Computational modeling for intestinal permeation focuses on predicting a drug’s ability to cross the intestinal epithelium and enter the portal circulation, which is an essential process for oral drug absorption. Drug bioavailability and absorption are influenced by the interplay between drug solubility and intestinal permeability. Intestinal permeation is a complex process involving both passive diffusion and active transport.
Computational models predict intestinal permeation using various approaches:
- In Silico Permeability Prediction (QSAR and Machine Learning Models): These empirical approaches predict permeability (e.g., Caco-2, PAMPA) by correlating molecular descriptors with experimentally measured properties.
- Molecular Descriptors: Common descriptors include logP, topological polar surface area (TPSA), molecular weight, and hydrogen bonding capacity. More advanced models may incorporate 2D/3D structural features, quantum mechanical properties, and features derived from machine learning.
- Statistical Algorithms: Modeling techniques include linear regression, partial least squares, support vector machines, random forest, and deep learning architectures.
- Caco-2 Permeability Models: Many models specifically simulate in vitro membrane permeation assays like Caco-2, MDCK, or PAMPA, as these have been shown to be useful indicators of in vivo drug absorption. These models are constructed using large datasets of experimentally measured apparent permeability (Papp) values, with molecular descriptors serving as input features. They can be classification-based (e.g., predicting high vs. low permeability) or regression-based (predicting continuous Papp values). A QSAR model using 674 compounds achieved 78–82% accuracy in classifying Caco-2 permeability.
- Limitations: Challenges for Caco-2 models include inter- and intra-laboratory variability, differences in transporter expression, and their inability to fully capture active transport mechanisms.
- Mechanistic and Physiologically Based Pharmacokinetic (PBPK) Models: These models simulate drug absorption by integrating physicochemical, biopharmaceutical, and physiological parameters.
- They can account for regional differences in intestinal permeability, transporter expression, and dynamic processes such as dissolution, precipitation, and metabolism.
- Software Platforms: Key platforms that implement these models include GastroPlus™, Simcyp®, and GI-Sim. For example, GastroPlus™ and Simcyp® have been used to simulate the oral absorption of structurally diverse low-solubility drugs, incorporating Caco-2 permeability data, solubility, and physicochemical properties as inputs. The predicted fraction absorbed (Fa) from these simulations showed reasonable accuracy when compared to clinical data. GastroPlus™ also accounts for the effects of drug movement through the gastrointestinal tract and its absorption into each compartment using the Advanced Compartmental Absorption and Transit (ACAT) model.
- Molecular Dynamics (MD) Simulations: These provide atomistic insights into the passive permeation of small molecules through lipid bilayers.
- MD simulations capture the dynamic interactions between drug molecules and membrane components.
- Enhanced Sampling Techniques: Techniques such as umbrella sampling, metadynamics, and replica exchange are used to calculate potential of mean force (PMF) profiles and estimate permeability coefficients.
- Case Study: MD simulations were used to compare the permeability of withaferin-A and withanone across a POPC/cholesterol membrane, revealing that withaferin-A had higher permeability due to favorable interactions with membrane phosphate groups.
- Influence of Ionization State: The ionization state of a compound significantly affects both its solubility and permeability, influencing its absorption profile. The charge of a molecule can be determined using its ionization constant (pKa) and the environmental pH. Unionized forms are typically more lipophilic and cross membranes easily, while ionized forms generally have higher aqueous solubility. Several commercial and publicly available programs provide pKa estimation based on the input structure, aiding in these predictions.
- Active Transporters: The process of intestinal permeation involves both passive diffusion and active transport. Influx and efflux transporters located in intestinal epithelial cells can either increase or decrease oral absorption. Predicting interactions with these transporters is crucial for accurate absorption modeling.
Overall, computational modeling for intestinal permeation provides an in silico means to understand and predict how drugs cross the intestinal barrier, which is vital for optimizing oral drug delivery and predicting pharmacokinetic behavior.
Unit 2: Computational Modeling of Drug Disposition: Podcast link
Introduction, Modeling Techniques: Drug Absorption, Solubility, Intestinal Permeation, Drug Distribution ,Drug Excretion, Active Transport; P-gp, BCRP, Nucleoside Transporters, hPEPT1, ASBT, OCT, OATP, BBB-Choline Transporter.
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