Prediction of the First-Pass Metabolism of a Drug After Oral Intake Based on Structural Parameters and Physicochemical Properties.

BACKGROUND AND OBJECTIVE: The oral first-pass metabolism is a crucial factor that plays a key role in a drug's pharmacokinetic profile. Prediction of the oral first-pass metabolism based on chemical structural parameters can be useful in the drug-design process. Developing an orally administered drug with an acceptable pharmacokinetic profile is necessary to reduce the cost and time associated with evaluating the extent of the first-pass metabolism of a candidate compound in preclinical studies. The aim of this study is to estimate the first-pass metabolism of an orally administered drug. METHODS: A set of compounds with reported first-pass metabolism data were collected. Moreover, human intestinal absorption percentage and oral bioavailability data were extracted from the literature to propose a classification system that split the drugs up based on their first-pass metabolism extents. Various structural parameters were calculated for each compound. The relations of the structural and physicochemical values of each compound to the class the compound belongs to were obtained using logistic regression. RESULTS: Initial analysis showed that compounds with logD7.4 > 1 or a rugosity factor of > 1.5 are more likely to have high first-pass metabolism. Four different models that can predict the oral first-pass metabolism with acceptable error were introduced. The overall accuracies of the models were in the range of 72% (for models with simple descriptors) to 78% (for models with complex descriptors). Although the models with simple descriptors have lower accuracies compared to complex models, they are more interpretable and easier for researchers to utilize. CONCLUSION: A novel classification of drugs based on the extent of the oral first-pass metabolism was introduced, and mechanistic models were developed to assign candidate compounds to the appropriate proposed classes.

Determination of indoxyl sulfate by spectrofluorimetric method in human plasma through extraction with deep eutectic solvent.

A rapid and efficient analytical method was established to quantify indoxyl sulfate (IS) in plasma through extraction technique with a deep eutectic solvent (DES) and spectrofluorimetric method. DES (choline chloride: urea) was mixed with plasma samples for the extraction of IS, followed by the addition of dipotassium hydrogen phosphate (K2HPO4) solution to form an aqueous two-phase system. The fluorescence intensity of IS which was first extracted to the DES-rich-phase and then back-extracted into the salt-rich-phase, was measured by spectrofluorimetric method. Some key factors such as pH, centrifugation speed and time, the volume ratio of DES/salt, and salt concentration were optimized. Under the optimized conditions, the suggested method had a dynamic range between 20 and 160 µg/mL with a coefficient of determination (R2) of 0.99. Precision (relative standard deviation) was less than 15% and accuracy (% relative recovery) was ± 15% at the nominal concentration level. In addition, results showed that IS levels in real samples were higher than 40 µg/mL which was compatible with reported IS levels in end-stage renal disease (ESRD) patients. Overall, all the results reflect the fact that the presented analytical method can potentially be used for the determination of IS in real plasma samples.

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 (ßCD) 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 ßCD. In this study, the solubility enhancement of some drugs in the presence of 10mM ßCD at 25°C was experimentally determined or collected from the literature. Two different models to predict the solubilization by ßCD 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 ßCD. Moreover, other descriptors related to hydrophobicity and the capability of hydrogen bonding formation of molecules could improve the accuracy of the established models.

Comments on "Cocrystallization of 5-fluorouracil with gallic acid: A novel 5-fluorouracil cocrystal displaying synergistic anti-tumor activity both in oral and intraperitoneal injection administration".

This commentary highlights the significance of the solution stability and preparation of a solution of cocrystal in evaluating anti-tumor activity. It introduces a challenging issue regarding improving the biological activity of an API in cocrystal form.

Neural Network Models for Predicting Solubility and Metabolism Class of Drugs in the Biopharmaceutics Drug Disposition Classification System (BDDCS).

BACKGROUND AND OBJECTIVE: The biopharmaceutics drug disposition classification system (BDDCS) categorizes drugs into four classes on the basis of their solubility and metabolism. This framework allows for the study of the pharmacokinetics of transporters and enzymatic metabolization on biopharmaceuticals, as well as drug-drug interactions in the body. The objective of the present study was to develop computational models by neural network models and structural parameters and physicochemical properties to estimate the class of a drug in the BDDCS system. METHODS: In this study, deep learning methods were utilized to explore the potential of artificial and convolutional neural networks (ANNs and CNNs) in predicting the BDDCS class of 721 substances. The structural parameters and physicochemical properties [Abraham solvation parameters, octanol-water partition (log P) and over the pH range 1-7.5 (log D), number of rotatable bonds, hydrogen bond acceptor numbers, as well as hydrogen bond donor count] are calculated with various software. These compounds were then split into a training set consisting of 602 molecules and a test set of 119 compounds to validate the models. RESULTS: The results of this study showed that neural network models using applied parameters of the drug, i.e., log D and Abraham solvation parameters, are able to predict the class of solubility and metabolism in the BDDCS system with good accuracy. CONCLUSIONS: Neural network models are well equipped to deal with the relations between the structural parameters and physicochemical properties of drugs and BDDCS classes. In addition, log D is a more suitable parameter compared with log P in predicting BDDCS.

Solubility of Some Drugs in Aqueous Solutions of Choline Chloride-Based Deep Eutectic Solvent Systems: Experimental Data, Modeling, and the Impact of Solution pH.

Background: The solubility of drugs in water and organic solvents is a crucial factor in numerous pharmaceutical processes. In recent years, a new type of solvent called deep eutectic solvents (DESs) has been developed as a useful solvent for drugs. Choline chloride-glycerol/urea (ChCl-G/U) systems are DESs recognized as a novel category of environmentally friendly solvents. One recent application of this type of DES in water is the solubilization of drugs. Objectives: This study aimed to investigate the solubility of certain drugs in ChCl-G/U. In addition, the solubilization mechanisms of the DESs studied, and quantitative structure-property relationship (QSPR) models for solubilization were proposed. Methods: The solubility of 13 drugs in an aqueous solution of the ChCl-G/U system was investigated using the shake flask method. The study was conducted at 10% and 50% mass fractions of the studied systems. Multiple linear regression models were used to develop mathematical relationships between the solubilization of the studied compounds in the presence of ChCl-G/U + water mixture using QSPR models. Results: The solubility of the compounds showed a significant increase upon adding ChCl-G/U to the aqueous solutions. Based on the data obtained, QSPR models were developed using solubilization ratio and structural descriptors. Conclusions: The experimental data demonstrates the potential of utilizing ChCl-G/U as a medium to enhance the solubility of poorly soluble drugs in water. Solubilization of solutes in ChCl-G/U + water mixtures could be correlated with the structural properties of drugs. Moreover, the final pH of the solutions in ChCl-U is a critical factor that must be considered when using this system for solubilization.

Comments on "Overview and thermodynamic modelling of deep eutectic solvents as co-solvents to enhance drug solubilities in water".

This communication highlights the significance of the final pH of the solution and the potential instability of deep eutectic solvent (DES) within aqueous solutions, and introducing a challenging aspect regarding solute solubilization by DESs.

Developing an analytical method for quantification of trientine based on modified silver nanoparticles.

Trientine or (N,N´-bis(2-aminoethyl)-1,2-ethanediamine (TETA) is a copper chelator and used in Wilson's disease, is aliphatic amine that does not have UV absorbing groups. In this study, the modified silver nanoparticles (AgNPs) by sodium lauryl sulfate have been used to develop an analytical method for quantification of TETA. Different concentrations of TETA were added into a particular concentration of AgNPs and absorbance of each sample was measured at 397 nm under the optimal conditions which include pH, time, salt and AgNPs volume. It was optimized by a design of experiments using response surface methodology. Then, the calibration curve was obtained based on the concentrations of TETA solution versus decrease in the absorbance of AgNPs. Selectivity of the developed method was performed in plasma and presence of common cations i.e. copper, zinc and ferrous. Under optimum conditions, linear range of this method was between 10 and 40 ng.mL- 1 with correlation coefficient (R2) of 0.996 with limit of detection and quantification of 3 ng.mL- 1 and 10 ng.mL- 1, respectively. Selectivity of established method in presence of cations eliminated by diluting because of high sensitivity of the established analytical techniques based on AgNPs. This method is suitable and low costing for quantification of TETA and does not require high equipment.

Online preconcentration and chiral separation of ofloxacin in exhaled breath condensate by capillary electrophoresis.

Breath analysis is an effective method of monitoring systemic or respiratory ailments. A simple chiral capillary electrophoresis method coupled with an online field-amplified sample injection stacking method is presented for ultratrace quantification of the enantiomers of ofloxacin in exhaled breath condensate (EBC). The study is focused on the use of EBC as an easily available biological sample to monitor ofloxacin's enantiomers levels with good patient compliance. The proposed method was validated in accordance with FDA guidelines over the concentration range of 0.004-1.0 µg mL-1 of racemic ofloxacin. Inter- and intra-day precision and accuracy were within the acceptable limit (below 8.50 %). The method was specific for routine analysis of ofloxacin's enantiomers. A small volume of EBC samples from seven patients under ofloxacin therapy was analyzed using the proposed method in which the concentrations of "R" and "S" enantiomers were between 0.0026 and 0.056 µg mL-1.

Serum Levels of Indoxyl Sulfate and P-cresol in Type II Diabetic Patients With and Without Nephropathy.

INTRODUCTION: Indoxyl sulfate (IS) and para-cresol (p-cresol) are uremic toxins with high protein bonding index that accumulate in the body with decreasing kidney function. The main purpose of the current investigation was to compare the concentration of p-cresol and IS in serum of the type II diabetic individuals with and without nephropathy. METHODS: Fifty-five patients with type II diabetes mellitus were divided into two groups: case and control. The case group consisted of 26 diabetic patients with nephropathy (proteinuria and serum creatinine below 1.5 mg/dL) without any other kidney diseases. The control group included 29 patients without diabetic nephropathy. Patients with advanced heart disease, cerebrovascular accident and other inflammatory or infectious diseases were excluded. Five mL of venous blood was taken from each patient in the morning fasting state. Then other laboratory tests including serum uric acid and creatinine levels, serum urea nitrogen, lipids and glucose were measured by standard methods. P-Cresol and IS levels were measured by the spectrofluorimetric method after extraction. We also filled out a checklist with information regarding the duration of their disease, medication history (oral or injectable), and other demographic information. There were no significant differences between the two groups regarding the investigated factors Results. There were no significant difference among the investigated factors between the two groups (P > .05) except for the serum creatinine, proteinuria and estimated glomerular filtration rate, where the mean values of cases were considerably higher than those of the controls. Serum IS and p-cresol levels were also significantly higher in the case group (P < .05). CONCLUSION: According to the findings, it seems that IS, and p-cresol may play a role in the development of diabetic nephropathy and other complications of diabetes mellitus.  DOI: 10.52547/ijkd.7266.

Solid-state characterization of ibuprofen-isonicotinamide cocrystals prepared by electrospraying and solvent evaporation.

Aim: Electrospraying (ELS) was used to prepare micronized ibuprofen-isonicotinamide cocrystal (IBU-INA-ELS) and its properties were compared with the solvent evaporated cocrystal (IBU-INA-SE). Methods: Solid-state characterization of crystalline phase, production yield, particle size, powder flow, wettability, solution mediated phase transformation (SMPT), and dissolution rate were measured. Results: The ELS produced phase pure particles of IBU-INA with a size of 1.46 µm and yield of 72.3%. This cocrystal improved the intrinsic dissolution rate and powder dissolution rate of IBU by 3.6- and 1.7-fold, respectively. Our experiments showed that the dissolution of IBU-INA was affected by particle size, solubility, SMPT and wettability. Conclusion: ELS produced micronized cocrystals for improving dissolution of ibuprofen with a high yield in a single step and mild conditions.

The intestinal absorption of ibuprofen is limited by low dissolution rate in the gastrointestinal fluids. This drug needs applied in an appropriate method to enhance its dissolution. One way to improve dissolution of ibuprofen is preparing micronized cocrystal of this drug and a water soluble compound, isonicotinamide. In this study, we prepared and characterized micronized particles of ibuprofen-isonicotinamide cocrystal by electrospraying, a single step and continuous method without need for any added material. Our experiments showed that the prepared micronized cocrystal could improve the dissolution of ibuprofen but the cocrystal is rapidly precipitated as ibuprofen crystals in contact with dissolution medium. This precipitation hampered the expected increase in dissolution. Therefore, solution mediated phase transformation should be considered in formulating micronized cocrystals.

Comparison of various methods for validity evaluation of QSAR models.

BACKGROUND: Quantitative structure-activity relationship (QSAR) modeling is one of the most important computational tools employed in drug discovery and development. The external validation of QSAR models is the main point to check the reliability of developed models for the prediction activity of not yet synthesized compounds. It was performed by different criteria in the literature. METHODS: In this study, 44 reported QSAR models for biologically active compounds reported in scientific papers were collected. Various statistical parameters of external validation of a QSAR model were calculated, and the results were discussed. RESULTS: The findings revealed that employing the coefficient of determination (r2) alone could not indicate the validity of a QSAR model. The established criteria for external validation have some advantages and disadvantages which should be considered in QSAR studies. CONCLUSION: This study showed that these methods alone are not only enough to indicate the validity/invalidity of a QSAR model.

Copper (II) complexes with N, S donor pyrazole-based ligands as anticancer agents.

A group of bidentate nitrogen and sulfur donor pyrazole derivative ligands abbreviated as Na[RNCS(Pz)], Na[RNCS(PzMe2)], Na[RNCS(PzMe3)], Na[RNCS(PzPhMe)], Na[RNCS(PzPh2)], where (R = Et, Ph), and their Cu (II) complexes were synthesized and characterized by spectroscopic and physicochemical methods. The crystal structure of [Cu(PhNCSPzMe3)2] was determined by X-ray crystallography analysis and the results described a distorted square planar coordination geometry for this complex. Also, the cyclic voltammetry investigations indicated that the synthesized copper complex is an electrochemically active species. Moreover, the cytotoxic activity of all of the twenty synthesized compounds was evaluated using MTT assay against the MCF-7 (human breast carcinoma) cell lines, in vitro. Cu (II) complexes indicate significant cytotoxicity against the MCF-7 cell lines as compared with the free ligands. The docking studies showed that the copper complexes have better interactions with EGFR and CDK2 proteins, compared to the free ligands, and most of the studied compounds have a higher value of binding energy relative to the studied controls. The results of QSAR analysis suggest that dipole moment is in direct correlation with the obtained IC50 values, and it strongly impact the anticancer effects generated by the compounds. Our findings suggest that the developed copper complexes can be good candidates for further evaluations as chemotherapeutic agents in the treatment of cancer.

In silico models to predict tubular secretion or reabsorption clearance pathway using physicochemical properties and structural characteristics.

Renal clearance is one of the main pathways for a drug to be cleared from plasma. The aim of this study is to develop in-silico models to find out the relationship between the type of renal clearance, and structural parameters.Literature data were used to categorise the drugs into those that undergo tubular secretion and those that undergo reabsorption. Different structural descriptors (VolSurf descriptors, Abraham solvation parameters, data warrior descriptors, logarithm of distribution coefficient at pH = 7.4 (logD7.4)) were applied to develop a mechanistic model for estimating renal clearance class whether its secretion or reabsorption.The results of this study show that logD7.4 and the number of hydrogen bond donors, as well as available uncharged species (AUS7.4), are the most effective descriptors to establish mechanistic models for predicting renal clearance class. The classification models were established with a level of accuracy of more than 75%.Developed models of this study can be helpful to predict renal clearance class for new drug candidates with an acceptable error. Hydrophilicity and hydrogen bond formation ability of drugs are among the main descriptors.

Predicting the Drug Clearance Pathway with Structural Descriptors.

BACKGROUND AND OBJECTIVE: The clearance, by renal elimination or hepatic metabolism, is one of the most important pharmacokinetic parameters of a drug. It allows the half-life, bioavailability, and drug-drug interactions to be predicted, and it can also affect the dose regimen of a drug. Predicting the clearance pathways of new chemical candidates during drug development is vital in order to minimize the risks of possible side effects and drug interactions. Many in vivo methods have been established to predict drug clearance in humans, and these mainly rely on data from in vivo studies in preclinical species-mainly rats, dogs, and monkeys. They are also time consuming and expensive. The aim of this study was to find the relationship between structural parameters of drugs and their clearance pathways. METHODS: The clearance pathway of each drug was obtained from the literature. Various structural descriptors [Abraham solvation parameters, topological polar surface area, numbers of hydrogen-bond donors and acceptors, number of rotatable bonds, molecular weight, logarithm of the partition coefficient (logP), and logarithm of the distribution coefficient at pH 7.4 (logD7.4)] were applied to develop a mechanistic model for predicting clearance pathways. RESULTS: The results of this study indicate that compounds with logD7.4 > 1 or with zero or one hydrogen-bond donor undergo hepatic metabolism, whereas the clearance pathway for chemicals with logD7.4 < - 2 is renal elimination. Furthermore, models established using logistic regression based on five structural parameters for compounds with - 2 < logD7.4 < 1 could be used in a clearance pathway prediction tool. The overall prediction accuracies of the first and second models were 84.8% and 84.4%, respectively. CONCLUSION: The developed model can be used to find the clearance pathways of new drug candidates with acceptable accuracy. The main descriptors that are used to evaluate this parameter are the hydrophobicity and the number of hydrogen-bonding functional groups of the compound.

Crystal engineering of valproic acid and carbamazepine to improve hygroscopicity and dissolution profile.

Sodium valproate, the most common solid form of valproic acid, is highly hygroscopic and carbamazepine has extremely low aqueous solubility. Producing a salt form of valproic acid with tromethamine and a cocrystal form of valproic acid with carbamazepine have been studied as two approaches to improve physicochemical properties of the intended drugs. Characterization methods including differential scanning calorimetry (DSC), powder X-ray diffraction (PXRD) and Fourier transform infrared spectroscopy (FTIR) are applied to characterize the synthesized salt and cocrystal. The stability of sodium valproate and tromethamine valproate were examined in 33, 53, 75 and 100 percent of relative humidity. The dissolution profile studies were performed in phosphate buffer media (pH = 6.8) for carbamazepine (a low soluble drug) and carbamazepine-valprocic acid cocrystal. Tromethamine valproate was physically more stable than sodium valproate in exposure to humidity. Carbamazepine-valproic acid cocrystal did not show an extreme improvement in dissolution profile when compared with carbamazepine, however after 24 hcocrystal was more soluble.

Beware of Bar Charts for Plotting Calibration Curves for Analytical Method Development.

Some researchers, in their published articles in authentic scientific journals, plot calibration curves using bar charts instead of scatter plots using common software such as Excel. Bar charts can significantly affect the apparent linear range and sensitivity of the developed method, and using bar charts as calibration curves gives the wrong results. Therefore, this issue should be considered by researchers in developing analytical methods.

Prediction of the Oral Bioavailability Correlation Between Humans and Preclinical Animals.

BACKGROUND: Experimental determination of a drug's oral bioavailability in humans is cost and time consuming; therefore, is usually performed on preclinical animals. It is believed that animal data are predictive of human data; however, there are some doubts regarding the reliability of this concept. The aim of this study is to clarify whether it is possible to predict a correlation between human and animal bioavailability data based on structural parameters. METHODS: Oral bioavailability data of drugs for humans and preclinical animals (rats and dogs) were collected from the literature. Structural descriptors [Abraham solvation parameters, topological polar surface area (TPSA), logarithm of partition coefficient (logP) and logarithm of distribution coefficient at pH = 6.8 (logD6.8)] were calculated by ACD/Labs software. Data were divided into two classes by percentage deviation (PD) of oral bioavailability between humans and preclinical animals (PD < 40%: class I and PD > 40%: class II). Classification-based models were used to predict the class of each drug using structural parameters. RESULTS: The results of this study revealed that logD6.8 is the main parameter for evaluation of the correlation between oral bioavailability in humans and animals. Moreover, the developed models using logistic regression based on logP, TPSA and Abraham solvation parameters are able to predict the class of a drug with 75% accuracy. CONCLUSION: The structural parameters and developed models in this study can be used to find compounds that have an acceptable correlation between oral bioavailabilities in animals, i.e., rats and dogs, and humans.