Predicting Bile and Lipid Interaction for Drug Substances

POxload: Machine Learning Estimates Drug Loadings of Polymeric Micelles

Block copolymers, composed of poly(2-oxazoline)s and poly(2-oxazine)s, can serve as drug delivery systems; they form micelles that carry poorly water-soluble drugs. Many recent studies have investigated the effects of structural changes of the polymer and the hydrophobic cargo on drug loading. In this work, we combine these data to establish an extended formulation database. Different molecular properties and fingerprints are tested for their applicability to serve as formulation-specific mixture descriptors. A variety of classification and regression models are built for different descriptor subsets and thresholds of loading efficiency and loading capacity, with the best models achieving overall good statistics for both cross- and external validation (balanced accuracies of 0.8). Subsequently, important features are dissected for interpretation, and the DrugBank is screened for potential therapeutic use cases where these polymers could be used to develop novel formulations of hydrophobic drugs. The most promising models are provided as an open-source software tool for other researchers to test the applicability of these delivery systems for potential new drug candidates.

Exploring molecular fingerprints of different drugs having bile interaction: a stepping stone towards better drug delivery

Bile acids are amphiphilic substances produced naturally in humans. In the context of drug delivery and dosage form design, it is critical to understand whether a drug interacts with bile inside the gastrointestinal (GI) tract or not. This study focuses on the identification of structural fingerprints/features important for bile interaction. Molecular modelling methods such as Bayesian classification and recursive partitioning (RP) studies are executed to find important fingerprints/features for the bile interaction. For the Bayesian classification study, the ROC score of 0.837 and 0.950 are found for the training set and the test set compounds, respectively. The fluorine-containing aliphatic/aromatic group, the branched chain of the alkyl group containing hydroxyl moiety and the phenothiazine ring etc. are identified as good fingerprints having a positive contribution towards bile interactions, whereas, the bad fingerprints such as free carboxylate group, purine, and pyrimidine ring etc. have a negative contribution towards bile interactions. The best tree (tree ID: 1) from the RP study classifies the bile interacting or non-interacting compounds with a ROC score of 0.941 for the training and 0.875 for the test set. Additionally, SARpy and QSAR-Co analyses are also been performed to classify compounds as bile interacting/non-interacting. Moreover, forty-six recently FDA-approved drugs have been screened by the developed SARpy and QSAR-Co models to assess their bile interaction properties. Overall, this attempt may facilitate the researchers to identify bile interacting/non-interacting molecules in a faster way and help in the design of formulations and target-specific drug development.

Bile Is a Selective Elevator for Mucosal Mechanics and Transport

Mucus mechanically protects the intestinal epithelium and impacts the absorption of drugs, with a largely unknown role for bile. We explored the impacts of bile on mucosal biomechanics and drug transport within mucus. Bile diffused with square-root-of-time kinetics and interplayed with mucus, leading to transient stiffening captured in Brillouin images and a concentration-dependent change from subdiffusive to Brownian-like diffusion kinetics within the mucus demonstrated by differential dynamic microscopy. Bile-interacting drugs, Fluphenazine and Perphenazine, diffused faster through mucus in the presence of bile, while Metoprolol, a drug with no bile interaction, displayed consistent diffusion. Our findings were corroborated by rat studies, where co-dosing of a bile acid sequestrant substantially reduced the bioavailability of Perphenazine but not Metoprolol. We clustered over 50 drugs based on their interactions with bile and mucin. Drugs that interacted with bile also interacted with mucin but not vice versa. This study detailed the dynamics of mucus biomechanics under bile exposure and linked the ability of a drug to interact with bile to its abbility to interact with mucus.

Structural Investigation on How Guest Loading of Poly(2-oxazoline)-Based Micelles Affects the Interaction with Simulated Intestinal Fluids

Drug loading of polymer micelles can have a profound effect on their particle size and morphology as well as their physicochemical properties. In turn, this influences performance in biological environments. For oral delivery of drugs, the intestinal environment is key, and consequently, a thorough structural understanding of what happens at this material-biology interface is required to understand in vivo performance and tailor improved delivery vehicles. In this study, we address this interface in vitro through a detailed structural characterization of the colloidal assemblies of polymeric micelles based on poly(2-oxazolines) with three different guest loadings with the natural product curcumin (17-52 wt %) in fed-state simulated intestinal fluids (FeSSIF). For this, we employ NMR spectroscopy, in particular, 1H NMR, 1H-1H-NOESY, and 1H DOSY experiments complemented by quantum chemical calculations and cryo-TEM measurements. Through this mixture of methods, we identified curcumin-taurocholate interactions as central interaction patterns alongside interactions with the polymer and lipids. Furthermore, curcumin molecules can be exchanged between polymer micelles and bile colloids, an important prerequisite for their uptake. Finally, increased loading of the polymer micelles with curcumin resulted in a larger number of vesicles as taurocholate─through coordination with Cur─is less available to form nanoparticles with the lipids. The loading-dependent behavior found in this study deviates from previous work on a different drug substance highlighting the need for further studies including different drug molecules and polymer types to improve the understanding of events on the molecular level.

Molecular Dynamics Simulations of Self-Assembling Colloids in Fed-State Human Intestinal Fluids and Their Solubilization of Lipophilic Drugs

Bioavailability of oral drugs often depends on how soluble the active pharmaceutical ingredient is in the fluid present in the small intestine. For efficient drug discovery and development, computational tools are needed for estimating this drug solubility. In this paper, we examined human intestinal fluids collected in the fed state, with coarse-grained molecular dynamics simulations. The experimentally obtained concentrations in aspirated duodenal fluids from five healthy individuals were used in three simulation sets to evaluate the importance of the initial distribution of molecules and the presence of glycerides in the simulation box when simulating the colloidal environment of the human intestinal fluid. We observed self-assembly of colloidal structures of different types: prolate, elongated, and oblate micelles, and vesicles. Glycerides were important for the formation of vesicles, and their absence was shown to induce elongated micelles. We then simulated the impact of digestion and absorption on the different colloidal types. Finally, we looked at the solubilization of three model compounds of increasing lipophilicity (prednisolone, fenofibrate, and probucol) by calculating contact ratios of drug-colloid to drug-water. Our simulation results of colloidal interactions with APIs were in line with experimental solubilization data but showed a dissimilarity to solubility values when comparing fasted-/fed-state ratios between two of the APIs. This work shows that coarse-grained molecular dynamics simulation is a promising tool for investigation of the intestinal fluids, in terms of colloidal attributes and drug solubility.