In Silico ADME/T Properties of Quinine Derivatives using SwissADME and pkCSM Webservers

Abstract

Aim: Malaria is among the most devastating and widespread tropical parasitic diseases. To overcome antimalarial drug resistance, new drugs need to be developed. This study is designed to establish the pharmacokinetic profile and toxicity of nine quinine derivatives as potential antimalarial drugs using in silico approaches by SwissADME and pkCSM.
 Methodology: The structures of investigated compounds were translated into canonical SMILES format and then submitted to SwissADME web tool that gives free access to physicochemical properties, pharmacokinetics, drug-likeness and medicinal chemistry friendliness of compounds, and pkCSM webserver for predicting and optimizing pharmacokinetic and toxicity properties.
 Results: SwissADME mainly used to predict the physicochemical properties of compounds and their drug-likeness revealed that all quinine derivatives have good bioavailability and satisfied the Lipinski’s rule of five. The pkCSM results on the absorption, distribution, metabolism, excretion and toxicity show that all investigated compounds have a good pharmacokinetic profile and they are safe since they belong to class 4 of the Globally Harmonized System (300 < Category 4 ≤ 2000 mg/kg/day).
 Conclusion: Drug-likeness and ADME/T predictions of nine investigated quinine derivatives revealed that they are good candidates to oral drug formulation and thus they can be used in a broader context of overcoming the development of resistance by Plasmodium protozoans against most of the drugs currently used to treat malaria. As future prospects, further studies on bioevaluation of compounds are needed to elucidate their potential pharmacological activities.

SwissADME and pkCSM Webservers Predictors: an integrated Online Platform for Accurate and Comprehensive Predictions for In Silico ADME/T Properties of Artemisinin and its Derivatives

“…[ [16], [19], [20], [21], [22], [23]]. For the desired molecule to be established and used as a drug, the subsequent step in the computer-aided drug model pipeline to deal with is the pre-clinical optimization.…”

“…The physicochemical property is a molecular attribute which affects efficacy, safety, or metabolism and can be anticipated utilizing Lipinski's rule of five, Veber's rule, or Muegge's rule. In this research, we employed Lipinski's rule to create an orally active medication, which proves the number of hydrogen bonds acceptor (HBA) of less or equal to 10, hydrogen bonds donor (HBD) of less or equal to 5, molecular weight (MW) of less than 500 Da, and Log P of less or equal to 5 [16]. Artemisinin, as a reference chemical, and its synthesized variants are uploaded to the SwissADME website one by one in the standard SMILES format.. Lipophilicity and solubility are the other two major determinants that are examined for optimal medication development.…”

“…Furthermore, the molecular refractivity of a drug molecule must not exceed 130 m 3 .mol -1 and must not be less than 40 m 3 .mol -1 [16]. All derivatives have a m 3 .mol -1 range of 69.90 -92.46 m 3 .mol -1 .…”

“…The goal of this study is to use SwissADME [10] and pkCSM [11] webservers to forecast the physicochemical qualities, drug-likeness properties, ADME (absorption, distribution, metabolism, and excretion), and toxicity of five artemisinin derivatives to understand their pharmacokinetic behaviour. In addition to being free, the webservers utilized in this work have undergone several comparison trials that show that SwissADME and pkCSM present as well as or improved than numerous other frequently used techniques [ [10], [11], [12], [13], [14], [15], [16]].…”

The Prediction of Pharmacokinetic Properties of Compounds in <i>Hemigraphis alternata</i> (Burm.F.) T. Ander Leaves Using pkCSM

“…Graphical modeling results from an understandable and well-established mathematical description of chemical entities. Different predictors, including molecular structure and chemistry, may be retrieved using pkCSM [14][15][16]. Despite the diversity in the size of the data set and the distribution of experimental values, the pkCSM model was able to establish a strong correlation with experimental results through regression analysis of the ADME predictors [8].…”