Novel chemometric strategy based on the application of artificial neural networks to crossed mixture design for the improvement of recombinant protein production in continuous culture

The optimal blends of six compounds that should be present in culture media used in recombinant protein production were determined by means of artificial neural networks (ANN) coupled with crossed mixture experimental design. This combination constitutes a novel approach to develop a medium for cultivating genetically engineered mammalian cells. The compounds were collected in two mixtures of three elements each, and the experimental space was determined by a crossed mixture design. Empirical data from 51 experimental units were used in a multiresponse analysis to train artificial neural networks which satisfy different requirements, in order to define two new culture media (Medium 1 and Medium 2) to be used in a continuous biopharmaceutical production process. These media were tested in a bioreactor to produce a recombinant protein in CHO cells. Remarkably, for both predicted media all responses satisfied the predefined goals pursued during the analysis, except in the case of the specific growth rate (μ) observed for Medium 1. ANN analysis proved to be a suitable methodology to be used when dealing with complex experimental designs, as frequently occurs in the optimization of production processes in the biotechnology area. The present work is a new example of the use of ANN for the resolution of a complex, real life system, successfully employed in the context of a biopharmaceutical production process.     

Fabrication of polystyrene hollow microspheres as laser fusion targets by optimized density-matched emulsion technique and characterization

Inertial confinement fusion, frequently referred to as ICF, inertial fusion, or laser fusion, is a means of producing energy by imploding small hollow microspheres containing thermonuclear fusion fuel. Polymer microspheres, which are used as fuel containers, can be produced by solution-based micro-encapsulation technique better known as density-matched emulsion technique. The specifications of these microspheres are very rigorous, and various aspects of the emulsion hydrodynamics associated with their production are important in controlling the final product. This paper describes about the optimization of various parameters associated with density-matched emulsion method in order to improve the surface smoothness, wall thickness uniformity and sphericity of hollow polymer microspheres. These polymer microshells have been successfully fabricated in our lab, with 3–30 μm wall thickness and 50–1600 μm diameters. The sphericity and wall thickness uniformity are better than 99%. Elimination of vacuoles and high yield rate has been achieved by adopting the step-wise heating of W₁/O/W₂ emulsion for solvent removal.     

Complementary use of partial least-squares and artificial neural networks for the non-linear spectrophotometric analysis of pharmaceutical samples

The complementary use of partial least-squares (PLS) multivariate calibration and artificial neural networks (ANNs) for the simultaneous spectrophotometric determination of three active components in a pharmaceutical formulation has been explored. The presence of non-linearities caused by chemical interactions was confirmed by a recently discussed methodology based on Mallows augmented partial residual plots. Ternary mixtures of chlorpheniramine, naphazoline and dexamethasone in a matrix of excipients have been resolved by using PLS for the two major analytes (chlorpheniramine and naphazoline) and ANNs for the minor one (dexamethasone). Notwithstanding the large number of constituents, their high degree of spectral overlap and the occurrence of non-linearities, rapid and simultaneous analysis has been achieved, with reasonably good accuracy and precision. No extraction procedures using non-aqueous solvents are required.     

Hippocampal lesions facilitate instrumental learning with delayed reinforcement but induce impulsive choice in rats

Background  

Animals must frequently act to influence the world even when the reinforcing outcomes of their actions are delayed. Learning with action-outcome delays is a complex problem, and little is known of the neural mechanisms that bridge such delays. When outcomes are delayed, they may be attributed to (or associated with) the action that caused them, or mistakenly attributed to other stimuli, such as the environmental context. Consequently, animals that are poor at forming context-outcome associations might learn action-outcome associations better with delayed reinforcement than normal animals. The hippocampus contributes to the representation of environmental context, being required for aspects of contextual conditioning. We therefore hypothesized that animals with hippocampal lesions would be better than normal animals at learning to act on the basis of delayed reinforcement. We tested the ability of hippocampal-lesioned rats to learn a free-operant instrumental response using delayed reinforcement, and what is potentially a related ability – the ability to exhibit self-controlled choice, or to sacrifice an immediate, small reward in order to obtain a delayed but larger reward.     

Synthesis and Reactivity of Nickel‐Stabilised μ2:η2,η2‐P2, As2 and PAs Units

The reactivity of two paramagnetic nickel(I) compounds, CpNi(NHC) (where Cp=cyclopentadienyl; NHC=1,3‐bis(2,4,6‐trimethylphenyl)imidazol‐2‐ylidene (IMes) or 1,3‐bis(2,6‐diisopropylphenyl)imidazol‐2‐ylidene (IPr)), towards [Na(dioxane)_x][PnCO] (Pn=P, As) is described. These reactions afford symmetric bimetallic compounds (μ²:η²,η²‐Pn₂){Ni(NHC)(CO)}₂. Several novel intermediates en route to such species are identified and characterised, including a compound containing the PCO^? anion in an unprecedented μ²:η²,η²‐binding mode. Ultimately, on treatment of the (μ²:η²,η²‐Pn₂){Ni(IMes)(CO)}₂ compounds with carbon monoxide, the Pn₂ units can be released, affording P₄ in the case of the phosphorus‐containing species, and elemental arsenic in the case of (μ²:η²,η²‐As₂){Ni(IMes)(CO)}₂.     

Teaching Chemometrics with a Bioprocess: Analytical Methods Comparison Using Bivariate Linear Regression

We present an advanced analytical chemistry laboratory experiment involving chemometrics. Students perform a comparison of two analytical methods by checking several analyte concentrations within a certain range by using least-squares linear regression. They obtain statistical information such as the presence of constant and proportional biases. The exercise is based on the determination of glucose levels using two colorimetric methods (enzymatic and Somogyi—Nelson) in a very simple batch system formed by an infusion of tea, glucose, and a combination of a yeast (Schizosacaromyces pombe) and a bacteria (Acetobacter xylimun), usually named Kombucha. Several samples are collected during a week of laboratory work, and measurements are performed in a subsequent four-hour laboratory class. Although commercial computer software exists for a variety of statistical applications, specific programs for the application of statistics to analytical chemistry are not prevalent. In order to solve this particular problem, a Matlab 5.3 routine is presented.     

Simultaneous determination of phenobarbital and phenytoin in tablet preparations by multivariate spectrophotometric calibration

The use of multivariate spectrophotometric calibration for the simultaneous determination of the active components of antiepileptic tablets is presented. The resolution of binary mixtures of phenobarbital and phenytoin has been accomplished by using partial least squares (PLS-1) regression analysis. Although the components show an important degree of spectral overlap, they have been simultaneously determined with high accuracy, with no interference from tablet excipients. A comparison is presented with the related multivariate method of classical least squares (CLS) analysis, which is shown to yield less reliable results due to the severe spectral overlap presented by the studied compounds. A statistical measure for the spectral overlap is proposed.     

Chemometric resolution of fully overlapped CE peaks: Quantitation of carbamazepine in human serum in the presence of several interferences

Drug monitoring in serum samples was performed using second‐order data generated by CE‐DAD, processed with a suitable chemometric strategy. Carbamazepine could be accurately quantitated in the presence of its main metabolite (carbamazepine epoxide), other therapeutic drugs (lamotrigine, phenobarbital, phenytoin, phenylephrine, ibuprofen, acetaminophen, theophylline, caffeine, acetyl salicylic acid), and additional serum endogenous components. The analytical strategy consisted of the following steps: (i) serum sample clean‐up to remove matrix interferences, (ii) data pre‐processing, in order to reduce the background and to correct for electrophoretic time shifts, and (iii) resolution of fully overlapped CE peaks (corresponding to carbamazepine, its metabolite, lamotrigine and unexpected serum components) by the well‐known multivariate curve resolution‐alternating least squares algorithm, which extracts quantitative information that can be uniquely ascribed to the analyte of interest. The analyte concentration in serum samples ranged from 2.00 to 8.00 mg/L. Mean recoveries were 102.6% (s=7.7) for binary samples, and 94.8% (s=13.5) for spiked serum samples, while CV (%)=4.0 was computed for five replicate, indicative of the acceptable accuracy and precision of the proposed method.