The optimisation of the pharmaceutical properties of carboxylic acid drugs is often conducted by salt formation. Often, the salt with the best solubility is not chosen due to other factors such as stability, solubility, dissolution and bioavailability that are taken into consideration during the preformulation stage. This work uses advanced imaging techniques to give insights into the preformulation properties that can aid in the empirical approach often used in industry for the selection of salts. Gemfibrozil (GEM) was used as a model poorly soluble drug. Four salts of GEM were made using cyclopropylamine (CPROP), cyclobutylamine (CBUT), cyclopentylamine (CPENT) and cyclohexylamine (CHEX) as counterions. DSC, XRD and SEM were used to confirm and characterise salt formation. IDR obtained using UV-imaging up to 10 min for all the salts showed that an increase in the chain length of the counterion caused a decrease in the IDR. Past the 10 min mark, there was an increase in the IDR value for the CPROP salt, which was visualised using UV-imaging. The developed interfacial (surface) area ratio (Sdr) showed significant surface gains for the compacts. Full dosage form (capsule) imaging showed an improvement over the GEM for all the salts with an increase in chain length of the counterion bringing about a decrease in dissolution which correlated with the obtained UV-imaging IDR data.
Submission: European Journal of Pharmaceutics and Biopharmaceuticsthe dissolution of indomethacin. This work thus highlights the importance of having both complimentary IDR and whole dosage imaging techniques in giving a better understanding of solid dispersion systems.
The utilising of innovative combinatorial chemistry and high-throughput screening tools in drug discovery has led to more active pharmaceutical ingredients with poorly soluble properties reaching clinical stages of drug development processes. It is estimated that two out of five pharmaceutical compounds in the US market are considered poorly soluble (Fahr & Liu, 2007). Poorly soluble drugs are very challenging during pharmaceutical development as solubility, and further dissolution tends to be the rate-limiting step for these compounds entering the systemic circulations and giving the desired therapeutic response (Conway and Asare-Addo, 2016). To overcome this issue, several techniques are used. These techniques can be classified into three main categories (Savjani, Gajjar, & Savjani, 2012) : I: physical modification such as particle size reduction or crystal habit (polymorphs and amorphous forms) II: Chemical modification such as salt formation. III: Miscellaneous techniques such as supercritical fluids,
This study presents a modelling framework to predict the flowability of various commonly used pharmaceutical powders. The flowability models were trained and validated on 86 samples including single components and binary mixtures. Two modelling paradigms based on artificial intelligence (AI) namely, a radial basis function (RBF) and an integrated network were employed to model the flowability represented by the flow function coefficient (FFC) and the bulk density (RHOB). Both approaches were utilized to map the input parameters (i.e. particle size, shape descriptors and various materials and mixtures) to the flow properties. The input parameters of the blends were determined from the particle size and shape properties of the single components. The results clearly indicated that the integrated network outperformed the single RBF network in terms of the predictive performance and the generalization capabilities. For the integrated network, the coefficient of determination of the testing data set (not used for training the model) for 2 FFC was 2 = 0.93, reflecting an acceptable predictive power of this model. Since the flowability of the blends can be predicted from single component size and shape descriptors, the integrated network can assist formulators in selecting excipients and their concentrations to improve flowability with minimal experimental effort and material. The presented modelling approach can thus be employed instead of actual measurements throughout the process development stage resulting in the (i) minimization of the time required, (ii) exploration and examination of the design space, and (iii) minimization of material waste.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.