Comparative study of different methods for the prediction of drug-polymer solubility

YesIn this study, a comparison of different methods to predict drug−polymer solubility was carried out on binary systems consisting of five model drugs (paracetamol, chloramphenicol, celecoxib, indomethacin, and felodipine) and polyvinylpyrrolidone/vinyl acetate copolymers (PVP/VA) of different monomer weight ratios. The drug−polymer solubility at 25 °C was predicted using the Flory−Huggins model, from data obtained at elevated temperature using thermal analysis methods based on the recrystallization of a supersaturated amorphous solid dispersion and two variations of the melting point depression method. These predictions were compared with the solubility in the low molecular weight liquid analogues of the PVP/VA copolymer (N-vinylpyrrolidone and vinyl acetate). The predicted solubilities at 25 °C varied considerably depending on the method used. However, the three thermal analysis methods ranked the predicted solubilities in the same order, except for the felodipine−PVP system. Furthermore, the magnitude of the predicted solubilities from the recrystallization method and melting point depression method correlated well with the estimates based on the solubility in the liquid analogues, which suggests that this method can be used as an initial screening tool if a liquid analogue is available. The learnings of this important comparative study provided general guidance for the selection of the most suitable method(s) for the screening of drug−polymer solubility.The Irish Research Council and Eli Lilly S.A. through an Irish Research Council Enterprise Partnership Scholarship for C.M.B., in part by The Royal Society in the form of Industrial Fellowship awarded to G.A., and in part by a research grant from Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2275 (for A.M.H., L.T., K.P., and A.K.)

The design and scale-up of spray dried particle delivery systems

INTRODUCTION: The rising demand for pharmaceutical particles with tailored physicochemical properties has opened new markets for spray drying especially for solubility enhancement, improving inhalation medicines and stabilization of biopharmaceuticals. Despite this, the spray drying literature is scattered and often does not address the principles underpinning robust development of pharmaceuticals. It is therefore necessary to present clearer picture of the field and highlight the factors influencing particle design and scale-up. Areas covered: The review presents a systematic analysis of the trends in development of particle delivery systems using spray drying. This is followed by exploring the mechanisms governing particle formation in the process stages. Particle design factors including those of equipment configurations and feed/process attributes were highlighted. Finally, the review summarises the current industrial approaches for upscaling pharmaceutical spray drying. Expert opinion: Spray drying provides the ability to design particles of the desired functionality. This greatly benefits the pharmaceutical sector especially as product specifications are becoming more encompassing and exacting. One of the biggest barriers to product translation remains one of scale-up/scale-down. A shift from trial and error approaches to model-based particle design helps to enhance control over product properties. To this end, process innovations and advanced manufacturing technologies are particularly welcomed

Multiple linear regression modelling to predict the stability of polymer-drug solid dispersions: comparison of the effects of polymers and manufacturing methods on solid dispersion stability

Solid dispersions can be a successful way to enhance the bioavailability of poorly soluble drugs. Here 60 solid dispersion formulations were produced using ten chemically diverse, neutral, poorly soluble drugs, three commonly used polymers, and two manufacturing techniques, spray drying and melt extrusion. Each formulation underwent a six-month stability study at accelerated conditions, 40 °C and 75% relative humidity (RH). Significant differences in times to crystallisation (onset of crystallisation) were observed between both the different polymers and the two processing methods. Stability from zero days to over one year was observed. The extensive experimental dataset obtained from this stability study was used to build multiple linear regression models to correlate physicochemical properties of the active pharmaceutical ingredients (API) with the stability data. The purpose of these models is to indicate which combination of processing method and polymer carrier is most likely to give a stable solid dispersion. Six quantitative mathematical multiple linear regression-based models were produced based on selection of the most influential independent physical and chemical parameters from a set of 33 possible factors, one model for each combination of polymer and processing method, with good predictability of stability. Three general rules are proposed from these models for the formulation development of suitably stable solid dispersions. Namely, increased stability is correlated with increased glass transition temperature (Tg) of solid dispersions, as well as decreased number of H-bond donors and increased molecular flexibility (such as rotatable bonds and ring count) of the drug molecule

Mechanochemical Behaviour of Solid Pharmaceuticals during Milling: Experimental and Modelling Studies

Milling is a commonly used technique in the processing of active pharmaceutical ingredients (APIs) or excipients to control the size and dissolution rate of poorly soluble drugs. However, one of the major challenges of the milling process is the physical and chemical changes arising from the mechanical treatment (mechanochemistry) of the material which might adversely impact the pharmaceutical performances. The common practice to optimise the milling of a specific solid pharmaceutical is to conduct extensive trial and error experiments. However, this method is costly and ineffective, particularly in the early drug development stage, where, a limited amount of API is available. Hence, a methodology that allows anticipating the milling behaviour of particular pharmaceutical solids would be highly desirable. The review section is set to identify the knowledge gap to enable designing a methodology that can address the lack of understanding of the mechanistic behaviour of solid pharmaceutical during milling including the extent of particles size reduction (comminution), and potential mechanochemistry (i.e. amorphisation). This is achieved through determining the key material properties that influence the milling behaviour of the sample, such as the mechanical properties which are controlled by their underlying crystal structure and molecular properties. And through evaluating the critical milling parameters that control the level of the energy available for the treatment of particles during milling, including the type of mill, speed, and time of milling. The material properties influencing the mechanistic behaviour of solid pharmaceuticals were predicted using computational chemistry through studying the sample intrinsic characteristics at the molecular level. The key predicted properties include crystal habit, mechanical properties, and any potential slip plane/system. Two solid pharmaceutical candidates were used in the modelling work, L-Glutamic Acid (β-LGA) and Diaqua-bis(Omeprazolate)-magnesium(II) dihydrate (DABOMD) which are employed for cancer inhibition and stomach acid reflux applications respectively. The outcome of the properties prediction indicates that DABOMD is anticipated to experience large comminution and amorphisation due to its propensity to brittle failure and plastic deformation owed to its moderate elastic modulus and hardness, presence of slip plane and the allocation of water molecules near its slip system. Whereas β-LGA is expected to experience larger comminution and lower amorphisation compared to DABOMD which is related to the prevailing hydrogen network holding its crystal structure, higher elastic modulus and hardness values, and to the lack of slip planes in its structure. To verify the results of the predicted work, milling was performed on DABOMD using a planetary ball mill and a single ball mill and on β-LGA using a planetary ball mill at different times. The outcome of the empirical work shows that DABOMD undergoes a prominent comminution and amorphisation processes that occur parallel to each other, with planetary ball mill causing slightly higher comminution and amorphisation compared to the single ball mill. Whereas, milling of β-LGA shows that it undergoes larger dominant comminution followed by partial amorphisation and recrystallisation. To establish a relationship between the degree of comminution, amorphisation, and the intensity of milling. The energies of planetary ball mill and a single ball mill were quantified using a collision model derived from the literature, and through tracking the milling jar with high speed-camera respectively and were validated through the DEM simulations of the mills. It was found that the planetary ball mill produces higher energy than single ball mill which explains the difference in the comminution and amorphisation obtained in the two mills. The energy produced using DEM simulation of the planetary ball mill agrees with the calculated energies from the collision model. However, the energy calculated from the DEM simulation is lower than that generated from tracking the milling jar in the single ball using a high-speed camera since the DEM tracks the movement of the ball and the powder instead of the movement of the jar. This methodology will enable determining the type and amount of changes that raise with the milling of a solid (i.e. comminution, amorphisation), the time at which it occurs, and the energy required to cause this change

Experimental and Computational Prediction of Glass Transition Temperature of Drugs

Glass transition temperature (<i>T</i>_g) is an important inherent property of an amorphous solid material which is usually determined experimentally. In this study, the relation between <i>T</i>_g and melting temperature (<i>T</i>_m) was evaluated using a data set of 71 structurally diverse druglike compounds. Further, in silico models for prediction of <i>T</i>_g were developed based on calculated molecular descriptors and linear (multilinear regression, partial least-squares, principal component regression) and nonlinear (neural network, support vector regression) modeling techniques. The models based on <i>T</i>_m predicted <i>T</i>_g with an RMSE of 19.5 K for the test set. Among the five computational models developed herein the support vector regression gave the best result with RMSE of 18.7 K for the test set using only four chemical descriptors. Hence, two different models that predict <i>T</i>_g of drug-like molecules with high accuracy were developed. If <i>T</i>_m is available, a simple linear regression can be used to predict <i>T</i>_g. However, the results also suggest that support vector regression and calculated molecular descriptors can predict <i>T</i>_g with equal accuracy, already before compound synthesis