Spray drying of drug-swellable dispersant suspensions for preparation of fast-dissolving,high drug-loaded,surfactant-free nanocomposites

Bioavailability of a poorly soluble drug can be improved by preparing a drug nanosuspension and subsequently drying it into nanocomposite microparticles (NCMPs). Unfortunately, drug nanoparticles aggregate during milling and drying, causing incomplete recovery and slow dissolution. The aim of this study is to investigate the impact of various classes of dispersants on drug dissolution from drug NCMPs, with the ultimate goal of enhancing the bioavailability of poorly water-soluble drugs via high drug nanoparticle loaded, surfactant-free NCMPs. Precursor suspensions of griseofulvin (GF, model drug) nanoparticles in the presence of various dispersants were prepared via wet stirred media milling and spray dried to form the NCMPs. Hydroxypropyl cellulose (HPC, polymer) alone and with sodium dodecyl sulfate (SDS, surfactant) was used as a base-line stabilizer/dispersant during milling. Two swellable crosslinked polymers, croscarmellose sodium (CCS) and sodium starch glycolate (SSG), and a conventional soluble matrix former, Mannitol, were used in addition to HPC. Besides being used as-received, CCS was also wet co-milled with GF for two different durations to examine the impact of CCS particle size. Laser diffraction, scanning electron microscopy, powder X-ray diffraction (XRD), UV spectroscopy, NCMP redispersion and dissolution tests were used for characterization. The results show that incorporation of CCS/SSG, preferably wet-milled to a wide particle size distribution, into the spray-dried NCMPs resulted in fast release and dispersion of drug nanoparticle clusters. The swellable dispersants were superior to Mannitol in dissolution enhancement, and could achieve fast release comparable to SDS, demonstrating the feasibility of spray drying to prepare high drug-loaded, surfactant-free nanocomposites.     

Comparative analysis of requirements change prediction models: manual,linguistic, and neural network

Requirement change propagation, if not managed, may lead to monetary losses or project failure. The a posteriori tracking of requirement dependencies is a well-established practice in project and change management. The identification of these dependencies often requires manual input by one or more individuals with intimate knowledge of the project. Moreover, the definition of these dependencies that help to predict requirement change is not currently found in the literature. This paper presents two industry case studies of predicting system requirement change propagation through three approaches: manually, linguistically, and bag-of-words. Dependencies are manually and automatically developed between requirements from textual data and computationally processed to develop surrogate models to predict change. Two types of relationship generation, manual keyword selection and part-of-speech tagging, are compared. Artificial neural networks are used to create surrogate models to predict change. These approaches are evaluated on three connectedness metrics: shortest path, path count, and maximum flow rate. The results are given in terms of search depth needed within a requirements document to identify the subsequent changes. The semi-automated approach yielded the most accurate results, requiring a search depth of 11 %, but sacrifices on automation. The fully automated approach is able to predict requirement change within a search depth of 15 % and offers the benefits of full minimal human input.     

Lipid Metabolism and Cardiovascular Morbidity and Mortality in Hemodialysis Patients: Role of Factors Modulating Cytosolic Calcium

Animal studies indicate that insulin resistance and glucose intolerance leading to dyslipidemia in uremic rats are associated with increased cytosolic calcium ([Ca⁺⁺ i]). The resistance and intolerance are reversed with verapamil, but recur after its discontinuation. This finding suggests that hyperparathyroid‐induced [Ca ⁺⁺ i] increase is responsible for the metabolic derangement. We retrospectively examined, over a 12‐year period, the effects of factors that lower [Ca ⁺⁺i] on total serum cholesterol and triglycerides in 332 hemodialysis (HD) patients. Because the study was retrospective, detailed lipid profiles were not available. We therefore relied on morbidity and mortality outcomes related to atherosclerotic vascular disease. Patients with diabetes mellitus were excluded, because their dyslipidemia and vascular disease are mediated via a different mechanism. Four groups emerged: group I [high parathormone (PTH) in the absence of calcium channel blockers (CCBs), n = 107], representing the highest [Ca⁺⁺ i]; group II (high PTH in the presence of CCBs, n = 76) and group III (lower PTH in the absence of CCBs, n = 66), representing intermediate [Ca ⁺⁺ i]; and group IV (lower PTH in the presence of CCBs, n = 83) representing the lowest [Ca ⁺⁺i]. The theoretically lower [Ca ⁺⁺ i] was achieved via CCB therapy or lower PTH, or both. The mean serum cholesterol in group I was 322 ± 24 mg/dL and the level of triglycerides was 398 ± 34 mg/dL. Group II had mean serum cholesterol of 196 ± 16 mg/dL and triglycerides of 157 ± 17 mg/dL. Group III had a mean serum cholesterol of 202 ± 19 mg/dL and triglycerides of 160 ± 15 mg/dL. Group IV had a mean serum cholesterol of 183 ± 9 mg/dL and triglycerides of 94 ± 6 mg/dL. The differences in cholesterol and triglyceride levels among four groups were significant (p < 0.001) by one‐way analysis of variance (ANOVA). The incidence of cardiovascular morbidity and mortality events was 61% in group I, 24% in group II, 28% in group III, and 18% in group IV (χ ² = 47.7, p < 0.001). We conclude that, in non diabetic HD patients, hyperparathyroidism, especially in the absence of CCBs, is associated with severe dyslipidemia and increased risk of cardiovascular morbidity and mortality. Dyslipidemia may be related to a hyperparathyroid‐induced increase in cytosolic calcium [Ca⁺⁺i]. Lowering [Ca⁺⁺i] by decreasing PTH or by blocking calcium entry into cells (via CCBs), or both, is associated with less dyslipidemia and improved long‐term cardiovascular morbidity and mortality. Prospective randomized studies, with actual measurement of [Ca ⁺⁺i], are needed to verify the results of this study.     

Reasons for change propagation: a case study in an automotive OEM

This paper focuses on identifying the reasons for change propagation during the production phase of the product life cycle. Unlike the traditional change propagation study where the focus is within the product, this study is focused to understand the propagation effects of change on other functional silos in the manufacturing firm. First, the reasons for the changes are identified using archival analysis through which it is found that 77.0?% of changes are due to internal reasons while 23.0?% are external. Second, these changes are distinguished into genesis, and propagated changes using a matrix-based modeling approach from which the reasons for propagation are identified. It is inferred that 32.4?% of the total changes are due to propagated changes such as inventory issues, manufacturing issues, and design error rectification. The majority of reasons for these propagated changes include document error rectification such as BOM error, drawing error, incorrect introduction date in engineering change note, and design error rectification such as design limitations. The findings indicate nearly one-third of time spent by the engineers can be reduced by developing appropriate controls during the change release process.     

Predicting requirement change propagation through investigation of physical and functional domains

Requirements play a critical role in the design process and are important to the project’s success. The design process is iterative, and requirements are constantly changed and updated to reflect stakeholders’ expectations, design changes, regulations, and resource limitations. Since requirements drive product development from initial development of concepts to final production of the finished product, mismanaged requirement changes can lead to monetary and time losses. The ability to assess a requirement change, predict its propagation, and evaluate the impact early in the design process will enable engineers to make informed decisions regarding change implementation. Prior research performed by Morkos culminated in the Automated Requirement Change Propagation Prediction (ARCPP) tool to mitigate issues due to requirement change propagation. The ARCPP tool utilized syntactic natural language data, part of speech (POS) elements in requirement statements, as relators to form relationships between requirements. The resulting requirement network serves to predict change propagation as a result of an initiating requirement change using the performance metric of the tool, requirement review depth. Whereas the prior research proved that change propagation can be predicted using requirements, the purpose of this research is to understand why requirements can be used. Specifically, what parts of a requirement affect its ability to predict change propagation? This is performed by addressing three key research questions (RQs): (1) Is the requirement review depth affected by the number of relators selected to relate requirements, (2) Is the requirement review depth affected by the frequency of relators selected to relate requirements, and (3) Which element of a requirement, the physical or functional domain, is responsible for instigating change propagation? The results indicate that the review depth, an indicator of the performance of the ARCPP tool, is not affected by the number and frequency of relators, but rather by the ability of relators in capturing the propagating relationships. Further, the physical domain is found to contribute more towards predicting change propagation than the functional domain. Finally, a recommendation on selecting the number of requirement relators is presented.     

Extending the Applicability of Exact Nuclear Overhauser Enhancements to Large Proteins and RNA

Distance‐dependent nuclear Overhauser enhancements (NOEs) are one of the most popular and important experimental restraints for calculating NMR structures. Despite this, they are mostly employed as semiquantitative upper distance bounds, and this discards the wealth of information that is encoded in the cross‐relaxation rate constant. Information that is lost includes exact distances between protons and dynamics that occur on the sub‐millisecond timescale. Our recently introduced exact measurement of the NOE (eNOE) requires little additional experimental effort relative to other NMR observables. So far, we have used eNOEs to calculate multistate ensembles of proteins up to approximately 150 residues. Here, we briefly revisit eNOE methodology and present two new directions for the use of eNOEs: applications to large proteins and RNA.