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 68 Matches

Brand Name(s) include: Jasoprim, Malirid, Neo-Quipenyl, Pimaquin, Pmq, Primachina, Primacin, Primaquina, Primaquine, Primaquine diphosphate, Primaquine Phosphate, and Remaquin

Disease: Malaria, Plasmodium vivax, Plasmodium ovale

Drug Class: Antimalarial

Related Files: Carboxyprimaquine (metabolite)

Date Updated: March 2022

 The model at-a-glance

Absorption Model

  • First-Order

Volume of Distribution 

  • Full PBPK (Method 2)

Routes of Elimination

  • 89% MAO (entered using ‘user-UGT’ as a surrogate in the Simulator), 11% CYP2D6

Perpetrator DDI

  • CYP1A2 Inhibitor (in vitro)

Validation

  •  6 studies with single (15 to 45 mg) and multiple (15 mg QD) dosing. 100% of Cmax and AUC values within 1.5-fold.
  • No clinical DDI studies to verify contribution of metabolic routes

Limitations

  •  The active metabolites of primaquine have not characterized due to their instability. Therefore, a PBPK model for active metabolites cannot be developed in their own right.
  • Qualitative data suggests a role of P-gp, however, Jmax and Km values have not been measured.
  • There is evidence of enantiomer specific metabolism for primaquine which has not been considered in the current model.

Updates in Version 19

  • Updated in vitro protein and blood binding data and subsequent back calculation of CLint (retrograde approach)
    •  fu: 0.19 -> 0.26
    • B:P: 1 -> 0.82
  • Converted from minimal PBPK model to full PBPK model

 

Dihydroartemisinin (DHA) from Artesunate

Brand Name(s) include: Camoquin (FDC with amodiaquine)

Disease: Malaria

Drug Class: Antimalarials

Related Drugs: DHA, Amodiaquine

Date Updated: March 2022

The model at-a-glance

  Absorption Model

  • First-Order

  Volume of Distribution

  • Full PBPK (Method 2)

Note: Kp scalar used

  Route of Elimination

  • UGT1A9 (50%); UGT2B7(50%)

  Perpetrator DDI

  • CYP1A2 Inhibitor

  Validation

  • One clinical study describing single dose exposure of DHA was used to verify the PBPK model.  100% of studies were within 2-fold, of which 100% were within 1.5-fold. 

  Limitations

  • The absorption model does not consider the formation of ‘DHA from artesunate’ mechanistically. Instead, an optimized ka and fa were applied to the DHA model to describe the observed plasma concentration-time curve of DHA. The remainder of the DHA model was identical to the DHA model which is described above.
  • The model does not account for the differences in plasma fraction unbound observed in patients compared to healthy volunteers.
  • Verification needed for perpetrator DDI assessment as literature data is unavailable at this time.

  Updates in V19

  • Updated in vitro data
    • Propranolol Papp: 30 cm/s x 106
  • Converted model to full PBPK with Vss predicted through Method 2
  • Updated retrograde clearance

 

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.

Brand Name(s) include: Lariam, Mephaquin, Mefliam

Disease: Malaria

Drug Class: Antimalarials

Date Updated: November 2021

The model at-a-glance

  Absorption Model

First-Order

  Volume of Distribution

Full PBPK (Method 2)

  Route of Elimination

CYP3A4 (fm =100); renal clearance (fe = 0.05)

  Perpetrator DDI

  • CYP2C9 Inhibitor
  • CYP2D6 Inhibitor
  • CYP3A4 Inhibitor

  Validation

  • Six clinical studies describing single and multiple dose exposure of mefloquine were used the verify the PBPK model.  Most of the studies (83%) were within 1.5-fold, with all simulations falling within 2-fold of the observed values. 
  • Two clinical DDI studies where mefloquine was the victim of a CYP3A4-mediated DDI were accurately recovered using the PBPK model.

  Limitations

  • Only profiles of plasma concentrations assessed, many studies report blood concentrations​
  • Mefloquine has significant uptake into erythrocytes and haematocrit levels typically not reported​
  • Could be important in disease population (Possible time-varying B/P for Malaria patients?)​
  • Cmax for doses > 750 mg over predicted ​
  • fa possibly decreases with dose, more data needed to fully determine the cause​
  • Most literature data extracted from graphs of mean data, difficulty determining accurate early time points due to poor image quality​
  • Verification needed for perpetrator DDI assessment as literature data is unavailable at this time

  Updates in V19

  • Updated in vitro­ data
    • fup: 0.016 -> 0.015
    • B:P ratio 1.7 -> 1.1 and subsequent re-calculation of CLint using the retrograde approach
  • Converted model to full PBPK distribution model with Vss predicted through Method 2
  • Sensitivity analysis of ka

 

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