Search the PBPK Model Repository

  Absorption Model

First-Order

  Volume of Distribution

Full PBPK (Method 2)

  Route of Elimination

Formed by CYP2C19, CYP3A4; unknown clearance mechanism

  Perpetrator DDI

• CYP2D6 Inhibitor

  Validation

• Proguanil and cycloguanil files were built using in vitro and clinical (Jeppersen et al., 1997) data
• 5 clinical studies describing single and multiple dose exposure of cycloguanil were used to verify the PBPK model. 60% of studies were within 2-fold, of which 40% were within 1.5-fold.
• A clinical DDI study where proguanil was the victim of a CYP2C19-mediated DDI was accurately recovered using the PBPK model.  

  Limitations

• Prediction of cycloguanil exposure was complicated by not knowing the polymorphism classification of subjects in each study, hence the model performance was deemed acceptable using the criteria of being within 2-fold of observed.
• Verification needed for perpetrator DDI assessment as literature data is unavailable at this time

  Updates in V19

• Recalculated fm values using the corrected dose administered in Jeppesen et al., 1997
• Previous version used paper calculation of total clearance which did not account for the weight of the salt in the 200 mg dose administered
• Model converted from minimal to full PBPK distribution model
• Updated in vitro data
• Updated CYP2D6 IC₅₀

 Publication

Kimoto, E., Vourvahis, M., Scialis, R. J., Eng, H., Rodrigues, A. D., & Varma, M. V. S. (2019). Mechanistic Evaluation of the Complex Drug-Drug Interactions of Maraviroc: Contribution of Cytochrome P450 3A, P-Glycoprotein and Organic Anion Transporting Polypeptide 1B1. Drug metabolism and disposition: the biological fate of chemicals, 47(5), 493–503.

 Simcyp Version

V15

 Published Model Application

DDI prediction

 Absorption Model

ADAM; includes P-gp in the intestines

Full PBPK

 Route of Elimination

• CYP3A4
• Renal clearance
• Includes hepatic biliary clearance by OATP1B1

 Advantages and Limitations

• Model was developed to evaluate DDI of maraviroc as victim.
• Model was verified with IV and oral data.
• Model was verified as a victim of interactions with ketoconazole, ritonavir, efavirenz and rifampin

 Model Compound Files

• v15_res_maraviroc_simcyp_kimoto
• v15_res_maraviroc_simcyp_kimoto_iv_3mg
• v15_res_maraviroc_simcyp_kimoto_iv_10mg
• v15_res_maraviroc_simcyp_kimoto_iv_30mg
• v15_res_maraviroc_simcyp_kimoto_po_150mg_bid

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)
  •  f_u: 0.19 -> 0.26
  • B:P: 1 -> 0.82

• Converted from minimal PBPK model to full PBPK model

 Absorption Model

•   First-Order

 Volume of Distribution 

•   Full PBPK (Method 2)

  Note: Kp scalar used

 Route of Elimination

•   CYP2C19, CYP3A4, renal clearance

 Perpetrator DDI

•   CYP2D6 Inhibitor

 Validation

• Proguanil and cycloguanil files were built using in vitro and clinical (Jeppersen et al., 1997) data
• 3 clinical studies describing single and multiple dose exposure of proguanil were used to verify the PBPK model. 66% of studies were within 2-fold, of which 33% were within 1.5-fold. 
• A clinical DDI study where proguanil was the victim of a CYP2C19-mediated DDI was accurately recovered using the PBPK model.  

 Limitations

• Prediction of proguanil exposure was complicated by not knowing the polymorphism classification of subjects in each study, hence the model performance was deemed acceptable using the criteria of being within 2-fold of observed.
• Verification needed for perpetrator DDI assessment as literature data is unavailable at this time
• With a large CL_Rcomponent and chemical relation to metformin, we hypothesise that proguanil may be a substrate for active transport in the kidney. However, owing to a lack of mechanistic information relating to active transport this cannot be built into the model.​

 Updates in V19

• Modification of fm values
• Model converted from minimal to full PBPK distribution model
• Updated CYP2D6 IC50