Applying Linear and Non-Linear Methods for Parallel Prediction of Volume of Distribution and Fraction of Unbound Drug

Abstract

Volume of distribution and fraction unbound are two key parameters in pharmacokinetics. The fraction unbound describes the portion of free drug in plasma that may extravasate, while volume of distribution describes the tissue access and binding of a drug. Reliable in silico predictions of these pharmacokinetic parameters would benefit the early stages of drug discovery, as experimental measuring is not feasible for screening purposes. We have applied linear and nonlinear multivariate approaches to predict these parameters: linear partial least square regression and non-linear recursive partitioning classification. The volume of distribution and fraction of unbound drug in plasma are predicted in parallel within the model, since the two are expected to be affected by similar physicochemical drug properties. Predictive models for both parameters were built and the performance of the linear models compared to models included in the commercial software Volsurf+. Our models performed better in predicting the unbound fraction (Q2 0.54 for test set compared to 0.38 with Volsurf+ model), but prediction accuracy of the volume of distribution was comparable to the Volsurf+ model (Q2 of 0.70 for test set compared to 0.71 with Volsurf+ model). The nonlinear classification models were able to identify compounds with a high or low volume of distribution (sensitivity 0.81 and 0.71, respectively, for test set), while classification of fraction unbound was less successful. The interrelationship between the volume of distribution and fraction unbound is investigated and described in terms of physicochemical descriptors. Lipophilicity and solubility descriptors were found to have a high influence on both volume of distribution and fraction unbound, but with an inverse relationship.

In vitro membrane binding and protein binding (IAM MB/PB technology) to estimate in vivo distribution: applications in early drug discovery

“…Lipophilicity has been recognised for a long time as the principal parameter that influences solubility [20,21], permeability [22], tissue binding, protein binding [23,24], toxicity [10], promiscuity [1], clearance [25] etc. Several ligand efficiency parameters contain the lipophilicity and propose to consider the potency relative to the lipophilicity of the compounds, such as Ligand Lipophilicity Efficiency, LLE [7,26].…”

Quantitative Structure – Pharmacokinetics Relationships Analysis of Basic Drugs: Volume of Distribution

“…The models are derived by different statistical and machine learning methods as artificial neural networks (ANN) (7,9,13), multiple linear regression (MLR) (8,10,12,14,15,18,19), partial least squares (PLS) (10 -12, 16, 18, 20), Bayesian neural networks (BNN) (16), classification and regression trees (CART) (16), mixed determinant analysis -random forest (MDA -RF) (17), recursive partitioning classification (RPC) (20).…”

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.…”