New frames of reference for mapping drugs in the four classes of the BCS and BDDCS into regions with clear boundaries

Analyzing parametric influences driving age-associated changes in absorption using a PBPK-GSA approach

The advanced age population may be susceptible to an increased risk of adverse effects due to increased drug exposure after oral dosing. Factors such as high-interindividual variability and lack of data has led to poor characterization of absorption's role in pharmacokinetic changes in this population. Physiologically based pharmacokinetic (PBPK) models are increasingly being used during the drug development process, as their unique qualities are advantageous in atypical scenarios such as drug-drug interactions or special populations such as older people. Along with relying on various sources of data, auxiliary tools including parameter estimation and sensitivity analysis techniques are employed to support model development and other applications. However, sensitivity analyses have mostly been limited to localized techniques in the majority of reported PBPK models using them. This is disadvantageous, since local sensitivity analyses are unsuitable for risk analysis, which require assessment of parametric interactions and proper coverage of the input space to better estimate and subsequently mitigate the effects of the phenomenon of interest. For this reason, this study seeks to integrate a global sensitivity analysis screening method with PBPK models based in PK-Sim® to characterize the consequences of potential changes in absorption that are often associated with advanced age. The Elementary Effects (Morris) method and visualization of the results are implemented in R and three model drugs representing Biopharmaceutical Classification System classes I-III that are expected to exhibit some sensitivity to three age-associated hypotheses were successfully tested.

Investigating pharmacokinetic profiles of Centella asiatica using machine learning and PBPK modelling

Physiologically based pharmacokinetic (PBPK) modeling serves as a valuable tool for determining the distribution and disposition of substances in the body of an organism. It involves a mathematical representation of the interrelationships among crucial physiological, biochemical, and physicochemical parameters. A lack of the values of pharmacokinetic parameters can be challenging in constructing a PBPK model. Herein, we propose an artificial intelligence framework to evaluate a key pharmacokinetic parameter, the intestinal effective permeability (Peff). The publicly available Peff dataset was utilized to develop regression machine learning models. The XGBoost model demonstrates the best test accuracy of R-squared (R2, coefficient of determination) of 0.68. The model is then applied to compute the Peff of asiaticoside and madecassoside, the parent compounds found in Centella asiatica. Subsequently, PBPK modeling was conducted to evaluate the biodistribution of the herbal substances following oral administration in a rat model. The simulation results were evaluated and validated, which agreed with the existing in vivo studies in rats. This in silico pipeline presents a potential approach for investigating the pharmacokinetic parameters and profiles of drugs or herbal substances, which can be used independently or integrated into other modeling systems.

An Investigation into the Factors Governing Drug Absorption and Food Effect Prediction Based on Data Mining Methodology

Drug absorption is a complex process governed by a number of interrelated physicochemical, biopharmaceutical, and pharmacokinetic factors. In order to explore complex relationships among these factors, multivariate exploratory analysis was performed on the dataset of drugs with diverse bioperformance. The investigated dataset included subset of drugs for which bioequivalence between solid dosage form and oral solution has been reported, and subset of drugs described in the literature as low solubility/low permeability compounds. Discriminatory power of hierarchical clustering on principal components was somewhat higher when applied on the data subsets of drugs with similar bioperformance, while analysis of the integrated dataset indicated existence of two groups of drugs with the boundaries reflected in P_eff value of approximately 2 × 10^-4 cm/s and Fa and Fm values higher than 85% and 50%, respectively. Majority of the investigated drugs within the integrated dataset were grouped within their initial subset indicating that overall drug bioperformance is closely related to its physicochemical, biopharmaceutical and pharmacokinetic properties. Classification models constructed using the random forest (RF) and support vector machine with polynomial kernel function were able to predict food effect based on drug dose/solubility ratio (D/S), effective permeability (P_eff), percent of dose metabolized (Fm), and elimination half-life (τ_1/2). Although both models performed well during training and testing, only RF kept satisfying performance when applied on the external dataset (kappa value > 0.4). The results obtained indicate that data mining can be employed as useful tool in biopharmaceutical drug characterization which merits further investigation.

Application of data mining approach to identify drug subclasses based on solubility and permeability

Solubility and permeability are recognized as key parameters governing drug intestinal absorption and represent the basis for biopharmaceutics drug classification. The Biopharmaceutics Classification System (BCS) is widely accepted and adopted by regulatory agencies. However, currently established low/high permeability and solubility boundaries are the subject of the ongoing scientific discussion. The aim of the present study was to apply data mining analysis on the selected drugs data set in order to develop a human permeability predictive model based on selected molecular descriptors, and to perform data clustering and classification to identify drug subclasses with respect to dose/solubility ratio (D/S) and effective permeability (P_eff ). The P_eff values predicted for 30 model drugs for which experimental human permeability data are not available were in good agreement with the reported fraction of drug absorbed. The results of clustering and classification analysis indicate the predominant influence of P_eff over D/S. Two P_eff cut-off values (1 × 10^-4 and 2.7 × 10^-4  cm/s) have been identified indicating the existence of an intermediate group of drugs with moderate permeability. Advanced computational analysis employed in the present study enabled the recognition of complex relationships and patterns within physicochemical and biopharmaceutical properties associated with drug bioperformance.

Biopharmaceutic classification of drugs revisited

The biopharmaceutics classification system (BCS) was based on the tube model of the intestinal lumen. This model considers constant drug permeability along the intestines, a plug flow fluid with the suspended drug particles moving with the fluid, and dissolution in the small particle limit. Since then the research work focusing on drug gastrointestinal (GI) absorption phenomena and processes rely on the classical laws of transport, diffusion and kinetics; however, the homogeneous assumptions associated with the well-stirred Euclidean media, where the classical laws of diffusion and kinetics apply, have been questioned in the past. In this work we explore the biopharmaceutic classification of drugs using a heterogeneous pseudo steady-state model of oral drug absorption. The fraction of dose absorbed (F_abs) was expressed as a function of two time-dependent processes where time dependent coefficients govern drug absorption and non-absorption processes. Fundamental drug properties like the absorption potential are correlated with F_abs and allow the biopharmaceutic classification of drugs taking into account the heterogeneous aspects of oral drug absorption. This analysis reveals that for Class I drugs no time dependency is expected for both absorption and non absorption processes since the gastric emptying is controlling the absorption of Class I drugs while the completion of absorption (F_abs>90%) is terminated along the first part of the jejunum. Due to the biopharmaceutical properties of Class II, III and IV drugs, these drugs travel throughout the GI tract and therefore both absorption and non absorption processes will exhibit time dependency. Thus, the calculation of F_abs (<90%) for Class II, III and IV is dependent on the estimates of the time exponents of time dependent coefficients controlling drug absorption e.g. dissolution, uptake or non absorption e.g. precipitation.