Cellular manufacturing system design considering machines reliability and parts alternative process routings

Cell formation is an important problem in the design of cellular manufacturing systems (CMS). Most cell formation methods appeared in the literature assume that each part has one process plan, and all machines are 100% reliable with unlimited capacity. However, this is not realistic in manufacturing systems. Considering machines reliability in addition to machines capacity and machine duplicates during the part route selection process help to obtain better machine grouping and minimum total cost for CMS. Considering these factors in addition to operations sequence and production volumes makes the problem more complex but more realistic. Most of the methods appeared in the literature to solve such problems use mathematical programming procedures that take large amount of computational efforts. Procedures using similarity coefficient method are more flexible in incorporating various important production data and lend easily to computer applications. A new similarity coefficient equation that incorporates all these production factors is developed. Also, a procedure that captures the similarity between machine groups and minimises the total CMS cost is developed. The procedure utilises functional cells to eliminate intercellular moves and achieve ‘one-piece flow’ practise. The methodology is compared with other methods in the literature and found to be more effective.     

Drug self-assembly: A phenomenon at the nanometer scale with major impact in the structure–biological properties relationship and the treatment of disease

Under water-rich conditions, small amphiphilic and hydrophobic drug molecules self-assemble into supramolecular nanostructures. Thus, substantial modifications in their interaction with cellular structures and the ability to reach intracellular targets could happen. Additionally, drug aggregates could be more toxic than the non-aggregated counterparts, or vice versa. Moreover, since self-aggregation reduces the number of effective “monomeric” molecules that interact with the target, the drug potency could be underestimated. In other cases, the activity could be ascribed to the non-aggregated molecule while it stems from its aggregates. Thus, drug self-assembly could mislead from drug throughput screening assays to advanced preclinical and clinical trials. Finally, aggregates could serve as crystallization nuclei. The impact that this phenomenon has on the biological performance of active compounds, the inconsistent and often controversial nature of the published data and the need for recommendations/guidelines as preamble of more harmonized research protocols to characterize drug self-aggregation were main motivations for this review. First, the key molecular and environmental parameters governing drug self-aggregation, the main drug families for which this phenomenon and the methods used for its characterization are described. Then, promising nanotechnology platforms investigated to prevent/control it towards a more efficient drug development process are briefly discussed.     

Adaptive neuro-fuzzy inference system multi-objective optimization using the genetic algorithm/singular value decomposition method for modelling the discharge coefficient in rectangular sharp-crested side weirs

In the present article, the adaptive neuro-fuzzy inference system (ANFIS) is employed to model the discharge coefficient in rectangular sharp-crested side weirs. The genetic algorithm (GA) is used for the optimum selection of membership functions, while the singular value decomposition (SVD) method helps in computing the linear parameters of the ANFIS results section (GA/SVD-ANFIS). The effect of each dimensionless parameter on discharge coefficient prediction is examined in five different models to conduct sensitivity analysis by applying the above-mentioned dimensionless parameters. Two different sets of experimental data are utilized to examine the models and obtain the best model. The study results indicate that the model designed through GA/SVD-ANFIS predicts the discharge coefficient with a good level of accuracy (mean absolute percentage error?=?3.362 and root mean square error?=?0.027). Moreover, comparing this method with existing equations and the multi-layer perceptron–artificial neural network (MLP-ANN) indicates that the GA/SVD-ANFIS method has superior performance in simulating the discharge coefficient of side weirs.     

The influence of bile salts on the distribution of simvastatin in the octanol/buffer system

Abstract

Introduction: Distribution coefficient (D) is useful parameter for evaluating drugs permeability properties across biological membranes, which are of importance for drugs bioavailability. Given that bile acids are intensively studied as drug permeation-modifying and -solubilizing agents, the aim of this study was to estimate the influence of sodium salts of cholic (CA), deoxycholic (DCA) and 12-monoketocholic acids (MKC) on distribution coefficient of simvastatin (SV) (lactone [SVL] and acid form [SVA]) which is a highly lipophilic compound with extremely low water solubility and bioavailability.

Methods: LogD values of SVA and SVL with or without bile salts were measured by liquid–liquid extraction in n-octanol/buffer systems at pH 5 and 7.4. SV concentrations in aqueous phase were determined by HPLC-DAD. Chem3D Ultra program was applied for computation of physico-chemical properties of analyzed compounds and their complexes.

Results: Statistically significant decrease in both SVA and SVL logD was observed for all three studied bile salts at both selected pH. MKC exerted the most pronounced effect in the case of SVA while there were no statistically significant differences between observed bile salts for SVL. The calculated physico-chemical properties of analyzed compounds and their complexes supported experimental results.

Conclusions: Our data indicate that the addition of bile salts into the n-octanol/buffer system decreases the values of SV distribution coefficient at both studied pH values. This may be the result of the formation of hydrophilic complexes increasing the solubility of SV that could consequently impact the pharmacokinetic parameters of SV and the final drug response in patients.     

Development of a water-in-oil-in-water multiple emulsion system integrating biomimetic aqueous-core lipid nanodroplets for protein entity stabilization. Part II: process and product characterization

The aqueous-core enclosed in lipid nanoballoons integrating multiple emulsions of the type water-in-oil-in-water mimic, at least in theory, the environment within viable cells, thus being suitable for housing hydrophilic protein entities such as bioactive proteins, peptides and bacteriophage particles. This study reports a complete physicochemical characterization of optimized biomimetic aqueous-core lipid nanoballoons housing hydrophilic (BSA) protein entities, evolved from a statistical 2³×3¹ factorial design study (three variables at two levels and one variable at three levels) that was the subject of the first paper of a series of three, aiming at complete stabilization of the three-dimensional structure of protein entities attempted via housing the said molecular entities within biomimetic aqueous-core lipid nanoballoons integrating a multiple (W/O/W) emulsion. The statistical factorial design followed led to the production of an optimum W/O/W multiple emulsion possessing quite homogeneous particles with an average hydrodynamic size of (186.2?±?2.6) nm and average Zeta potential of (?36.5?±?0.9) mV, and exhibiting a polydispersity index of 0.206?±?0.014. Additionally, the results obtained for the diffusion coefficient of the lipid nanoballoons integrating the optimized W/O/W multiple emulsion were comparable and of the same order of magnitude (10^?12 m² s^?1) as those published by other authors since, typically, diffusion coefficients for molecules range from 10^?10 to 10^?7 m² s^?1, but diffusion coefficients for nanoparticles are typically of the order of magnitude of 10^?12 m² s^?1.     

Monitoring the Coefficient of Variation Using the Side Sensitive Group Runs Chart

Many quality characteristics have means and standard deviations that are not independent. Instead, the standard deviations of these quality characteristics are proportional to their corresponding means. Thus, monitoring the coefficient of variation (CV), for these quality characteristics, using a control chart has gained remarkable attention in recent years. This paper presents a side sensitive group runs chart for the CV (called the SSGR CV chart). The implementation and optimization procedures of the proposed chart are presented. Two optimization procedures are developed, i.e. (i) by minimizing the average run length (ARL) when the shift size is deterministic and (ii) by minimizing the expected average run length (EARL) when the shift size is unknown. An application of the SSGR CV chart using a real dataset is also demonstrated. Additionally, the SSGR CV chart is compared with the Shewhart CV, runs rules CV, synthetic CV and exponentially weighted moving average CV charts by means of ARLs and standard deviation of the run lengths. The performance comparison is also conducted using EARLs when the shift size is unknown. In general, the SSGR CV chart surpasses the other charts under comparison, for most upward and downward CV shifts. Copyright © 2015 John Wiley & Sons, Ltd.