Active Transport in Drug Dispostion Computational modelling

Active transporters are proteins located in cell membranes that actively move drugs and other molecules across biological barriers. They facilitate the uptake and efflux of drugs across various biological membranes, such as intestinal epithelial cells, kidney tubules, and the blood-brain barrier. This energy-regulated step can either increase (influx transporters) or decrease (efflux transporters) drug absorption and significantly influence drug distribution and excretion. Active transport involves moving molecules against their concentration gradient and natural thermodynamic fluidity. Two main types of active transporter systems are mentioned: adenosine triphosphate (ATP)-dependent binding cassette (ABC) transporters and solute carrier (SLC) systems.

Here’s a detailed explanation of the requested active transporters:

1. P-glycoprotein (P-gp)

P-glycoprotein (P-gp), also known as ABCB1, is a prototypical ATP-dependent efflux transporter. It is widely distributed in various organs and tissues, including the gastrointestinal tract, intestine, liver, kidney, blood-brain barrier (BBB), blood, brain, testes, and placenta.

Role in Drug Disposition:

P-gp significantly impacts drug disposition by reducing drug absorption and enhancing renal and hepatic excretion.
It is known to limit the intestinal absorption of certain drugs, such as the anticancer drug paclitaxel.
P-gp also restricts the central nervous system (CNS) penetration of drugs like HIV protease inhibitors.
Beyond its role in pharmacokinetics, P-gp is notably responsible for multidrug resistance in cancer chemotherapy.
It can influence drug-drug interactions by interacting with enzymes like cytochrome P450 (3A4) microsomal enzyme. P-gp inducers (e.g., rifampicin) can minimize bioavailability, while inhibitors (e.g., verapamil) can increase it.

Computational Modeling Approaches:

QSAR and Machine Learning (ML) models are extensively developed to predict P-gp substrates and inhibitors, utilizing descriptors such as molecular weight, hydrophobicity, hydrogen bond donors/acceptors, and topological indices. ML classifiers can achieve high accuracy in prediction.
Pharmacophore modeling identifies essential features for P-gp interaction and is used for virtual screening. Common chemical features identified include hydrophobes, hydrogen bond acceptors, and ring aromatic features, as well as their specific geometric arrangement.
With available P-gp crystal structures, structure-based docking and molecular dynamics (MD) simulations are employed to elucidate binding modes and predict ligand affinity.
In silico tools like SwissADME and the Human Intestinal Transporter Database provide P-gp substrate status and models for virtual profiling.

2. BCRP (Breast Cancer Resistance Protein)

BCRP, also known as ABCG2, is another ATP-dependent efflux transporter. It is co-expressed with P-gp at various barrier sites, including the intestine, liver, brain, placenta, and in hematological malignancies and solid tumors. It is also present in stem cells.

Role in Drug Disposition:

BCRP plays an intricate role in drug disposition behavior.
It confers resistance to a variety of anticancer agents like anthracyclines and mitoxantrone.
BCRP limits oral absorption, mediates biliary excretion, and contributes to multidrug resistance.
It provides fetus protection, assists in biliary elimination, and causes a decrease in reabsorption through the kidney, as well as protection of stem cells.
BCRP works as a high-gradient transporting system with specificity for molecules with negative or positive charges, organic anions, and conjugated sulfates.

Computational Modeling Approaches:

QSAR and ML models are developed for BCRP inhibition and substrate prediction. These models emphasize specific structural features, such as the presence of a 2,3-double bond in ring C and hydroxylation at position 5.
Pharmacophore modeling can be used, including ensemble pharmacophore models combined with SVM regression, to address BCRP’s broad binding promiscuity.
Structure-based docking studies are facilitated by BCRP structures to predict ligand binding and rationalize structure-activity relationships.
In silico tools like the PhE/SVM model and the Human Intestinal Transporter Database include BCRP models for virtual screening.

3. Nucleoside Transporters

Nucleoside transporters are responsible for the transportation of both naturally occurring nucleosides and synthetic nucleoside analogs, which are used as anticancer drugs (e.g., cladribine) and antiviral drugs (e.g., zalcitabine). They also regulate neuronal modulation and are essential for DNA/RNA synthesis.

Classification and Location:

They are classified into different types based on their mechanism and affinity:
- Concentrative nucleoside transporters (CNTs): CNT1, CNT2, CNT3. These are high-affinity, selective transporters mainly located in the epithelia of the intestine, kidney, liver, and brain. CNT1-3 systems are located in the renal epithelium.
- Equilibrative nucleoside transporters (ENTs): ENT1, ENT2. These are broad-affinity, low-selective transporters that are ubiquitously located. ENT1-2 and SLC29A1-2 systems are available in the basolateral membrane.
Their involvement in drug disposition includes absorption, distribution, and excretion.

Computational Modeling Approaches:

3D-QSAR and pharmacophore modeling techniques have been used to generate distinctive models for CNT1, CNT2, and ENT1.
These models often reveal common features required for nucleoside transporter-mediated transport, such as two hydrophobic features and one hydrogen bond acceptor on the pentose ring. Individual models also show subtle characteristic requirements for each specific transporter, indicating, for instance, that CNT2 is the most selective and ENT1 has the broadest inhibitor specificity.
Ligand-based QSAR and ML models predict substrate specificity and inhibitor potency.
Homology modeling based on related transporters is used for docking studies to predict binding and transport.
In silico tools like AutoDock are used for structure-based predictions.

4. hPEPT1 (Human Peptide Transporter 1)

hPEPT1, also known as SLC15A1, is a low-affinity, high-capacity oligopeptide transport system. It is a proton-coupled transporter. It is mainly expressed in the intestine and kidney, specifically on the apical membrane of the small intestine.

Role in Drug Disposition:

hPEPT1 transports a diverse range of substrates, including β-lactam antibiotics and angiotensin-converting enzyme (ACE) inhibitors. It also plays a role in the movement of nitrogen throughout the body.
By being expressed in the intestine and kidney, it affects drug absorption and excretion.
Oral hypoglycemic agents, such as sulfonylureas and biguanides, have been shown to inhibit this transporting system.

Computational Modeling Approaches:

Pharmacophore models based on high-affinity substrates (e.g., Gly-Sar, bestatin, enalapril) have identified two hydrophobic features, one hydrogen bond donor, one hydrogen bond acceptor, and one negative ionizable feature as hPEPT1 transport requirements.
This pharmacophore model has been successfully applied to screen databases for novel inhibitors, demonstrating the potential of in silico models in high-throughput database screening.
QSAR and ML classification models using molecular descriptors have also been developed to predict hPEPT1 substrates and inhibitors.
In silico tools that enable pharmacophore screening and virtual profiling are available, including the Human Intestinal Transporter Database which provides hPEPT1 models.

5. ASBT (Apical Sodium-Dependent Bile Acid Transporter)

ASBT, also known as SLC10A2, is a high-efficacy, high-capacity transporter.

Location and Role: It is primarily expressed on the apical membrane of ileal enterocytes and cholangiocytes. ASBT plays a key role in the reabsorption of bile acids and their analogs from the gut lumen. By assisting bile acid absorption, it provides an additional intestinal target for improving drug absorption. It is also active against liver disease, hyperglycemia, and hyperlipoproteinemia. The ASBT system contains 348 amino acids with a molecular weight of 38 kilodaltons and two glycosylation sites at N10 and N328.
Computational Modeling Approaches:
- Pharmacophore modeling has been used to identify its transport requirements. A model based on a training set of 17 chemically diverse ASBT inhibitors revealed that one hydrogen bond donor, one hydrogen bond acceptor, one negative charge, and three hydrophobic centers are required for ASBT transport.
- These requirements are consistent with findings from a previous 3D-QSAR model derived from the structure and activity of 30 ASBT inhibitors and substrates.
- The Human Intestinal Transporter Database includes ASBT models for virtual profiling, aiding in substrate and inhibitor prediction.

6. OCT (Organic Cation Transporters)

Organic Cation Transporters (OCTs) are crucial for the uptake of many cationic drugs.

Location and Role: OCTs are found across different barrier membranes in the kidney, liver, and intestine epithelia. They influence the pharmacokinetics of drugs such as antiarrhythmics, β-adrenoreceptor blocking agents, antihistamines, antiviral agents, and skeletal muscle-relaxing agents. Three main human OCTs have been identified: OCT1, OCT2, and OCT3. These transporters comprise 550-560 amino acids with 12 transmembrane alpha helices, including an intracellular loop and a large extracellular loop with proper glycosylation factors, directly relating to the uptake of hydrophilic compounds.
Computational Modeling Approaches:
- A pharmacophore model for human OCT1, developed by analyzing inhibition of TEA uptake in HeLa cells, suggested transport requirements as three hydrophobic features and one positive ionizable feature.
- 2D- and 3D-QSAR analyses have been performed to identify and differentiate binding requirements for human OCT2 and rabbit OCT2, showing similarities in chemical features but distinct orientations for a critical hydrogen bonding feature. This highlights the sensitivity of in silico modeling in distinguishing similar transporters.
- QSAR and Machine Learning models use descriptors like charge, molecular weight, and lipophilicity to predict OCT substrates and inhibitors.
- The Human Intestinal Transporter Database provides OCT1 models for virtual screening.

7. OATP (Organic Anion Transporting Polypeptides)

Organic Anion Transporting Polypeptides (OATPs) are influx transporters that significantly influence the plasma concentration of many drugs.

Location and Role: OATPs are expressed in a diverse range of tissue membranes, including the liver, intestine, lung, and brain. While initially thought to transport only organic anionic drugs, their broad substrate specificity means they also transport organic cationic drugs. There are currently 11 human OATPs identified. OATP1B1 and OATP1B3 are responsible for hepatocellular drug uptake, while OATP2B1 and OATP1A2 relate to intestinal absorption.
Computational Modeling Approaches:
- The substrate binding requirements of the best-studied OATP1B1 have been successfully modeled using a metapharmacophore approach. This model, based on 18 diverse molecules, identified three hydrophobic features flanked by two hydrogen bond acceptor features as essential for OATP1B1 transport. Similar requirements were derived from a 3D-QSAR study based on rat Oatp1a5.
- QSAR and Machine Learning models for OATP1B1, OATP1B3, and OATP2B1 use molecular descriptors to predict inhibitors and substrates.
- Recent cryo-electron microscopy (cryo-EM) structures of OATP1B1 and OATP1B3 now facilitate structure-based docking studies to understand substrate specificity and drug-drug interactions.
- Proteochemometric modeling integrates protein sequence and ligand descriptors to predict interactions across OATP isoforms.
- The Human Intestinal Transporter Database provides OATP2B1 models for substrate and inhibitor prediction.

8. BBB-Choline Transporter (Blood-Brain Barrier Choline Transporter)

The BBB-choline transporter (BBB-ChT) is a native nutrient transporter that plays a role in brain drug delivery.

Location and Role: It transports choline, a charged cation, across the blood-brain barrier (BBB) into the central nervous system (CNS). This active transport assists the BBB penetration of choline-like compounds. Understanding its structural requirements is crucial for predicting BBB permeation more accurately. It is also responsible for the biosynthesis of acetylcholine from choline. The system has two subtypes, ChT1 and ChT2, with ChT1 being responsible for choline uptake from the extracellular system and ChT2 for choline oxidation in mitochondria. Hemicholinium is known to block the activity of both ChT1 and ChT2.
Computational Modeling Approaches:
- Even though the BBB-choline transporter has not been cloned, studies have applied a combination of empirical and theoretical methodologies.
- 3D-QSAR models, built with empirical Ki data from in situ rat brain perfusion experiments using a structurally diverse set of compounds, identified three hydrophobic interactions and one hydrogen bonding interaction surrounding the positively charged ammonium moiety as important for BBB-choline transporter recognition.
- Homology modeling based on related transporters (e.g., ChT1) is used for molecular docking and 3D-QSAR studies.
- Docking and virtual screening tools like AutoDock and AutoDock Vina are used to predict binding affinities and identify important structural features for BBB-ChT interaction.
- These in silico tools, including custom homology models and docking platforms, are utilized for virtual screening of compound libraries to identify potential BBB-ChT substrates.