Comparative Analysis of Chemical Descriptors by Machine Learning Reveals Atomistic Insights into Solute-Lipid Interactions.

This study explores the research area of drug solubility in lipid excipients, an area persistently complex despite recent advancements in understanding and predicting solubility based on molecular structure. To this end, this research investigated novel descriptor sets, employing machine learning techniques to understand the determinants governing interactions between solutes and medium-chain triglycerides (MCTs). Quantitative structure-property relationships (QSPR) were constructed on an extended solubility data set comprising 182 experimental values of structurally diverse drug molecules, including both development and marketed drugs to extract meaningful property relationships. Four classes of molecular descriptors, ranging from traditional representations to complex geometrical descriptions, were assessed and compared in terms of their predictive accuracy and interpretability. These include two-dimensional (2D) and three-dimensional (3D) descriptors, Abraham solvation parameters, extended connectivity fingerprints (ECFPs), and the smooth overlap of atomic position (SOAP) descriptor. Through testing three distinct regularized regression algorithms alongside various preprocessing schemes, the SOAP descriptor enabled the construction of a superior performing model in terms of interpretability and accuracy. Its atom-centered characteristics allowed contributions to be estimated at the atomic level, thereby enabling the ranking of prevalent molecular motifs and their influence on drug solubility in MCTs. The performance on a separate test set demonstrated high predictive accuracy (RMSE = 0.50) for 2D and 3D, SOAP, and Abraham Solvation descriptors. The model trained on ECFP4 descriptors resulted in inferior predictive accuracy. Lastly, uncertainty estimations for each model were introduced to assess their applicability domains and provide information on where the models may extrapolate in chemical space and, thus, where more data may be necessary to refine a data-driven approach to predict solubility in MCTs. Overall, the presented approaches further enable computationally informed formulation development by introducing a novel in silico approach for rational drug development and prediction of dose loading in lipids.

Interactions of dimethylaminoethyl methacrylate copolymer with non-acidic drugs demonstrated high solubilization in vitro and pronounced sustained release in vivo.

Recent work demonstrated remarkable solubilization effects of methacrylate-copolymer Eudragit EPO (EPO) not only with acidic drugs but interestingly also with poorly soluble basic compounds. The current work studied EPO-mediated solubilization effects first in vitro using felodipine (FLP) and tamoxifen (TMX) as model compounds. EPO-containing solutions were subsequently compared in a rat pharmacokinetic study against reference solutions and suspensions. Surprisingly, solution formulations with EPO did not result in an increased relative oral bioavailability. Exposure was reduced for both drugs and plasma-profiles of the EPO solutions showed a delayed and lower maximum plasma concentration compared to the reference formulations. This sustained in vivo release was likely due to combined effects of strong drug-polymer interactions and pH-dependent precipitation of the polymer in the rat intestine. Remarkable was that in vitro drug-polymer coprecipitates did not reveal crystalline drug by polarized light microscopy. Thus, such a formulation approach provides a rather simple opportunity to modify drug release in vivo. However, this may be rather an approach for preclinical formulations, if high peak-to-trough ratios of plasma levels are problematic regarding adverse effects related to Cmax or if plasma concentrations drop too fast below required pharmacological concentrations.

The quest for exceptional drug solubilization in diluted surfactant solutions and consideration of residual solid state.

Solubility screening in different surfactant solutions is an important part of pharmaceutical profiling. A particular interest is in low surfactant concentrations that mimic the dilution of an oral dosage form. Despite of intensive previous research on solubilization in micelles, there is only limited data available at low surfactant concentrations and generally missing is a physical state analysis of the residual solid. The present work therefore studied 13 model drugs in 6 different oral surfactant solutions (0.5%, w/w) by concomitant X-ray diffraction (XRPD) analysis to consider effects on solvent-mediated phase transformations. A particular aspect was potential occurrence of exceptionally high drug solubilization. As a result, general solubilization correlations were observed especially between surfactants that share chemical similarity. Exceptional solubility enhancement of several hundred-fold was evidenced in case of sodium dodecyl sulfate solutions with dipyridamole and progesterone. Furthermore, carbamazepine and testosterone showed surfactant-type dependent hydrate formation. The present results are of practical relevance for an optimization of surfactant screenings in preformulation and early development and provide a basis for mechanistic modeling of surfactant effects on solubilization and solid state modifications.

Unexpected Solubility Enhancement of Drug Bases in the Presence of a Dimethylaminoethyl Methacrylate Copolymer.

The methacrylate copolymer Eudragit EPO (EPO) has previously shown to greatly enhance solubilization of acidic drugs via ionic interactions and by multiple hydrophobic contacts with polymeric side chains. The latter type of interaction could also play a role for solubilization of other compounds than acids. The aim of this study was therefore to investigate the solubility of six poorly soluble bases in presence and absence of EPO by quantitative ultrapressure liquid chromatography with concomitant X-ray powder diffraction analysis of the solid state. For a better mechanistic understanding, spectra and diffusion data were obtained by 1H nuclear magnetic resonance (NMR) spectroscopy. Unexpected high solubility enhancement (up to 360-fold) was evidenced in the presence of EPO despite the fact that bases and polymer were both carrying positive charges. This exceptional and unexpected solubilization was not due to a change in the crystalline solid state. NMR spectra and measured diffusion coefficients indicated both strong drug-polymer interactions in the bulk solution, and diffusion data suggested conformational changes of the polymer in solution. Such conformational changes may have increased the accessibility and extent of hydrophobic contacts thereby leading to increased overall molecular interactions. These initially surprising solubilization results demonstrate that excipient selection should not be based solely on simple considerations of, for example, opposite charges of drug and excipient, but it requires a more refined molecular view. Different solution NMR techniques are especially promising tools to gain such mechanistic insights.

Can we estimate the critical micelle concentration of amphiphilic drug bases from molecular connectivity indices?

Self-aggregation of drugs is since many years an important topic in the pharmaceutical sciences. Drugs can aggregate similar to surfactants and thereby exhibit a critical micelle concentration (CMC). The present work focused on amphiphilic drug bases and it was aimed to predict log(CMC) based on chemical structure alone. A dataset of 35 compounds was gathered mostly form the literature and complemented with own measurements based on ultrasonic resonator technology. The hydrophilic-lipophilic balance (HLB) values of the protonated bases were calculated and provided a range of 22.9-27.4. Based on a hypothesis from surfactant sciences, it was tried to predict log(CMC) with connectivity and shape indices as well as molecular dipole moment. A fairly good model was obtained using the Randix index (RI), 3 D Wiener number (WN) and molecular dipole moment (DM) (R2 = 0.824). Interestingly, a simple linear regression of log(CMC) with the Randic index alone, resulted in an acceptable model (R2 = 0.755). The present work should help with early identification of drug bases that exhibit surfactant-like behavior and an estimation of log(CMC) values is proposed. An improved understanding of drug aggregation and prediction of log(CMC) helps to better cope with physical consequences like, for example, "anomalous" drug solubility in drug discovery and development.

A Systematic Study of Molecular Interactions of Anionic Drugs with a Dimethylaminoethyl Methacrylate Copolymer Regarding Solubility Enhancement.

The methacrylate-copolymer Eudragit EPO (EPO) has raised interest in solubility enhancement of anionic drugs. Effects on aqueous drug solubility at rather low polymer concentrations are barely known despite their importance upon dissolution and dilution of oral dosage forms. We provide evidence for substantial enhancement (factor 4-230) of aqueous solubility of poorly water-soluble anionic drugs induced by low (0.1-5% (w/w)) concentration of EPO for a panel of seven acidic crystalline drugs. Diffusion data (determined by 1H nuclear magnetic resonance spectroscopy) indicate that the solubility increasing effect monitored by quantitative ultraperformance liquid chromatography was caused primarily by molecular API polymer interactions in the bulk liquid phase. Residual solid API remained unaltered as tested by X-ray powder diffraction. The solubility enhancement (SE) revealed a significant rank correlation (rSpearman = -0.83) with rDiffAPI, where SE and rDiffAPI are defined ratios of solubility and diffusion coefficient in the presence and absence of EPO. SE decreased in the order of indomethacin, mefenamic acid, warfarin, piroxicam, furosemide, bezafibrate, and tolbutamide. The solubilizing effect was attributed to both ionic and hydrophobic interactions between drugs and EPO. The excellent solubilizing properties of EPO are highly promising for pharmaceutical development, and the data set provides first steps toward an understanding of drug-excipient interaction mechanisms.