Selection of effective cocrystals former for dissolution rate improvement of active pharmaceutical ingredients based on lipoaffinity index

A review on advancement of cocrystallization approach and a brief on screening, formulation and characterization of the same

The objective of this review is, to discuss recent advancements in screening methods for co-formers, evaluation cum confirmation methods and co-crystallization with examples. Co-crystals are considered as a new form of an old drug entity. Co-crystals improve the stability, hygroscopicity, solubility, dissolution, and physicochemical properties of pure drugs without altering chemical and pharmacological properties. Advancement in co-crystal formulation methods like electrospray and laser-irradiation methods are showing potential for solvent-free co-crystallization and tends to give better yield and lesser loss of materials. Screening methods are also transformed from trial and error to in-silico methods, which facilitate the selection process by reducing the time of screening and increasing the number of co-formers to be screened. Advanced evaluation methods like Raman and solid-state NMR spectroscopy provide a better understanding of crystal lattice by pinpointing the interaction between drug/co-former molecules. The same evaluation methods can also differentiate between the formation of salt and co-crystals. Co-crystals are helping open a new door in pharmaceutical industries in the field of formulation for the improvement of physicochemical properties in existing old molecules and several new molecules. With a motto of "making a good drug better", co-crystals show scope for vast research and give researchers an ocean of opportunities to make the impossible, possible.

Least Absolute Shrinkage and Selection Operator-Based Prediction of the Binding Constant of p-Sulfonatocalix[6]/[8]arenes with Alkaloids

p-Sulfonatocalix[n]arenes (SCnA) have demonstrated great potential for drug encapsulation through host-guest complexation to improve solubility, stability, and bioavailability. In this study, the solubilization effect of SCnA (n = 4, 6, 8) on 95 active compounds derived from traditional Chinese medicine (TCM) was investigated. Based on the significant solubilization effect on alkaloids, SC6A/SC8A and 76 alkaloids were selected as the host and guest, respectively, to determine the binding constant by competitive fluorescence titration. LASSO regression was adopted to investigate the mechanism of the complex of SCnA with alkaloids. The binding constant of alkaloids-SC6A and alkaloids-SC8A was related to the alkaloid alkalinity. Also, the electronegativity, polarization, first ionization potential, hydrogen bond potential, the molecular size, and shape of alkaloids are critical properties to determine alkaloids-SC6A binding constant as well as electronegativity, polarization, hydrophobicity, and the molecular size and shape of alkaloids play an important role for the alkaloids-SC8A binding constant.

Optimization and characterization of self-nanoemulsifying drug delivery system of iloperidone using box-behnken design and desirability function

PURPOSE

Iloperidone (IP) is an antipsychotic drug which belongs to Biopharmaceutical Classification System (BCS) II exhibiting poor aqueous solubility. The current investigation explores the possibility of enhancement of solubility and dissolution characteristics of IP by formulation of liquid self-nano emulsifying drug delivery system (L-SNEDDS) utilizing Box-Behnken Design (BBD) and desirability function.

METHODS

The oils, surfactants and co-surfactants used in the study were selected based on solubility of the drug and their emulsification ability. Optimization of the formulation was performed using BBD by employing four response variables such as globule size (nm), percentage transmittance (%), self-emulsification time (sec) and percent drug released in 15min. 2D contour plots and 3D response surface plots were constructed using Design Expert software.

RESULTS

The developed optimal L-SNEDDS of IP through BBD approach resulted in improvement of solubility and dissolution rate as compared with the pure drug. Based on desirability function, optimized formulation was prepared and was assessed for response variables (globule size, percentage transmittance, self-emulsification time and percent drug dissolved in 15min). The characterization studies revealed droplet size to be 21.80±2.41nm, 99.584±0.65% transmittance, 24.43±2.12sec emulsification time and 95.31±1.57% cumulative drug release in 15min.

CONCLUSION

The results conclude the potentiality of prepared L-SNEDDS in improving solubility and dissolution rate of IP.

Pharmaceutical Co-crystal of Antiviral Agent Efavirenz with Nicotinamide for the Enhancement of Solubility, Physicochemical Stability, and Oral Bioavailability

The present research work attempted to improve the oral bioavailability of the antiviral drug Efavirenz (EFV) using a pharmaceutical cocrystallization technique. EFV comes under BCS-II and has extremely low water solubility, and results in low oral bioavailability. EFV and nicotinamide (NICO) were selected in a (1:1) stoichiometric ratio and efavirenz nicotinamide cocrystal (ENCOC) was prepared through the liquid-assisted grinding method (LAG). The confirmation of the formation of a new solid phase was done through spectroscopic techniques like Fourier transmission infrared (FTIR), Raman, and ¹³C solid-state nuclear magnetic resonance (¹³C ssNMR). Thermal techniques like differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), and hot stage microscopy (HSM) illustrated the thermal behavior and melting patterns of ENCOC, EFV, and NICO. The X-ray powder diffraction (XRPD) confirms the formation of a new crystalline phase in ENCOC. The Morphology was determined through scanning electron microscopy (FESEM). The results of saturated solubility studies and in vitro drug release studies exhibited 8.9-fold enhancement in solubility and 2.56-fold enhancement in percentage cumulative drug release. The percentage drug content of ENCOC was found higher than 97% and cocrystal exhibits excellent accelerated stability. The oral bioavailability of EFV (C_max, 799.08 ng/mL) exhibits significant enhancement after cocrystallization (C_max, 5597.09 ng/mL) than EFV and Efcure®-200 tablet (2896.21 ng/mL). The current work investigates the scalable and cost-effective method for enhancement of physicochemical stability, solubility, and oral bioavailability of an antiviral agent EFV.

New Screening Protocol for Effective Green Solvents Selection of Benzamide, Salicylamide and Ethenzamide

New protocol for screening efficient and environmentally friendly solvents was proposed and experimentally verified. The guidance for solvent selection comes from computed solubility via COSMO-RS approach. Furthermore, solute-solvent affinities computed using advanced quantum chemistry level were used as a rationale for observed solvents ranking. The screening protocol pointed out that 4-formylomorpholine (4FM) is an attractive solubilizer compared to commonly used aprotic solvents such as DMSO and DMF. This was tested experimentally by measuring the solubility of the title compounds in aqueous binary mixtures in the temperature range between 298.15 K and 313.15 K. Additional measurements were also performed for aqueous binary mixtures of DMSO and DMF. It has been found that the solubility of studied aromatic amides is very high and quite similar in all three aprotic solvents. For most aqueous binary mixtures, a significant decrease in solubility with a decrease in the organic fraction is observed, indicating that all systems can be regarded as efficient solvent-anti-solvent pairs. In the case of salicylamide dissolved in aqueous-4FM binary mixtures, a strong synergistic effect has been found leading to the highest solubility for 0.6 mole fraction of 4-FM.

Solvent Screening for Solubility Enhancement of Theophylline in Neat, Binary and Ternary NADES Solvents: New Measurements and Ensemble Machine Learning

Theophylline, a typical representative of active pharmaceutical ingredients, was selected to study the characteristics of experimental and theoretical solubility measured at 25 °C in a broad range of solvents, including neat, binary mixtures and ternary natural deep eutectics (NADES) prepared with choline chloride, polyols and water. There was a strong synergistic effect of organic solvents mixed with water, and among the experimentally studied binary systems, the one containing DMSO with water in unimolar proportions was found to be the most effective in theophylline dissolution. Likewise, for NADES, the addition of water (0.2 molar fraction) resulted in increased solubility compared to pure eutectics, with the highest solubilisation potential offered by the composition of choline chloride with glycerol. The ensemble of Statistica Automated Neural Networks (SANNs) developed using intermolecular interactions in pure systems has been found to be a very accurate model for solubility computations. This machine learning protocol was also applied as an extensive screening for potential solvents with higher solubility of theophylline. Such solvents were identified in all three subgroups, including neat solvents, binary mixtures and ternary NADES systems. Some methodological considerations of SANNs applications for future modelling were also provided. Although the developed protocol is focused exclusively on theophylline solubility, it also has general importance and can be used for the development of predictive models adequate for solvent screening of other compounds in a variety of systems. Formulation of such a model offers rational guidance for the selection of proper candidates as solubilisers in the designed solvents screening.

Cocrystals of Apixaban with Improved Solubility and Permeability: Formulation, Physicochemical Characterization, Pharmacokinetic Evaluation, and Computational Studies

The objective of the current study was to develop new cocrystals of Apixaban (APX) to improve its solubility and permeability. The molecular interaction between APX and caffeine (CFFN) was further studied by Raman spectroscopy. The results of all eight studied conformers revealed that the synthesized APX-CFFN cocrystals had the highest solubility and permeability. The water solubility and permeability of APX in the cocrystal were simultaneously enhanced as compared with pure APX in the physiological pH environment (pH 6.8 and pH 7.4). The X-ray diffraction analysis revealed that the cocrystal has a component molar ratio of 1:1. This was dominated by a three-dimensional hydrogen bonding supramolecular structure. The in vivo pharmacokinetic (PK) study indicated that the mean area under curve (AUC) of APX from the synthesized cocrystal was enhanced more than three-folds than the pure APX. Tablets of APX and APX-CFFN cocrystals were prepared using direct compression method and evaluated for in vitro dissolution profile in phosphate buffers (pH 6.8 and pH 7.4). Computational investigations with molecular dynamics simulations also supported the formation of stable cocrystals. The drug release of APX from the tablets was considerably increased when compared with the pure APX in both pH conditions and it was found to increase with an increase in media pH. The present investigation represents an alternative approach for optimizing physicochemical and PK properties of Biopharmaceutical Classification System class-III drugs without changing its molecular structure and intrinsic bioactivities.

Cocrystal Prediction Using Machine Learning Models and Descriptors

Cocrystals are of much interest in industrial application as well as academic research, and screening of suitable coformers for active pharmaceutical ingredients is the most crucial and challenging step in cocrystal development. Recently, machine learning techniques are attracting researchers in many fields including pharmaceutical research such as quantitative structure-activity/property relationship. In this paper, we develop machine learning models to predict cocrystal formation. We extract descriptor values from simplified molecular-input line-entry system (SMILES) of compounds and compare the machine learning models by experiments with our collected data of 1476 instances. As a result, we found that artificial neural network shows great potential as it has the best accuracy, sensitivity, and F1 score. We also found that the model achieved comparable performance with about half of the descriptors chosen by feature selection algorithms. We believe that this will contribute to faster and more accurate cocrystal development.

The use of fast molecular descriptors and artificial neural networks approach in organochlorine compounds electron ionization mass spectra classification

Developing of theoretical tools can be very helpful for supporting new pollutant detection. Nowadays, a combination of mass spectrometry and chromatographic techniques are the most basic environmental monitoring methods. In this paper, two organochlorine compound mass spectra classification systems were proposed. The classification models were developed within the framework of artificial neural networks (ANNs) and fast 1D and 2D molecular descriptor calculations. Based on the intensities of two characteristic MS peaks, namely, [M] and [M-35], two classification criterions were proposed. According to criterion I, class 1 comprises [M] signals with the intensity higher than 800 NIST units, while class 2 consists of signals with the intensity lower or equal than 800. According to criterion II, class 1 consists of [M-35] signals with the intensity higher than 100, while signals with the intensity lower or equal than 100 belong to class 2. As a result of ANNs learning stage, five models for both classification criterions were generated. The external model validation showed that all ANNs are characterized by high predicting power; however, criterion I-based ANNs are much more accurate and therefore are more suitable for analytical purposes. In order to obtain another confirmation, selected ANNs were tested against additional dataset comprising popular sunscreen agents disinfection by-products reported in previous works.

Predicting Value of Binding Constants of Organic Ligands to Beta-Cyclodextrin: Application of MARSplines and Descriptors Encoded in SMILES String

The quantitative structure–activity relationship (QSPR) model was formulated to quantify values of the binding constant (lnK) of a series of ligands to beta–cyclodextrin (β-CD). For this purpose, the multivariate adaptive regression splines (MARSplines) methodology was adopted with molecular descriptors derived from the simplified molecular input line entry specification (SMILES) strings. This approach allows discovery of regression equations consisting of new non-linear components (basis functions) being combinations of molecular descriptors. The model was subjected to the standard internal and external validation procedures, which indicated its high predictive power. The appearance of polarity-related descriptors, such as XlogP, confirms the hydrophobic nature of the cyclodextrin cavity. The model can be used for predicting the affinity of new ligands to β-CD. However, a non-standard application was also proposed for classification into Biopharmaceutical Classification System (BCS) drug types. It was found that a single parameter, which is the estimated value of lnK, is sufficient to distinguish highly permeable drugs (BCS class I and II) from low permeable ones (BCS class II and IV). In general, it was found that drugs of the former group exhibit higher affinity to β-CD then the latter group (class III and IV).

Cocrystal Assembled by Pyrene Derivative and 1,4-Diiodotetrafluorobenzene via a C=O···I Halogen Bond

Cocrystal formation is a strategy used to modify the solid-state properties of a given molecule. In this study, a new cocrystal assembled by 1,4-Diiodotetrafluorobenzene (1,4-DITFB) and a pyrene derivative, 1-acetyl-3-phenyl-5-(1-pyrenyl)-pyrazoline (APPP), was synthesized. Due to the twisted structure of APPP, the crystal structure is greatly different with some large π-conjugated compounds, which exhibits edge-to-face π-stacked arrangement between 1,4-DITFB and pyrene rings, rather than the face-to-face π-stacked arrangement. Hirshfeld surface analysis and the shift of characteristic vibration band of the carbonyl group in FT-IR spectroscopy suggest the formation of a C=O···I halogen bond.

Ginsenoside Drug Nanocomposites Prepared by the Aerosol Solvent Extraction System for Enhancing Drug Solubility and Stability

Ginsenosides are the pharmacologically active constituents of ginseng. So far, more than 30 ginsenosides have been identified and widely used in pharmaceutical formulations. However, the therapeutic applications of ginsenosides are hampered by their poor solubility and low bioavailability. In this study, we selected two of the most important ginsenosides-Re and Rh₂ as model drugs to prepare ginsenoside drug nanocomposites (NanoGS) using the simple aerosol solvent extraction system (ASES) technique to address the poor solubility and bioavailability of these compounds. Compared with raw ginsenosides, NanoGS exhibited significantly enhanced dissolution rate owing to their low crystallinity and high surface area. Furthermore, in vitro cellular investigations showed that NanoGS-Rh₂ exhibited outstanding anticancer activity against MCF-7 cancer cells. Therefore, this study is expected to provide a promising strategy that could optimize and broaden the applications of ginsenosides, as well as other water-insoluble drugs in pharmaceutical formulations.

Recent advances in improving oral drug bioavailability by cocrystals

Introduction: Oral drug delivery is the most favored route of drug administration. However, poor oral bioavailability is one of the leading reasons for insufficient clinical efficacy. Improving oral absorption of drugs with low water solubility and/or low intestinal membrane permeability is an active field of research. Cocrystallization of drugs with appropriate coformers is a promising approach for enhancing oral bioavailability.

Methods: In the present review, we have focused on recent advances that have been made in improving oral absorption through cocrystallization. The covered areas include supersaturation and its importance on oral absorption of cocrystals, permeability of cocrystals through membranes, drug-coformer pharmacokinetic (PK) interactions, conducting in vivo-in vitro correlations for cocrystals. Additionally, a discussion has been made on the integration of nanocrystal technology with supramolecular design. Marketed cocrystal products and PK studies in human subjects are also reported.

Results: Considering supersaturation and consequent precipitation properties is necessary when evaluating dissolution and bioavailability of cocrystals. Appropriate excipients should be included to control precipitation kinetics and to capture solubility advantage of cocrystals. Beside to solubility, cocrystals may modify membrane permeability of drugs. Therefore, cocrystals can find applications in improving oral bioavailability of poorly permeable drugs. It has been shown that cocrystals may interrupt cellular integrity of cellular monolayers which can raise toxicity concerns. Some of coformers may interact with intestinal absorption of drugs through changing intestinal blood flow, metabolism and inhibiting efflux pumps. Therefore, caution should be taken into account when conducting bioavailability studies. Nanosized cocrystals have shown a high potential towards improving absorption of poorly soluble drugs.

Conclusions: Cocrystals have found their way from the proof-of-principle stage to the clinic. Up to now, at least two cocrystal products have gained approval from regulatory bodies. However, there are remaining challenges on safety, predicting in vivo behavior and revealing real potential of cocrystals in the human.