Computational modeling of drug absorption

Computational modeling of drug absorption is a crucial aspect of pharmaceutical sciences, aiming to predict how drugs enter the systemic circulation from their administration site, most commonly the gastrointestinal (GI) tract for orally administered drugs. Historically, a significant portion of drug candidates failed in later clinical trials due to undesirable pharmacokinetic properties, including absorption, leading to a shift towards early in vitro and in silico evaluation of ADMET properties. Computational models translate physiological, biochemical, and molecular processes into mathematical equations to simulate drug behavior under various conditions.

Computational models predict drug absorption by considering several critical factors:

Solubility:
- Importance: A drug generally must dissolve before it can be absorbed from the intestinal lumen. Poor solubility can lead to low and variable bioavailability, erratic pharmacokinetics, and clinical failure. The Biopharmaceutical Classification System (BCS) categorizes drugs based on their solubility and intestinal permeability, with BCS Class II (low solubility, high permeability) and Class IV (low solubility, low permeability) compounds posing significant challenges. The Developability Classification System (DCS) further refines BCS Class II into dissolution-rate-limiting (Class-IIa) and solubility-rate-limiting (Class-IIb) compounds.
- Modeling Approaches:
  - QSPR (Quantitative Structure-Property Relationship) and QSAR (Quantitative Structure-Activity Relationship) Models: These models correlate molecular descriptors (numerical representations of chemical structure) with experimentally measured solubility. Common descriptors include molecular weight, logP, polar surface area, hydrogen bond donors/acceptors, and melting point. The target property for most models is the logarithm of solubility (logS).
  - Mechanistic and Thermodynamic Models: These simulate the physical and chemical processes governing solubility, such as dissolution and ionization. The “general solubility equation” can estimate solubility by measuring a drug’s logP (log of the partition coefficient between water and n-octanol) and its melting point.
  - Machine Learning (ML) and Artificial Intelligence (AI): Deep learning architectures, such as graph neural networks, can predict solubility directly from molecular structure.
  - Biorelevant Solubility Prediction: Computational models also predict solubility in biorelevant media, such as fasted state simulated intestinal fluid (FaSSIF) and fed state simulated intestinal fluid (FeSSIF), which are more predictive of in vivo absorption than pure water solubility.
- Software Tools: Commercial programs like GastroPlus calculate regional solubility along the GI tract using the Henderson–Hasselbalch equation, incorporating pKa values and the effects of bile salts and micelles. ADMET Predictor is a machine learning-based suite that predicts aqueous and biorelevant solubility. SwissADME is a free web-based tool that predicts water solubility.
Intestinal Permeation:
- Importance: This describes the ability of drugs to cross the intestinal mucosa separating the gut lumen from the portal circulation, an essential process for drugs to reach systemic circulation. It involves both passive diffusion and active transport.
- Modeling Approaches:
  - In Silico Permeability Prediction: QSAR and ML models predict permeability (e.g., Caco-2, PAMPA) using molecular descriptors and training data from experimental assays.
  - In Silico Caco-2 Permeability Models: These models are built using large datasets of experimentally measured apparent permeability (Papp) values. Caco-2 and MDCK cell monolayers are widely used to simulate membrane permeability as an in vitro estimation of in vivo absorption.
  - Mechanistic and PBPK (Physiologically Based Pharmacokinetic) Models: These models integrate physicochemical, biopharmaceutical, and physiological parameters to simulate drug absorption. They can account for regional differences in intestinal permeability, transporter expression, and dynamic processes.
  - Molecular Dynamics (MD) Simulations: These provide atomistic insights into the passive permeation of small molecules through lipid bilayers, capturing dynamic interactions between drug molecules and membrane components.
Ionization State (pKa):
- The ionization state of a compound significantly affects both its solubility and permeability, thereby influencing its absorption profile.
- The charge of a molecule can be determined using its ionization constant value (pKa) and the environmental pH.
- Unionized forms of a drug are typically more lipophilic and can more easily cross non-polar biological membranes via passive diffusion, while ionized forms generally have higher aqueous solubility.
- Several commercial and publicly available programs provide pKa estimation based on the input structure, including SCSpKa, Pallas/pKalc, ACD/pKa, and SPARC online calculator.
Active Transporters:
- Both influx and efflux transporters are located in intestinal epithelial cells and can either increase or decrease oral absorption.
- Influx transporters (e.g., human peptide transporter 1 (hPEPT1), apical sodium bile acid transporter (ASBT), and nucleoside transporters) actively transport drugs that mimic their native substrates into the epithelial cell.
- Efflux transporters (e.g., P-glycoprotein (P-gp), multidrug resistance-associated protein (MRP), and breast cancer resistance protein (BCRP)) actively pump absorbed drugs back into the intestinal lumen.
- Commercial programs like GastroPlus and ADME/Tox WEB have implemented capabilities for modeling active transport.
Other Considerations:
- Drug absorption modeling also accounts for factors like dissolution rate, GI transit time, and first-pass metabolism in intestinal epithelial cells by cytochrome P450 enzymes.
- Dissolution is modeled using approaches like Noyes-Whitney, incorporating parameters like particle size, solubility, and formulation characteristics. GI transit is modeled as a series of compartments with specific transit times and absorption properties.
- Commercial packages such as GastroPlus and iDEA are available to predict oral absorption and other pharmacokinetic properties. They are often based on advanced models like the Compartmental Absorption and Transit (CAT) model, which incorporates the effects of drug movement through the GI tract and absorption into each compartment simultaneously.

In summary, computational models for drug absorption integrate a diverse range of data and techniques—from molecular descriptors and ionization states to complex physiological and transport mechanisms—to provide a comprehensive in silico prediction of how a drug will be absorbed in the body.

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.