Niederquell, Andreas
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Study of disordered mesoporous silica regarding intrinsic compound affinity to the carrier and drug-accessible surface area
There is increasing research interest in using mesoporous silica for the delivery of poorly water-soluble drugs that are stabilized in a noncrystalline form. Most research has been done on ordered silica, whereas far fewer studies have been published on using nonordered mesoporous silica, and little is known about intrinsic drug affinity to the silica surface. The present mechanistic study uses inverse gas chromatography (IGC) to analyze the surface energies of three different commercially available disordered mesoporous silica grades in the gas phase. Using the more drug-like probe molecule octane instead of nitrogen, the concept of a “drug-accessible surface area” is hereby introduced, and the effect on drug monolayer capacity is addressed. In addition, enthalpic interactions of molecules with the silica surface were calculated based on molecular mechanics, and entropic energy contributions of volatiles were estimated considering molecular flexibility. These free energy contributions were used in a regression model, giving a successful comparison with experimental desorption energies from IGC. It is proposed that a simplified model for drugs based only on the enthalpic interactions can provide an affinity ranking to the silica surface. Following this preformulation research on mesoporous silica, future studies may harness the presented concepts to guide formulation scientists. © 2023 American Chemical Society.
Study and computational modeling of fatty acid effects on drug solubility in lipid-based systems
Lipid-based systems have many advantages in formulation of poorly water-soluble drugs but issues of a limited solvent capacity are often encountered in development. One of the possible solubilization approaches of especially basic drugs could be the addition of fatty acids to oils but currently, a systematic study is lacking. Therefore, the present work investigated apparently neutral and basic drugs in medium chain triglycerides (MCT) alone and with added either caproic acid (C6), caprylic acid (C8), capric acid (C10) or oleic acid (C18:1) at different levels (5 – 20%, w/w). A miniaturized solubility assay was used together with X-ray diffraction to analyze the residual solid and finally, solubility data were modeled using the conductor-like screening model for real solvents (COSMO-RS). Some drug bases had an MCT solubility of only a few mg/ml or less but addition of fatty acids provided in some formulations exceptional drug loading of up to about 20% (w/w). The solubility changes were in general more pronounced the shorter the chain length was and the longest oleic acid even displayed a negative effect in mixtures of celecoxib and fenofibrate. The COSMO-RS prediction accuracy was highly specific for the given compounds with root mean square errors (RMSE) ranging from an excellent 0.07 to a highest value of 1.12. The latter was obtained with the strongest model base pimozide for which a new solid form was found in some samples. In conclusion, targeting specific molecular interactions with the solute combined with mechanistic modeling provides new tools to advance lipid-based drug delivery.
Machine Estimation of Drug Melting Properties and Influence on Solubility Prediction
There has been much recent interest in machine learning (ML) and molecular quantitative structure property relationships (QSPR). The present research evaluated modern ML-based methods implemented in commercial software (COSMOquick and Molecular Modeling Pro), compared to a classical group contribution approach (Joback and Reid method), to estimate melting points and enthalpy of fusion values. A broad data set of market compounds was gathered from the literature, together with new data measured by differential scanning calorimetry for drug candidates. The highest prediction accuracy was achieved by QSPR using stochastic gradient boosting. The model deviations were discussed, particularly the implications on thermodynamic solubility modeling, as this typically requires estimation of both melting point and enthalpy of fusion. The results suggested that despite considerable advancement in prediction accuracy, there are still limitations especially with complex drug candidates. It is recommended that in such cases, melting properties obtained in silico should be used carefully as input data for thermodynamic solubility modeling. Future research will show how the prediction limits of thermophysical drug properties can be further advanced by even larger data sets and other ML algorithms or also by using molecular simulations.
Partial Solvation Parameters of Drugs as a New Thermodynamic Tool for Pharmaceutics
Partial solvation parameters (PSP) have much in common with the Hansen solubility parameter or with a linear solvation energy relationship (LSER), but there are advantages based on the sound thermodynamic basis. It is, therefore, surprising that PSP has so far not been harnessed in pharmaceutics for the selection of excipients or property estimation of formulations and their components. This work introduces PSP calculation for drugs, where the raw data were obtained from inverse gas chromatography. It was shown that only a few probe gases were needed to get reasonable estimates of the drug PSPs. Interestingly, an alternative calculation of LSER parameters in silico did not reflect the experimentally obtained activity coefficients for all probe gases as well, which was attributed to the complexity of the drug structures. The experimental PSPs were proven to be helpful in predicting drug solubility in various solvents and the PSP framework allowed calculation of the different surface energy contributions. A specific benefit of PSP is that parameters can be readily converted to either classical solubility or LSER parameters. Therefore, PSP is not just about a new definition of solvatochromic parameters, but the underlying thermodynamics provides a unified approach, which holds much promise for broad applications in pharmaceutics.
Corrigendum to “Powder cohesion and energy to break an avalanche. Can we address surface heterogeneity?” [Int. J. Pharm. 626 (2022) 122198]
Hydroxypropyl Cellulose for Drug Precipitation Inhibition: From the Potential of Molecular Interactions to Performance Considering Microrheology
There has been recent interest in using hydroxypropyl cellulose (HPC) for supersaturating drug formulations. This study investigated the potential for molecular HPC interactions with the model drug celecoxib by integrating novel approaches in the field of drug supersaturation analysis. Following an initial polymer characterization study, quantum-chemical calculations and molecular dynamics simulations were complemented with results of inverse gas chromatography and broadband diffusing wave spectroscopy. HPC performance was studied regarding drug solubilization and kinetics of desupersaturation using different grades (i.e., HPC-UL, SSL, SL, and L). The results suggested that the potential contribution of dispersive interactions and hydrogen bonding depended strongly on the absence or presence of the aqueous phase. It was proposed that aggregation of HPC polymer chains provided a complex heterogeneity of molecular environments with more or less excluded water for drug interaction. In precipitation experiments at a low aqueous polymer concentration (i.e., 0.01%, w/w), grades L and SL appeared to sustain drug supersaturation better than SSL and UL. However, UL was particularly effective in drug solubilization at pH 6.8. Thus, a better understanding of drug–polymer interactions is important for formulation development, and polymer blends may be used to harness the combined advantages of individual polymer grades.
Mechanistic aspects of drug loading in liquisolid systems with hydrophilic lipid-based mixtures
Relevance of the theoretical critical pore radius in mesoporous silica for fast crystallizing drugs
Formulation of poorly water-soluble drugs with mesoporous silica has become a thriving field of pharmaceutics. The theoretical critical pore diameter has been introduced as a maximum value below which an undesired drug crystallization is suppressed by spatial confinement. Currently, only few values have been reported and study of fast crystallising drugs is missing especially at relevant storage temperatures. This study investigated the critical pore diameter of three model drugs with a poor glass-forming ability (i.e. haloperidol, carbamazepine and benzamide) using different mesoporous carriers (Parteck® SLC 500, Neusilin® US2, Syloid® XDP 3050 and Aeroperl® 300 Pharma) and subsequently monitored physical formulation stability over three months by X-ray powder diffraction. The selected drugs showed clear differences in their estimated critical pore diameters, whereas a temperature dependence was barely relevant for pharmaceutical storage conditions. Superior stability was noted for the formulations containing benzamide in line with its predicted relatively large critical pore diameter of 29.5 nm. Contrarily, impaired physical stability depending on drug loading was observed in the case of haloperidol representing a compound with a rather small critical pore diameter (8.4 nm). These findings confirm the importance of estimating the critical pore diameter, especially for poor glass-forming drugs.
Ultra-sub-stoichiometric “Dynamic” Bioconjugation Reduces Viscosity by Disrupting Immunoglobulin Oligomerization
Monoclonal antibodies (mAb) are a major focus of the pharmaceutical industry, and polyclonal immunoglobulin G (IgG) therapy is used to treat a wide variety of health conditions. As some individuals require mAb/IgG therapy their entire life, there is currently a great desire to formulate antibodies for bolus injection rather than infusion. However, to achieve the required doses, very concentrated antibody solutions may be required. Unfortunately, mAb/IgG self-assembly at high concentration can produce an unacceptably high viscosity for injection. To address this challenge, this study expands the concept of "dynamic covalent chemistry" to "dynamic bioconjugation" in order to reduce viscosity by interfering with antibody-antibody interactions. Ultra-sub-stoichiometric amounts of dynamic PEGylation agents (down to the nanomolar) significantly reduced the viscosity of concentrated antibody solutions by interfering with oligomerization.