A Functional Approach to Solubility Parameter Computations

Expanding lignin thermal property space by fractionation and covalent modification

To fully exploit kraft lignin's potential in material applications, we need to achieve tight control over those key physicochemical lignin parameters that ultimately determine, and serve as proxy for, the properties of lignin-derived materials. Here, we show that fractionation combined with systematic (incremental) modification provides a powerful strategy to expand and controllably tailor lignin property space. In particular, the glass transition temperature (Tg) of a typical kraft lignin could be tuned over a remarkable and unprecedented 213 °C. Remarkably, for all fractions the Tg proved to be highly linearly correlated with the degree of derivatisation by allylation, offering such tight control over the Tg of the lignin and ultimately the ability to 'dial-in' this key property. Importantly, such control over this proxy parameter indeed translated well to lignin-based thiol-ene thermosetting films, whose Tgs thus covered a range from 2-124 °C. This proof of concept suggests this approach to be a powerful and generalisable one, allowing a biorefinery or downstream operation to consciously and reliably tailor lignins to predictable specifications which fit their desired application.

Recruiting T cells and sensitizing tumors to NKG2D immune surveillance for robust antitumor immune response

Although recruiting T cells to convert cold tumors into hot can prevent some tumors from evading immune surveillance, tumors have evolved more mechanisms to achieve immune evasion, such as downregulating major histocompatibility complex I (MHC I) molecules expression to prevent T cells from recognizing tumor-antigens, or secreting immune suppression cytokines that disable T cells. Tumor immune evasion not only promotes tumor growth, but also weakens the efficacy of existing tumor immunotherapies. Therefore, recruiting T cells while reshaping innate immunity plays an important role in preventing tumor immune escape. In this study, we constructed a long-acting in situ forming implant (ISFI) based on the Atrigel technology, co-encapsulated with G3-C12 and sulfisoxazole (SFX) as a drug depot in the tumor site (SFX + G3-C12-ISFI). First, G3-C12 could recruit T cells, and transform cold into hot tumors. Furthermore, SFX could inhibit tumor-derived exosomes secretion, reduce the shedding of NKG2D ligand (NKG2DL), repair NKG2D/NKG2DL pathway, reinvigorate natural killer (NK) cells, and evade the effects of MHC I molecules missing. In the humanized cold tumor model, our strategy showed an excellent anti-tumor effect, providing a smart strategy for solving tumor evasion immune surveillance.

Automated Quantum Dots Purification via Solid Phase Extraction

The separation of colloidal nanocrystals from their original synthesis medium is an essential process step towards their application, however, the costs on a preparative scale are still a constraint. A new combination of approaches for the purification of hydrophobic Quantum Dots is presented, resulting in an efficient scalable process in regard to time and solvent consumption, using common laboratory equipment and low-cost materials. The procedure is based on a combination of solvent-induced adhesion and solid phase extraction. The platform allows the transition from manual handling towards automation, yielding an overall purification performance similar to one conventional batch precipitation/centrifugation step, which was investigated by thermogravimetry and gas chromatography. The distinct miscibility gaps between surfactants used as nanoparticle capping agents, original and extraction medium are clarified by their phase diagrams, which confirmed the outcome of the flow chemistry process. Furthermore, the solubility behavior of the Quantum Dots is put into context with the Hansen solubility parameters framework to reasonably decide upon appropriate solvent types.

Design of Over-1000 nm Near-Infrared Fluorescent Polymeric Micellar Nanoparticles by Matching the Solubility Parameter of the Core Polymer and Dye

Polymeric micellar nanoparticles (PNPs) encapsulating over-thousand-nanometer (OTN) near-infrared (NIR) fluorescent dye molecules in block polymers having hydrophobic and hydrophilic chains are promising agents for the dynamic imaging of deep tissue. To achieve OTN-NIR fluorescent PNPs (OTN-PNPs) having high brightness, it is crucial to increase the affinity between the core polymer and dye molecules by matching their polarities; thus, criteria and methods to evaluate the affinity are required. In this study, we used the Hansen solubility parameter (HSP), including the polarity term, to evaluate the affinity between the two substances. HSP values of the OTN-NIR fluorescent dye IR-1061 and four core polymers, poly(lactic-co-glycolic acid) (PLGA), poly(lactic acid) (PLA), poly(ε-caprolactone) (PCL), and polystyrene (PSt), were calculated using the Hansen solubility sphere method and molecular group contribution method, respectively. The relative energy density between IR-1061 and each core polymer calculated using their HSP values revealed that the affinities of PLGA and PLA for IR-1061 are higher than those of PCL and PSt. Therefore, OTN-PNPs composed of PLGA, PLA, and PCL core polymers were prepared and compared. The OTN-PNPs having PLGA and PLA cores could be loaded with larger amounts of IR-1061, had higher photoluminescence intensities, and showed higher stability in phosphate buffered saline than those having PCL cores. Moreover, the OTN-PNPs having PLGA or PLA cores were used for the dynamic imaging of live mice. Thus, matching the solubility parameters of the core polymer and dye molecule is a useful approach for designing high-performance OTN-NIR fluorescent probes.

Towards a framework for evaluating and reporting Hansen solubility parameters: applications to particle dispersions

A thorough understanding of complex interactions within particulate systems is a key for knowledge-based formulations. Hansen solubility parameters (HSP) are widely used to assess the compatibility of the dispersed phase with the continuous phase. At present, the determination of HSP is often based on a liquid ranking list obtained by evaluating a pertinent dispersion parameter using only one pre-selected characterization method. Furthermore, one cannot rule out the possibility of subjective judgment especially for liquids for which it is difficult to decipher the compatibility or underlying interactions. As a result, the end value of HSP might be of little or no information. To overcome these issues, we introduce a generalized and technology-agnostic combinatorics-based procedure. We discuss the principles of the procedure and the implications of evaluating and reporting particle HSP values. We demonstrate the procedure by using SiN _x particles synthesized in the gas phase. We leverage the analytical centrifugation data to evaluate stability trajectories of SiN _x dispersions in various liquids to deduce particle-liquid compatibility.

Soft-chemistry synthesis, solubility and interlayer spacing of carbon nano-onions

Carbon nano-onions (CNOs), as one of the allotropes of carbon, have attracted great attention because of their excellent performance in many fields, especially in capacitors. Developing soft-chemistry synthesis methods is critically of importance, while the forming mechanism in this area is not clear. In this paper, we present a critical review of CNOs regarding the structure, especially interlayer spacing, and synthesis processes, elaborating the recent progress on soft-chemistry methods. Hansen solubility parameter theory is applied to predict and regulate the solubility of CNOs. This article would be inspirational and give new insights into understanding the formation and properties of CNOs.

Application of Multivariate Adaptive Regression Splines (MARSplines) for Predicting Hansen Solubility Parameters Based on 1D and 2D Molecular Descriptors Computed from SMILES String

A new method of Hansen solubility parameters (HSPs) prediction was developed by combining the multivariate adaptive regression splines (MARSplines) methodology with a simple multivariable regression involving 1D and 2D PaDEL molecular descriptors. In order to adopt the MARSplines approach to QSPR/QSAR problems, several optimization procedures were proposed and tested. The effectiveness of the obtained models was checked via standard QSPR/QSAR internal validation procedures provided by the QSARINS software and by predicting the solubility classification of polymers and drug-like solid solutes in collections of solvents. By utilizing information derived only from SMILES strings, the obtained models allow for computing all of the three Hansen solubility parameters including dispersion, polarization, and hydrogen bonding. Although several descriptors are required for proper parameters estimation, the proposed procedure is simple and straightforward and does not require a molecular geometry optimization. The obtained HSP values are highly correlated with experimental data, and their application for solving solubility problems leads to essentially the same quality as for the original parameters. Based on provided models, it is possible to characterize any solvent and liquid solute for which HSP data are unavailable.

Application of the solubility parameter concept to assist with oral delivery of poorly water-soluble drugs – a PEARRL review

Objectives

Solubility parameters have been used for decades in various scientific fields including pharmaceutics. It is, however, still a field of active research both on a conceptual and experimental level. This work addresses the need to review solubility parameter applications in pharmaceutics of poorly water-soluble drugs.

Key findings

An overview of the different experimental and calculation methods to determine solubility parameters is provided, which covers from classical to modern approaches. In the pharmaceutical field, solubility parameters are primarily used to guide organic solvent selection, cocrystals and salt screening, lipid-based delivery, solid dispersions and nano- or microparticulate drug delivery systems. Solubility parameters have been applied for a quantitative assessment of mixtures, or they are simply used to rank excipients for a given drug.

Summary

In particular, partial solubility parameters hold great promise for aiding the development of poorly soluble drug delivery systems. This is particularly true in early-stage development, where compound availability and resources are limited. The experimental determination of solubility parameters has its merits despite being rather labour-intensive because further data can be used to continuously improve in silico predictions. Such improvements will ensure that solubility parameters will also in future guide scientists in finding suitable drug formulations.