Analysis and Artificial Neural Network Prediction of Melting Properties and Ideal Mole fraction Solubility of Cocrystals

Krishna, G. Rama; Rasmuson, Åke C.

doi:10.1021/acs.cgd.0c00182

Cited by 21 publications

(19 citation statements)

References 46 publications

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“…THP II exhibits a lower mole fraction solubility in chloroform than SA, and the THP:SA cocrystal has the lowest mole fraction solubility of the three solid phases. Similar cases have been reported previously in studies where the cocrystal displayed solubility outside of the range of the two coformers. ,, …”

Section: Results and Analysissupporting

confidence: 88%

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Nucleation of the Theophylline:Salicylic Acid 1:1 Cocrystal

McTague

Rasmuson

2021

Crystal Growth & Design

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The nucleation behavior of the theophylline–salicylic acid 1:1 (THP:SA) cocrystal in chloroform has been investigated and compared with the corresponding behavior of the pure compounds. Induction times have been determined at different supersaturations at 10 °C under each condition in approximately 40–80 repetition experiments in 20 mL vials. Nucleation times, extracted from the median induction times by accounting for a nucleus growth time, have been used to determine the interfacial energy and the pre-exponential factor within the classical nucleation theory. Results show that the cocrystal at equal driving force has a longer nucleation time, or to reach equal nucleation time, the cocrystal requires a higher driving force. Pure theophylline is easier to nucleate than pure salicylic acid, despite the latter having a smaller molecular size, higher solubility, and is expected to form dimers already in the solution. The cocrystal is found to have an interfacial energy in between the respective values for the pure compounds. However, the higher molecular volume of the cocrystal, taken as the volume of the 1:1 theophylline–salicylic acid assembly, leads to the highest nucleation work, which, together with a low pre-exponential factor, explains why the cocrystal is the most difficult to nucleate. The experimentally extracted pre-exponential factor of the cocrystal is very similar to that of THP, and similar trends are observed from theoretical expressions of volume-diffusion- and surface-integration-controlled nucleation, respectively.

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Section: Results and Analysissupporting

confidence: 88%

“…Similar cases have been reported previously in studies where the cocrystal displayed solubility outside of the range of the two coformers. 15 , 29 , 30 …”

Section: Results and Analysismentioning

confidence: 99%

Nucleation of the Theophylline:Salicylic Acid 1:1 Cocrystal

McTague

Rasmuson

2021

Crystal Growth & Design

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“…Among them, w (l) ji is the connection weight from neuron i to neuron j, and there is formula (8) for the output layer:…”

Section: Neural Network Prediction Methodmentioning

confidence: 99%

“…e model successfully predicted the corresponding melting properties of the cocrystal and the ideal solubility of the cocrystal from the molecular weight and melting temperature of the pure compound [8]. Pattnaik and Sutar study reveals a new computational prediction model to predict response, making material removal rate (MRR) more accurate.…”

Section: Related Workmentioning

confidence: 99%

Macroeconomic Forecast Model System Based on Digital Information and Blockchain Technology

2022

Mobile Information Systems

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Since the reform and opening up, China’s economy has developed rapidly, becoming the second largest economy in the world. The state of macroeconomic development has a great influence on the government’s policy introduction and the investment decisions of individual institutions. Therefore, forecasting the macroeconomy is of great significance to the country. This paper aims to study the macroeconomic forecasting model system based on digital information and blockchain technology. This paper proposes an artificial neural network prediction algorithm based on digital information and blockchain technology, and the artificial neural network and particle swarm algorithm are combined to become the hybrid artificial neural network algorithm, and the conception of the establishment of the macroeconomic forecast model is proposed. The experimental results of this paper show that according to the prediction results of artificial neural network, the prediction and actual error in 2010 is 0.10, while the new method proposed in this paper predicts the error of 0.056. In 2016, the prediction error based on artificial neural network was 0.14, while the prediction result of the new method proposed in this paper showed an error of 0.008. In each year’s economic forecast, the neural network model of the new method proposed in this paper has higher prediction accuracy and smaller error. It can be seen that the neural network model based on artificial neural network and PSO algorithm proposed in this paper is beneficial to macroeconomic forecasting.

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“…In this regard, the aforementioned studies testify to the need to gain further insights into the impact of different excipients on cocrystal properties. There have been a few studies reported that aim to develop the models using mathematical/numerical modeling techniques that predict the melting properties, ideal mole fraction solubility, and aqueous solubility product of pure cocrystals. − However, these studies have mainly focused on a mathematical model development for the prediction of pure cocrystal properties, and the impact of molecular-level interactions between cocrystal and excipients in formulations has not been considered. In this regard, a fundamental understanding of the types and nature of molecular interactions between cocrystal and excipients is essential in order to discern their effect on physicochemical properties such as solubility, dissolution rate, and stability.…”

Section: Introductionmentioning

confidence: 99%

Cocrystal Formulations: Evaluation of the Impact of Excipients on Dissolution by Molecular Simulation and Experimental Approaches

Cadden

Gupta

Kanaujia

et al. 2020

Crystal Growth & Design

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Cocrystallization has matured into an established technique for fine-tuning the physicochemical properties of active pharmaceutical ingredients (APIs). This technique has been adopted by pharmaceutical drug companies, with increasing numbers of cocrystal-based drug products now entering the market. Surprisingly, however, studies into the formulation aspects of cocrystal-based drugs are relatively few and far between compared to the vast literature on their design, synthesis, and characterization. We herein report the results of our investigations into cocrystal–excipient interactions in water that determine the dissolution properties of cocrystals in formulation by a combination of molecular dynamics (MD) simulation and experimental approaches. Two cocrystals of an antirheumatic drug, leflunomide (LEF) with 3-hydroxybenzoic acid (HBA) and 2-picolinic acid (PIC), were assessed in formulation with the frequently used excipients lactose and dibasic calcium phosphate (DCP). For comparison, the dissolution of neat LEF formulations with these excipients was also evaluated. The parameters deduced from MD simulations, such as solvent-accessible surface area, intermolecular hydrogen bonds among formulation ingredients and water, and interaction energy between the API (or cocrystal) and water were found to be essential indicators in the prediction of cocrystal formulation dissolution trends. It was found that the presence of lactose as an excipient improved the dissolution of the cocrystal formulation compared to the neat cocrystals, most notably for the LEF-PIC cocrystal. In contrast, DCP was seen to have a detrimental effect on the dissolution of cocrystal formulations, all exhibiting lower dissolution than their neat cocrystal counterparts and LEF. Careful analysis of these results revealed that the nature of the excipient plays a significant role in the dissolution properties. While the improved dissolution of the lactose formulations was attributed to its hydrophilic nature, the ionic and hydrophobic nature of DCP was likely responsible for its detrimental effect. The results of the MD simulations were found to be in excellent agreement with the experimentally observed dissolution hierarchy.

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Analysis and Artificial Neural Network Prediction of Melting Properties and Ideal Mole fraction Solubility of Cocrystals

Cited by 21 publications

References 46 publications

Nucleation of the Theophylline:Salicylic Acid 1:1 Cocrystal

Nucleation of the Theophylline:Salicylic Acid 1:1 Cocrystal

Macroeconomic Forecast Model System Based on Digital Information and Blockchain Technology

Cocrystal Formulations: Evaluation of the Impact of Excipients on Dissolution by Molecular Simulation and Experimental Approaches

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