Application of machine learning in prediction of hydrotrope-enhanced solubilisation of indomethacin

Damiati, Safa A.; Martini, Luigi G.; Smith, Norman W.; Lawrence, M. Jayne; Barlow, David J.

doi:10.1016/j.ijpharm.2017.07.048

Cited by 22 publications

(21 citation statements)

References 30 publications

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“…The final layer of an artificial neuron is the output layer, which consists of the outputs (targets). Moreover, by examining the magnitude of the ANN connection weights, ANNs can provide quantitative estimates of the relative importance of the input variables for the output in question (36). Figure 2 illustrates a schematic representation of a typical biological neuron (a) and an ANN (b).…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…This is true for oral, parenteral, ophthalmic, topical, and other routes of administration. Various solubilization techniques are used to improve the aqueous solubilities of drug substances, such as using surfactant, complexation, salt formation, using hydrotropes, or forming cocrystals (36,63,64).…”

Section: Machine Learning In Pharmaceutical Preformulationmentioning

confidence: 99%

“…The in silico prediction of the aqueous solubility of drug substances has gained significant interest using different computational approaches, such as molecular dynamics simulations (65) and machine learning techniques (36). For example, Damiati et al (2017) developed a machine learning application using ANNs to predict the solubility enhancement effect of several hydrotrope molecules.…”

Section: Machine Learning In Pharmaceutical Preformulationmentioning

confidence: 99%

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Digital Pharmaceutical Sciences

Damiati

2020

AAPS PharmSciTech

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Artificial intelligence (AI) and machine learning, in particular, have gained significant interest in many fields, including pharmaceutical sciences. The enormous growth of data from several sources, the recent advances in various analytical tools, and the continuous developments in machine learning algorithms have resulted in a rapid increase in new machine learning applications in different areas of pharmaceutical sciences. This review summarizes the past, present, and potential future impacts of machine learning technologies on different areas of pharmaceutical sciences, including drug design and discovery, preformulation, and formulation. The machine learning methods commonly used in pharmaceutical sciences are discussed, with a specific emphasis on artificial neural networks due to their capability to model the nonlinear relationships that are commonly encountered in pharmaceutical research. AI and machine learning technologies in common day-today pharma needs as well as industrial and regulatory insights are reviewed. Beyond traditional potentials of implementing digital technologies using machine learning in the development of more efficient, fast, and economical solutions in pharmaceutical sciences are also discussed.

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Section: Artificial Neural Networkmentioning

confidence: 99%

Section: Machine Learning In Pharmaceutical Preformulationmentioning

confidence: 99%

Section: Machine Learning In Pharmaceutical Preformulationmentioning

confidence: 99%

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Digital Pharmaceutical Sciences

Damiati

2020

AAPS PharmSciTech

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“…Further work is required for a definitive choice on hydrotrope affiliation. Up to that point, the idea of hydrotrope affiliation and MHC must not be disposed of, yet ought to be utilized with reservation [21,22].…”

Section: Self-association Of Hydrotropesmentioning

confidence: 99%

A Review on Hydrotropy: A Potential Approach for the Solubility Enhancement of Poorly Soluble Drug

Joshi¹,

NAINWAL²,

SAHARAN³

2019

Asian J Pharm Clin Res

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Solubility is one of the significant parameters to accomplish wanted centralization of medication in foundational flow for pharmacological reaction to appear. Medication adequacy can be seriously constrained by poor watery dissolvability, and a few medications additionally show reactions because of their poor solvency. There are numerous strategies which are utilized to upgrade the fluid solvency. The capacity to increment watery solvency would thus be able to be a profitable guide to expanding proficiency as well as lessening reactions for specific medications. The ability to augment watery dissolvability would subsequently have the option to be a beneficial manual for extending capability just as diminishing responses for explicit medications. Therapeutic adequacy of a medication relies on the bioavailability and at last on the solvency of medication particles. Solvency is one of the significant parameters to accomplish wanted convergence of medication in fundamental course for pharmacological reaction to appear. Because of cutting edge inquire about and advancement, there are assortments of new medications and their subordinates are accessible. In any case, over 40% of lipophilic medication up-and-comers neglect to achieve showcase because of poor bioavailability, despite the fact that these medications may display potential pharmacodynamic exercises. The lipophilic medication that achieves market requires a high portion to accomplish appropriate pharmacological activity. Hydrotropy is one of the solvency improvement methods which upgrade dissolvability to numerous folds with utilization of hydrotropes such as sodium benzoate, sodium citrate, urea, niacinamide, and so forth and have numerous focal points as, it does not require substance alteration of hydrophobic medications, utilization of natural solvents, or readiness of emulsion framework and so forth.

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“…Poor solubility of drugs poses a serious challenge to drug development. However, this problem can be overcome by the use of weakly amphiphilic organic molecules called hydrotropes [1][2][3][4][5]. Hydrotropes, when added to water, increase the solubility of hydrophobic drug molecules up to several orders of magnitude [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Effect of solute aggregation on solubilization

Shimizu

Kanasaki

2019

Journal of Molecular Liquids

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Strong self-association of hydrophobic solutes takes place in water. However, solute selfassociation has often been neglected in understanding the aqueous solubility of drugs as well as their solubilization by excipients, cosolvents and hydrotropes. Based on a rigorous statistical thermodynamic foundation, here we show how to estimate the contribution from solute selfassociation to solubility and solubilization, based on experimental data such as solubility and the osmotic second virial coefficients. Such data show that solute self-association can indeed be negligible in most common cases of hydrotropic solubilization, Setschenow coeffficients and the hydrophobic hydration.

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Application of machine learning in prediction of hydrotrope-enhanced solubilisation of indomethacin

Cited by 22 publications

References 30 publications

Digital Pharmaceutical Sciences

Digital Pharmaceutical Sciences

A Review on Hydrotropy: A Potential Approach for the Solubility Enhancement of Poorly Soluble Drug

Effect of solute aggregation on solubilization

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