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
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44 |
Page 45 |
Page 46 |
Page 47 |
Page 48 |
Page 49 |
Page 50 |
Page 51 |
Page 52 |
Page 53 |
Page 54 |
Page 55 |
Page 56 |
Page 57 |
Page 58 |
Page 59 |
Page 60 |
Page 61 |
Page 62 |
Page 63 |
Page 64 |
Page 65 |
Page 66 |
Page 67 |
Page 68 |
Page 69 |
Page 70 |
Page 71 |
Page 72 |
Page 73 |
Page 74 |
Page 75 |
Page 76 |
Page 77 |
Page 78 |
Page 79 |
Page 80 |
Page 81 |
Page 82 |
Page 83 |
Page 84 |
Page 85 |
Page 86 |
Page 87 |
Page 88 |
Page 89 |
Page 90 |
Page 91 |
Page 92 |
Page 93 |
Page 94 |
Page 95 |
Page 96 |
Page 97 |
Page 98 |
Page 99 |
Page 100 |
Page 101 |
Page 102 |
Page 103 |
Page 104 |
Page 105 |
Page 106 |
Page 107 |
Page 108 |
Page 109 |
Page 110 |
Page 111 |
Page 112 |
Page 113 |
Page 114 |
Page 115 |
Page 116 |
Page 117 |
Page 118 |
Page 119 |
Page 120 |
Page 121 |
Page 122 |
Page 123 |
Page 124 |
Page 125 |
Page 126 |
Page 127 |
Page 128 |
Page 129 |
Page 130 |
Page 131 |
Page 132 |
Page 133 |
Page 134 |
Page 135 |
Page 136 |
Page 137 |
Page 138 |
Page 139 |
Page 140 |
Page 141 |
Page 142 |
Page 143 |
Page 144 |
Page 145 |
Page 146 |
Page 147 |
Page 148 |
Page 149 |
Page 150 |
Page 151 |
Page 152 |
Page 153 |
Page 154