Integrating antibody–drug conjugate bioanalytical measures using PK–PD modeling & simulation Review
be cleared from the liver. The PBPK model was able to describe the PK of ADC and cys-mcMMAF in all the tissues and tumor reasonably well, following incorporation of ADC binding to CD70 antigen in the tumor compartment. This kind of PBPK models provide enhanced mechanistic insight into the deter- minants of ADC and unconjugated drug disposi- tion. Quantification of these determinates may allow a prior prediction of the clinical PK of ADC, and also support the development of novel ADC technologies. The PBPK model can also be used to correlate the tissue specific toxicity of an ADC with the simulated tissue concentrations of ADC or the released drug to establish a reliable exposure–toxicity relationship.
Integration of PD data Since most of the ADCs evaluated in the clinic so far are developed for oncology indications, the PD endpoints relate to the killing of cancer cells. In the in vitro experiments the PD endpoint is change in cell number (or viability), and in the in vivo setting the PD endpoint is either change in tumor volume (for solid tumors) or cell number (for disseminated tumors). Having a clinically relevant biomarker that can accu- rately represent the changes in the disease state is also a very desirable PD endpoint, however we are not aware of any published work that employs biomarker for assessing the efficacy of ADCs. The purpose of integrating the PK and PD data using mathematical model is to establish a reliable quantitative exposure– response relationship that can be used for simulating different scenarios. Additionally, PK–PD relationships established preclinically can also be used to predict the clinically efficacious dose and clinical performance of ADCs [8]. The PD models employed for characterizing the
PK–PD of ADCs so far are the same that have been developed for other chemotherapeutic drugs. This includes variations of the direct kill model (Figure 6A), signal-distribution model (Figure 6B) or cell-distribu- tion model (CDM) (Figure 6C). However, the distinc- tion that is associated with each of the different PK– PD model for ADCs is the use of different PK analytes to drive the efficacy of ADCs. Sukumaran et al. [14] have employed a direct kill
model to characterize the efficacy of TDCs in animal models. The PD model employed zero-order growth rate and linear/nonlinear killing function to charac- terize the changes in tumor volume. ADC concentra- tions in plasma were used to drive the efficacy. Since the plasma PK of each individual DAR species was available, the tumor-killing efficacy of each DAR species of the ADC was considered to be directly pro- portional to the DAR value. Thus, the PK–PD mod-
future science group
eling by Sukumaran et al. supported the notion that the amount of drug conjugated to the mAb confers the tumor killing capability to an ADC, and ADC efficacy is dependent on the drug load (i.e., DAR). Jumbe et al. [25] developed one of the first PK–PD
models to characterize the preclinical efficacy of T-DM1, using a variant of the CDM. They used ADC concentrations in plasma to drive the effi- cacy. The interesting feature of their model was an unusual tumor growth function, and a killing func- tion that was combined with the parameter charac- terizing the delay of drug efficacy. Nonetheless, PK– PD model was able to successfully characterize the preclinical efficacy of T-DM1 in several xenograft models, following multiple dose and dose fraction- ation studies. Using the estimated model parameters from their PK–PD model Jumbe et al. were able to mathematically derive a secondary parameter, coined tumor static concentration (TSC). TSC was consid- ered as a theoretical serum ADC concentration at which tumor growth and death rates are equal, and the tumor volume remains unchanged. TSC was used as a ‘threshold concentration’ for predicting the clinically efficacious dosing regimen for T-DM1. It was proposed that for a dosing regimen to be clini- cally efficacious the trough levels of T-DM1 between the two dosing interval should not fall below the TSC. Later on Haddish-Berhane et al. [26] developed another variant of CDM to enable preclinical-to- clinical translation of ADCs. They evaluated multi- ple models (including the one from Jumbe et al.) and established a novel model that was the most stable and appropriate for clinical translation of ADC efficacy. Based on the novel model a new equation for TSC was derived that was different than the one devel- oped by Jumbe et al. [25]. Contrary to Jumbe et al., Haddish-Berhane et al. [26] proposed that for an ADC to be efficacious in the clinic only the average ADC exposure in plasma between the two dosing interval should match TSC. Additionally, Haddish- Berhane et al. [26] proposed the use of 3–4 different xenograft models to create a range of TSCs to guide the clinical dosing regimen selection for an ADC. It was demonstrated that this approach for the clinical translation of ADC was applicable to T-DM1. The range of TSCs for T-DM1 predicted based on the preclinical efficacy data was 1–40 μg/ml, and the average concentration of T-DM1 in the clinic at the clinically efficacious dosing regimen of 3.6 mg/kg Q3W is reported to be 14 μg/ml. Since the concentrations of unconjugated drug inside
the tumor cells is the most relevant analyte for corre- lating with ADC efficacy, a PK–PD model that uses tumor drug concentrations to drive ADC efficacy is
www.future-science.com 1641
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