Hydrophobic deep eutectic solvent (HDES) as oil phase in lipid-based drug formulations.

There is increasing pharmaceutical interest in deep eutectic solvents not only as a green alternative to organic solvents in drug manufacturing, but also as liquid formulation for drug delivery. The present work introduces a hydrophobic deep eutectic solvent (HDES) to the field of lipid-based formulations (LBF). Phase behavior of a mixture with 2:1 M ratio of decanoic- to dodecanoic acid was studied experimentally and described by thermodynamic modelling. Venetoclax was selected as a hydrophobic model drug and studied by atomistic molecular dynamics simulations of the mixtures. As a result, valuable molecular insights were gained into the interaction networks between the different components. Moreover, experimentally the HDES showed greatly enhanced drug solubilization compared to conventional glyceride-based vehicles, but aqueous dispersion behavior was limited. Hence surfactants were studied for their ability to improve aqueous dispersion and addition of Tween 80 resulted in lowest droplet sizes and high in vitro drug release. In conclusion, the combination of HDES with surfactant(s) provides a novel LBF with high pharmaceutical potential. However, the components must be finely balanced to keep the integrity of the solubilizing HDES, while enabling sufficient dispersion and drug release.

Predictions of biorelevant solubility change during dispersion and digestion of lipid-based formulations.

Computational approaches are increasingly explored in development of drug products, including the development of lipid-based formulations (LBFs), to assess their feasibility for achieving adequate oral absorption at an early stage. This study investigated the use of computational pharmaceutics approaches to predict solubility changes of poorly soluble drugs during dispersion and digestion in biorelevant media. Concentrations of 30 poorly water-soluble drugs were determined pre- and post-digestion with in-line UV probes using the MicroDISS Profiler™. Generally, cationic drugs displayed higher drug concentrations post-digestion, whereas for non-ionized drugs there was no discernible trend between drug concentration in dispersed and digested phase. In the case of anionic drugs there tended to be a decrease or no change in the drug concentration post-digestion. Partial least squares modelling was used to identify the molecular descriptors and drug properties which predict changes in solubility ratio in long-chain LBF pre-digestion (R2 of calibration = 0.80, Q2 of validation = 0.64) and post-digestion (R2 of calibration = 0.76, Q2 of validation = 0.72). Furthermore, multiple linear regression equations were developed to facilitate prediction of the solubility ratio pre- and post-digestion. Applying three molecular descriptors (melting point, LogD, and number of aromatic rings) these equations showed good predictivity (pre-digestion R2 = 0.70, and post-digestion R2 = 0.68). The model developed will support a computationally guided LBF strategy for emerging poorly water-soluble drugs by predicting biorelevant solubility changes during dispersion and digestion. This facilitates a more data-informed developability decision making and subsequently facilitates a more efficient use of formulation screening resources.

Predictive computational models for assessing the impact of co-milling on drug dissolution.

Co-milling is an effective technique for improving dissolution rate limited absorption characteristics of poorly water-soluble drugs. However, there is a scarcity of models available to forecast the magnitude of dissolution rate improvement caused by co-milling. Therefore, this study endeavoured to quantitatively predict the increase in dissolution by co-milling based on drug properties. Using a biorelevant dissolution setup, a series of 29 structurally diverse and crystalline drugs were screened in co-milled and physically blended mixtures with Polyvinylpyrrolidone K25. Co-Milling Dissolution Ratios after 15 min (COMDR15 min) and 60 min (COMDR60 min) drug release were predicted by variable selection in the framework of a partial least squares (PLS) regression. The model forecasts the COMDR15 min (R2 = 0.82 and Q2 = 0.77) and COMDR60 min (R2 = 0.87 and Q2 = 0.84) with small differences in root mean square errors of training and test sets by selecting four drug properties. Based on three of these selected variables, applicable multiple linear regression equations were developed with a high predictive power of R2 = 0.83 (COMDR15 min) and R2 = 0.84 (COMDR60 min). The most influential predictor variable was the median drug particle size before milling, followed by the calculated drug logD6.5 value, the calculated molecular descriptor Kappa 3 and the apparent solubility of drugs after 24 h dissolution. The study demonstrates the feasibility of forecasting the dissolution rate improvements of poorly water-solube drugs through co-milling. These models can be applied as computational tools to guide formulation in early stage development.

A study of hydrophobic domain formation of polymeric drug precipitation inhibitors in aqueous solution.

Despite the widespread use of polymers as precipitation inhibitors in supersaturating drug formulations, the current understanding of their mechanisms of action is still incomplete. Specifically, the role of hydrophobic drug interactions with polymers by considering possible supramolecular conformations in aqueous dispersion is an interesting topic. Accordingly, this study investigated the tendency of polymers to create hydrophobic domains, where lipophilic compounds may nest to support drug solubilisation and supersaturation. Fluorescence spectroscopy with the environment-sensitive probe pyrene was compared with atomistic molecular dynamics simulations of the model drug fenofibrate (FENO). Subsequently, kinetic drug supersaturation and thermodynamic solubility experiments were conducted. As a result, the different polymers showed hydrophobic domain formation to a varying degree and the molecular simulations supported interpretation of fluorescence spectroscopy data. Molecular insights were gained into the conformational structure of how the polymers interacted with FENO in solution phase, which apart from nucleation and crystal growth effects, determined drug concentrations in solution. Notable was that even at the lowest polymer concentration of 0.01 %, w/v, there were polymer-specific solubilisation effects of FENO observed and the resulting reduction in apparent drug supersaturation provided relevant knowledge both from a mechanistic and practical perspective.

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.

Amorphous solid dispersion of a binary formulation with felodipine and HPMC for 3D printed floating tablets.

This study focuses on the combination of three-dimensional printing (3DP) and amorphous solid dispersion (ASD) technologies for the manufacturing of gastroretentive floating tablets. Employing hot melt extrusion (HME) and fused deposition modeling (FDM), the study investigates the development of drug-loaded filaments and 3D printed (3DP) tablets containing felodipine as model drug and hydroxypropyl methylcellulose (HPMC) as the polymeric carrier. Prior to fabrication, solubility parameter estimation and molecular dynamics simulations were applied to predict drug-polymer interactions, which are crucial for ASD formation. Physical bulk and surface characterization complemented the quality control of both drug-loaded filaments and 3DP tablets. The analysis confirmed a successful amorphous dispersion of felodipine within the polymeric matrix. Furthermore, the low infill percentage and enclosed design of the 3DP tablet allowed for obtaining low-density systems. This structure resulted in buoyancy during the entire drug release process until a complete dissolution of the 3DP tablets (more than 8 h) was attained. The particular design made it possible for a single polymer to achieve a zero-order controlled release of the drug, which is considered the ideal kinetics for a gastroretentive system. Accordingly, this study can be seen as an advancement in ASD formulation for 3DP technology within pharmaceutics.

Ten years of the manufacturing classification system: a review of literature applications and an extension of the framework to continuous manufacture.

The MCS initiative was first introduced in 2013. Since then, two MCS papers have been published: the first proposing a structured approach to consider the impact of drug substance physical properties on manufacturability and the second outlining real world examples of MCS principles. By 2023, both publications had been extensively cited by over 240 publications. This article firstly reviews this citing work and considers how the MCS concepts have been received and are being applied. Secondly, we will extend the MCS framework to continuous manufacture. The review structure follows the flow of drug product development focussing first on optimisation of API properties. The exploitation of links between API particle properties and manufacturability using large datasets seems particularly promising. Subsequently, applications of the MCS for formulation design include a detailed look at the impact of percolation threshold, the role of excipients and how other classification systems can be of assistance. The final review section focusses on manufacturing process development, covering the impact of strain rate sensitivity and modelling applications. The second part of the paper focuses on continuous processing proposing a parallel MCS framework alongside the existing batch manufacturing guidance. Specifically, we propose that continuous direct compression can accommodate a wider range of API properties compared to its batch equivalent.

Water-mediated phase transformations of posaconazole: An intricate jungle of crystal forms.

Posaconazole is a broad-spectrum antifungal agent exhibiting rich polymorphism. Up to now, a total of fourteen different crystal forms have been reported, sometimes with an ambiguous nomenclature, but less is known about their properties and stability relationships. Investigating the solid-state of a drug compound is essential to identify the most stable form under working conditions and to prevent the risk of undesired solid-phase transformations under processing and storage. In this paper, we study posaconazole polymorphism by providing a description of its polymorphs, hydrates, and solvates. Powder X-ray diffraction (PXRD), dynamic vapor sorption (DVS), spectroscopic and thermal techniques were employed to characterize the different forms. In addition, the solid-phase transformations of posaconazole in aqueous suspensions were studied by means of Raman microscopy. Surprisingly, we found that Form S, the crystal form contained in the marketed oral suspension, is not the most stable form in water. Form S readily converts to a more stable hydrate, i.e. Form A, after storage in water for two weeks. In the commercial oral formulation the conversion between the two forms is prevented by the presence of polysorbate 80. Such insights into the stabilizing excipient effects beyond particle dispersion are critical to formulators.

High loading of lipophilic compounds in mesoporous silica for improved solubility and dissolution performance.

Loading poorly soluble active pharmaceutical ingredients (API) into mesoporous silica can enable API stabilization in non-crystalline form, which leads to improved dissolution. This is particularly beneficial for highly lipophilic APIs (log D7.4 > 8) as these drugs often exhibit limited solubility in dispersion forming carrier polymers, resulting in low drug load and reduced solid state stability. To overcome this challenge, we loaded the highly lipophilic natural products coenzyme Q10 (CoQ10) and astaxanthin (ASX), as well as the synthetic APIs probucol (PB) and lumefantrine (LU) into the mesoporous silica carriers Syloid® XDP 3050 and Silsol® 6035. All formulations were physically stable in their non-crystalline form and drug loads of up to 50 % were achieved. At increasing drug loads, a marked increase in equilibrium solubility of the active ingredients in biorelevant medium was detected, leading to improved performance during biorelevant biphasic dissolution studies (BiPHa + ). Particularly the natural products CoQ10 and ASX showed substantial benefits from being loaded into mesoporous carrier particles and clearly outperformed currently available commercial formulations. Performance differences between the model compounds could be explained by in silico calculations of the mixing enthalpy for drug and silica in combination with an experimental chromatographic method to estimate molecular interactions.

Developing an in vitro lipolysis model for real-time analysis of drug concentrations during digestion of lipid-based formulations.

Understanding the effect of digestion on oral lipid-based drug formulations is a critical step in assessing the impact of the digestive process in the intestine on intraluminal drug concentrations. The classical pH-stat in vitro lipolysis technique has traditionally been applied, however, there is a need to explore the establishment of higher throughput small-scale methods. This study explores the use of alternative lipases with the aim of selecting digestion conditions that permit in-line UV detection for the determination of real-time drug concentrations. A range of immobilised and pre-dissolved lipases were assessed for digestion of lipid-based formulations and compared to digestion with the classical source of lipase, porcine pancreatin. Palatase® 20000 L, a purified liquid lipase, displayed comparable digestion kinetics to porcine pancreatin and drug concentration determined during digestion of a fenofibrate lipid-based formulation were similar between methods. In-line UV analysis using the MicroDISS ProfilerTM demonstrated that drug concentration could be monitored during one hour of dispersion and three hours of digestion for both a medium- and long-chain lipid-based formulations with corresponding results to that obtained from the classical lipolysis method. This method offers opportunities exploring the real-time dynamic drug concentration during dispersion and digestion of lipid-based formulations in a small-scale setup avoiding artifacts as a result of extensive sample preparation.

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.

Comparative Drug Solubility Studies Using Shake-Flask Versus a Laser-Based Robotic Method.

Drug solubility is of central importance to the pharmaceutical sciences, but reported values often show discrepancies. Various factors have been discussed in the literature to account for such differences, but the influence of manual testing in comparison to a robotic system has not been studied adequately before. In this study, four expert researchers were asked to measure the solubility of four drugs with various solubility behaviors (i.e., paracetamol, mesalazine, lamotrigine, and ketoconazole) in the same laboratory with the same instruments, method, and material sources and repeated their measurements after a time interval. In addition, the same solubility data were determined using an automated laser-based setup. The results suggest that manual testing leads to a handling influence on measured solubility values, and the results were discussed in more detail as compared to the automated laser-based system. Within the framework of unavoidable uncertainties of solubility testing, it is a possibility to combine minimal experimental testing that is preferably automated with mathematical modeling. That is a practical suggestion to support future pharmaceutical development in a more efficient way.

Lipid based formulations as supersaturating oral delivery systems: From current to future industrial applications.

Lipid-based formulations, in particular supersaturated lipid-based formulations, are important delivery approaches when formulating challenging compounds, as especially low water-soluble compounds profit from delivery in a pre-dissolved state. In this article, the classification of lipid-based formulation is described, followed by a detailed discussion of different supersaturated lipid-based formulations and the recent advances reported in the literature. The supersaturated lipid-based formulations discussed include both the in situ forming supersaturated systems as well as the thermally induced supersaturated lipid-based formulations. The in situ forming drug supersaturation by lipid-based formulations has been widely employed and numerous clinically available products are on the market. There are some scientific gaps in the field, but in general there is a good understanding of the mechanisms driving the success of these systems. For thermally induced supersaturation, the technology is not yet fully understood and developed, hence more research is required in this field to explore the formulations beyond preclinical studies and initial clinical trials.

Advancing algorithmic drug product development: Recommendations for machine learning approaches in drug formulation.

Artificial intelligence is a rapidly expanding area of research, with the disruptive potential to transform traditional approaches in the pharmaceutical industry, from drug discovery and development to clinical practice. Machine learning, a subfield of artificial intelligence, has fundamentally transformed in silico modelling and has the capacity to streamline clinical translation. This paper reviews data-driven modelling methodologies with a focus on drug formulation development. Despite recent advances, there is limited modelling guidance specific to drug product development and a trend towards suboptimal modelling practices, resulting in models that may not give reliable predictions in practice. There is an overwhelming focus on benchtop experimental outcomes obtained for a specific modelling aim, leaving the capabilities of data scraping or the use of combined modelling approaches yet to be fully explored. Moreover, the preference for high accuracy can lead to a reliance on black box methods over interpretable models. This further limits the widespread adoption of machine learning as black boxes yield models that cannot be easily understood for the purposes of enhancing product performance. In this review, recommendations for conducting machine learning research for drug product development to ensure trustworthiness, transparency, and reliability of the models produced are presented. Finally, possible future directions on how research in this area might develop are discussed to aim for models that provide useful and robust guidance to formulators.

Leveraging the use of in vitro and computational methods to support the development of enabling oral drug products: An InPharma commentary.

Due to the strong tendency towards poorly soluble drugs in modern development pipelines, enabling drug formulations such as amorphous solid dispersions, cyclodextrins, co-crystals and lipid-based formulations are frequently applied to solubilize or generate supersaturation in gastrointestinal fluids, thus enhancing oral drug absorption. Although many innovative in vitro and in silico tools have been introduced in recent years to aid development of enabling formulations, significant knowledge gaps still exist with respect to how best to implement them. As a result, the development strategy for enabling formulations varies considerably within the industry and many elements of empiricism remain. The InPharma network aims to advance a mechanistic, animal-free approach to the assessment of drug developability. This commentary focuses current status and next steps that will be taken in InPharma to identify and fully utilize 'best practice' in vitro and in silico tools for use in physiologically based biopharmaceutic models.

Polymer-embedded deep eutectic solvents (PEDES) as a novel bio-enabling formulation approach.

There is a growing interest in using deep eutectic solvents (DES) as a pharmaceutical delivery system for poorly water-soluble compounds. To reduce the risk of drug precipitation following oral administration, this study addresses the hypothesis that directly including a polymeric precipitation inhibitor (PI) in a DES mixture could obtain a polymer-embedded deep eutectic system (PEDES) as a novel bio-enabling formulation principle. Following broad formulation screening, a PEDES embedding 15% w/w of polyvinyl pyrrolidone K30 (PVP) in L-carnitine:ethylene glycol (1:4, molar ratio) DES was successfully formulated as a supersaturating formulation using indomethacin as model compound. The drug solubility of 175.6 mg/mL obtained in DES was remarkably high, and upon release (phosphate buffer, pH 6.5) a maximum supersaturation factor of 9.8 was recorded, whereby the release kinetics displayed a suitable "parachute effect". The formulation was further characterized to include a molecular dynamics simulation. It can be concluded that PEDES appears to be a viable novel formulation approach, setting solid grounds for further research to assess the full potential of this novel type of supersaturating drug delivery system.

Exploring the Cocrystal Landscape of Posaconazole by Combining High-Throughput Screening Experimentation with Computational Chemistry.

The development of multicomponent crystal forms, such as cocrystals, represents a means to enhance the dissolution and absorption properties of poorly water-soluble drug compounds. However, the successful discovery of new pharmaceutical cocrystals remains a time- and resource-consuming process. This study proposes the use of a combined computational-experimental high-throughput approach as a tool to accelerate and improve the efficiency of cocrystal screening exemplified by posaconazole. First, we employed the COSMOquick software to preselect and rank cocrystal candidates (coformers). Second, high-throughput crystallization experiments (HTCS) were conducted on the selected coformers. The HTCS results were successfully reproduced by liquid-assisted grinding and reaction crystallization, ultimately leading to the synthesis of thirteen new posaconazole cocrystals (7 anhydrous, 5 hydrates, and 1 solvate). The posaconazole cocrystals were characterized by PXRD, 1H NMR, Fourier transform-Raman, thermogravimetry-Fourier transform infrared spectroscopy, and differential scanning calorimetry. In addition, the prediction performance of COSMOquick was compared to that of two alternative knowledge-based methods: molecular complementarity (MC) and hydrogen bond propensity (HBP). Although HBP does not perform better than random guessing for this case study, both MC and COSMOquick show good discriminatory ability, suggesting their use as a potential virtual tool to improve cocrystal screening.

Physiological Buffer Effects in Drug Supersaturation - A Mechanistic Study of Hydroxypropyl Cellulose as Precipitation Inhibitor.

Phosphate buffer is predominantly used instead of the more physiological bicarbonate buffer, as the latter requires a technical solution of adequate gas mixing. Recent pioneering work on how bicarbonate buffer affected drug supersaturation revealed interesting effects that call for more mechanistic understanding. Therefore, this study used hydroxypropyl cellulose as a model precipitation inhibitor and real-time desupersaturation testing was conducted with the drugs bifonazole, ezetimibe, tolfenamic acid and triclabendazole. Specific buffer effects for the different compounds were noted and overall, statistical significance was found for the precipitation induction time (p = 0.0088). Interestingly, molecular dynamics simulation revealed a conformational effect of the polymer in the presence of the different buffer types. Subsequent molecular docking trials suggested a stronger interaction energy of drug and polymer in the presence of phosphate compared to bicarbonate buffer (p =0.0010). In conclusion, a better mechanistic understanding of how different buffers affect drug-polymer interactions regarding drug supersaturation was achieved. Further mechanisms may account for the overall buffer effects and additional research on drug supersaturation is certainly needed, but it can already be concluded that bicarbonate buffering should be used more often for in vitro testing in drug development.