dissolution rate
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Co-milling of a drug with a co-former is an efficient technique to improve the solubility of drugs. Besides the particle size reduction, the co-milling process induces a structural disorder and the creation of amorphous regions. The extent of drug solubility enhancement is dependent on the proper choice of co-milling co-former. The aim of this work was to compare the effects of different co-formers (meglumine and polyvinylpyrrolidone) on the dissolution rates of glass forming (indomethacin) and non-glass forming (mefenamic acid) model drugs. A positive impact of the co-milling on the dissolution behavior was observed in all co-milled mixtures, even if no substantial amorphization was observed. While meglumine exhibited pronounced effects on the dissolution rate of both drugs, the slightest enhancement was observed in mixtures with polyvinylpyrrolidone. The evaluation of specific release rate revealed the surface activation of drug particle is responsible for improving the dissolution rate of both drug types, but for the glass former, this surface activation could be persistent while maintaining a high dissolution rate even until a high fraction of drug is released. Our results, therefore, indicate that adequate co-former choice and consideration of drug glass forming ability are important for a successful co-milling approach to poorly water-soluble drugs.
- MeSH
- indomethacin MeSH
- léčivé přípravky * MeSH
- povidon * MeSH
- příprava léků MeSH
- rozpustnost MeSH
- velikost částic MeSH
- Publikační typ
- časopisecké články MeSH
The aim is to determine how well the rate parameter of the homogeneous model of dissolution can be estimated in dependency on the chosen times to measure the empirical data. The approach is based on the theory of Fisher information. We show that if the probability distribution of the measurement errors is known, the data should be collected at a single time instant or its close proximity in order to obtain the best estimate. This is in sharp contrast with commonly used experimental protocols. Further, from the properties of the Fisher information we deduce how suitable is the model of measurement error and we show that asymmetric distribution of data close to the time origin is unavoidable.
Tři různé přípravky obsahující 240 mg verapamil hydrochloridu v potahované tabletě se zpomale-ným uvolňováním byly porovnány zaslepeným disolučním testem. Množství uvolněné účinné látkyz tablety i její dynamika se po třetí hodině disoluce u všech tří přípravků významně lišily. U pří-pravku C (originální přípravek Isoptin SR, výrobce Knoll) se po sedmi hodinách uvolnilo 92,2 %účinné látky. U generického přípravku A bylo toto množství ve srovnání s originálním přípravkemvyšší, a u generického přípravku B nižší. Pouze u originálního přípravku byla dynamika uvolňováníúčinné látky lineární. Nepravidelné a nelineární uvolňování účinné látky z tablety může mít zanásledek klinicky suboptimální účinnost a bezpečnost generických přípravků.
Three different products containing 240 mg of verapamil hydrochloride in a coated tablet withsustained release were compared using the blinded dissolution test. The amount of the activesubstance released from the tablet and its dynamics differed significantly with all three productsafter 3 hours of dissolution. With product C (Isoptin SR, a proprietary product manufactured byKnoll), 92,2 % of the active substance was released after seven hours. With generic products A andB, the amounts were higher and lower, respectively. The dynamics of release of the active substancewas linear only with the proprietary product. Irregular and non-linear rates of active substancerelease from the tablet may result in clinically suboptimal efficacy and safety of generic products.
Prediction of poly(lactic-co-glycolic acid) (PLGA) micro- and nanoparticles' dissolution rates plays a significant role in pharmaceutical and medical industries. The prediction of PLGA dissolution rate is crucial for drug manufacturing. Therefore, a model that predicts the PLGA dissolution rate could be beneficial. PLGA dissolution is influenced by numerous factors (features), and counting the known features leads to a dataset with 300 features. This large number of features and high redundancy within the dataset makes the prediction task very difficult and inaccurate. In this study, dimensionality reduction techniques were applied in order to simplify the task and eliminate irrelevant and redundant features. A heterogeneous pool of several regression algorithms were independently tested and evaluated. In addition, several ensemble methods were tested in order to improve the accuracy of prediction. The empirical results revealed that the proposed evolutionary weighted ensemble method offered the lowest margin of error and significantly outperformed the individual algorithms and the other ensemble techniques.
The purpose of this study was to specify critical parameters (physicochemical characteristics) of drug substance that can affect dissolution profile/dissolution rate of the final drug product manufactured by validated procedure from various batches of the same drug substance received from different suppliers. The target was to design a sufficiently robust drug substance specification allowing to obtain a satisfactory drug product. For this reason, five batches of the drug substance and five samples of the final peroral drug products were analysed with the use of solid state analysis methods on the bulk level. Besides polymorphism, particle size distribution, surface area, zeta potential, and water content were identified as important parameters, and the zeta potential and the particle size distribution of the drug substance seem to be critical quality attributes affecting the dissolution rate of the drug substance released from the final peroral drug formulation.
The use of solid oral dosage forms depends on the degree of bioavailability of the active pharmaceutical ingredient. The rate and extent of the drug released from the dosage form and subsequently dissolved in the gastrointestinal fluids greatly affects its fate in the human body. In vitro dissolution test may provide an in-depth understanding of a drug formulation's behaviour in vivo, as long as it sufficiently simulates relevant gastrointestinal conditions. Therefore, the development of in vitro gastrointestinal systems, which reflects advanced technology and knowledge about the human body, is receiving considerable attention. This article is focused on the biorelevant dynamic apparatuses and their sophisticated design, overcoming many limitations of conventional dissolution devices and allowing a better correlation with in vivo behaviour of solid oral dosage forms.
Co-milling is an effective technique for improving dissolution rate limited absorption characteristics of poorly water-soluble drugs. However, there is a scarcity of models available to forecast the magnitude of dissolution rate improvement caused by co-milling. Therefore, this study endeavoured to quantitatively predict the increase in dissolution by co-milling based on drug properties. Using a biorelevant dissolution setup, a series of 29 structurally diverse and crystalline drugs were screened in co-milled and physically blended mixtures with Polyvinylpyrrolidone K25. Co-Milling Dissolution Ratios after 15 min (COMDR15 min) and 60 min (COMDR60 min) drug release were predicted by variable selection in the framework of a partial least squares (PLS) regression. The model forecasts the COMDR15 min (R2 = 0.82 and Q2 = 0.77) and COMDR60 min (R2 = 0.87 and Q2 = 0.84) with small differences in root mean square errors of training and test sets by selecting four drug properties. Based on three of these selected variables, applicable multiple linear regression equations were developed with a high predictive power of R2 = 0.83 (COMDR15 min) and R2 = 0.84 (COMDR60 min). The most influential predictor variable was the median drug particle size before milling, followed by the calculated drug logD6.5 value, the calculated molecular descriptor Kappa 3 and the apparent solubility of drugs after 24 h dissolution. The study demonstrates the feasibility of forecasting the dissolution rate improvements of poorly water-solube drugs through co-milling. These models can be applied as computational tools to guide formulation in early stage development.
