Prediction of Drug Solubility in Lipid Mixtures from the Individual Ingredients

Sacchetti, Mark; Nejati, Elham

doi:10.1208/s12249-012-9830-3

Cited by 24 publications

(23 citation statements)

References 9 publications

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“…The use of either the weighed mean or the geometric mean of the solubility for this purpose has been discussed elsewhere. 17 …”

Section: Resultsmentioning

confidence: 99%

“…The loading capacities of the LBFs were calculated for the nine model compounds by taking use of eq 1 : where S LBF is the total drug loading in the formulation and equal to the sum of the solubility in the pure excipient ( S e ), multiplied by the weight percentage of that excipient in the formulation ( W e ). 14 , 17 In other words, the drug loading capacity of the formulation is the sum of the drug solubility in all included excipients normalized by the contributing weight fraction of the excipients in the formulation. The above equation was modified from the original log–linear equation; 14 , 15 here the sum of the weighted mean of the solubility is used instead of the weighed geometric mean.…”

Section: Experimental Sectionmentioning

confidence: 99%

“… 16 − 18 Based on four model compounds, it was shown that the sum of the loading capacity in the LBF is equal to the solubility of each included excipient, when compensated for the fraction of the excipient in the formulation. 17 The weakness of this methodology is that it requires experimental measurements, which limits the applicability for rapid estimation of LBF loading capacity.…”

Section: Introductionmentioning

confidence: 99%

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Tools for Early Prediction of Drug Loading in Lipid-Based Formulations

2015

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Identification of the usefulness of lipid-based formulations (LBFs) for delivery of poorly water-soluble drugs is at date mainly experimentally based. In this work we used a diverse drug data set, and more than 2,000 solubility measurements to develop experimental and computational tools to predict the loading capacity of LBFs. Computational models were developed to enable in silico prediction of solubility, and hence drug loading capacity, in the LBFs. Drug solubility in mixed mono-, di-, triglycerides (Maisine 35-1 and Capmul MCM EP) correlated (R2 0.89) as well as the drug solubility in Carbitol and other ethoxylated excipients (PEG400, R2 0.85; Polysorbate 80, R2 0.90; Cremophor EL, R2 0.93). A melting point below 150 °C was observed to result in a reasonable solubility in the glycerides. The loading capacity in LBFs was accurately calculated from solubility data in single excipients (R2 0.91). In silico models, without the demand of experimentally determined solubility, also gave good predictions of the loading capacity in these complex formulations (R2 0.79). The framework established here gives a better understanding of drug solubility in single excipients and of LBF loading capacity. The large data set studied revealed that experimental screening efforts can be rationalized by solubility measurements in key excipients or from solid state information. For the first time it was shown that loading capacity in complex formulations can be accurately predicted using molecular information extracted from calculated descriptors and thermal properties of the crystalline drug.

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“…The use of either the weighed mean or the geometric mean of the solubility for this purpose has been discussed elsewhere. 17 …”

Section: Resultsmentioning

confidence: 99%

Section: Experimental Sectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Tools for Early Prediction of Drug Loading in Lipid-Based Formulations

2015

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“…The solubility study of DMF in lipid was based on previously described standard procedures. 17 , 18 An excess amount of DMF was added to the lipids maintained at 70±5 °C were stirred thoroughly and sonicated for maximum solubilization. The solution was sampled at 2, 6, 8, 24, 48 and 72 h to analyze dissolved drug.…”

Section: Methodsmentioning

confidence: 99%

Preparation and Statistical Modeling of Solid Lipid Nanoparticles of Dimethyl Fumarate for Better Management of Multiple Sclerosis

Ojha¹,

Kumar²

2018

Adv Pharm Bull

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Purpose: The objective of this study was to synthesize and statistically optimize dimethyl fumarate (DMF) loaded solid lipid nanoparticles (SLNs) for better management of multiple sclerosis (MS).Methods: SLNs were formulated by hot emulsion, ultrasonication method and optimized with response surface methodology (RSM). A three factor and three level box-behnken design was used to demonstrate the role of polynomial quadratic equation and contour plots in predicting the effect of independent variables on dependent responses that were particle size and % entrapment efficiency (%EE).Results: The results were analyzed by analysis of variance (ANOVA) to evaluate the significant differences between the independent variables. The optimized SLNs were characterized and found to have an average particle size of 300 nm, zeta potential value of -34.89 mv and polydispersity index value < 0.3. Entrapment efficiency was found to be 59% and drug loading was 15%. TEM microphotograph revealed spherical shape and no aggregation of nanoparticles. In-vitro drug release profile was an indicative of prolonged therapy. In-vivo pharmacokinetic data revealed that the relative bioavailability was enhanced in DMF loaded SLNs in Wistar rats.Conclusion: This study showed that the present formulation with improved characteristics can be a promising formulation with a longer half-life for the better management of MS.

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“…Solubility is an important parameter required to achieve the concentration of drugs needed for systemic circulation to produce the desired therapeutic effects. Poor solubility results in poor absorption of drugs in the gastrointestinal tract which often results in toxicity [ 4 ]. In many pharmaceutical companies, formulation and development of new chemical entities (NCE) or generic drugs are practically limited by poor water solubility, with more than 40% of NCE being insoluble [ 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

Polyamidoamine Dendrimers for Enhanced Solubility of Small Molecules and Other Desirable Properties for Site Specific Delivery: Insights from Experimental and Computational Studies

et al. 2018

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Clinical applications of many small molecules are limited due to poor solubility and lack of controlled release besides lack of other desirable properties. Experimental and computational studies have reported on the therapeutic potential of polyamidoamine (PAMAM) dendrimers as solubility enhancers in pre-clinical and clinical settings. Besides formulation strategies, factors such as pH, PAMAM dendrimer generation, PAMAM dendrimer concentration, nature of the PAMAM core, special ligand and surface modifications of PAMAM dendrimer have an influence on drug solubility and other recommendable pharmacological properties. This review, therefore, compiles the recently reported applications of PAMAM dendrimers in pre-clinical and clinical uses as enhancers of solubility and other desirable properties such as sustained and controlled release, bioavailability, bio-distribution, toxicity reduction or enhancement, and targeted delivery of small molecules with emphasis on cancer treatment.

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Prediction of Drug Solubility in Lipid Mixtures from the Individual Ingredients

Cited by 24 publications

References 9 publications

Tools for Early Prediction of Drug Loading in Lipid-Based Formulations

Tools for Early Prediction of Drug Loading in Lipid-Based Formulations

Preparation and Statistical Modeling of Solid Lipid Nanoparticles of Dimethyl Fumarate for Better Management of Multiple Sclerosis

Polyamidoamine Dendrimers for Enhanced Solubility of Small Molecules and Other Desirable Properties for Site Specific Delivery: Insights from Experimental and Computational Studies

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