Multiple approaches for achieving drug solubility: an in silico perspective

Solubilization of drugs using beta-cyclodextrin: Experimental data and modeling

Many drug candidates fail to complete the entire drug development process because of poor physicochemical properties. Solubility is an important physicochemical property which plays a vital role in various stages of drug discovery and development. Several methods have been proposed to enhance the solubility of drugs, and complex formation with cyclodextrins is among them. Beta-cyclodextrin (βCD) is a common excipient for solubilization of drugs. The aim of this study is to develop the mechanistic QSPR models to predict the solubility enhancement of a drug in the presence of βCD. In this study, the solubility enhancement of some drugs in the presence of 10mM βCD at 25°C was experimentally determined or collected from the literature. Two different models to predict the solubilization by βCD were developed by binary logistic regression using structural properties of drugs with more than 80% accuracy. Polar surface area and excess molar refraction are the main parameters for estimating solubilization by βCD. Moreover, other descriptors related to hydrophobicity and the capability of hydrogen bonding formation of molecules could improve the accuracy of the established models.

Experimental and Machine-Learning-Assisted Design of Pharmaceutically Acceptable Deep Eutectic Solvents for the Solubility Improvement of Non-Selective COX Inhibitors Ibuprofen and Ketoprofen

Deep eutectic solvents (DESs) are commonly used in pharmaceutical applications as excellent solubilizers of active substances. This study investigated the tuning of ibuprofen and ketoprofen solubility utilizing DESs containing choline chloride or betaine as hydrogen bond acceptors and various polyols (ethylene glycol, diethylene glycol, triethylene glycol, glycerol, 1,2-propanediol, 1,3-butanediol) as hydrogen bond donors. Experimental solubility data were collected for all DES systems. A machine learning model was developed using COSMO-RS molecular descriptors to predict solubility. All studied DESs exhibited a cosolvency effect, increasing drug solubility at modest concentrations of water. The model accurately predicted solubility for ibuprofen, ketoprofen, and related analogs (flurbiprofen, felbinac, phenylacetic acid, diphenylacetic acid). A machine learning approach utilizing COSMO-RS descriptors enables the rational design and solubility prediction of DES formulations for improved pharmaceutical applications.

Micro-Solvation of Propofol in Propylene Glycol-Water Binary Mixtures: Molecular Dynamics Simulation Studies

The water microstructure around propofol plays a crucial role in controlling their solubility in the binary mixture. The unusual nature of such a water microstructure can influence both translational and reorientational dynamics, as well as the water hydrogen bond network near propofol. We have carried out all-atom molecular dynamics simulations of five different compositions of the propylene glycol (PG)/water binary mixture containing propofol (PFL) molecules to investigate the differential behavior of water microsolvation shells around propofol, which is likely to control the propofol solubility. It is evident from the simulation snapshots for various compositions that the PG at high molecular ratio favors the water cluster and extended chainlike network that percolates within the PG matrix, where the propofol is in the dispersed state. We estimated that the radial distribution function indicates higher ordered water microstructure around propofol for high PG content, as compared to the lower PG content in the PG/water mixture. So, the hydrophilic PG regulates the stability of the water micronetwork around propofol and its solubility in the binary mixture. We observed that the translational and rotational mobility of water belonging to the propofol microsolvation shell is hindered for high PG content and relaxed toward the low PG molecular ratio in the PG/water mixture. It has been noticed that the structural relaxation of the hydrogen bond formed between the propofol and the water molecules present in the propofol microsolvation shell for all five compositions is found to be slower for high PG content and becomes faster on the way to low PG content in the mixture. Simultaneously, we calculated the intermittent residence time correlation function of the water molecules belonging to the microsolvation shell around the propofol for five different compositions and found a faster short time decay followed up with long time components. Again, the origin of such long time decay is primarily from the structural relaxation of the microsolvation shell around the propofol, where the high PG content shows the slower structural relaxation that turns faster as the PG content approaches to the other end of the compositions. So, our studies showed that the slower structural relaxation of the microsolvation shell around propofol for a high PG molecular ratio in the PG/water mixture correlate well with the extensive ordering of the water microstructure and restricted water mobility and facilitates the dissolution process of propofol in the binary mixture.

A new set of solute descriptors to calculate solubility of drugs in mono-solvents

A new set of solute parameters derived from a correlation model using Catalan parameters. The parameters represent the interaction of the solute with the mono-solvents at 298.15K. The computational procedure was adopted from Abraham's solvation model and the obtained results are promising. In this work, the calculated parameters were used to back-calculate the drugs solubility in various mono-solvents at different temperatures employing the van't Hoff's model as the skeleton on the derived model. The obtained mean percentage deviations (MPDs) were in the range of 3.1 to 88.5% with the overall MPD of 29.1%. (1) Derivation of a new set of solute parameters from a correlation model using Catalan parameters; (2) adoption of the calculation method of Abraham's solvation model with the skeleton of van't Hoff's equation; (3) using the achieved parameters for back-calculation of drugs solubility in various mono-solvents; (4) obtaining an overall acceptable mean percentage deviation of 29.1% from calculations.

In Silico Screening as a Tool to Prepare Drug-Drug Cocrystals of Ibrutinib-Ketoconazole: a Strategy to Enhance Their Solubility Profiles and Oral Bioavailability

Ibrutinib (IBR) is a biopharmaceutical classification system (BCS) class II drug and an irreversible Bruton's tyrosine kinase (BTK) inhibitor. IBR has an extremely low oral bioavailability due to the activity of the CYP3A4 enzyme. The current intention of the research was to enhance solubility followed by oral bioavailability of IBR using the hot melt extrusion (HME) technique by formulating drug-drug cocrystals (DDCs). Ketoconazole (KET) is an active CYP3A4 inhibitor and was selected based on computational studies and solubility parameter prediction. Differential scanning calorimetry (DSC), Fourier transform infrared spectroscopy (FT-IR), powder X-ray diffraction (PXRD), thermogravimetric analysis (TGA), proton nuclear magnetic resonance (1H NMR), and scanning electron microscopy (SEM) evaluations were employed for estimating the formation of IBR-KET DDCs. The IBR-KET DDC system was discovered to have a hydrogen bond (H-bond) and π-π-stacking interactions, in accordance with the computational results. Further, IBR-KET DDCs showed enhanced solubility, stability, powder dissolution, in vitro release, and flow properties. Furthermore, IBR-KET-DDCs were associated with enhanced cytotoxic activity in K562-CCL-243 cancer cell lines when compared with IBR and KET alone. In vivo pharmacokinetic studies have shown an enhanced oral bioavailability of up to 4.30 folds of IBR and 2.31 folds of KET through IBR-KET-DDCs compared to that of the IBR and KET suspension alone. Thus, the prepared IBR-KET-DDCs using the HME technique stand as a favorable drug delivery system that augments the solubility and oral bioavailability of IBR along with KET.

Prediction of the Mechanical Deformation Properties of Organic Crystals Based upon their Crystallographic Structures: Case Studies of Pentaerythritol and Pentaerythritol Tetranitrate

PURPOSE

Development of a quantitative model and associated workflow for predicting the mechanical deformation properties (plastic deformation or cleavage fracture) of organic single crystals from their crystallographic structures using molecular and crystallographic modelling.

METHODS

Intermolecular synthons, hydrogen bonding, crystal morphology and surface chemistry are modelled using empirical force fields with the data integrated into the analysis of lattice deformation as computed using a statistical approach.

RESULTS

The approach developed comprises three main components. Firstly, the identification of the likely direction of deformation based on lattice unit cell geometry; secondly, the identification of likely lattice planes for deformation through the calculation of the strength and stereochemistry of interplanar intermolecular interactions, surface plane rugosity and surface energy; thirdly, identification of potential crystal planes for cleavage fracture by assessing intermolecular bonding anisotropy. Pentaerythritol is predicted to fracture by brittle cleavage on the {001} lattice planes by strong in-plane hydrogen-bond interactions in the <110>, whereas pentaerythritol tetranitrate is predicted to deform by plastic deformation through the slip system {110} < 001>, with both predictions being in excellent agreement with known experimental data.

CONCLUSION

A crystallographic framework and associated workflow for predicting the mechanical deformation of molecular crystals is developed through quantitative assessment of lattice energetics, crystal surface chemistry and crystal defects. The potential for the de novo prediction of the mechanical deformation of pharmaceutical materials using this approach is highlighted for its potential importance in the design of formulated drug products process as needed for manufacture by direct compression.

Prediction of the Aqueous Solubility of Compounds Based on Light Gradient Boosting Machines with Molecular Fingerprints and the Cuckoo Search Algorithm

Aqueous solubility is one of the most important physicochemical properties in drug discovery. At present, the prediction of aqueous solubility of compounds is still a challenging problem. Machine learning has shown great potential in solubility prediction. Most machine learning models largely rely on the setting of hyperparameters, and their performance can be improved by setting the hyperparameters in a better way. In this paper, we used MACCS fingerprints to represent the structural features and optimized the hyperparameters of the light gradient boosting machine (LightGBM) with the cuckoo search algorithm (CS). Based on the above representation and optimization, the CS-LightGBM model was established to predict the aqueous solubility of 2446 organic compounds and the obtained prediction results were compared with those obtained with the other six different machine learning models (RF, GBDT, XGBoost, LightGBM, SVR, and BO-LightGBM). The comparison results showed that the CS-LightGBM model had a better prediction performance than the other six different models. RMSE, MAE, and R ² of the CS-LightGBM model were, respectively, 0.7785, 0.5117, and 0.8575. In addition, this model has good scalability and can be used to solve solubility prediction problems in other fields such as solvent selection and drug screening.

Development and Characterization of Eudragit® EPO-Based Solid Dispersion of Rosuvastatin Calcium to Foresee the Impact on Solubility, Dissolution and Antihyperlipidemic Activity

Poor solubility is the major challenge involved in the formulation development of new chemical entities (NCEs), as more than 40% of NCEs are practically insoluble in water. Solid dispersion (SD) is a promising technology for improving dissolution and, thereby, the bioavailability of poorly soluble drugs. This study investigates the influence of a pH-sensitive acrylate polymer, EPO, on the physicochemical properties of rosuvastatin calcium, an antihyperlipidemic drug. In silico docking was conducted with numerous polymers to predict drug polymer miscibility. The screened-out polymer was used to fabricate the binary SD of RoC in variable ratios using the co-grinding and solvent evaporation methods. The prepared formulations were assessed for physiochemical parameters such as saturation solubility, drug content and in vitro drug release. The optimized formulations were further ruled out using solid-state characterization (FTIR, DSC, XRD and SEM) and in vitro cytotoxicity. The results revealed that all SDs profoundly increased solubility as well as drug release. However, the formulation RSE-2, with a remarkable 71.88-fold increase in solubility, presented 92% of drug release in the initial 5 min. The molecular interaction studied using FTIR, XRD, DSC and SEM analysis evidenced the improvement of in vitro dissolution. The enhancement in solubility of RoC may be important for the modulation of the dyslipidemia response. Therefore, pharmacodynamic activity was conducted for optimized formulations. Our findings suggested an ameliorative effect of RSE-2 in dyslipidemia and its associated complications. Moreover, RSE-2 exhibited nonexistence of cytotoxicity against human liver cell lines. Convincingly, this study demonstrates that SD of RoC can be successfully fabricated by EPO, and have all the characteristics that are favourable for superior dissolution and better therapeutic response to the drug.

Solids Turn into Liquids—Liquid Eutectic Systems of Pharmaceutics to Improve Drug Solubility

The low solubility of active pharmaceutical ingredients (APIs) is a problem in pharmaceutical development. Several methodologies can be used to improve API solubility, including the use of eutectic systems in which one of the constituents is the API. This class of compounds is commonly called Therapeutic Deep Eutectic Systems (THEDES). THEDES has been gaining attention due to their properties such as non-toxicity, biodegradability, and being non-expensive and easy to prepare. Since the knowledge of the solid liquid diagram of the mixture and the ideal eutectic point is necessary to ascertain if a mixture is a deep eutectic or just a eutectic mixture that is liquid at ambient temperature, the systems studied in this work are called Therapeutic Liquid Eutectic Systems (THELES). Therefore, the strategy proposed in this work is to improve the solubility of chlorpropamide and tolbutamide by preparing THELES. Both APIs are sulfonylurea compounds used for the treatment of type 2 diabetes mellitus and have low solubility in water. To prepare the THELES, several coformers were tested, namely, tromethamine, L(+)-arginine, L-tryptophan, citric acid, malic acid, ascorbic acid, and p-aminobenzoic acid, in molar ratios of 1:1 and 1:2. To improve viscosity, water was added in different molar ratios to all systems. THELES were characterized by mid-infrared spectroscopy (MIR), and differential scanning calorimetry. Their viscosity, solubility, and permeability were also determined. Their stability at room temperature and 40 °C was accessed by MIR. Cytocompatibility was performed by metabolic activity and cell lysis evaluation, according to ISO10993-5:2009, and compared with the crystalline APIs. THELES with TRIS were successfully synthesized for both APIs. Results showed an increased solubility without a decrease in the permeability of the APIs in the THELES when compared with the pure APIs. The THELES were also considered stable for 8 weeks at ambient temperature. The cells studied showed that the THELES were not toxic for the cell lines used.

Contemporary Medicinal Chemistry Strategies for the Discovery and Development of Novel HIV-1 Non-nucleoside Reverse Transcriptase Inhibitors

Currently, HIV-1 non-nucleoside reverse transcriptase inhibitors (NNRTIs) are a major component of the highly active anti-retroviral therapy (HAART) regimen. However, the occurrence of drug-resistant strains and adverse reactions after long-term usage have inevitably compromised the clinical application of NNRTIs. Therefore, the development of novel inhibitors with distinct anti-resistance profiles and better pharmacological properties is still an enormous challenge. Herein, we summarize state-of-the-art medicinal chemistry strategies for the discovery of potent NNRTIs, such as structure-based design strategies, contemporary computer-aided drug design, covalent-binding strategies, and the application of multi-target-directed ligands. The strategies described here will facilitate the identification of promising HIV-1 NNRTIs.

Relative bioavailability enhancement of simvastatin via dry emulsion systems: comparison of spray drying and fluid bed layering technology

Comprehensive comparisons of similar lipid based drug delivery systems produced by different technologies are scarce. Spray drying and fluid bed layering technologies were compared with respect to the process and product characteristics of otherwise similar simvastatin loaded dry emulsion systems. Fluid bed layering provided higher process yield (83.3% vs 71.5%), encapsulation efficiency (80.0% vs 68.4 %), relative one month product stability (93.8% vs 85.5%), larger and more circular particles (336 µm vs 56 µm) and lower median oil droplet size after product reconstitution in water (2.85 µm vs 4.27 µm), compared to spray drying. However, spray dried products exhibited higher drug content (22.2 mg/g vs 9.34 mg/g). An in-vivo pharmacokinetic study in rats was performed and a pharmacokinetic model was developed in order to compare the optimised simvastatin loaded dry emulsion systems, a simvastatin glyceride mimetic loaded in the dry emulsion and a simvastatin loaded SMEDDS with a reference physical mixture. Of the formulation tested, fluid bed layered pellets excelled and provided a 115% relative increase in bioavailability. Among the two technologies, fluid bed layering provided dry emulsion products with higher relative bioavailability and better product characteristics for further processing into final dosage forms.

Prediction of small-molecule compound solubility in organic solvents by machine learning algorithms

Rapid solvent selection is of great significance in chemistry. However, solubility prediction remains a crucial challenge. This study aimed to develop machine learning models that can accurately predict compound solubility in organic solvents. A dataset containing 5081 experimental temperature and solubility data of compounds in organic solvents was extracted and standardized. Molecular fingerprints were selected to characterize structural features. lightGBM was compared with deep learning and traditional machine learning (PLS, Ridge regression, kNN, DT, ET, RF, SVM) to develop models for predicting solubility in organic solvents at different temperatures. Compared to other models, lightGBM exhibited significantly better overall generalization (logS  ± 0.20). For unseen solutes, our model gave a prediction accuracy (logS  ± 0.59) close to the expected noise level of experimental solubility data. lightGBM revealed the physicochemical relationship between solubility and structural features. Our method enables rapid solvent screening in chemistry and may be applied to solubility prediction in other solvents.

In vitro and in vivo correlation for lipid-based formulations: Current status and future perspectives

Lipid-based formulations (LBFs) have demonstrated a great potential in enhancing the oral absorption of poorly water-soluble drugs. However, construction of in vitro and in vivo correlations (IVIVCs) for LBFs is quite challenging, owing to a complex in vivo processing of these formulations. In this paper, we start with a brief introduction on the gastrointestinal digestion of lipid/LBFs and its relation to enhanced oral drug absorption; based on the concept of IVIVCs, the current status of in vitro models to establish IVIVCs for LBFs is reviewed, while future perspectives in this field are discussed. In vitro tests, which facilitate the understanding and prediction of the in vivo performance of solid dosage forms, frequently fail to mimic the in vivo processing of LBFs, leading to inconsistent results. In vitro digestion models, which more closely simulate gastrointestinal physiology, are a more promising option. Despite some successes in IVIVC modeling, the accuracy and consistency of these models are yet to be validated, particularly for human data. A reliable IVIVC model can not only reduce the risk, time, and cost of formulation development but can also contribute to the formulation design and optimization, thus promoting the clinical translation of LBFs.

Molecular Simulation and Statistical Learning Methods toward Predicting Drug-Polymer Amorphous Solid Dispersion Miscibility, Stability, and Formulation Design

Amorphous solid dispersions (ASDs) have emerged as widespread formulations for drug delivery of poorly soluble active pharmaceutical ingredients (APIs). Predicting the API solubility with various carriers in the API-carrier mixture and the principal API-carrier non-bonding interactions are critical factors for rational drug development and formulation decisions. Experimental determination of these interactions, solubility, and dissolution mechanisms is time-consuming, costly, and reliant on trial and error. To that end, molecular modeling has been applied to simulate ASD properties and mechanisms. Quantum mechanical methods elucidate the strength of API-carrier non-bonding interactions, while molecular dynamics simulations model and predict ASD physical stability, solubility, and dissolution mechanisms. Statistical learning models have been recently applied to the prediction of a variety of drug formulation properties and show immense potential for continued application in the understanding and prediction of ASD solubility. Continued theoretical progress and computational applications will accelerate lead compound development before clinical trials. This article reviews in silico research for the rational formulation design of low-solubility drugs. Pertinent theoretical groundwork is presented, modeling applications and limitations are discussed, and the prospective clinical benefits of accelerated ASD formulation are envisioned.

Mechanistic Understanding From Molecular Dynamics Simulation in Pharmaceutical Research 1: Drug Delivery

In this review, we outline the growing role that molecular dynamics simulation is able to play as a design tool in drug delivery. We cover both the pharmaceutical and computational backgrounds, in a pedagogical fashion, as this review is designed to be equally accessible to pharmaceutical researchers interested in what this new computational tool is capable of and experts in molecular modeling who wish to pursue pharmaceutical applications as a context for their research. The field has become too broad for us to concisely describe all work that has been carried out; many comprehensive reviews on subtopics of this area are cited. We discuss the insight molecular dynamics modeling has provided in dissolution and solubility, however, the majority of the discussion is focused on nanomedicine: the development of nanoscale drug delivery vehicles. Here we focus on three areas where molecular dynamics modeling has had a particularly strong impact: (1) behavior in the bloodstream and protective polymer corona, (2) Drug loading and controlled release, and (3) Nanoparticle interaction with both model and biological membranes. We conclude with some thoughts on the role that molecular dynamics simulation can grow to play in the development of new drug delivery systems.