

Software Functional Complexity Using a New Set of Criteria

With the rapid technology advances, there is an emerging consensus that the size and complexity of software designs are increasing so rapidly that they proportionally affect the magnitude of administrative and development efforts. An important consideration is how to estimate software complexity. This subject continues to be a research topic in the literature. The software design process researched here uses the Unified Modeling Language (UML) diagrams and the database design for extracting pertinent information. The Entity Relationship (ER) model of Peter Chen (of MIT) is a conceptual method of describing the data in a relational structure. An Entity Relationship Diagram (ERD) and an Entity Relationship Schema (ERS) represents the ER model, containing the entities, attributes, primary and foreign keys, and the relationships between the entities. Extending this ERS modeling construct, this paper uses an additional enhanced schema, called the Object Relationship Schema (ORS), which, together with the existing ERS, creates an enhanced view of the requirements and the design of the database. In addition, functional dependency, security, computational complexity, use cases, component structure and interpretations are considered for estimating functional complexity of modern software systems which is very valuable in higher education for new workforce development.

p-adic numbers encode complex networks

The Erdős-Rényi (ER) random graph G(n, p) analytically characterizes the behaviors in complex networks. However, attempts to fit real-world observations need more sophisticated structures (e.g., multilayer networks), rules (e.g., Achlioptas processes), and projections onto geometric, social, or geographic spaces. The p-adic number system offers a natural representation of hierarchical organization of complex networks. The p-adic random graph interprets n as the cardinality of a set of p-adic numbers. Constructing a vast space of hierarchical structures is equivalent for combining number sequences. Although the giant component is vital in dynamic evolution of networks, the structure of multiple big components is also essential. Fitting the sizes of the few largest components to empirical data was rarely demonstrated. The p-adic ultrametric enables the ER model to simulate multiple big components from the observations of genetic interaction networks, social networks, and epidemics. Community structures lead to multimodal distributions of the big component sizes in networks, which have important implications in intervention of spreading processes.

GraphER: Token-Centric Entity Resolution with Graph Convolutional Neural Networks

Entity resolution (ER) aims to identify entity records that refer to the same real-world entity, which is a critical problem in data cleaning and integration. Most of the existing models are attribute-centric, that is, matching entity pairs by comparing similarities of pre-aligned attributes, which require the schemas of records to be identical and are too coarse-grained to capture subtle key information within a single attribute. In this paper, we propose a novel graph-based ER model GraphER. Our model is token-centric: the final matching results are generated by directly aggregating token-level comparison features, in which both the semantic and structural information has been softly embedded into token embeddings by training an Entity Record Graph Convolutional Network (ER-GCN). To the best of our knowledge, our work is the first effort to do token-centric entity resolution with the help of GCN in entity resolution task. Extensive experiments on two real-world datasets demonstrate that our model stably outperforms state-of-the-art models.

Identifying semantic characteristics of user interaction datasets through application of a data analysis

The study goal is to identify semantics characteristics of datasets, at the moment of data collecting, from dataset's structures found on export data interfaces available on user’s interactions analysis tools, on Internet communication channels, and on statistical data access tools involved in a scientific journal management process, thru an application of data analysis and data model techniques. The research universe was delimited to exportable dataset's structures, found in journal publishing systems, online social networks statistics, search engines, and web analytics tools. The sample analyzed was restricted to dataset's structures, available in reports found in Open Journal Systems (OJS), Google Analytics, Google Search Console, Twitter Analytics, and Facebook Insights. These resources did not present any version control numbering, except by OJS (2.6). The data was collected in September' 2017 from "Electronic Journal Digital Skills for Family Farming" accounts. It was adopted an exploratory analysis methodology to identify characteristics about how data are available and structured on those data resources, contemplating a systematically describing process of datasets, entities, and attributes related to the interaction between users and communications channels from a scientific journal. A total of 255 exportable datasets were found, distributed in 5 file formats: Comma-Separated Values (CSV) (82), Google Docs Spreadsheet File Format (69), Excel Microsoft Office Open XML Format Spreadsheet file (50), Portable Document Format (50), and Excel Binary File Format (3). Except for CSV, all other file formats were discarded, mainly because CSV is a machine-readable, open file format, and available in every export data interfaces analyzed. It was collected 82 CSV datasets from Google Analytics (50), Google Search (20), Open Journal Systems (7), Facebook Insights (3), and Twitter Analytics (2). In order to systematize the analysis, it was applied concepts from Entity-Relationship (ER) Model (Silberschatz, Korth, & Sudarshan, 2010) with entities to store data collected from i) services, ii) resources available in the services, iii) datasets available in the resources, and iv) attributes available in the datasets. Also, it was developed two auxiliary tables i) format, to store file format types available on datasets, and ii) data type to store data types: "a named (and in practice finite) set of values" (Date, 2016, p. 228). This applied ER Model provides a structure to store data from entities and attributes from each dataset. Applying this ER structure on data collected in this study was possible to identify 82 entities, 2280 attributes, with a subset of 1342 unique attribute labels. The ER structure and data was stored in a Google Spreadsheet file. After that, the file was uploaded to a DataBase Management System (DBMS) to a further data analysis. It was developed a Python script to reorder the data stored in DBMS to a new data structure, adopting the Online Analytical Processing (OLAP) cube as representation with Service (s), Entity (e), and Attribute (a) data used as dimensions (Gray, Bosworth, Lyaman, & Pirahesh, 1996; Inmon, 1996; Kimball & Ross, 2011). The collected data was reordered to OLAP cube dimensions by a pivot table process (Cornell, 2005). It was intended to observe on intersections of OLAP cube the characteristics shared internally and externally by services, entities and, attributes that can affect semantics aspects on data collecting. The results show that 88.69% of attributes doesn't it relate to any description about its content. Added to that, all attributes that share equal labels between distinct services came without description on collecting. This subset of attributes had a significant importance to interoperability applicability of those datasets, with a capability to distinguish the context on collecting process and also be part of a group of potential primary keys or unique fields, helping to build relationships between data from this sources, or even in a geographic, timing or linguistic determination.