Patrick J. O'DWyer

A solubilizing receiver medium has been documented to increase drug flux in vitro, but the mechanisms underlying this effect remain poorly understood. This study investigated these mechanisms and established a mathematical model to describe the increase in apparent permeability. Flow rate experiments were performed to quantify the individual boundary layer and membrane resistances associated with diffusion. The impact of nine solubilizing receiver additives, including surfactants, cyclodextrins, and bovine serum albumin, on the flux of griseofulvin was investigated. The increase in apparent permeability followed the rank-order, though not the magnitude, of the solubility enhancement in the receiver (Spearman's ρ = 0.93, p < 0.001, n = 20). The mechanistic model, termed the reduced-resistances model, demonstrates that a solubilizing receiver reduces diffusional resistance in the membrane and in the receiver-side boundary layer. At high ratios of receiver to donor solubility, a hyperbolic relationship was observed where diffusion through the donor-side boundary layer becomes rate-limiting. Additional drug cocktail permeability studies with antipyrine, phenytoin, and meloxicam confirmed the broader applicability of this model. These findings provide a framework for informed receiver selection in permeability assays and underscore the importance of considering the receiver medium when comparing results across experiments.

Co-milling with excipients offers an effective solvent-free strategy to enhance the dissolution of drugs in the pharmaceutical industry. Nonetheless, the range of excipients available for co-milling, as well as the selection criteria, remains relatively unexplored. Therefore, this study aims to evaluate the effect of co-milling with crosslinked swellable polymers on the dissolution of poorly water-soluble drugs. A structurally diverse set of crystalline compounds (n = 13) were co-milled with the three commonly used tablet disintegrants: Croscarmellose sodium, sodium starch glycolate and crospovidone. In vitro dissolution profiles were assessed to evaluate the excipient performance and identify crucial drug descriptors relating to co-milling induced dissolution enhancement. All 39 co-milled formulations showed an increased drug intrinsic dissolution rate (IDR), with drug properties such as molecular size and polar surface area showing a positive correlation with the extent of IDR enhancement following co-milling. Moreover, co-milling consistently yielded greater initial dissolution (AUC _0-5 min ) across all excipients compared to milling the drug without an excipient. On average, milling with croscarmellose sodium produced the greatest improvement in the early stages of dissolution for neutral and basic compounds, while sodium starch glycolate was more effective in enhancing the dissolution of co-milled acidic drugs. Furthermore, an assessment within the refined Developability Classification System (rDCS) revealed that co-milling was effective to overcome dissolution rate limitations for all rDCS class IIa drugs. This study establishes a practical framework for the broader application of co-milling with tablet disintegrants and demonstrates its relevance within the latest rDCS paradigm for overcoming dissolution rate-limited absorption. Graphical Abstract

More than a decade since its introduction, the polymeric excipient Soluplus continues to receive considerable attention for its application in the development of amorphous solid dispersions (ASDs) and its utility as a solubilizer for drugs exhibiting solubility limited absorption. While it is well-recognized that Soluplus forms micelles, the impact of its lower critical solution temperature of approximately 40 °C remains an underexplored aspect. This study investigated the phase behavior of Soluplus in fasted-state simulated intestinal fluid (FaSSIF-V1). It was demonstrated that Soluplus forms a dispersed polymer-rich coacervate phase, which coexists with Soluplus micelles at 37 °C. This behavior was confirmed by cloud point measurements, visually discernible phases after centrifugation, as well as multi-angle dynamic light scattering (MADLS) measurements, and quantitative ¹H-nuclear magnetic resonance (NMR) spectroscopy of Soluplus concentrations in the supernatant pre- and post-centrifugation. The practical relevance of these findings was contextualized by solvent shift experiments and dissolution testing of spray-dried ASD. The results demonstrated that the poorly water-soluble drug RO6897779 resided in a polymer-rich coacervate phase and was spun down during centrifugation, which resulted in an amorphous pellet exhibiting the characteristics of a viscous liquid. The entrapment of the drug within the polymer-rich phase was further analyzed by temperature- and time-dependent MADLS experiments. The findings of this study are of particular relevance for a mechanistic understanding, relevant to comprehending in vitro-in vivo relationships of Soluplus-based ASDs. Low sampled drug concentrations in FaSSIF-V1 at 37 °C may originate not only from limited drug release and precipitation but also from the formation of a drug-containing, polymer-rich Soluplus phase. Therefore, a liquid-liquid phase separation occurring from Soluplus-based formulations in a biorelevant medium can be excipient-driven, which is different from the common perception that phase separation in the solution state is triggered primarily by high drug concentrations exceeding their amorphous solubility.

Advanced predictive modeling approaches have harnessed data to fuel important innovations at all stages of drug development. However, the need for a machine-readable drug product library which consolidates many aspects of formulation design and performance remains largely unmet. This study presents a scripted, reproducible approach to database curation and explores its potential to streamline oral medicine development. The Product Information files for all centrally authorized drug products containing a small molecule active ingredient were retrieved programmatically from the European Medicines Agency Web site. Text processing isolated relevant information, including the maximum clinical dose, dosage form, route of administration, excipients, and pharmacokinetic performance. Chemical and bioactivity data were integrated through automated linking to external curated databases. The capability of this database to inform oral medicine development was assessed in the context of drug-likeness evaluation, excipient selection, and prediction of oral fraction absorbed. Existing filters of drug-likeness, such as the Rule of Five, were found to poorly capture the chemical space of marketed oral drug products. Association rule learning identified frequent patterns in tablet formulation compositions that can be used to establish excipient combinations that have seen clinical success. Binary prediction models of oral fraction absorbed constructed exclusively from regulatory data achieved acceptable performance (balanced accuracy_test = 0.725), demonstrating its modelability and potential for use during early stage molecule prioritization tasks. This study illustrates the impact of highly linked drug product data in accelerating clinical translation and underlines the ongoing need for accuracy and completeness of data reported in the regulatory datasphere.

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.