Evaluation of hNav1.9 Screening Cascade for Analgesic Drug Discovery

Authors

Graham D. Smith, Katie L. Puddefoot, Sergiy Tokar, Alexander S. Haworth, Catherine M. Hodgson, Thomas D. M. Hill, Ayesha Jinat, Anthony M. Rush, Edward B. Stevens, Gary S. Clark

  • Metrion Biosciences Ltd., Cambridge, UK

Introduction

Encoded by the SCN11A gene, NaV1.9 is a voltage-gated sodium (NaV) channel highly expressed in trigeminal ganglion neurons and small-diameter nociceptors in the dorsal root ganglion. NaV1.9 acts as a threshold channel with a lower activation threshold, slower biophysical properties and a large window current compared to the other NaV isoforms1. These characteristics are important for its role in the regulation of neuronal excitability and the modulation of inflammatory and neuropathic pain. Clinically, NaV1.9 dysfunction has been implicated in altered pain perception in humans (Figure 1), evidencing its potential as a non opioid pain target2-4.

Figure 1 – Schematic of pain-related mutations in hNaV1.9 adapted from Kabata et al.5

High-throughput NaV1.9 drug discovery programmes have been hindered to date by the lack of the cellular tools and screening assays. Hence, the generation of a robust NaV1.9 screening cascade would greatly accelerate the development of selective NaV1.9 modulators without the side-effects associated with current pain treatment options.

Materials and Methods

  • Cell culture - A stably-expressing monoclonal CHO-hNaV1.9 cell line was generated in-house.
  • Automated patch clamp electrophysiology – For biophysical assessment, cells were held at -140 mV with 100 ms steps from -100 mV to +50 mV in 10 mV increments. For compound screening, cells stepped to -40mV (50 ms) from -140mV (0.05 Hz). IV stimulation was the same as manual patch, except 100 ms steps. Recordings were madeusing multi-hole on a Qube 384 (Sophion Bioscience).

Results

1. Biophysical assessment of hNaV1.9 using QPatch and Qube automated patch clamp

Figure 2 - hNaV1.9 channels have distinct biophysical properties compared to the other NaV isoforms1 (A). The IV relationship (B) and conductance (C) of hNaV1.9 currents recorded from 126 Qube 384 multi-hole wells. Voltage dependence of fast and slow Inactivation displayed in D. Recovery from fast and slow inactivation shown in E. Data for inactivation kinetics obtained using QPatch48.

2. Effects of GTPγS on hNaV1.9 on biophysics/pharmacology

Figure 3 - Enhanced G-protein signalling has been shown to potentiate NaV1.9 current amplitudes6. Addition of up to 500 µM intracellular GTPγS resulted in larger hNaV1.9 currents, with a depolarising shift V0.5 of activation (A,B). Importantly, GTPγS concentration did not alter hNaV1.9 pharmacology (C,D). A concentration of 200µM was used for routine screening.

3.Pharmacological assessment of hNaV1.9 and rNaV1.9 using Qube

Figure 4 – Validated Qube 384 assays testing a selection of NaV inhibitors with a range of potencies against hNaV1.9 (A) and rNaV1.9 (B). Representative I-t plots of vehicle or compound (at 10 µM) against hNaV1.9 are shown in C. Table with calculated IC50 values (D).

4. Blinded assessment of hNaV1.9 pharmacology using spiked plated approach

Figure 5 - The robustness of the Qube 384 assay was further validated by assessing the potency of TC-N 1752, using a randomised spiked plate approach (plate map - A). Vehicle response and TC-N 1752 potency correlated well between control and test wells (B, C). In test wells, the vehicle response displayed low variability with the TC-N 1752 response (at >0.1 µM) easily discernible above the mean vehicle response + 3 SD threshold (D).

Conclusions

  • hNaV1.9 biophysics from this cell line match the characteristics of native hNaV1.9
  • Pharmacology obtained for a variety of known sodium channel inhibitors against both hNaV1.9 and rNaV1.9
  • A robust screen sequence has been developed based around hNaV1.9 Qube 384 automated patch clamp assay to accelerate the development of selective NaV1.9 modulators for utility in the treatment of pain

References

  1. Dib-Hajj S. et al. Trends Neurosci. 2002;25(5):253-259
  2. Leipold E. et al. Nat. Genet. 2013;45(11):1399-1404
  3. Zhang X. Y. et al. Am, J. Hum. Genet. 2013;93(5):957-966
  4. Huang J. et al. Am. J. Hum. Genet. 2014;137(6):1627-1642
  5. Kabata R. et al. PLoSOne2018;13(12):e0208516
  6. VanoyeC.G.etal.J.Gen.Physiol.2013;141(2):193-202
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