Review Khot, Sharma & Shah
A v2
peripheral compartment
CLd2
V1 central compartment
DAR7 DAR6
K7-6 K6-5
v3
peripheral compartment
CLd3
DAR5 DAR4 DAR3 DAR2 DAR1 DAR0
K5-4 K4-3 K3-2
K2-1 K1-0
B LLHH 2*kH
CL in vivo Kplasma
LLH *V1 CL TT kH LL kH 2*kL L kL kH DAR0 CL DAR0
constant for antibody degradation in plasma; L: Light-chain site of conjugation. (A) Adapted from [6]. (B) Adapted from [14].
position of ADC and released drug [9]. The model suggests that generally the tumor concentrations of unconjugated drug are very high compared with plasma concentrations, however the amount of unconjugated drug generated in the tumor is very small compared with the rest of the body. Thus, the degradation of ADC in the tumor does not contribute significantly toward the unconjugated drug concentrations in plasma. The model also suggests that while the unconjugated drug distributing from plasma to the tumor contributes notably toward the tumor intersti- tial drug concentration initially, the unconjugated drug generated within the cancer cells is the most dominant source of drug concentrations in tumor interstitial space. According to the model, initially the drug brought in the tumor cells by ADC internalization is the predominant contributor toward intracellular drug concentrations, but as the time progresses, binding of the drug within the cells becomes the major contributor for retaining unbound drug within the cell. The tumor PK model also points out that the rate of drug efflux out of the cell, expression level of intracellular target, tumor size, and the dissocia- tion rate of the drug from ADC, are few key parameters that determine the tumor concentrations of the unconju- gated drug, which is the main moiety responsible for the pharmacological effect of ADC [9].
Tissue PK of ADC Understanding and mathematically characterizing the
1638 Bioanalysis (2015) 7(13)
Figure 3. Plasma PK of individual drug–antibody ratio components of antibody–drug conjugates. (A) PK model developed by Bender et al. to characterize the plasma PK of each individual DAR species of T-DM1 in rat, monkey and human. (B) PK model developed by Sukumaran et al. to characterize the plasma PK of individual DAR species of MMAE-based TDCs. DAR4 is shown as LLHH, DAR3 is shown as LLH or LHH, DAR2 is shown as LL or LH or HH,and DAR1 is shown as L or H. CL: Clearance; CLd: Distributional clearance; DAR: Drug–antibody ratio; H: Heavy-chain site of conjugation; k: Rate of dissociation of the drug from individual DAR species; Ki
: Rate constant for DM1 deconjugation; Kplasma : Rate H
2*kL LH
2*kL LHH 2*kH kL kL HH 2*kH CL DAR1 CL DAR2 CL DAR3 CL DAR4
PK of ADC and its components in different tissues is important for establishing a reliable exposure–toxicity relationship and predicting the potential of drug–drug interaction (DDI). This is mainly done using physi- ologically based PK (PBPK) models, which are capable of characterizing or predicting the tissue PK of drugs, and very amicable to clinical translation. However, in order to develop the PBPK model that can simultane- ously characterize the PK of both ADC and the released drug, one needs to develop two different PBPK models and combine them mechanistically [19,20], because the physiological processes governing the disposition of large molecules like ADC and small molecules like the unconjugated drug are very different. Depending on whether the purpose of PBPK model
is to characterize the tissue PK of ADC and the released drug, or to predict DDI, the requirement for the bio- analytical measurements needed to develop the model changes. In order to develop a PBPK model capable of predicting DDI between clinically used drugs and the unconjugated drug generated from ADC, the tissue concentration data are not required. However, an array of in vitro data generated from the tissue cells that are expected to be responsible for the DDI is required. Addi- tionally, plasma PK of ADC, the unconjugated drug, and the drug that is being tested for DDI potential is also required. Chen et al. [21] have developed one such mini- mal PBPK model to predict the clinical DDI between
future science group
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