Description of the Interfacial Behavior of Benzonitrile at Icy Surfaces by Grand Canonical Monte Carlo Simulations.

The adsorption of benzonitrile at the surface of crystalline (Ih) and low-density amorphous (LDA) ice has been investigated by grand canonical Monte Carlo simulations at temperatures ranging from 50 to 200 K. It is found that, in spite of its rather large dipole moment of 4.5 D, benzonitrile molecules can only form a highly unsaturated monolayer on LDA ice, reaching not more than 50% of the surface concentration of the saturated monolayer even at the lowest temperature considered, and they practically do not adsorb on Ih ice. In spite of the observed weak ability of the benzonitrile molecules for being adsorbed, the estimated heat of adsorption at an infinitely low surface concentration of -66.8 ± 2.2 kJ/mol is rather large. This value includes the contribution of roughly -30 to -35 kJ/mol of a benzene ring, about -10 kJ/mol of a large molecular dipole moment, and about -20 to -25 kJ/mol of a benzonitrile-water H-bond, as estimated from comparisons with the heat of adsorption values of similar molecules. The surprisingly weak ability of benzonitrile for adsorption is thus attributed to the unusually strong cohesion between the molecules, considerably exceeding their adhesion to ice, as reflected in the 70-80 kJ/mol difference of the lateral and ice contributions to the binding energy of surface benzonitrile molecules in the presence of condensed benzonitrile.

Explainable deep recurrent neural networks for the batch analysis of a pharmaceutical tableting process in the spirit of Pharma 4.0.

Due to the continuously increasing Cost of Goods Sold, the pharmaceutical industry has faced several challenges, and the Right First-Time principle with data-driven decision-making has become more pressing to sustain competitiveness. Thus, in this work, three different types of artificial neural network (ANN) models were developed, compared, and interpreted by analyzing an open-access dataset from a real pharmaceutical tableting production process. First, the multilayer perceptron (MLP) model was used to describe the total waste based on 20 raw material properties and 25 statistical descriptors of the time series data collected throughout the tableting (e.g., tableting speed and compression force). Then using 10 process time series data in addition to the raw material properties, the cumulative waste, during manufacturing was also predicted by long short-term memory (LSTM) and bidirectional LSTM (biLSTM) recurrent neural networks (RNN). The LSTM network was used to forecast the waste production profile to allow preventive actions. The results showed that RNNs were able to predict the waste trajectory, the best model resulting in 1096 and 2174 tablets training and testing root mean squared errors, respectively. For a better understanding of the process, and the models and to help the decision-support systems and control strategies, interpretation methods were implemented for all ANNs, which increased the process understanding by identifying the most influential material attributes and process parameters. The presented methodology is applicable to various critical quality attributes in several fields of pharmaceutics and therefore is a useful tool for realizing the Pharma 4.0 concept.

Real-time release testing of in vitro dissolution and blend uniformity in a continuous powder blending process by NIR spectroscopy and machine vision.

Continuous manufacturing is gaining increasing interest in the pharmaceutical industry, also requiring real-time and non-destructive quality monitoring. Multiple studies have already addressed the possibility of surrogate in vitro dissolution testing, but the utilization has rarely been demonstrated in real-time. Therefore, in this work, the in-line applicability of an artificial intelligence-based dissolution surrogate model is developed the first time. NIR spectroscopy-based partial least squares regression and artificial neural networks were developed and tested in-line and at-line to assess the blend uniformity and dissolution of encapsulated acetylsalicylic acid (ASA) - microcrystalline cellulose (MCC) powder blends in a continuous blending process. The studied blend is related to a previously published end-to-end manufacturing line, where the varying size of the ASA crystals obtained from a continuous crystallization significantly affected the dissolution of the final product. The in-line monitoring was suitable for detecting the variations in the ASA content and dissolution caused by the feeding of ASA with different particle sizes, and the at-line predictions agreed well with the measured validation dissolution curves (f2 = 80.5). The results were further validated using machine vision-based particle size analysis. Consequently, this work could contribute to the advancement of RTRT in continuous end-to-end processes.

Surface Properties of N,N-Dimethylformamide-Water Mixtures, As Seen from Computer Simulations.

The liquid-vapor interface of N,N-dimethylformamide (DMF)-water mixtures, spanning the entire composition range, is investigated in detail at 298 K by molecular dynamics simulation and intrinsic surface analysis. DMF molecules are found to adsorb strongly at the liquid surface, but this adsorption extends only to the first molecular layer. Water and DMF molecules mix with each other on the molecular scale even in the surface layer; thus, no marked self-association of any of the components is seen at the liquid surface. The major surface component prefers such orientation in which the molecular dipole vector lays parallel with the macroscopic plane of the surface. On the other hand, the preferred orientation of the minor component is determined, at both ends of the composition range, by the possibility of H-bond formation with the major component. The lack of H-donating ability of DMF leads to a rapid breakup of the percolating H-bond network at the surface; due to the strong adsorption of DMF, this breakup occurs below the bulk phase DMF mole fraction of 0.03. The disruption of the surface H-bond network also accelerates the exchange of both species between the liquid surface and bulk liquid phase, although, for water, this effect becomes apparent only above a bulk phase DMF mole fraction of 0.4. H-bonds formed by a DMF and a water molecule live, on average, 25-60% longer than those formed by two water molecules at the liquid surface. A similar, but smaller (i.e., about 10-20%) difference is seen in the bulk liquid phase. The enhanced surface mobility of the molecules results in 2-6 times larger diffusion coefficient and 2-5 times shorter H-bond lifetime values at the liquid surface than in the bulk liquid phase. The diffusion of both molecules is slowed down in the presence of the other species; in the case of DMF, this effect is caused by the formation of water-DMF H-bonds, whereas for water, steric hindrances imposed by the bulky DMF neighbors are responsible for this slowing down.

Calculation of the Free Energy of Mixing as a Tool for Assessing and Improving Potential Models: The Case of the N,N-Dimethylformamide-Water System.

The Helmholtz free energy, energy, and entropy of mixing of N,N-dimethylformamide (DMF) and water are calculated in the entire composition range by means of Monte Carlo computer simulations and thermodynamic integration using all possible combinations of five DMF and three widely used water models. Our results reveal that the mixing of DMF and water is highly non-ideal. Thus, in their dilute solutions, both molecules induce structural ordering of the major component, as evidenced by the concomitant decrease in the entropy. Among the 15 model combinations considered, only 4 reproduce the well-known full miscibility of DMF and water, 3 of which strongly exaggerate the thermodynamic driving force of the miscibility. Thus, the combination of the CS2 model of DMF and the TIP4P/2005 water model reproduces the properties of the DMF-water mixtures far better than the other combinations tested. Our results also reveal that moving a fractional negative charge from the N atom to the O atom of the DMF molecule, leading to the increase in its dipole moment, improves the miscibility of the model with water. Starting from the CS2 model and optimizing the charge to be moved, we propose a new model of DMF that reproduces very accurately both the Helmholtz free energy of mixing of aqueous DMF solutions in the entire composition range (when used in combination with the TIP4P/2005 water model) and also the internal energy of neat DMF.