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 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

ADAM (Solution)

Full PBPK (Method 2)

 Route of Elimination

• CYP3A4 = 19.5 %, CYP2C19 = 33%, additional HLM = 20%, biliary clearance =26 %, renal clearance = 1.5% at steady state

 Perpetrator DDI

• CYP3A4 competitive inhibition, Mechanism-Based Inhibition and Induction
• P-gp Inhibition

 Validation

The refined model was able to recover clinically observed concentration-time profiles of nelfinavir following single and multiple dosing.

Six clinical DDI studies where nelfinavir was administered with either ritonavir, rifampicin, rifabutin, efavirenz, and nevirapine were used to verify the PBPK model of nevirapine as a victim. In comparison of predicted vs. observed AUC, 100% of the studies were within 1.5-fold.

Nine clinical DDI studies where nevirapine was administered with either alfentanil, midazolam, simvastatin, atorvastatin, rifabutin, or digoxin were used to verify the PBPK model of nevirapine as a perpetrator. In comparison of predicted vs. observed AUC, 100% of the studies were within 2-fold and 78% were within 1.25-fold.

 Limitations

• Single oral dose exposure is underpredicted (generally within 2-fold)

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