Quantitative structure-pharmacokinetic relationship (QSPkP) analysis of the volume of distribution values of anti-infective agents from j group of the ATC classification in humans

Abstract

There is a constant need to develop new anti-infective drug molecules because the antibiotics and antiviral drugs always face a threat of resistance development, which may eventually lead to therapeutic failure. Drug discovery and development is a lengthy and costly process and many drug candidates fail at the later stages of development due to poor pharmacokinetic (PK) properties, i.e., absorption, distribution, metabolism and excretion (ADME) (1). The success rate in drug discovery can be increased by predicting the PK properties of virtual molecules in the early stages and this will also reduce the cost of drug development. Computational or in silico methods are increasingly used in drug discovery to predict the properties of virtual molecules (2). Quantitative structure--activity relationships (QSARs) are mathematical models that attempt to relate the struc-

Predicting volume of distribution with decision tree-based regression methods using predicted tissue:plasma partition coefficients

“…Concerning dataset variations, there are several other studies based on Obach et al’s dataset [14], including [5,22,35,45]. However, unlike our work, most of those studies tend to use a substantially smaller version of Obach et al’s dataset, focusing on a single type of compounds or removing compounds that are more difficult to predict for some reason.…”

“…However, unlike our work, most of those studies tend to use a substantially smaller version of Obach et al’s dataset, focusing on a single type of compounds or removing compounds that are more difficult to predict for some reason. In particular, the dataset used by Louis and Agrawal [5] contains drugs that belong to the category of anti-infective (J) and sub-categories antibacterial (J01), antimycotics (J02) antimycobacterial (J04) and antiviral (J05) according to the anatomical therapeutic classification (ATC). That dataset contains only 126 compounds, whose Vss varies from 0.05 to 33 L/Kg.…”

“…It is known that acidic drugs tend to have relatively small values of Vss, compared with basic drugs, due to the fact that acids tend to have extensive binding to plasma proteins [41]. Indeed, in the dataset used by Zhivkova and Doytchinova, the compounds’ Vss values range from 0.04 to 15 L/kg, an even smaller range than the one in the dataset used by Louis and Agrawal [5]. Again, the smaller Vss range in the dataset used by Zhivkova and Doytchinova helps to improve the predictive accuracy of the models, at the expense of the models’ applicability being restricted to acidic drugs.…”

“…Although this apparent Vss has no physiological meaning, its estimation is important because it predicts the drug’s plasma concentration for a given amount of drug in the body and it influences the drug’s half-life [2,3], which in turn is very important to determine the correct dosage regimen that clinicians should prescribe to patients [4,5]. Hence, one needs to estimate or predict Vss using an in vivo , in vitro or in silico approach [6-9].…”

“…[5,22]. On one hand, this large compound diversity makes it difficult to discover a model that predicts Vss with a high accuracy; but on the other hand the models produced in this study have a wider domain of applicability than other Vss models more specialized for certain classes of drugs.…”