Search the PBPK Model Repository

Quickly find freely available drug and population models in our PBPK model repository.

The models provided have been collated from published examples which authors have shared in our Published Model Collection or developed as part of various global health projects in our Global Health Collection. This search facility searches both model collections simultaneously.

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Found 84 Matches

Cholesterol_4beta-hydroxycholesterol_RES_V23_Simcyp_20240930

The 4β-hydroxycholesterol (4β-OHC) file is an endogenous biomarker for CYP3A activity mainly measured to evaluated potential CYP3A induction in vivo. The 4β-OHC file was developed as a primary metabolite of the parent compound, cholesterol. As the compound requires specific system parameter inputs for endogenous CYP abundance scaling, the parent-metabolite file is available within a workspace containing the required system parameters. The parent-metabolite file has been developed in the Simcyp Simulator V23 and details of the file performance are described in Karkhanis et al., 2024 (PMID 39458613) and in a compound summary that provides the key pharmacokinetic features of cholesterol and 4β-OHC considered within the model.

Simvastatin_Acid_Lactone_RES_V23R2_Simcyp_20241122

The RES-Simvastatin lactone and RES-Simvastatin acid models within the Simcyp Compound Repository have been developed as substrates of CYP3A4, CYP2C8, BCRP (simvastatin lactone), CES1 (simvastatin lactone) and OATP1B1 (simvastatin acid). Additionally, the models account for the interconversion between the lactone and acid forms in the acidic environment of the stomach. Note: Before running a simulation, modify the population to account for gastric luminalmetabolism. To do this, follow these steps:

  1. Go to Population > GI Tract > LuminalMetabolism> Compound > Expression
  2. Set the relative activity to 0 for all GI segments except the stomach

This document provides:

  1. Examples of model performance
  2. A summary of the key pharmacokinetic features of simvastatin lactone and simvastatin acid considered within the model
Rivaroxaban_V17R1_NationalUniversityofSingapore_20200923

https://dmd.aspetjournals.org/content/47/11/1291/tab-article-info This workspace was developed to recapitulate the magnitude of drug-drug interaction reported between Rivaroxaban and Verapamil as reported by Greenblatt et al. (https://pubmed.ncbi.nlm.nih.gov/29194698/) Note 1: In Table 1 of the publication the Caco-2 Papp (pH 7.4:7.4) was reported as 8 x 10-6 cm/s; however, the Rivaroxaban file in the workspace is using a Caco-2 Papp (pH 7.4:7.4) of 21.8 x 10-6 cm/s. This Papp is in line with the reported scalar in the EXCEL outputs and the Table 1. The obtained Rivaroxaban plasma concentration time profile is in line with the reported Figure 2C in the publication. Note 2: In Table 1 of the publication, input data for Mech KiM are stated; however, the Rivaroxaban file in the workspace is using a User Input for the renal clearance of 3.1 L/h; while the input data for Mech KiM are included in the compound file, they are not activated within the workspace, which is mimicking a DDI with Verapamil and Norverapamil. Note 3: Bile:micelle parameters were changed from 3.4 to 3.5.

Pyronaridine

Brand Name(s) include: Pyramax

Disease: Malaria

Drug Class: Antimalarials

Date Updated: March 2022

Related files: Artesunate (fixed dose combination – Pyramax)

The model at-a-glance

  Absorption Model

  • First-Order

  Volume of Distribution

  • Full PBPK (Method 3)
  • Note: Kp scalar used

  Route of Elimination

  • CYP1A2, CYP2B6, CYP2C8, CYP2D6 and CYP3A4

  Perpetrator DDI

  • CYP2D6 Inhibitor
  • P-gp Inhibitor

  Validation

  • Two clinical studies describing pyronaridine exposure were available for model verification.  100% of predicted Cmax were within 1.5-fold of those observed whereas 40% of AUC were predicted within 1.5-fold of observed. This can be explained as observed exposure at 9mg/kg dose was lower than at 6 mg/kg.  The model recovered the observed data at the 6 mg/kg dose but then over predicted that at the higher dose.

  Limitations

  • One challenge in the verification of the model is the diverse ethnicities of subjects in reported clinical data and how best to reflect this in simulations.  In the absence of virtual Korean populations within the Simulator, the Caucasian population was modified in terms of bodyweight.  In the absence of supporting information, no changes to enzyme abundance (pmol/mg) were made to the population, although changes to liver weight (as a function of body weight) and hence total CYP abundance were propagated into the model.

  Updates in V19

  • Switched to Method 3 to facilitate like for like comparisons for covid- 19     repurposing strategies

 

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