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.

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.

Development of novel ligands against SARS-CoV-2 Mpro enzyme: an in silico and in vitro Study.

BACKGROUND: Despite tremendous efforts made by scientific community during the outbreak of COVID-19 pandemic, this disease still remains as a public health concern. Although different types of vaccines were globally used to reduce the mortality, emergence of new variants of SARS-CoV-2 is a challenging issue in COVID-19 pharmacotherapy. In this context, target therapy of SARS-CoV-2 by small ligands is a promising strategy. METHODS: In this investigation, we applied ligand-based virtual screening for finding novel molecules based on nirmatrelvir structure. Various criteria including drug-likeness, ADME, and toxicity properties were applied for filtering the compounds. The selected candidate molecules were subjected to molecular docking and dynamics simulation for predicting the binding mode and binding free energy, respectively. Then the molecules were experimentally evaluated in terms of antiviral activity against SARS-CoV-2 and toxicity assessment. RESULTS: The results demonstrated that the identified compounds showed inhibitory activity towards SARS-CoV-2 Mpro . CONCLUSION: In summary, the introduced compounds may provide novel scaffold for further structural modification and optimization with improved anti SARS-CoV-2 Mpro activity.

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.