In the actual drug development process, approximately 50% of candidate drugs fail due to low oral availability. On the other hand, poor oral bioavailability can lead to inefficiency of drugs and high inter-individual variability in the use of drugs, triggering some unpredictable drug reactions in the human body. Higher oral availability of the drug can reduce the amount of administration required to achieve the expected pharmacological effect because it can reduce the side effects and toxicity risks brought by the drug. If intravenous administration is used, the human body can use the blood to deliver the drug to the site where it can exert pharmacological effects through the systemic circulation. Human oral bioavailability (HOB) is an important pharmacokinetic parameter that measures the amount of a drug that actually enters circulation within the body after ingestion.
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Because oral administration is convenient and does not damage the skin or mucous membranes, 80% of the world’s drugs are administered orally. Thus, the accurate prediction of these properties is becoming increasingly important in drug discovery.Īmong the ADMET properties, one of the most important pharmacokinetic characteristics of newly developed drugs is high oral bioavailability. However, experimental testing of ADMET properties is time-consuming and costly. Therefore, ADMET assessments of candidate compounds during the early stages of drug discovery have become critical for improving the success rate of drug discovery and reducing the risk of late-stage attrition. Poor pharmacokinetic properties, including absorption, distribution, metabolism, excretion, and toxicity (ADMET), are the key reasons of late-stage failures in drug development. The results from this study provide an accurate and easy-to-use tool for screening of drug candidates based on HOB, which may be used to reduce the risk of failure in late stage of drug development. The model is available as a web server at for quick assessment of oral bioavailability for small molecules. The analysis of the importance of the input variables allowed the identification of the main molecular descriptors that affect the HOB class value. The consensus model shows excellent prediction accuracies on two independent test sets with two cutoffs of 20% and 50% for classification of molecules.
In this study, a total of 1588 drug molecules with HOB data were collected from the literature for the development of a classifying model that uses the consensus predictions of five random forest models. The use of computational models to evaluate HOB before the synthesis of new drugs will be beneficial to the drug development process. HOB is conventionally measured using expensive and time-consuming experimental tests.
Human oral bioavailability (HOB) is a key factor in determining the fate of new drugs in clinical trials.
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