1 research outputs found
Preclinical Development of an anti-5T4 Antibody–Drug Conjugate: Pharmacokinetics in Mice, Rats, and NHP and Tumor/Tissue Distribution in Mice
The
pharmacokinetics of an antibody (huA1)–drug (auristatin
microtubule disrupting MMAF) conjugate, targeting 5T4-expressing cells,
were characterized during the discovery and development phases in
female nu/nu mice and cynomolgus monkeys after a single dose and in
S-D rats and cynomolgus monkeys from multidose toxicity studies. Plasma/serum
samples were analyzed using an ELISA-based method for antibody and
conjugate (ADC) as well as for the released payload using an LC-MS/MS
method. In addition, the distribution of the Ab, ADC, and released
payload (cys-mcMMAF) was determined in a number of tissues (tumor,
lung, liver, kidney, and heart) in two tumor mouse models (H1975 and
MDA-MB-361-DYT2 models) using similar LBA and LC-MS/MS methods. Tissue
distribution studies revealed preferential tumor distribution of cys-mcMMAF
and its relative specificity to the 5T4 target containing tissue (tumor).
Single dose studies suggests lower CL values at the higher doses in
mice, although a linear relationship was seen in cynomolgus monkeys
at doses from 0.3 to 10 mg/kg with no evidence of TMDD. Evaluation
of DAR (drug–antibody ratio) in cynomolgus monkeys (at 3 mg/kg)
indicated that at least half of the payload was still on the ADC 1
to 2 weeks after IV dosing. After multiple doses, the huA1 and conjugate
data in rats and monkeys indicate that exposure (AUC) increases with
increasing dose in a linear fashion. Systemic exposure (as assessed
by <i>C</i><sub>max</sub> and AUC) of the released payload
increased with increasing dose, although exposure was very low and
its pharmacokinetics appeared to be formation rate limited. The incidence
of ADA was generally low in rats and monkeys. We will discuss cross
species comparison, relationships between the Ab, ADC, and released
payload exposure after multiple dosing, and insights into the distribution
of this ADC with a focus on experimental design as a way to address
or bypass apparent obstacles and its integration into predictive models