Exploring the temperature-dependent proportions of lidocaine conformers equilibria in supercritical carbon dioxide via NOESY

Khodov, Ilya A.; Belov, Konstantin V.; Sobornova, V.V.; Dyshin, A. A.; Киселев, М. Г.

doi:10.1016/j.molliq.2023.122620

Cited by 5 publications

(3 citation statements)

References 57 publications

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“…Notable examples include high-pressure nuclear magnetic resonance (NMR) spectroscopy and techniques such as nuclear Overhauser effect spectroscopy (NOESY). The increased knowledge of molecular structure is made possible by conformation management, and this knowledge can influence various aspects of pharmaceuticals, including their bioavailability [116,117].…”

Section: Challenges and Prospects In Drug Bioavailability Researchmentioning

confidence: 99%

The Bioavailability of Drugs—The Current State of Knowledge

Stielow,

Witczyńska,

Kubryń

et al. 2023

Molecules

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Drug bioavailability is a crucial aspect of pharmacology, affecting the effectiveness of drug therapy. Understanding how drugs are absorbed, distributed, metabolized, and eliminated in patients’ bodies is essential to ensure proper and safe treatment. This publication aims to highlight the relevance of drug bioavailability research and its importance in therapy. In addition to biochemical activity, bioavailability also plays a critical role in achieving the desired therapeutic effects. This may seem obvious, but it is worth noting that a drug can only produce the expected effect if the proper level of concentration can be achieved at the desired point in a patient’s body. Given the differences between patients, drug dosages, and administration forms, understanding and controlling bioavailability has become a priority in pharmacology. This publication discusses the basic concepts of bioavailability and the factors affecting it. We also looked at various methods of assessing bioavailability, both in the laboratory and in the clinic. Notably, the introduction of new technologies and tools in this field is vital to achieve advances in drug bioavailability research. This publication also discusses cases of drugs with poorly described bioavailability, providing a deeper understanding of the complex challenges they pose to medical researchers and practitioners. Simultaneously, the article focuses on the perspectives and trends that may shape the future of research regarding bioavailability, which is crucial to the development of modern pharmacology and drug therapy. In this context, the publication offers an essential, meaningful contribution toward understanding and highlighting bioavailability’s role in reliable patient treatment. The text also identifies areas that require further research and exploration.

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Section: Challenges and Prospects In Drug Bioavailability Researchmentioning

confidence: 99%

The Bioavailability of Drugs—The Current State of Knowledge

Stielow,

Witczyńska,

Kubryń

et al. 2023

Molecules

View full text Add to dashboard Cite

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“…NOESY spectra are often used to analyze the spatial structure of organic compound molecules, such as configuration and conformation [32][33][34][35]. The basic principle is that when two nuclei are very close in space, if a double resonance experimental magnetic field (B2) is irradiated on one of the nuclei to saturate, the nuclear Overhauser effect (NOE) will occur, leading to an enhancement of the resonance signal of another nucleis [32].…”

Section: Structural Analysis By Nuclear Magnetic Resonance (Nmr)mentioning

confidence: 99%

How to More Effectively Obtain Ginsenoside Rg5: Understanding Pathways of Conversion

Cheng,

Luo,

et al. 2023

Molecules

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Ginsenoside Rg5, a relatively uncommon secondary ginsenoside, exhibits notable pharmacological activity and is commonly hypothesized to originate from the dehydration of Rg3. In this work, we compared different conversion pathways using Rb1, R-Rg3 and S-Rg3 as the raw material under simple acid catalysis. Interestingly, the results indicate that the conversion follows this reaction activity order Rb1 > S-Rg3 > R-Rg3, which is contrary to the common understanding of Rg5 obtained from Rg3 by dehydration. Our experimental results have been fully confirmed by theoretical calculations and a NOESY analysis. The DFT analysis reveals that the free energies of S-Rg3 and R-Rg3 in generating carbocation are 7.56 mol/L and 7.57 mol/L, respectively, which are significantly higher than the free energy of 1.81 mol/L when Rb1 generates the same carbocation. This finding aligns with experimental evidence suggesting that Rb1 is more prone to generating Rg5 than Rg3. The findings from the nuclear magnetic resonance (NMR) analysis suggest that the fatty chains (C22–C27) in R-Rg3 and S-Rg3 adopt a Gauche conformation and an anti conformation with C16–C17 and C13–C17, respectively, due to the relatively weak repulsive van der Waals force. Therefore, the configuration of R-Rg3 is more conducive to the formation of intramolecular hydrogen bonds between 20C–OH and 12C–OH, whereas S-Rg3 lacks this capability. Consequently, this also explains the fact that S-Rg3 is more prone to dehydration to generate Rg5 than R-Rg3. Additionally, our research reveals that the synthetic route of Rg5 derived from protopanaxadiol (PPD)-type ginsenosides (including Rb1, Rb2, Rb3, Rc and Rd) exhibits notable advantages in terms of efficacy, purity and yield when compared to the pathway originating from Rg3. Moreover, this study presents a highly effective and practical approach for the extensive synthesis of Rg5, thereby facilitating the exploration of its pharmacological properties and potential application in drug discovery.

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“…Supercritical carbon dioxide (scCO 2 ) has demonstrated significant efficacy in various fields including extraction of essential oil, − seed oil, − nanoparticle formation, − impregnation, − control of polymorphic forms, − polymer synthesis, etc.…”

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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Exploring the temperature-dependent proportions of lidocaine conformers equilibria in supercritical carbon dioxide via NOESY

Cited by 5 publications

References 57 publications

The Bioavailability of Drugs—The Current State of Knowledge

The Bioavailability of Drugs—The Current State of Knowledge

How to More Effectively Obtain Ginsenoside Rg5: Understanding Pathways of Conversion

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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