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.

Novel styryl-thiazole hybrids as potential anti-Alzheimer's agents

In this study, combining the thiazole and cinnamoyl groups into the styryl-thiazole scaffold, a series of novel styryl-thiazole hybrids (6a-p) was rationally designed, synthesized, and evaluated by the multi-target-directed ligands strategy as potential candidates for the treatment of Alzheimer's disease (AD). Hybrids 6e and 6i are the most promising among the synthesized hybrids since they are able to significantly increase cell viabilities in Aβ1-42-exposed-human neuroblastoma cell line (6i at the concentration of 50 μg mL-1 and 6e at the concentration of 25 μg mL-1 resulted in ∼34% and ∼30% increase in cell viabilities, respectively). Compounds 6e and 6i exhibit highly AChE inhibitory properties in the experimental AD model at 375.6 ± 18.425 mU mL-1 and 397.6 ± 32.152 mU mL-1, respectively. Moreover, these data were also confirmed by docking studies and in vitro enzyme inhibition assays. Compared to hybrid 6e and according to the results, 6i also has the highest potential against Aβ1-42 aggregation with over 80% preventive activity. The in silico prediction of the physicochemical properties confirms that 6i possesses a better profile compared to 6e. Therefore, compound 6i presents a promising multi-targeted active molecular profile for treating AD considering the multifactorial nature of AD, and it is reasonable to deepen its mechanisms of action in an in vivo experimental model of AD.

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

1. 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.2. Literature data were used to categorize 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.3. 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%.4. 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 (logD_7.4)] were applied to develop a mechanistic model for predicting clearance pathways.

RESULTS

The results of this study indicate that compounds with logD_7.4 > 1 or with zero or one hydrogen-bond donor undergo hepatic metabolism, whereas the clearance pathway for chemicals with logD_7.4 < - 2 is renal elimination. Furthermore, models established using logistic regression based on five structural parameters for compounds with - 2 < logD_7.4 < 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.