Computational modeling for drug excretion focuses on predicting how drugs and their metabolites are eliminated from the body, primarily via renal (urine) and biliary (feces) routes. This process is crucial for determining a drug’s half-life and establishing appropriate dosing regimens.
The excretion or clearance of a drug is quantified by plasma clearance, defined as the plasma volume completely cleared of the drug per unit of time. The two main components of plasma clearance are hepatic and renal clearances. These processes are influenced by a drug’s physicochemical properties, the expression and activity of drug transporters, and the physiological state of the excretory organs.
Challenges in Modeling: Currently, no model has been reported that is capable of predicting plasma clearance solely from computed drug structures. Existing modeling efforts are mainly focused on estimating in vivo clearance from in vitro data. The complexity of hepatic and renal clearance processes is further complicated by the presence of active transporters.
Computational Modeling Approaches for Drug Excretion:
- Physiologically Based Pharmacokinetic (PBPK) Models:
- PBPK models are considered the “gold standard” for simulating drug excretion. They incorporate detailed representations of kidney and liver physiology, including transporter-mediated processes.
- Parameters such as intrinsic clearance, glomerular filtration rate, and transporter activity are integrated to simulate excretion kinetics.
- Modern PBPK platforms like Simcyp®, GastroPlus™, and PK-Sim® provide user-friendly interfaces and extensive physiological databases, allowing researchers to simulate drug excretion in healthy and special populations. These platforms can also simulate the impact of transporter inhibition on renal clearance.
- Quantitative Structure-Activity/Property Relationship (QSAR/QSPR) Models:
- These models utilize statistical and machine learning methods to correlate molecular descriptors with pharmacokinetic endpoints, such as renal clearance, biliary excretion, or the fraction excreted unchanged.
- For renal excretion, QSAR models predict the fraction excreted unchanged in urine (fe), renal clearance (CLr), and the likelihood of active secretion or reabsorption.
- For biliary excretion, models often use molecular weight, polarity, and the presence of specific functional groups to predict the probability and extent of biliary elimination. One model for human plasma clearance used partial least square analysis on a database of 754 molecules and was validated with enhanced leave-analog methods. Another approach used support vector machines and metabolism-like descriptors to predict the elimination of xenobiotics.
- Machine Learning (ML) Models:
- ML models can be trained on large datasets of drug-transporter interactions, clearance values, and excretion fractions. They can incorporate diverse data types, including chemical structure, gene expression, and clinical pharmacokinetics.
- In Silico Transporter Modeling:
- Active transporters are integral to any ADMET modeling program due to their ubiquitous presence on barrier membranes and the substantial overlap between their substrates and many drugs. They play a crucial role in drug absorption, distribution, and excretion by mediating the uptake and efflux of drugs across biological membranes.
- P-glycoprotein (P-gp) is an ATP-dependent efflux transporter that affects drug disposition by reducing absorption and enhancing renal and hepatic excretion. It is also responsible for multidrug resistance in cancer chemotherapy. Computational models, including pharmacophore and QSAR models, have been developed to understand its complex effects on drug disposition.
- Breast Cancer Resistance Protein (BCRP) is another ATP-dependent efflux transporter expressed in the intestine, liver, and brain, implicating its role in drug disposition. It contributes to multidrug resistance and impacts biliary elimination and reduced reabsorption through the kidney.
- Nucleoside transporters are involved in drug absorption, distribution, and excretion.
- The human peptide transporter (hPEPT1), expressed in the intestine and kidney, also affects drug absorption and excretion.
- Organic Cation Transporters (OCTs) facilitate the uptake of many cationic drugs across different barrier membranes from kidney, liver, and intestine epithelia, impacting their disposition.
- Organic Anion Transporting Polypeptides (OATPs) influence the plasma concentration of many drugs by actively transporting them across various tissue membranes like liver, intestine, lung, and brain.
- In silico models, often using ligand-based (molecular descriptors, statistical methods) or structure-based (homology modeling, molecular docking, MD simulations) approaches, are used to predict whether a compound is a substrate or inhibitor of a given transporter.
Factors Influencing Excretion Modeling:
- The ionization state of a drug affects its solubility and permeability, thus influencing its absorption profile. Since excretion is pH-dependent, and weakly acidic or basic environments influence molecules, ionization state plays a role in reabsorption and excretion.
- Knowledge of the structural requirements for transporters is critical for predicting clearance for a given structure.
- Data quality remains a limiting factor in ADMET modeling, including excretion. Continued efforts are needed to investigate less-understood transporters to get a more complete understanding of drug pharmacokinetic profiles.
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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