Theoretical modeling study on preparation of nanosized drugs using supercritical-based processing: Determination of solubility of Chlorothiazide in supercritical carbon dioxide

Li, Yuanci; Alameri, Ameer A.; Farhan, Zainab A.; AI_Sadi, Hafidh l.; Alosaimi, Manal E.; AbdalSalam, Ahmed Ghaleb; Jasim, Dheyaa Jumaah; Hadrawi, Salema K.; Al-Taee, Muataz Mohammed; Lafta, Ali H.; Othman, Hakeem A.; Alzahrani, Saleh; Moniem, Ashraf A.; Alqadi, Taha

doi:10.1016/j.molliq.2022.120984

Cited by 11 publications

(3 citation statements)

References 33 publications

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“…In recent years, with the continuous improvement of the application value of solid–liquid equilibrium, , a large number of experienced and activity coefficient equations related to it have emerged one after another, so that many corresponding thermodynamic models have been established, which can provide more theoretical basis for the determination of solubility . This work selects models (containing the van’t Hoff model, λ h model, modified Apelblat model, Yaws model, and NRTL model) to fit the experimental solubility data.…”

Section: Theoretical Modelsmentioning

confidence: 99%

Solubility, Thermodynamic Parameters, and Dissolution Properties of 17-α Hydroxyprogesterone in 13 Pure Solvents

Zhao,

Pan,

Xiong

et al. 2024

ACS Omega

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The static gravimetric method was used to measure the solubility of 17-α hydroxyprogesterone (OHP) in 13 pure solvents ranging from 278.15 to 323.15 K. The results indicate that the experimental solubility of OHP increases with increasing temperature. The experimental solubility data were correlated by the selected van’t Hoff model, λh model, modified Apelblat model, Yaws model, and nonrandom two-liquid (NRTL) model. The fitting results show that the Yaws model can give better correlation results by fitting 13 different pure solvent systems. Based on the NRTL equation, the thermodynamic analysis of solubility data showed that the mixing process was spontaneous. The Hansen solubility parameters (HSPs) and solvent effect were applied to explore these solubility characteristics. Finally, the thermodynamic properties Δsol H°, Δsol S°, Δsol G°, %ξH, and %ξTS were calculated by the van’t Hoff model equation. The results showed that Δsol H°, Δsol S°, and Δsol G° are all positive values, indicating that the dissolution of OHP in the selected solvent is an endothermic reaction with increasing entropy.

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Section: Theoretical Modelsmentioning

confidence: 99%

Solubility, Thermodynamic Parameters, and Dissolution Properties of 17-α Hydroxyprogesterone in 13 Pure Solvents

Zhao,

Pan,

Xiong

et al. 2024

ACS Omega

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“…Another approach to enhancing the therapeutic effectiveness of poorly soluble drugs by reducing their particle size involves the supercritical CO 2 -assisted technique (Figure 3) [41][42][43][44][45][46]. A substance reaches a supercritical fluid state when its temperature and pressure surpass the critical point.…”

Section: Supercritical Technologiesmentioning

confidence: 99%

Pharmaceutical Nanoparticles Formation and Their Physico-Chemical and Biomedical Properties

Shabatina,

Gromova,

Vernaya

et al. 2024

Pharmaceuticals

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The use of medicinal substances in nanosized forms (nanoforms, nanoparticles) allows the therapeutic effectiveness of pharmaceutical preparations to be increased due to several factors: (1) the high specific surface area of nanomaterials, and (2) the high concentration of surface-active centers interacting with biological objects. In the case of drug nanoforms, even low concentrations of a bioactive substance can have a significant therapeutic effect on living organisms. These effects allow pharmacists to use lower doses of active components, consequently lowering the toxic side effects of pharmaceutical nanoform preparations. It is known that many drug substances that are currently in development are poorly soluble in water, so they have insufficient bioavailability. Converting them into nanoforms will increase their rate of dissolution, and the increased saturation solubility of drug nanocrystals also makes a significant contribution to their high therapeutic efficiency. Some physical and chemical methods can contribute to the formation of both pure drug nanoparticles and their ligand or of polymer-covered nanoforms, which are characterized by higher stability. This review describes the most commonly used methods for the preparation of nanoforms (nanoparticles) of different medicinal substances, paying close attention to modern supercritical and cryogenic technologies and the advantages and disadvantages of the described methods and techniques; moreover, the improvements in the physico-chemical and biomedical properties of the obtained medicinal nanoforms are also discussed.

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“…At present, one of the most promising approaches for such a task involves the use of machine learning (ML) methods, as evidenced by the recent growth in the literature dedicated to the topic. − However, it is important to note that a majority of the constructed QSPR models are mostly used for the inter- or extrapolation of data, based on a rather small initial set. Notably, there are two noteworthy works , which utilize a larger data set of over a hundred drugs for model training, claiming that their models can successfully predict the properties of new compounds, expanding beyond the original training set.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Drug-like Compounds Solubility in Supercritical Carbon Dioxide: A Comparative Study between Classical Density Functional Theory and Machine Learning Approaches

Makarov,

Kalikin,

Budkov

2024

Ind. Eng. Chem. Res.

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Supercritical carbon dioxide (scCO 2 ) plays an essential role in various technological procedures, making the solubility of drugs in scCO 2 a crucial aspect of the drug formulation process. This study focuses on utilizing theoretical approaches to predict the solubility of drug-like compounds in scCO2 in order to select the optimum parameters for subsequent experimental procedures. Several machine learning models were developed and compared with a previously established theoretical approach based on classical density functional theory (cDFT). The CatBoost model, utilizing alvaDesc descriptors, demonstrated reasonably accurate predictions for the solubility of 187 drugs (AARD = 1.8%). Meanwhile, the CatBoost model, incorporating CDK descriptors and melting points of drugs as input parameters, exhibited satisfactory accuracy (AARD = 14.3%) in extrapolating predictions for new compounds. Comparing the results between the machine learning approach and the cDFT-based one revealed, on average, a higher accuracy and faster prediction speed for the former. However, cDFT demonstrated a more physical behavior of solubility isotherms compared with the machine learning models. This was particularly evident when the ML models struggled to accurately extrapolate solubility values beyond the experimental range of parameters in the supercritical state. Model CatBoost/CDK is freely accessible at http://chem-predictor.isc-ras.ru/ individual/scco/.

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Theoretical modeling study on preparation of nanosized drugs using supercritical-based processing: Determination of solubility of Chlorothiazide in supercritical carbon dioxide

Cited by 11 publications

References 33 publications

Solubility, Thermodynamic Parameters, and Dissolution Properties of 17-α Hydroxyprogesterone in 13 Pure Solvents

Solubility, Thermodynamic Parameters, and Dissolution Properties of 17-α Hydroxyprogesterone in 13 Pure Solvents

Pharmaceutical Nanoparticles Formation and Their Physico-Chemical and Biomedical Properties

Prediction of Drug-like Compounds Solubility in Supercritical Carbon Dioxide: A Comparative Study between Classical Density Functional Theory and Machine Learning Approaches

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