Azathioprine is an immunosuppressive drug for several inflammatory disorders. Due to its clinical relevance, to explore the solid-state properties for excipient compatibility in the product quality review process is essential. Fourier transform infrared spectroscopy, powder X-ray diffraction and thermal analysis (thermogravimetry/derivative thermogravimetry (TG/DTG), differential thermal analysis (DTA), and differential scanning calorimetry (DSC)) were applied. The compatibility studies evidenced that starch pregelatinized, colloidal silicon dioxide, and talc are fully compatible with azathioprine. However, stearic acid, magnesium stearate, and mannitol are incompatible after heat supply at temperatures easily reached by industrial processing. The nonlinear Vyazovkin isoconversional treatment performed the kinetic study of the thermal degradation. The activation energies were determined to clarify the influence of each excipient on the thermal drug stability, an essential procedure in the pharmaceutical development, and all over the commercial live span, in Good Manufacturing Practices.

A general kinetic equation to simulate differential scanning calorimetry (DSC) data was employed along this work. Random noises are used to generate a thousand data, which are considered to evaluate the performance of Levenberg-Marquardt (LM) and a Hopfield neural network (HNN) based algorithm in the fitting process. The HNN-based algorithm showed better results for two different initial conditions: exact and approximated values. After this statistical analysis, DSC experimental data at three heating rates for losartan potassium, an antihypertensive drug, was adjusted by the HNN method using different initial conditions to obtain the activation energy and frequency factor. Additionally, it was possible to recover the parameters for the kinetic model with accuracy, showing that the conversion is described by a complex process, once these values do not correspond to any ideal models described in the literature.

Thermal analysis can be used to determine shelf-life and kinetic parameters in pharmaceutical systems. This work investigates the kinetic of lumefantrine thermal decomposition, an antimalarial, using non-isothermal and isothermal experimental data. The non-isothermal conditions are analyzed applying Vyazovkin method, while isothermal conditions employ models fitting procedure and artificial neural network. Lumefantrine was characterized by powder X-ray diffraction and Fourier transform infrared spectroscopy. The initial stage of lumefantrine thermal decomposition, about 5% of conversion, corresponds to the loss of chlorine and hydroxyl, being correctly predicted by the neural network as a complex event. At room temperature, the D3 model is appropriate to describe the process, once the half-life time is 19 months, in agreement with manufacturer. Isoconversional model determined the activation energy along the whole process while isothermal methodology determined the global value considering the entire process. The results provide important information for the pharmaceutical industry to assay levels of acceptable lumefantrine contents.

What is an ill-posed inverse problem? The answer to this question is the main objective of the present paper and the pre-requisite to follow the material requires only elementary calculus. The first mathematical formulation of an inverse problem, due to N. H. Abel, together with the fundamental work by Jacques Hadamard, are explored at the beginning of the paper. A prototype system is used to consider the regularization concept. Three numerical methods, the Tikhonov regularization, the decomposition into singular values and the Hopfield neural networks, applied to remove the singularity are examined. General aspects of the ill-posed inverse problems in chemistry with emphasis in thermodynamics and a set of general rules for other areas of science are also analyzed.

Este trabalho concentra-se no cálculo do segundo coeficiente virial quântico, a partir de um potencial desenvolvido recentemente. Este coeficiente foi determinado com 4-5 algarismos significativos na faixa de temperatura de 3 a 100 K. Nossos resultados estão dentro do erro experimental. Três contribuições para o valor total deste coeficiente são o espalhamento quântico (contribuição de estados no contínuo), o estado ligado (contribuição de estados discretos) e o gás ideal quântico; discutimos estas contribuições separadamente. A contribuição mais importante é do espalhamento quântico, enquanto que as contribuições menores são dos estados discretos. Uma análise da sensibilidade foi realizada em função da temperatura para um parâmetro na região de curto alcance do potencial e para três parâmetros na região de longo alcance do potencial. Para ambas as temperaturas consideradas, 10 e 100 K, o coeficiente de dispersão C6 foi o mais significativo, e o termo dispersão C10 foi o menos significativo para o resultado total. Em geral, a precisão exigida para descrever os potenciais diminui com o aumento da temperatura. A precisão total e a relação dos parâmetros com os erros experimentais são discutidas.

This paper focuses on the calculation of the quantum second virial coefficient, under a recently developed potential. This coefficient was determined to within 4-5 significant figures in the temperature range from 3 to 100 K. Our results are within experimental error. The three contributions to the overall value of the coefficient are the quantum scattering (continuum state contribution), the bound state (discrete state contribution) and the quantum ideal gas; we discuss these contributions separately. The most significant contribution is from the scattering states, whereas the smaller contributions are from the discrete states. A sensitivity analysis was performed as a function of temperature for one parameter in the short-range region of the potential and for three parameters in the long-range regions of the potential. For both temperatures considered, 10 and 100 K, the C6 dispersion coefficient was the most significant, and the C10 dispersion term was the least significant to the overall result. In general, the precision required to describe the potential decays as the temperature increases. The overall accuracy and the relationship of the parameters to the experimental errors are discussed.

Methane combustion was studied by the Westbrook and Dryer model. This well-established simplified mechanism is very useful in combustion science, for computational effort can be notably reduced. In the inversion procedure to be studied, rate constants are obtained from [CO] concentration data. However, when inherent experimental errors in chemical concentrations are considered, an ill-conditioned inverse problem must be solved for which appropriate mathematical algorithms are needed. A recurrent neural network was chosen due to its numerical stability and robustness. The proposed methodology was compared against Simplex and Levenberg-Marquardt, the most used methods for optimization problems.

Potential parameters sensitivity analysis for helium unlike molecules, HeNe, HeAr, HeKr and HeXe is the subject of this work. Number of bound states these rare gas dimers can support, for different angular momentum, will be presented and discussed. The variable phase method, together with the Levinson's theorem, is used to explore the quantum scattering process at very low collision energy using the Tang and Toennies potential. These diatomic dimers can support a bound state even for relative angular momentum equal to five, as in HeXe. Vibrational excited states, with zero angular momentum, are also possible for HeKr and HeXe. Results from sensitive analysis will give acceptable order of magnitude on potentials parameters.

A neural network procedure to solve inverse chemical kinetic problems is discussed in this work. Rate constants are calculated from the product concentration of an irreversible consecutive reaction: the hydrogenation of Citral molecule, a process with industrial interest. Simulated and experimental data are considered. Errors in the simulated data, up to 7% in the concentrations, were assumed to investigate the robustness of the inverse procedure. Also, the proposed method is compared with two common methods in nonlinear analysis; the Simplex and Levenberg-Marquardt approaches. In all situations investigated, the neural network approach was numerically stable and robust with respect to deviations in the initial conditions or experimental noises.