Review Khot, Sharma & Shah A ×104 ×104
10 12 14
100
0 2 4 6 8
10-1 102 Antigen (nM) 104 D ×104
10 12 14
2 4 6 8
100 0
10-1 Kg0 10-1 10-2 (1/day) 100 10-3 101 Dose (mg/kg) ×104
10 12
0 2 4 6 8
1 2 3 4 5 6
10-1 0 100 Dose (mg/kg) ose (mg/kg) 101 101 102 10 103 Kd (mm3 10 104 /day)
Figure 7. Results from different clinical trial simulations conducted using the Brentuximab-Vedotin PK–PD model. Z-axis on all the panels represents tumor volume in mm3
regression. (A) Dose versus antigen concentration relationship. (B) Dose versus Kd relationship. (C) Dose versus kout (D) Dose versus kg0 relationship. (E) Dose versus kg relationship. (F) At a fixed dose, Ag versus Kd relationship. Ag: Antigen concentration; Kd: Antibody–drug conjugate-antigen affinity; kg: Linear tumor growth rate; kg0 growth rate; kout
: Unconjugated drug efflux rate.
Adapted from [8]. For color images please see online
www.future-science.com/doi/full/10.4155/bio.15.85
in cynomolgus monkeys. They employed total anti- body concentrations in the plasma to drive the toxicity. They characterized the monkey toxicity of 10 different ADCs containing the same drug-linker (vcMMAE) and DAR, but targeting 10 different antigen, using the same PK–TD model. The model was able to capture all the profiles reasonably well. However, it was found that despite the same drug-linker being employed for all 10 ADCs, there was a 30-fold difference in the toxicity of ADCs. Thus, their results raised the doubt about a general belief that different ADCs with similar drug- linker would demonstrate similar toxicity profile. The authors also claimed that the PK–TD model employed for monkeys should be amicable to interspecies transla- tion to the clinic, and the exposure–toxicity relation- ships for ADCs in the clinic can be predicted based on the monkey toxicity data [28]. Mugundu et al. [29] have also developed a PK–TD
model develop based on the TD model of Friberg et al. for characterizing the thrombocytopenia induced by inotuzumab ozogamicin (CMC-544) in the clinic. They established a relationship between exposure of
1644 Bioanalysis (2015) 7(13)
mAb (i.e., inotuzumab) or total drug (i.e., conjugated + unconjugated calicheamicin) and the decrease in the platelet count. The model did a reasonable job in char- acterizing the exposure and toxicity data with either mAb or total drug as the driver for the toxicity. Inter- estingly, using the PK–TD modeling, baseline platelet count was identified as an important parameter that determined the probability of achieving the toxicity with the ADC. When the patients had higher baseline platelet count they were less likely to experience severe thrombocytopenia. Thus, the integrated PK–TD mod- eling of ADC/drug concentration and platelet count provided the means to quantify the magnitude of plate- let suppression and identify dosing schedules that would mitigate the incidences of thrombocytopenia [29]. A more sophisticated version of the Friberg et al.
model has been developed by Bender et al. [30] to char- acterize T-DM1 induced thrombocytopenia in the clinic. Figure 8B describes the structure of T-DM1 PK-TD model. Interestingly, rather than using a con- tinuous PK profile of any analyte to drive the TD model, authors employed the average ADC concen-
future science group 105 101 100 Dose (mg/kg)
10 12
0 2 4 6 8
10 12 14
0 2 4 6 8
10-1 100 Dose (mg/kg) E ×105 ×10 105
5.0 4.5
1.0 2.0 3.0 4.0
3.5 2.5 1.5 0.5
×104
12 10 8 6 4 2 0
Dose (mg/kg) 101 100 Kd ( M) Kd (nM) F ×104
10 9 8 7 6 5
100 102 Ag (nM) 104 10-2 100 Kd (nM) 102 1
4 3 2 1
102 1 ×104 B ×104
10 12
2 4 6 8
C ×10
10 12 14
102
0 2 4 6 8
100 Kout (1/day) 10-2101 100 Dose (mg/kg) 104 ×104
10 12
4 6 8
10-1 1
2 0
. Red color in the 3D mesh represents tumor growth and blue color indicates tumor relationship.
: Exponential tumor
Tumor volume (mm3
)
Tumor volume (mm3
)
Tumor volume (mm3
)
Tumor volume (mm3
)
Tumor volume (mm3
)
Tumor volume (mm3
)
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