scholarly journals Thermal Stability Kinetics and Shelf Life Estimation of the Redox-Active Therapeutic and Mimic of Superoxide Dismutase Enzyme, Mn(III) meso-Tetrakis(N-ethylpyridinium-2-yl)porphyrin Chloride (MnTE-2-PyPCl5, BMX-010)

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Clarissa G. C. Maia ◽  
Bárbara C. R. de Araujo ◽  
Maria B. de Freitas-Marques ◽  
Israel F. da Costa ◽  
Maria Irene Yoshida ◽  
...  
Keyword(s):  
Neural Network ◽  
Thermal Stability ◽  
Artificial Neural Network ◽  
Superoxide Dismutase ◽  
Solid State ◽  
Shelf Life ◽  
Expiration Date ◽  
Redox Active ◽  
Stability Kinetics ◽  
Kinetics Of

Mn(III) meso-tetrakis(N-ethylpyridinium-2-yl)porphyrin chloride (MnTE-2-PyPCl5, BMX-010, and AEOL10113) is among the most studied superoxide dismutase (SOD) mimics and redox-active therapeutics, being currently tested as a drug candidate in a phase II clinical trial on atopic dermatitis and itch. The thermal stability of active pharmaceutical ingredients (API) is useful for estimating the expiration date and shelf life of pharmaceutical products under various storage and handling conditions. The thermal decomposition and kinetic parameters of MnTE-2-PyPCl5 were determined by thermogravimetry (TG) under nonisothermal and isothermal conditions. The first thermal degradation pathway affecting Mn-porphyrin structural integrity and, thus, activity and bioavailability was associated with loss of ethyl chloride via N-dealkylation reaction. The thermal stability kinetics of the N-dealkylation process leading to MnTE-2-PyPCl5 decomposition was investigated by using isoconversional models and artificial neural network. The new multilayer perceptron (MLP) artificial neural network approach allowed the simultaneous study of ten solid-state kinetic models and showed that MnTE-2-PyPCl5 degradation is better explained by a combination of various mechanisms, with major contributions from the contraction models R1 and R2. The calculated activation energy values from isothermal and nonisothermal data were about 90 kJ mol–1 on average and agreed with one another. According to the R1 modelling of the isothermal decomposition data, the estimated shelf life value for 10% decomposition ( t 90 % ) of MnTE-2-PyPCl5 at 25°C was approximately 17 years, which is consistent with the high solid-state stability of the compound. These results represent the first study on the solid-state decomposition kinetics of Mn(III) 2-N-alkylpyridylporphyrins, contributing to the development of this class of redox-active therapeutics and SOD mimics and providing supporting data to protocols on purification, handling, storage, formulation, expiration date, and general use of these compounds.

Artificial neural network hybridized with a genetic algorithm for optimization of lipase production from Penicillium roqueforti ATCC 10110 in solid-state fermentation

2021 ◽  
Vol 31 ◽  
pp. 101885
Author(s):  
Luiz Henrique Sales de Menezes ◽  
Lucas Lima Carneiro ◽  
Iasnaia Maria de Carvalho Tavares ◽  
Pedro Henrique Santos ◽  
Thiago Pereira das Chagas ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Solid State ◽  
Solid State Fermentation ◽  
Lipase Production ◽  
Penicillium Roqueforti ◽  
Artificial Neural

Thermal Behavior Prediction of MDPE Nanocomposite/Cloisite Na+ Using Artificial Neural Network and Neuro-Fuzzy Tools

2010 ◽  
Vol 1 (4) ◽  
Author(s):  
J. Sargolzaei ◽  
B. Ahangari
Keyword(s):  
Neural Network ◽  
Thermal Stability ◽  
Artificial Neural Network ◽  
Thermal Behavior ◽  
Fuzzy Inference ◽  
Air Atmosphere ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Inference Systems ◽  
Thermal Stability Of

Recently, we successfully prepared medium density polyethylene (MDPE) nanocomposite with 3 wt %, 6 wt %, and 9 wt % cloisite Na+ and the thermal stability of nanocomposite was investigated using the thermogravimetric analysis (TGA). The TGA in air atmosphere showed significantly improved thermal stability of 3 wt %, 6 wt %, and 9 wt % cloisite Na+ nanocomposite in comparison to pure MDPE. In this paper, the results of TGA of MDPE/cloisite Na+ nanocomposites were predicted by the artificial neural network (ANN). The ANN and adaptive neural fuzzy inference systems (ANFIS) models were developed to predict the degradation of MDPE/cloisite Na+ nanocomposite with temperature. The results revealed that there was a good agreement between predicted thermal behavior and actual values. The findings of this study also showed that the artificial neural networks and ANFIS techniques can be applied as a powerful tool.

Prediction of HPLC retention times of tebipenem pivoxyl and its degradation products in solid state by applying adaptive artificial neural network with recursive features elimination

Talanta ◽  
2015 ◽  
Vol 137 ◽  
pp. 174-181 ◽  
Author(s):  
Mikołaj Mizera ◽  
Alicja Talaczyńska ◽  
Przemysław Zalewski ◽  
Robert Skibiński ◽  
Judyta Cielecka-Piontek
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Solid State ◽  
Degradation Products ◽  
Artificial Neural

scholarly journals Hydrogen Sorption Properties, Thermal Stability and Kinetics of Hydrogen Desorption From the Hydride Phase of The MgH2 of a Mechanical Alloys of Magnesium with Ti, Ni and Y

2020 ◽  
Vol 21 (1) ◽  
pp. 167-175
Author(s):  
O. G. Ershova ◽  
V. D. Dobrovolsky ◽  
Yu. M. Solonin
Keyword(s):  
Thermal Stability ◽  
Thermodynamic Stability ◽  
Hydrogen Desorption ◽  
Sorption Properties ◽  
Hydrogen Sorption ◽  
Hydride Phase ◽  
Phase Content ◽  
Stability Kinetics ◽  
Kinetics Of

The mechanical alloys-composite MАs (Mg +10 % wt.Ti + 5 % wt.Y and Mg +10 % wt.Ni + 5 % wt.Y) were synthesized. The phase content, microstructure, the thermal stability, kinetics of hydrogen desorption from the MgH2 hydride phase of the obtained MAs were studiedby using XRD, SEM, TDS methods. It has been established that the addition of Ti + Y and Ni + Y to magnesium leads to significant improvement in the kinetics of hydrogen desorption from the  MgH2 hydride phase, which is evidenced by a significant reduction (in 6 and 15 times)in the time of release of all hydrogen from MA1 and MA2, respectively. Due to, Ti, Ni,Y alloying, the decrease in the thermodynamic stability of MgH2 is not found.

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