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Bioavailability refers to the proportion of an unaltered drug that, after administration, enters the systemic circulation and can be distributed to the desired action site. Factors such as gastrointestinal (GI) absorption and liver biotransformation influence the bioavailability of a drug when it is administered orally. When a drug is administered intravenously, it enters the systemic circulation directly; by definition, its bioavailability is assumed to be 100%. The bioavailability of an...
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Bioavailability refers to the proportion of an administered drug that reaches the systemic circulation in its active, unaltered form. It is a crucial pharmacokinetic parameter that determines the effectiveness of a drug in achieving its intended therapeutic outcomes. The route of administration significantly influences bioavailability, with intravenous administration achieving 100% bioavailability as the drug directly enters the bloodstream. In contrast, oral administration often results in...
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Body:Bioavailability is a critical pharmacological concept that measures the extent and rate at which an active drug ingredient or therapeutic moiety enters the systemic circulation, remaining unchanged. It's a pivotal factor in determining a drug's efficacy and safety.The Biopharmaceutics Classification System (BCS) plays an essential role in drug development by categorizing drugs into four classes based on their solubility and permeability. This classification aids in understanding drug...
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Bioavailability is a crucial pharmacokinetic parameter that quantifies the proportion of an administered drug that reaches the systemic circulation and is available for therapeutic action. Regulatory agencies mandate the assessment of bioavailability, typically measured as the area under the drug plasma concentration-versus-time curve (AUC), to ensure the efficacy and safety of pharmaceutical products. These evaluations are categorized as absolute and relative bioavailability studies.Absolute...
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Pharmacodynamic methods provide insights into a drug's effects on physiological processes over time and play a crucial role in understanding bioavailability and therapeutic efficacy. These methods can be broadly classified into acute pharmacological and therapeutic response approaches, each with distinct mechanisms and applications.The acute pharmacological response method directly correlates a drug's physiological effects, such as ECG or pupil diameter changes, to its time course in the body.
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Pharmacokinetics is a vital branch of pharmacology that examines how drugs are absorbed, distributed, metabolized, and excreted by the body. Two key methodologies in pharmacokinetics are plasma drug concentration studies and urinary drug excretion analyses, both of which provide critical insights into a drug's therapeutic efficacy and bioavailability.Plasma Drug Concentration-Time StudiesPlasma drug concentration-time studies involve analyzing blood samples at specific intervals to quantify...
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Oral bioavailability property prediction based on task similarity transfer learning.

Chen Zeng1, Chengcheng Xu1, Yingxu Liu1

  • 1Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, Nanjing, 211198, China.

Molecular Diversity
|September 10, 2025
PubMed
Summary
This summary is machine-generated.

Predicting human oral bioavailability (HOB) is crucial for drug development. A novel transfer learning framework (TS-GTL) effectively uses molecular graphs and physicochemical properties to improve HOB predictions, especially in data-scarce situations.

Keywords:
ADMETMolecular property predictionOral bioavailabilityTransfer learning

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Area of Science:

  • Pharmacokinetics and Drug Discovery
  • Computational Chemistry
  • Artificial Intelligence in Medicine

Background:

  • Accurate prediction of human oral bioavailability (HOB) is vital for optimizing drug candidates and reducing clinical trial failures.
  • Experimental determination of HOB is resource-intensive, while existing AI models face data dependency challenges.
  • Developing efficient and interpretable computational methods for ADMET property prediction is a key area of research.

Purpose of the Study:

  • To develop an efficient and interpretable computational framework for predicting human oral bioavailability (HOB), particularly in data-scarce environments.
  • To combine physicochemical properties with graph-based deep learning to enhance HOB prediction accuracy.
  • To introduce a novel transfer learning approach that leverages task similarity for improved predictive performance.

Main Methods:

  • Proposed a similarity-guided transfer learning framework, Task Similarity-guided Transfer Learning based on Molecular Graphs (TS-GTL).
  • Developed a deep learning model, PGnT (pKa Graph-based Knowledge-driven Transformer), incorporating molecular descriptors and leveraging GNNs and Transformer encoders.
  • Introduced MoTSE to quantify task similarity between physicochemical properties and HOB, with pretraining on logD properties showing optimal transfer learning performance.

Main Results:

  • The TS-GTL framework demonstrated superior performance compared to traditional machine learning algorithms and existing deep learning predictive tools.
  • Transfer learning, particularly when pretraining on logD properties, significantly improved HOB prediction accuracy.
  • The study highlighted the importance of task similarity in optimizing transfer learning for drug property prediction.

Conclusions:

  • The TS-GTL framework offers an efficient and interpretable alternative to experimental and current computational methods for ADMET property prediction.
  • Leveraging physicochemical properties and graph-based deep learning with task similarity is a promising strategy for improving HOB prediction in data-limited scenarios.
  • This approach advances the application of AI in drug discovery, enabling better optimization of drug candidates.