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DDI-Fusion: Human DDI Predictor

Predicts human DDI from in vitro CYP interaction data and chemical structure

  • Predicts human DDI (midazolam AUCR) using a proprietary algorithm that takes in vitro CYP interaction data (reversible inhibition, time dependent inhibition and induction) and chemical structure as inputs.
  • Customizable for alternative CYP3A4 substrates or alternative CYP enzymes.
  • chemPK™ v2 predicts key human pharmacokinetic parameters directly from structure. The predicted systemic and gut wall Cmax values are used as inputs for DDI-Fusion.
  • DDI-Fusion has several advantages over the regulatory mechanistic static model in that:
    • it uses a data-driven optimization approach requiring fewer assumptions.
    • human Cmax is predicted so no clinical in vivo data are required.
  • Provides early stage filter for directing chemistry and prioritizing screening.
  • Superior approach which uses PBPK model optimized from human clinical (in vivo) CYP3A4 data.
A tool for integrating in vitro ADME data with chemical structure to predict human DDI.


DDI-Fusion Input Requirements
  • Structural information input requirements
    • Chemical structure., e.g., SMILES, mol or sdf
    • Net charge at pH 7.4, calculable from pKas
  • Dosing information
    • Dosing regimen of perpetrator (dose, route, frequency, duration)
  • In vitro data
    • Reversible (Ki or IC50), TDI (KI and kinact) and induction (EC50 and Emax) parameters
    • Fraction unbound in plasma (fu,p)
DDI-Fusion Data Delivery
  • Predicted AUCR of victim for oral delivery (plus liver and gut contributions)

How does it work?

DDI-Fusion integrates liver and gut PK data from chemPK™ with in vitro CYP interaction data in a KNIME workflow-based approach which executes the process illustrated in Figure 1.

In silico human DDI prediction
Figure 1
DDI-Fusion workflow process.

KNIME can be downloaded easily and for free. Cyprotex can then provide the bespoke KNIME nodes (chemPK™ and DDI-Fusion) for the workflow.

Initially chemPK™ v2 predicts plasma and gut wall Cmax values. These parameters are used along with the in vitro inhibition and /or induction data as inputs to the DDI-Fusion node. This node then predicts the AUCRliver and AUCRgut for estimation of the AUCR.

Calculation of AUCR by DDI-Fusion

DDI-Fusion predicts AUCR of a co-administered victim drug (e.g., midazolam) in the presence of the perpetrator drug (test article).

AUCR= AUC of victim in presence of perpetrator
AUC of victim in absence of perpetrator

If inhibitory effects dominate, AUCR>1
If induction effects dominate, AUCR<1

Additionally, DDI-Fusion predicts the contributions of DDI in (i) the gut (AUCRgut), and (ii) the liver (AUCRliver).

AUCRtotal = AUCRgut wall x AUCRliver



Data from DDI-Fusion

In silico PK prediction

In silico DDI prediction
Figure 2
Comparison of (a). the mechanistic static model as recommended by regulatory authorities, and (b). Cyprotex’s DDI-Fusion model for predicting AUCR of reversible CYP3A4 inhibition against midazolam. 

The data illustrate how the regulatory mechanistic static model over-predicts AUCR for inhibition leading to false positives and possible unwanted compound attrition.
 Regulatory Mechanistic Static ModelDDI-Fusion
R2 0.63 0.70
RMSE 3.67 2.53
GMFE 1.58 1.36
Table 1
Statistics comparing the regulatory mechanistic static model with Cyprotex's DDI-Fusion model.

We are looking for partners with whom we can further develop the DDI-Fusion model. If you would like to be involved then please get in touch.

Contact for a demonstration.

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