Senior Principal Scientist Certara UK (Simcyp Division) SHEFFIELD, United Kingdom
Drug-target interactions can affect both small- and large-molecule drug pharmacokinetics (PK), with implications for dosing, efficacy and safety. In the case of monoclonal antibodies (mAbs), this manifests in the form of nonlinear PK when binding to the cell membrane antigens results in additional time-, concentration- and affinity-dependent receptor-mediated clearance. Mechanistic insight into TMDD is especially relevant in the case of antibody-drug conjugates (ADCs), as drug internalization will be related to the intracellular concentration of the small molecule toxic payload moiety.
Unlike plasma proteins, biochemical methods like ELISA or Western blotting are either unsuitable or insufficiently accurate in the case of tissue-embedded membrane proteins, which can compromise the predictions of target engagement. Quantitative protein mass spectroscopy data found in the PaxDb database contains concentration values for most of the proteome from different species and organs but is tabulated in parts per million (ppm) units which are not directly applicable to pharmacokinetic modelling. In this presentation, we demonstrate how to convert protein ppm concentration values from the PaxDb database into more appropriate molar values and then apply the insight obtained to analyze the tissue targeting specificity of mAbs against two widely used cancer targets: EGFR and HER2.
PBPK modelling predicts (and is supported by experimental data) that in humans less than 0.1% of the injected mAb or ADC dose may end up in solid tumour, the rest being catabolized in healthy tissues involving target-dependent and -independent processes. This questions the concept of mAbs as ‘Magic Bullets’ and we wish to discuss the constraints to tissue targeting that are set by physiology, the target and the mAb.
Learning Objectives:
Use publicly available protein mass-spectrometric data to obtain the tissue concentrations of membrane antigens. The values can then be used to parameterize PBPK models.
Use the PBPK models constructed to predict tissue distribution efficacy and interpret experimental PK and PD data.
Optimize the drug properties in silico (affinity and dosing) for maximum tissue targeting efficacy.
Gain better insight into tissue distribution, penetration and target engagement of biologics.