Prediction of Oral Bioavailability in Rats: Transferring Insights from in Vitro Correlations to (Deep) Machine Learning Models Using in Silico Model Outputs and Chemical Structure Parameters

Abstract

Oral administration of drug products is a strict requirement in many medical indications. Therefore, bioavailability prediction models are of high importance for prioritization of compound candidates in the drug discovery process. However, oral exposure and bioavailability are difficult to predict, as they are the result of various highly complex factors and/or processes influenced by the physicochemical properties of a compound, such as solubility, lipophilicity, or charge state, as well as by interactions with the organism, for instance, metabolism or membrane permeation. In this study, we assess whether it is possible to predict intravenous (iv) or oral drug exposure and oral bioavailability in rats. As input parameters, we use (i) six experimentally determined in vitro and physicochemical endpoints, namely, membrane permeation, free fraction, metabolic stability, solubility, pK a value, and lipophilicity; (ii) the outputs of six in silico absorption, distribution, metabolism, and excretion models trained on the same endpoints, or (iii) the chemical structure encoded as fingerprints or simplified molecular input line entry system strings. The underlying data set for the models is an unprecedented collection of almost 1900 data points with high-quality in vivo experiments performed in rats. We find that drug exposure after iv administration can be predicted similarly well using hybrid models with in vitro-or in silico-predicted endpoints as inputs, with fold change errors (FCE) of 2.28 and 2.08, respectively. The FCEs for exposure after oral administration are higher, and here, the prediction from in vitro inputs performs significantly better in comparison to in silico-based models with FCEs of 3.49 and 2.40, respectively, most probably reflecting the higher complexity of oral bioavailability. Simplifying the prediction task to a binary alert for low oral bioavailability, based only on chemical structure, we achieve accuracy and precision close to 70%.

In Silico Prediction of Intestinal Permeability by Hierarchical Support Vector Regression

“…Drug absorption mainly relies on solubility and intestinal permeability [8], which is also termed as intestinal absorption [9], since oral drugs must permeate the gastrointestinal barrier before they can be absorbed by the bodies [9]. In fact, solubility and permeability have been adopted by the biopharmaceutics drug disposition classification system (BDDCS), which suggests that the intestinal permeability rate is closely correlated with the extent of metabolism [10] Nevertheless, intestinal permeability is an extremely complicated process since drugs can pass through the intestinal epithelium to enter blood vessel by active transport as well as passive diffusion, as illustrated by Figure 3 of Dahlgren and Lennernäs [11].…”

Machine learning in pharmacometrics: Opportunities and challenges

“…98 Cortes-Ciriano used compared the predictive performance of: GBM, partial least squares, RFR and SVR for predicting the potency of a broad range compounds from their physiochemical descriptors. 99 QSAR has also been used to predict plasma protein binding, [100][101][102][103][104] blood brain barrier permeability, [105][106][107] clearance, [108][109][110] volume of distribution, [111][112][113] half-life, 114 bioavailability 115 and toxicity. 116 ML-based QSAR has shown utility in in drug metabolism and PK to predict compound metabolism, 117,118 transporter transcriptional upregulation, 119 uptake, efflux and inhibition.…”

Modeling and Simulation of Process Technology for Nanoparticulate Drug Formulations—A Particle Technology Perspective

“…One potential approach to overcome those limitations in data availability while simultaneously utilizing the established mechanistic insights in the future are so-called hybrid modeling approaches [78]. This might be realized in data-driven models combined with additional mechanistic input, e.g., meaningful chemical descriptors originating from quantum mechanical simulations or by directly coupling neural networks with mechanistic equations [79]. An AI-based evaluation of data may be applied to tackle complex issues of multi-component systems.…”