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Integrating antibody–drug conjugate bioanalytical measures using PK–PD modeling & simulation Review


CYP3A4 modulating drugs (midazolam, rifampicin and ketoconazole) and unconjugated MMAE from MMAE containing ADCs (anti-CD22-vc-MMAE and brentuximab-vedotin). MMAE is one of the most widely used drug component of the clinically tested ADCs, which is known to be a substrate of CYP3A4. Thus, any alterations in the activity of CYP3A4 enzyme could lead to clinically important changes in the PK of unconjugated MMAE. The PK model had two sub- models, one for characterizing the systemic PK of ADC (which was reported as antibody conjugated MMAE or acMMAE) and the other for characterizing the systemic PK unconjugated MMAE. Both the models are shown in Figure 5A. The clinical PK of acMMAE was fitted by the ADC submodel, and the clearance of acMMAE was treated as the input for the unconjugated MMAE submodel. The submodel for characterizing the PK of unconjugated MMAE was a PBPK model, developed


PK


Bolus dose ADC


ADC peripheral CLADC Drug peripheral CLD


the tumor compartment (surface or vascular exchange). Once inside the tumor extracellular matrix (ADCFree ADC is either allowed to interact with the cell surface antigen (Ag, konADC to the systemic circulation. The surface bound ADC (ADCBound


), be eliminated (CLADC


where it is assumed to be degraded. Each molecule of degraded ADC is assumed to generate certain molecules of drug in the cell, equivalent to the DAR of the ADC at the given time. The free drug molecules in the cell (DFree


manner as ADC (surface or vascular exchange), or allowed to go back in the tumor cells (kinD systemic circulation, the drug is allowed to distribute to the peripheral compartment (CLDD


and bound antibody–drug conjugate concentration in tumor extracellular space; Ag: Total antigen; DFree Free and bound drug concentration in cancer cell; Kdis KinD


or distribute back to the tumor compartment. ADC: Antibody–drug conjugate; CLADC


KonADC KonD KoutD


: Plasma clearance; CLDADC


: Drug nonspecific uptake rate in cancer cell; KintAg and KoffADC


and KoffD : Efflux rate of drug from the cancer cell. Adapted from [8]. future science group www.future-science.com 1639 : Distribution clearance; ADCFree


the extracellular space the free drug can also be generated by its dissociation from free or bound ADC (kdis drug from tumor extracellular matrix (DFree


is allowed to bind to the target (konD, koffD and DBound ) or allowed to exit to the extracellular matrix (koutD ). In ). Free


) is allowed to exchange between the plasma and tumor in a similar ). Once inside the


), be eliminated (CLD and ADCBound


), : Free


: Dissociation rate of drug from antibody–drug conjugate; : Internalization rate of antigen inside the cell;


and DBound


: Association and dissociation rate constants between antibody–drug conjugate and antigen; : Association and dissociation rate constants between drug and intracellular drug target;


: ) is allowed to internalize into the cancer cell (kintAg and koffADC


Figure 4. Tumor PK model for antibody–drug conjugates. After administration of ADC into the systemic circulation (Bolus dose), the ADC can distribute to the peripheral compartment (CLDADC


) or distribute to ), the


) or allowed to diffuse back ), ) CLDD CLDADC


Surface exchange ADC


ADC plasma DAR kdis kdis Drug plasma


Vascular exchange Drug


Surface exchange DFree


koutD kinD


Vascular exchange ADCFree koffADC


the observed data from clinical DDI studies [21]. In order to develop a PBPK model that can a pri- ori predict the tissue concentrations of ADC and


Tumor konADC ADCBound kintAg konD DFree koffD DBound


based on the in silico and in vitro data. Hepatic clearance for MMAE was scaled up from the intrinsic clearance value estimated from human hepatocytes. Based on the human mass balance study and a bile-duct cannulated rat study, 50% unconjugated MMAE was assumed to excrete via biliary pathway. The minimal PBPK model was successfully able to predict the PK of unconjugated MMAE in the clinic following anti-CD22-vc-MMAE or brentuximab-vedotin administration. The model also predicted no effect of MMAE on midazolam area under the concentration–time curve (AUC), twofold decrease in MMAE AUC due to rifampicin, and one-and-half fold increase in MMAE AUC due ketoconazole. All the model-simulated DDIs, expressed as AUC or maximum concentration (Cmax


) ratios, were within the twofold of


Cell


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