Drug Disposition Modeling

Question 1

1.Importance of drug disposition

Answer 1

1. Predictive power.
2. Reduction experimental cost
3. Optimization of drug design
4. Regulatory approval

Question 2

2. Key concepts:

Answer 2

1. PK : tmax, cmax, Vd, clearance
2. PD: therapeutic window
3. Mechanistic modeling
4. Empirical modeling

Question 3

3. Computational approach

Answer 3

1. Software simulation.
2. QSAR
3. machine learning with AI.

Question 4

4. Application In drug development

Answer 4

1. Clinical research
2. Clinical trials
3. Personalized medicines

Question 5

5. Challenges

Answer 5

1. Data integration.
2. Validation
3. Complex diseases

Question 6

5. Future Directions

Answer 6

1. Real time modeling

Question 7

[2.]Modeling techniques in drug disposition used for:

Answer 7

1. Absorption
2. Solubility
3. Intestinal permeation
   4.distribution
   5.Excretion

Question 8

2.1. Absorption

Answer 8

1. PBPK model : detail, mechanistic, drug absorption: parameters: transit time, pH , enzyme activity, regional permeability. Software : gastroplus simcyp.
2. Compartment models : drug movement between compartment
3. Absorption simulation software :
   ADAM ( advance dissolution advance metabolism) and gastro plus simulate absorption, considering dissolution precipitation and permeability

Question 9

2. 2. Solubility

Answer 9

1. QSAR: Uses statistical techniques to relate molecular description ( eg. Size, polarity)
   Model built: regression analysis, machine learning AI.
2. Molecular Dynamics ( MD) Simulations: Simulate interactions between drug molecules and solvent to predict solubility.
   Provide insights on solvation process and molecular interaction at atomic level.
3. Predictive software: programs like ACD / Percepta and Schrodinger’s Jaguar predict solubility using QSPR and other computation chemistry techniques.

Question 10

2.3 intestinal permeation:

Answer 10

1. Caco2cell model: mimic intestinal barrier also can be trained on experimental data.
2. In silico tools: software like PAMPA( PARALLE articifical membrane permeability assay) predict permeability based on molecular descriptors and physiocochemical properties.
3. Mechanistic models: incorporate intestinal transport, passive diffusion , active transport efflux process.

Question 11

2. 4 distribution

Answer 11

1. PBPD models: considering factors like blood flow rates, tissue composing, binding affinities.
2. Compartment model: vd and log p key inputs
3. Tissue partitioning model: predicts distribution based on tissue specific properties and drug physiochemical characteristics. Use algorithm to estimated log p between plasma and tissues

Question 12

2. 5 Excretion

Answer 12

1. PBPK model: renal and hepatic pathways, glomerular filtration tubular secretion and bilary excretion.
   Parameters such as clearance rates, enzyme activities, transport affinities used.
2. Mechanistic models: filtration, secretions, reabsorption.
   Factors like urine flow rate pH transporter expression
3. QSP(pharmacokinetic)R: predicts clearance based on molecular and structural features.

Question 13

ACTIVE TRANSPORT

Answer 13

Active Transport in ADMET Modeling
Importance: Transporters significantly affect drug movement across membranes.
They are present in barrier tissues and handle many drug-like molecules.
Current Challenge: Most ADMET models lack mechanisms to account for active transport.
Potential Solution:
Large datasets of in vitro transporter data are available.
These can be used to build models (pharmacophore, QSAR) to predict transporter effects.
Benefit: Including these models in ADMET programs would improve prediction of drug behavior.

Question 14

P-gp (P-glycoprotein)

Answer 14

Function: ATP-dependent efflux transporter that pumps various substances out of cells.
Impact on Drugs:
Reduces absorption of drugs in the intestine.
Increases excretion of drugs by kidneys and liver.
Examples:
Limits absorption of anti-cancer drug paclitaxel.
Restricts brain penetration of HIV protease inhibitors.
Importance: Extensively studied due to its role in drug response and cancer treatment.
Computational Models: Developed to predict inhibition of P-gp by drugs.
Based on diverse experimental data involving different cell systems.
Key Features for P-gp Substrates:
Hydrophobic groups
Hydrogen bond acceptor groups
Aromatic rings
Specific spatial arrangement of these features

Question 15

P-glycoprotein (P-gp)

Answer 15

1. Mechanistic Models: detailed kinetic data such as transporter expression levels, binding affinities, and ATPase activity
2. PBPK Models with P-gp Component : P-gp-mediated transport as a parameter in compartments like the intestines, liver, and brain
3. QSAR Models:whether a drug is a substrate or inhibitor of P-gp based on its chemical structure.
   4.Molecular Dynamics (MD) Simulations: interaction between drug molecules and P-gp at the molecular level.
4. Machine Learning Models : trained on large datasets of known P-gp substrates and inhibitors to predict new compounds’ interactions with P-gp.

Question 16

Breast Cancer Resistance Protein (BCRP)

Answer 16

Function: ATP-dependent efflux transporter that reduces effectiveness of anticancer drugs.
Examples: anthracyclines, mitoxantrone
Expression: Found in intestine, liver, and brain, affecting drug movement in these organs.
3D-QSAR Model: Developed to predict BCRP interaction with drugs based on structure-activity data of flavonoid analogs.
Highlights specific structural features for BCRP substrates (e.g., 2,3-double bond in ring C, hydroxylation at specific position).
Limitations of Model:
Not all BCRP substrates will be identified by the model.
No in silico model can perfectly predict real-world interactions.

Question 17

Breast Cancer Resistance Protein (BCRP)

Answer 17

1. Mechanistic Models: detailed kinetic data such as transporter expression levels, binding affinities, and ATPase activity
2. PBPK Models with P-gp Component : BCRP- mediated transport as a parameter in compartments like the intestines, liver, and brain
3. QSAR Models:whether a drug is a substrate or inhibitor of P-gp based on its chemical structure.
   4.Molecular Dynamics (MD) Simulations: interaction between drug molecules and P-gp at the molecular level.
4. Machine Learning Models : trained on large datasets of known P-gp substrates and inhibitors to predict new compounds’ interactions with P-gp.

Question 18

Applications in Drug Development( transporter)

Answer 18

1.Prediction of Drug Absorption : Computational models predict how modifications in drug structure can reduce efflux by these transporters.

2. Brain Penetration: Both P-gp and BCRP limit drug penetration into the brain. model can help.
3. Drug-Drug Interactions: Efflux transporters are often involved . predict potential adverse effects and guide dosage adjustments.
4. Personalized Medicine: Computational models can integrate genetic and phenotypic data to predict individual variations in drug disposition, supporting personalized treatment strategies.

Question 19

Nucleoside Transporters

Answer 19

Nucleoside transporters (e.g., ENT1, ENT2) are responsible for the uptake and efflux of nucleosides and nucleoside analogs, which are often used as antiviral and anticancer agents.

Mechanistic Models: These models describe the kinetics of nucleoside transporters, incorporating parameters such as binding affinities, transport rates, and substrate specificity.

PBPK Models: PBPK models can include nucleoside transporters in relevant tissues (e.g., liver, intestines) to predict the systemic disposition of nucleoside analogs.

QSAR Models: QSAR approaches predict the interaction of drugs with nucleoside transporters based on molecular structure and physicochemical properties.

Molecular Dynamics (MD) Simulations: MD simulations provide insights into the binding interactions and translocation mechanisms of nucleoside analogs within transporter proteins.

Question 20

Human Peptide Transporter 1 (hPEPT1)

Answer 20

hPEPT1 is a proton-coupled oligopeptide transporter primarily expressed in the small intestine, facilitating the absorption of di- and tripeptides and peptide-like drugs.

Mechanistic Models: These models include hPEPT1-mediated transport kinetics, such as substrate affinity, maximum transport rate (Vmax), and pH dependence.

PBPK Models: Incorporating hPEPT1 into PBPK models helps predict the oral bioavailability of peptide-like drugs by simulating their absorption in the intestines.

QSAR Models: QSAR models predict hPEPT1 substrate affinity based on drug molecular structure, aiding in the design of drugs with optimal intestinal absorption.

MD Simulations: MD simulations elucidate the interaction of substrates with hPEPT1 at the molecular level, providing insights into binding sites and transport mechanisms.

Question 21

Apical Sodium-dependent Bile Acid Transporter (ASBT)

Answer 21

ASBT is involved in the enterohepatic circulation of bile acids, influencing the absorption of bile acid-derived drugs.

Mechanistic Models: These models describe the kinetics of ASBT-mediated transport, including substrate binding and transport rates.

PBPK Models: PBPK models incorporating ASBT can simulate the enterohepatic recycling of bile acids and bile acid-derived drugs, influencing their systemic exposure.

QSAR Models: QSAR approaches predict ASBT substrate affinity, helping in the design of drugs that utilize bile acid transport pathways.

MD Simulations: MD simulations provide detailed insights into the binding interactions of substrates with ASBT, helping to understand the structural requirements for transport.

Question 22

Organic Cation Transporters (OCT)

Answer 22

OCTs (e.g., OCT1, OCT2) mediate the uptake and efflux of organic cations, affecting the distribution and elimination of many drugs.

Mechanistic Models: These models incorporate kinetic parameters such as affinity constants (Km), Vmax, and transporter expression levels.

PBPK Models: PBPK models can include OCTs in relevant tissues (e.g., liver, kidneys) to predict the impact on drug distribution and renal excretion.

QSAR Models: QSAR models predict the interaction of drugs with OCTs based on molecular descriptors, aiding in the optimization of drug transport properties.

MD Simulations: MD simulations offer insights into the binding and transport mechanisms of organic cations within OCT proteins.

Question 23

Organic Anion Transporting Polypeptides (OATP)

Answer 23

OATPs (e.g., OATP1B1, OATP1B3) are involved in the hepatic uptake of various drugs, influencing their metabolism and clearance.

Mechanistic Models: These models describe the kinetics of OATP-mediated transport, including substrate specificity, binding affinities, and transport rates.

PBPK Models: PBPK models incorporating OATPs can simulate the hepatic uptake and subsequent metabolism of drugs, affecting their overall pharmacokinetics.

QSAR Models: QSAR approaches predict OATP substrate affinity, facilitating the design of drugs with favorable hepatic uptake profiles.

MD Simulations: MD simulations provide detailed insights into the interaction of substrates with OATPs, revealing key binding sites and transport mechanisms.

Question 24

Blood-Brain Barrier (BBB) Choline Transporter

Answer 24

The BBB choline transporter facilitates the uptake of choline and choline-like drugs into the brain.

Mechanistic Models: These models describe the kinetics of choline transport, including binding affinities and transport rates.

PBPK Models: PBPK models that include the BBB choline transporter can predict the brain penetration of choline-like drugs, informing CNS drug development.

QSAR Models: QSAR models predict the interaction of drugs with the BBB choline transporter based on molecular structure, aiding in the design of CNS-active drugs.

MD Simulations: MD simulations provide insights into the binding and transport mechanisms of choline and choline-like drugs across the BBB.

Question 25

Contents

Answer 25

1. Importance of drug disposition
2. Key concepts and models
   3.