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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

  Absorption Model

• First-Order (dose and food-dependent fa – saved in different models)

  Volume of Distribution

• Full PBPK (Method 2)
• Notes: Includes a Kp scalar and Kpadipose

  Route of Elimination

• CYP3A4 (80%), CYP2C9 (10%), CYP2C19 (10%)

  Perpetrator DDI

• CYP3A4 Inhibitor

  Validation

• Two clinical studies with fasted and fed groups at varying dose levels describing single and multiple dose exposure of piperaquine were used to verify the PBPK model. All of the simulated studies were within 1.5-fold of the observed values. 
• A clinical DDI study where piperaquine was the victim of a CYP3A4-mediated DDI was accurately recovered using the PBPK model as well as a CYP3A4 perpetrator DDI with the sensitive substrate midazolam.

  Limitations

• Requires separate files for low and high dose due to dose-dependant fa​
• Cmax overprediction, likely due to formulation differences​
• Additional verification for DDIs would be ideal although studies are currently not available in literature

  Updates in V19

• Updated in vitro­ data
• LogP
• Converted model to full PBPK with Vss predicted through Method 2