PF-05089771

Crucial for cell activities, ion channels are key drug discovery targets. Although small-molecule and peptide modulators dominate ion channel drug discovery, antibodies are emerging as an alternative modality. However, challenges persist in generating potent antibodies, especially for channels with limited extracellular epitopes. We herein present a bi-epitopic crosslinking strategy to overcome these challenges, focusing on Na_V1.7, a potential analgesic target. Aiming to crosslink two non-overlapping epitopes on voltage-sensing domains II and IV, we construct bispecific antibodies and ligand-antibody conjugates. Enhanced affinity and potency are observed in comparison to the monospecific controls. Among them, a ligand-antibody conjugate (1080-PEG₇-ACDTB) displays a two-orders-of-magnitude improvement in potency (IC₅₀ of 0.06 ± 0.01 nM) and over 1,000-fold selectivity for Na_V1.7. Additionally, this conjugate demonstrates robust analgesic effects in mouse pain models. Our study introduces an approach to developing effective antibodies against Na_V1.7, thereby initiating a promising direction for the advancement of pain therapeutics.

Physiologically based pharmacokinetic (PBPK) models are valuable for translating in vitro absorption, distribution, metabolism, and excretion (ADME) data to predict clinical pharmacokinetics, and can enable discovery and early clinical stages of pharmaceutical research. However, in predicting pharmacokinetics of organic anion transporting polypeptide (OATP) 1B substrates based on in vitro transport and metabolism data, PBPK models typically require additional empirical in vitro-to-in vivo scaling factors (ESFs) in order to accurately recapitulate observed clinical profiles. As model simulation is very sensitive to ESFs, a critical evaluation of ESF estimation is prudent. Previously studies have applied classic 'two-stage' and 'naïve pooled data' approaches in identifying a set of compound independent ESFs. However, the 'two-stage' approach has the parameter identification issue in separately fitting data for individual compounds, while the 'naïve pooled data' approach ignores interstudy variability, leading to potentially biased ESF estimates.

In this study, we have applied a nonlinear mixed-effect approach in estimating ESF of the PBPK model and incorporated additional data from 86 runs of in vitro uptake assay and 49 clinical studies of 12 training compounds in model development to further enhance the translation of in vitro data to predict the pharmacokinetics of OATP1B substrate drugs. To test predication accuracy of the model, a 'leave-one-out' analysis has been performed.

The established model can reasonably describe the clinical observations, with both mean values and interstudy variabilities quantified for ESF and volume of distribution parameters. The mean estimates are largely consistent with values in the previous reports. The interstudy variabilities of these parameters are estimated to be at least 50% (as coefficient of variation). Most compounds can be reasonably predicted in the 'leave-one-out' analysis.

This study improves the confidence in predicting the pharmacokinetics of OATP1B substrates in individual studies of small sample sizes, and quantifies the variability associated with the prediction.