PL

Dziękujemy, Jerzy Kowalski

Wkrótce się z Tobą skontaktujemy.

Chemistry & Life Science

Newsletter

ADMEWORKS DDI Simulator

DDI Simulator quantitatively predicts the extent of drug-drug interactions arising from co-administration of drugs, an important study in drug development, through time course simulation of the concentrations of each drug in the body using physiologically-based pharmacokinetic (PBPK) mathematical models.

Advantages:
- More accurate predictions than simple approximation method.
- Allows prediction using in-house compound as inhibitor or substrate.
- Evaluate risk based on AUC ratio, Cmax ratio or changes in elimination half-life.

DDI Simulator was developed based on the results of the Human Animal Bridging (HAB) Research Organization's Project in Japan. Development of new additional features, such as the database of drugs (containing in vivo Ki values), is currently under the supervision of Dr. Kazuya Maeda from the Graduate School of Pharmaceutical Sciences, University of Tokyo.
Inhibition
Enzyme(Competitive + MBI) and Transporter Inhibition models

Prediction of drug-drug interaction due to both enzyme inhibition and transporter inhibition is possible.



The enzyme inhibition model allows users to simulate simultaneous competitive and mechanism-based inhibitions that actually occur in clinical studies by integrating the two inhibition models into one.

The transporter inhibition model allows users to simulate simultaneous inhibitions of the metabolizing enzymes (competitive) and the uptake and/or efflux transporters in both the kidney and the liver.

 
PBPK
Physiologically-based Pharmacokinetic Model

DDI Simulator uses PBPK models to simulate DDIs due to metabolizing enzyme and transporter inhibitions. The following shows the PBPK model used for the enzyme inhibition model (combined Competitive and MBI).



As opposed to simple approximation by “1+[I]/Ki” where the concentration is constant, DDI Simulator uses PBPK model to simulate more accurate time-dependent concentrations. The comparison graphs below show several drugs give better agreement with experimentally observed results using the PBPK model.

Kato M et al., Pharm Res. 25(8):1891-1901(2008)


 
Drugs
Drugs Database (includes drugs in the FDA 2012 revised draft guidance)

The database contains a total of 77 drugs(Substrate 47 drugs and Inhibitor 30 drugs) which includes FDA-recommended substrates and inhibitors for study in all major CYP isoforms (1A2, 2C8, 2C9, 2C19, 2D6, 3A4) making it possible for users to readily simulate DDI with their own compounds.



The registered inhibitors contain human in vivo Ki values obtained by parameter optimization using actual data from DDI clinical studies. For in-house drugs in pre-clinical phase where in vivo Ki is unknown, accurate prediction of in vivo Ki from in vitro Ki is possible based on a formula using only the octanol-water partition coefficient (logP).

 
CYP Isoforms
Inhibition of Multiple CYP Isoforms

Accurate prediction of DDI is possible for drugs metabolized by several CYP isoforms. By simply assigning each CYP’s contribution to the total metabolism of the substrate, the effect of simultaneous inhibition of multiple CYP isoforms can be studied.

Kantola T et al., Eur J ClinPharmacol. 56(3):225-9(2000)


 
Intestinal Metabolism
Inhibition of Intestinal Metabolism

Drug metabolizing enzymes like CYP3A4 exists not only in the liver but also in the small intestines. Inhibition may occur in both places that would affect the risk level of DDI.

Neuvonen PJ et al., Clin Pharmacol Ther. 63(3):332-41(1998)


Accurate prediction of the increase in FaFg (when intestinal metabolism is inhibited) reduces overestimated predictions of DDI as compared to setting the FaFg to a maximum value (FaFg=1).

 
Risk
Optimization of Dosing Regimen to Minimize Risk

Flexible settings allow the user to study the best dosing regimen for both the substrate and the inhibitor in order to minimize the risk of DDI. Also, simulation of the maximum risk when administering repeated doses of the inhibitor is automatically done by calculating the right substrate dose timing at the steady-state.





 

ADMEWORKS DDI Simulator

DDI Simulator quantitatively predicts the extent of drug-drug interactions arising from co-administration of drugs, an important study in drug development, through time course simulation of the concentrations of each drug in the body using physiologically-based pharmacokinetic (PBPK) mathematical models.

Advantages:
- More accurate predictions than simple approximation method.
- Allows prediction using in-house compound as inhibitor or substrate.
- Evaluate risk based on AUC ratio, Cmax ratio or changes in elimination half-life.

DDI Simulator was developed based on the results of the Human Animal Bridging (HAB) Research Organization's Project in Japan. Development of new additional features, such as the database of drugs (containing in vivo Ki values), is currently under the supervision of Dr. Kazuya Maeda from the Graduate School of Pharmaceutical Sciences, University of Tokyo.
Enzyme(Competitive + MBI) and Transporter Inhibition models

Prediction of drug-drug interaction due to both enzyme inhibition and transporter inhibition is possible.



The enzyme inhibition model allows users to simulate simultaneous competitive and mechanism-based inhibitions that actually occur in clinical studies by integrating the two inhibition models into one.

The transporter inhibition model allows users to simulate simultaneous inhibitions of the metabolizing enzymes (competitive) and the uptake and/or efflux transporters in both the kidney and the liver.

 
Physiologically-based Pharmacokinetic Model

DDI Simulator uses PBPK models to simulate DDIs due to metabolizing enzyme and transporter inhibitions. The following shows the PBPK model used for the enzyme inhibition model (combined Competitive and MBI).



As opposed to simple approximation by “1+[I]/Ki” where the concentration is constant, DDI Simulator uses PBPK model to simulate more accurate time-dependent concentrations. The comparison graphs below show several drugs give better agreement with experimentally observed results using the PBPK model.

Kato M et al., Pharm Res. 25(8):1891-1901(2008)


 
Drugs Database (includes drugs in the FDA 2012 revised draft guidance)

The database contains a total of 77 drugs(Substrate 47 drugs and Inhibitor 30 drugs) which includes FDA-recommended substrates and inhibitors for study in all major CYP isoforms (1A2, 2C8, 2C9, 2C19, 2D6, 3A4) making it possible for users to readily simulate DDI with their own compounds.



The registered inhibitors contain human in vivo Ki values obtained by parameter optimization using actual data from DDI clinical studies. For in-house drugs in pre-clinical phase where in vivo Ki is unknown, accurate prediction of in vivo Ki from in vitro Ki is possible based on a formula using only the octanol-water partition coefficient (logP).

 
Inhibition of Multiple CYP Isoforms

Accurate prediction of DDI is possible for drugs metabolized by several CYP isoforms. By simply assigning each CYP’s contribution to the total metabolism of the substrate, the effect of simultaneous inhibition of multiple CYP isoforms can be studied.

Kantola T et al., Eur J ClinPharmacol. 56(3):225-9(2000)


 
Inhibition of Intestinal Metabolism

Drug metabolizing enzymes like CYP3A4 exists not only in the liver but also in the small intestines. Inhibition may occur in both places that would affect the risk level of DDI.

Neuvonen PJ et al., Clin Pharmacol Ther. 63(3):332-41(1998)


Accurate prediction of the increase in FaFg (when intestinal metabolism is inhibited) reduces overestimated predictions of DDI as compared to setting the FaFg to a maximum value (FaFg=1).

 
Optimization of Dosing Regimen to Minimize Risk

Flexible settings allow the user to study the best dosing regimen for both the substrate and the inhibitor in order to minimize the risk of DDI. Also, simulation of the maximum risk when administering repeated doses of the inhibitor is automatically done by calculating the right substrate dose timing at the steady-state.