Inhibition of sodium conductance by cannabigerol contributes to a reduction of dorsal root ganglion neuron excitability

Abstract

Background and Purpose:

Cannabigerol (CBG), a non-psychotropic phytocannabinoid and a precursor of Δ⁹-tetrahydrocannabinol and cannabidiol, has been suggested to act as an analgesic. A previous study reported that CBG (10 μM) blocks voltage-gated sodium (Na_v) currents in CNS neurons, although the underlying mechanism is not well understood. Genetic and functional studies have validated Na_v1.7 channels as an opportune target for analgesic drug development. The effects of CBG on Na_v1.7 channels, which may contribute to its analgesic properties, have not been previously investigated.

Experimental Approach:

To determine the effects of CBG on Na_v channels, we used stably transfected HEK cells and primary dorsal root ganglion (DRG) neurons to characterize compound effects using experimental and computational techniques. These included patch-clamp, multielectrode array, and action potential modelling.

Key Results:

CBG is a ~10-fold state-dependent Na_v channel inhibitor (K_I-K_R: ~2–20 μM) with an average Hill-slope of ~2. We determined that, at lower concentrations, CBG predominantly blocks sodium G_max and slows recovery from inactivation. However, as the concentration is increased, CBG also induces a hyperpolarizing shift in the half-voltage of inactivation. Our modelling and multielectrode array recordings suggest that CBG attenuates DRG excitability.

Conclusions and Implications:

Inhibition of Na_v1.7 channels in DRG neurons may underlie CBG-induced neuronal hypoexcitability. As most Na_v1.7 channels are inactivated at the resting membrane potential of DRG neurons, they are more likely to be inhibited by lower CBG concentrations, suggesting functional selectivity against Na_v1.7 channels, compared with other Na_v channels (via G_max block).

1 |. INTRODUCTION

A key non-psychotropic phytocannabinoid, cannabigerol (CBG) (Pagano et al., 2015), is a precursor for the widely studied cannabis derivatives, Δ⁹-tetrahydrocannabinol (THC) and cannabidiol (CBD) (Nachnani et al., 2021). CBG has been proposed for many therapeutic applications (Mammana et al., 2019). The diversity of the disorders for which CBG is suggested as a potential therapeutic agent is paralleled by claims made for CBD (Ghovanloo & Ruben, 2021). Importantly, CBG has been suggested as an analgesic compound (Evans, 1991). However, the use of CBG is yet to be substantiated in a human clinical trial against any excitability-related conditions.

The pharmacological properties of CBG on some targets appear to share features with both THC and CBD (Nachnani et al., 2021). However, the mechanisms underlying CBG’s actions are less well understood than those underlying the actions of THC or CBD. CBG is known to modulate the activities of several targets, including various TRP channels (Muller et al., 2019). Previous investigations indicate that CBG (10 μM) decreases current density of voltage-gated sodium (Na_v) channels and action potential (AP) frequency in rat CA1 hippocampal neurons (Hill et al., 2014). However, the exact pharmacological nature of the interactions between CBG and Na_v channels and whether these interactions could be part of CBG’s potential anti-pain/analgesic behaviour remain unknown.

Altering the function of Na_v channels can disrupt electrical signalling (Catterall, 2012). Mutations in the Na_v channel isoforms of the peripheral nervous system (PNS), Na_v1.6–1.9, elicit pain-related conditions (Dib-Hajj & Waxman, 2019). The Na_v1.7 channel is regarded as the major Na_v isoform that acts as a threshold channel to trigger action potentials (AP) in DRG neurons. Gain-of-function mutations of Na_v1.7 channels produce several pain syndromes (Dib-Hajj et al., 2005; Fertleman et al., 2006). Recessive mutations in SCN9A that cause Na_v1.7 loss-of-function cause DRG neurons to become hypoexcitable and are linked with complete insensitivity to pain, which is not accompanied by motor, cardiac, or cognitive deficits (Cox et al., 2006; Goldberg et al., 2007). These findings showed Na_v1.7 channels to be a highly validated target for analgesic drug development. However, the efforts that have gone into the development of selective blockers of these channels have been clinically unsuccessful, thus far.

This failure has been proposed to be due to drug occupancy problems, in which drug administration many fold above the IC₅₀ results in loss of selectivity and, hence, off-target effects (Bankar et al., 2018). A more effective target engagement could be achieved if a compound exhibits two properties, in particular, ultra-hydrophobicity and functional selectivity. The compound hydrophobicity could be crucial, in that it would enhance absorption into the neuronal cells that are in fatty tissues. The functional selectivity could reduce likelihood of unwanted side effects. CBG, which has a calculated −LogD = 7.04, fits the hydrophobicity criteria. Here, we sought to determine whether CBG also fits the second criteria, with respect to functionally targeting of DRG excitability, at least in part, via inhibition of Na_v channels. Because the physicochemical properties of CBG and CBD are similar, and as CBD has been thoroughly described with respect to Na_v channel interactions (Ghovanloo & Ruben, 2021), we used the earlier CBD studies to guide our investigations of CBG activity in this study.

2 |. METHODS

2.1 |. Cell culture

Human embryonic kidney (HEK-293) (CLS Cat# 300192/p777_HEK293, RRID:CVCL_0045; ATCC, Manassas, VA, USA) cells were used for automated patch-clamp experiments. HEK-293 cells were stably transfected with human Na_v1.7 channels (Klugbauer et al., 1995). The human SCN1B cDNA construct was transfected into the cell line. All cells were incubated at 37°C/5% CO₂.

2.2 |. Automated patch-clamp

Automated patch-clamp recording was used for all HEK293 experiments. Sodium currents were measured in the whole-cell configuration using a Qube-384 (Sophion A/S, Copenhagen, Denmark) automated voltage-clamp system. Intracellular solution contained (in mM): 120 CsF, 10 NaCl, 2 MgCl₂, 10 HEPES, adjusted to pH 7.2 with CsOH. The extracellular recording solution contained (in mM): 145 NaCl, 3 KCl, 1 MgCl₂, 1.5 CaCl₂, 10 HEPES, adjusted to pH 7.4 with NaOH. Liquid junction potentials calculated to be ~7 mV were not adjusted for. Currents were low pass filtered at 5 kHz and recorded at 25-kHz sampling frequency. Series resistance compensation was applied at 100% and leak subtraction enabled. The Qube-384 temperature controller was used to maintain recording chamber temperature for all experiments at 22 ± 2°C at the recording chamber. Appropriate filters for cell membrane resistance (typically >500 MOhm), series resistance (<10 MOhm), and Nav current magnitude (>500 pA at a test pulse from a resting HP of −120 mV) were routinely applied to exclude poor quality recordings. Vehicle controls were run on each plate to enable correction for any compound-independent decrease of currents over time. Baselines were established after 20 min in vehicle. Fractional inhibition was measured as current amplitude from baseline to maximal inhibition after 20-min exposure to test compound unless otherwise noted. Normalized mean inhibition data were fit to the Hill–Langmuir equation:

$$\mathrm{Y}=[\mathrm{C}{]}^{\mathrm{h}}/\left({{\mathrm{I}\mathrm{C}}_{50}}^{\mathrm{h}}+[\mathrm{C}{]}^{\mathrm{h}}\right)$$ (1)

to estimate the half maximal inhibitory concentration (${\mathrm{I}\mathrm{C}}_{50}$ value), where $\mathrm{Y}$ is the normalized inhibition, $\mathrm{C}$ the compound concentration, ${\mathrm{I}\mathrm{C}}_{50}$ the concentration of test compound to inhibit the currents 50%, and h the Hill coefficient. Data analysis was performed using Analyzer (Sophion A/S, Copenhagen, Denmark) and Prism (GraphPad Software Inc., La Jolla, CA, USA) software. All HEK voltage-clamp experiments were done using the Qube.

2.3 |. Manual patch-clamp

Whole-cell patch-clamp recordings were performed using the same solution as above. All recordings were made using an EPC-10 patch-clamp amplifier (HEKA Elektronik, Lambrecht, Germany) digitized at 20 kHz via an ITC-16 interface (Instrutech, Great Neck, NY, USA). Voltage-clamping and data acquisition were controlled using PatchMaster/FitMaster (FITMASTER, RRID:SCR_016233) software (HEKA Elektronik, Lambrecht, Germany) running on a Windows computer. Current was low pass filtered at 10 kHz. Leak subtraction was performed automatically by software using a P/4 procedure following the test pulse. Giga-Ohm seals were allowed to stabilize in the on-cell configuration for 1 min prior to establishing the whole-cell configuration. Series resistance was less than 5 MΩ for all recordings. Series resistance compensation up to 80% was used when necessary. Before each protocol, the membrane potential was hyperpolarized to −100 mV to ensure complete availability of currents. All experiments were conducted at 22 ± 2°C. Analysis and graphing were done using FitMaster software (HEKA Elektronik) and Igor Pro (Wavemetrics, Lake Oswego, OR, USA) (IGOR Pro, RRID:SCR_000325). All DRG voltage-clamp experiments were done using manual patch-clamp.

2.4 |. Isolation of DRG neurons

Primary sensory neurons, to be used for multi-electrode array (MEA)/patch-clamp studies, were isolated from Sprague Dawley rat pups (Envigo. Indianapolis, IN, USA), following a protocol approved by the Animal Use Committee of the Department of Veterans Affairs West Haven Hospital. Animal studies are reported in compliance with the ARRIVE guidelines (Percie du Sert et al., 2020) and with the recommendations made by the British Journal of Pharmacology (Lilley et al., 2020).

DRGs from rat pups (P0–P5) were collected and neurons were dissociated as described previously (Cummins et al., 2009). Briefly, DRGs were incubated for 20 min at 37°C in complete saline solution (CSS) (in mM: 137 NaCl, 5.3 KCl, 1 MgCl₂, 25 sorbitol, 3 CaCl₂, and 10 HEPES, adjusted to pH 7.2 with NaOH) containing in 1.5 mg·ml⁻¹ Collagenase A (Roche) and 0.6-mM EDTA, followed by a 20-min incubation at 37°C in CSS containing 1.5 mg·ml⁻¹ Collagenase D (Roche), 0.6-mM EDTA, and 30 U·ml⁻¹ papain; DRGs were then triturated in 0.5 ml of DRG media (DMEM/F12 with 100 U·ml⁻¹ penicillin, 0.1 mg·ml⁻¹ streptomycin [Invitrogen], 2-mM L-glutamine, and 10% fetal bovine serum [Hyclone]) containing 1.5 mg·ml⁻¹ BSA (low endotoxin) and 1.5 mg·ml⁻¹ trypsin inhibitor (Sigma). After trituration, cell suspension was filtered with 70-μm cell strainer (Becton Dickinson). The cell suspension was centrifuged (100 g for 3 min). After neurons were settled for 50 min, each well was fed with 1.44 ml of DRG media (final volume 1.5 ml) supplemented with nerve growth factor (50 ng·ml⁻¹) and glial cell line-derived neurotrophic factor (50 ng·ml⁻¹) and maintained at 37°C in a 95% air/5% CO₂ (v/v) incubator for 3 days before MEA recording.

For patch-clamp recordings in DRG neurons, the same procedure as for DRG neuron isolation for MEA was used except that after trituration, 100 μl of cell suspension was seeded directly onto each poly-D-lysine/laminin-coated coverslips (BD) and incubated at 37°C in a 95% air/5% CO₂ (v/v) incubator. After 45 min for neurons to attach to the coverslips, DRG media was added into each well to a final volume of 1.0 ml, and the neurons were maintained at 37°C in a 95% air/5% CO₂ (v/v) incubator before patch-clamp recording.

Overall, we used six animals and four preps for DRG experiments, with the associated n number for each dataset being presented in respective figure legends.

2.5 |. Compound preparation

Powdered CBG was dissolved in 100% DMSO to yield a stock solution (20 mM). The stock solution was used to prepare drug solutions in extracellular solutions at various concentrations with no more than 0.5% total DMSO content. TTX was dissolved in water and diluted in extracellular solutions.

2.6 |. Activation protocols

To determine the voltage dependence of activation, we measured the peak current amplitude at test pulse potentials ranging from −120 mV to +30 mV in increments of +5 mV for 500 ms. Channel conductance ($\mathrm{G}$) was calculated from peak ${\mathrm{I}}_{\mathrm{N}\mathrm{a}}$:

$${\mathrm{G}}_{\mathrm{N}\mathrm{a}}={\mathrm{I}}_{\mathrm{N}\mathrm{a}}/\mathrm{V}-{\mathrm{E}}_{\mathrm{N}\mathrm{a}}$$ (2)

where ${\mathrm{G}}_{\mathrm{N}\mathrm{a}}$ is conductance, ${\mathrm{I}}_{\mathrm{N}\mathrm{a}}$ is peak sodium current in response to the command potential $\mathrm{V}$, and ${\mathrm{E}}_{\mathrm{N}\mathrm{a}}$ (measured on IV relationships) is the Nernst equilibrium potential. Calculated values for conductance were fit with the Boltzmann equation:

$$\mathrm{G}/{\mathrm{G}}_{\mathrm{m}\mathrm{a}\mathrm{x}}=1/\left(1+\mathrm{e}\mathrm{x}\mathrm{p}\left[{\mathrm{V}}_{1/2}-{\mathrm{V}}_{\mathrm{m}}\right]/\mathrm{k}\right)$$ (3)

where $\mathrm{G}/{\mathrm{G}}_{\mathrm{m}\mathrm{a}\mathrm{x}}$ is normalized conductance amplitude, ${\mathrm{V}}_{\mathrm{m}}$ is the command potential, ${\mathrm{V}}_{1/2}$ is the midpoint voltage, and k is the slope.

2.7 |. Steady-state inactivation protocols

The voltage dependence of fast inactivation was measured by preconditioning the channels from −120 to +30 mV in increments of 5 mV for 500/200 ms, followed by a 10-ms test pulse during which the voltage was stepped to −20 mV. Normalized current amplitudes from the test pulse were fit as a function of voltage using the Boltzmann equation:

$$\mathrm{I}/{\mathrm{I}}_{\mathrm{m}\mathrm{a}\mathrm{x}}=1/\left(1+\mathrm{e}\mathrm{x}\mathrm{p}\left[{\mathrm{V}}_{1/2}-{\mathrm{V}}_{\mathrm{m}}\right]/\mathrm{k}\right)$$ (4)

where ${\mathrm{I}}_{\mathrm{m}\mathrm{a}\mathrm{x}}$ is the maximum test pulse current amplitude. The steady-state slow inactivation protocols involved step pulses from −120 mV to 20 mV for 5 or 10 s, followed by 100 ms recovery interval at −120 mV, followed by a test pulse to −20 mV.

2.8 |. State dependence protocols

To determine state dependence, potency was measured from three different holding potentials (−110, −100, and −90 mV). The protocol started with a holding potential of −110 mV followed by 180 × 20 ms depolarizing pulses to 0 mV at 1 Hz. Then, the holding potential was depolarized by 10 mV, and the 180-pulse protocol was repeated until −90 mV was reached.

2.9 |. Recovery from inactivation protocols

Recovery from inactivation was measured by holding the channels at −120 mV, followed by a depolarizing pulse to 0 mV, then the potential was returned to −120 mV. This was followed by a depolarizing 10-ms test pulse to 0 mV to measure availability. Recovery from inactivation was measured after pre-pulse durations of 20 ms, 500 ms, and 5 s and fit with a bi-exponential function of the form:

$$\mathrm{S}\mathrm{p}\mathrm{a}\mathrm{n}\mathrm{F}\mathrm{a}\mathrm{s}\mathrm{t}=(\mathrm{Y}\mathrm{O}-\mathrm{P}\mathrm{l}\mathrm{a}\mathrm{t}\mathrm{e}\mathrm{a}\mathrm{u})\mathrm{*}\mathrm{P}\mathrm{e}\mathrm{r}\mathrm{c}\mathrm{e}\mathrm{n}\mathrm{t}\mathrm{F}\mathrm{a}\mathrm{s}\mathrm{t}\mathrm{*}0.01$$ (5)

$$\mathrm{S}\mathrm{p}\mathrm{a}\mathrm{n}\mathrm{S}\mathrm{l}\mathrm{o}\mathrm{w}=(\mathrm{Y}\mathrm{O}-\mathrm{P}\mathrm{l}\mathrm{a}\mathrm{t}\mathrm{e}\mathrm{a}\mathrm{u})\mathrm{*}(100-\mathrm{P}\mathrm{e}\mathrm{r}\mathrm{c}\mathrm{e}\mathrm{n}\mathrm{t}\mathrm{F}\mathrm{a}\mathrm{s}\mathrm{t})\mathrm{*}0.01$$ (6)

$$\mathrm{Y}=\mathrm{P}\mathrm{l}\mathrm{a}\mathrm{t}\mathrm{e}\mathrm{a}\mathrm{u}+\mathrm{S}\mathrm{p}\mathrm{a}\mathrm{n}\mathrm{F}\mathrm{a}\mathrm{s}\mathrm{t}\mathrm{*}\mathrm{e}\mathrm{x}\mathrm{p}(-\mathrm{K}\mathrm{F}\mathrm{a}\mathrm{s}\mathrm{t}\mathrm{*}\mathrm{t})+\mathrm{S}\mathrm{p}\mathrm{a}\mathrm{n}\mathrm{S}\mathrm{l}\mathrm{o}\mathrm{w}\mathrm{*}\mathrm{e}\mathrm{x}\mathrm{p}(-\mathrm{K}\mathrm{S}\mathrm{l}\mathrm{o}\mathrm{w}\mathrm{*}\mathrm{t})$$ (7)

where $\mathrm{t}$ is time in seconds, $\mathrm{Y}0$ is the $\mathrm{Y}$ intercept at $\mathrm{t}=0$, KFast and KSlow are rate constants in units the reciprocal of t, and PercentFast is the fraction of the $\mathrm{Y}$ signal attributed to the fast-decaying component of the fit.

2.10 |. Kinetics of inhibition

The kinetics of CBG block were measured at three potentials at 15 μM. The channels were held at respective holding potentials followed by pulses to −20 mV. The blocked sodium current was normalized to vehicle and subsequently fit with a single exponential function:

$$\mathrm{Y}=(\mathrm{Y}\mathrm{O}-\mathrm{P}\mathrm{l}\mathrm{a}\mathrm{t}\mathrm{e}\mathrm{a}\mathrm{u})\mathrm{*}\mathrm{e}\mathrm{x}\mathrm{p}(-\mathrm{K}\mathrm{*}\mathrm{t})+\mathrm{P}\mathrm{l}\mathrm{a}\mathrm{t}\mathrm{e}\mathrm{a}\mathrm{u}$$ (8)

2.11 |. Onset of closed-state fast inactivation

Channels were held at −120 mV followed by a depolarizing pulse to −60 mV for increasing time intervals, followed by a 10-ms test pulse to −20 mV, then the channels were brought back to −120 mV for the subsequent pulse.

2.12 |. Action potential modelling

Neuronal action potential modelling was based on a modified Hodgkin–Huxley model (Hodgkin & Huxley, 1952; Verma et al., 2020). The model was modified to match the properties of DRG cells (Hodgkin & Huxley, 1952; Verma et al., 2020). The modified parameters were based on electrophysiological results obtained from whole-cell patch-clamp experiments in this study. The model accounted for activation voltage dependence, steady-state inactivation voltage dependence, and peak sodium currents. The model included both TTX-S (sensitive) and TTX-R (resistant) sodium current (as per the equations provided in the original Verma et al. paper). The modifications to the model parameters were performed with assumption that CBG is a non-selective compound in modulating both types of currents.

2.13 |. Multi-electrode array recordings

Multi-electrode array (MEA) experiments were performed at 22°C with a multi-well MEA system (Maestro, Axion Biosystems) according to a recently developed protocol (Yang et al., 2016). Briefly, DRGs were dissociated and cultured on MEA plates, maintained at 37°C in a 5% CO₂ incubator. A 12-well recording plate was used, embedded with a total of 768 electrodes. For each experiment, multiple wells were used to assess rat DRGs. Each well of 12-well MEA plate (Axion Biosystems) was coated with poly-D-lysine (50 μg·ml⁻¹) and laminin (10 μg·ml⁻¹).

2.14 |. Data analysis and statistics

The data analysis comply with the British Journal of Pharmacology recommendations on experimental design and analysis in pharmacology (Curtis et al., 2018). Normalization was performed in order to control the variations in sodium channel expression and inward current amplitude and in order to be able to fit the recorded data with Boltzmann function (for voltage dependences) or an exponential/bi-exponential function (for time courses of inactivation). The Sophion Qube is an automated electrophysiology instrument that is blinded to cell selections and experimentation, and selection was performed in a randomized manner. All subsequent data filtering and analysis was performed in a non-biased manner, in which automated filters were applied to the entire dataset from a given Qube run. Fitting and graphing were carried out using Prism 9 software (Graphpad Software Inc., San Diego, CA) (PRISM, RRID:SCR_005375) (GraphPad, RRID:SCR_000306), unless otherwise noted. All statistical P values report the results obtained from tests that compared experimental conditions to the control conditions. One-way ANOVA when multiple concentrations were each being compared with vehicle, and the t test when overall two conditions were being compared. A level of significance α = 0.05 was used with P values less than 0.05 being considered to be statistically significant. All values are reported as means ± SEM or errors in fit, when appropriate, for n recordings or samples. The declared group size (n) is the number of independent values, and the data were analysed statistically using these independent values.

2.15 |. Materials

CBG and TTX were purchased from Cayman Chemicals (Ann Arbor, Michigan, USA).

2.16 |. Nomenclature of targets and ligands

Key protein targets and ligands in this article are hyperlinked to corresponding entries in http://www.guidetopharmacology.org, and are permanently archived in the Concise Guide to PHARMACOLOGY 2021/22 (Alexander, Christopoulos, et al., 2021; Alexander, Mathie, et al., 2021).

3 |. RESULTS

3.1 |. CBG is a state-dependent inhibitor of sodium currents

Our first goal for this study was to determine the concentration–response of CBG on Na_v currents and to find out whether there is a state-dependence for inhibition. To do this, we recorded sodium currents in HEK293 stably expressing hNa_v1.7 channels. We used a protocol to examine state-dependent inhibition across a range of holding potentials where channel inactivation varied (Kuo & Bean, 1994). We first held channels at a holding potential of −110 mV where channels are almost all in the resting state, while pulsing the channels 180 times at 1 Hz to reach equilibrium (vehicle addition is shown in Figure S1). Then, we depolarized the holding potential by 10 mV two more times and repeated the pulse train at each voltage (Figure 1a). To construct the concentration–response, individual cells were exposed to single concentrations of CBG, then normalized inhibition at each concentration was pooled and fit with a Hill–Langmuir equation, yielding values for IC₅₀ (4.6–18.8 μM) and Hill slopes (1.2–1.6). Representative current traces are shown in Figure 1a, and the fractional block of sodium currents from the last pulse from each holding-potential is shown in Figure 1b. Interestingly, the sodium current inhibition has steep Hill slopes ranging 1.2–2.3 across all three holding potentials (Figure 1b). This suggests that multiple interactions contribute to CBG inhibition.

FIGURE 1.

State dependence of CBG as a voltage-dependent sodium channel inhibitor. (a) Pulse protocol showing 180 pulses run at 1 Hz at each holding potential and representative current traces. (b) CBG potency at three holding potentials at pulse 180 (3 min) in Na_v1.7 channels (IC₅₀ [μM]: −110 mV = 18.8 ± 2.9, −100 mV = 9.3 ± 1.0, −90 mV = 4.6 ± 1.1; slope: −110 mV = 1.2 ± 0.3, −100 mV = 2.3 ± 0.5, −90 mV = 1.6 ± 0.4; data shown as means ± SEM; n = 9–19). Structure of CBG is shown at the top left. (c) Kinetics of inhibition of Na_v1.7 channels at the noted holding potentials. (d) Distribution of kinetic time constants; data shown as individual values with mean ± SEM (n = 7–14). (e) Apparent Kd at different voltages was well fitted with a 4-state model invoking different potencies for resting and inactivated-state block. (f) Manual voltage-clamp of primary rat DRG neurons; data shown as individual values with mean ± SEM (n = 5). The experiments in panels (a)–(e) were performed using automated, and the data in panel (f) were performed using manual patch-clamp

To ensure that CBG and the Na_v1.7 channels reached equilibrium, we performed another set of experiments, where we pulsed the channels 240 times at 1 Hz (4 min) and compared the current amplitude at 3 min and at 4 min; the results (Figure S2) indicated that there was no difference between the data obtained at these two time points.

Next, we investigated the kinetics of the inhibition by CBG of Na_v1.7 channels, by measuring peak I_Na amplitude over 3 min during a series of pulses to −20 mV from the same three holding potentials as above. The observed rates of equilibration (time constant observed, τ_obs) of inhibition were measured by fitting single exponential decays to inhibition of currents. The fraction of inhibition was normalized against the response in vehicle and plotted against the time elapsed after CBG (15 μM) addition, which was set at t = 0 (Figure 1c,d). We found that the rate at which 15-μM CBG inhibits Na_v1.7 channels becomes faster as the holding potential is depolarized. This voltage-dependent change in inhibition kinetics is congruent with the IC₅₀ relationships in Figure 1b and is consistent with the idea of CBG being a state-dependent Na_v inhibitor.

Figure 1e shows a plot of the inverse of the apparent IC₅₀ fit with a 4-state binding model that used parameters obtained from the Boltzmann fit of the voltage dependence of steady-state inactivation (SSI) from a 500-ms pre-pulse (Featherstone et al., 1996; Ghovanloo et al., 2021; Kuo & Bean, 1994; Richmond et al., 1998). The potency numbers were based on the results shown in Figure 1b. This established that the apparent potency is directly related to the proportion of inactivated channels at different holding-potentials. These results demonstrate that CBG inhibits the sodium current from both rest (~23 μM) and inactivated states (~2 μM). The potency of CBG is about ~11-fold greater for inhibiting inactivated compared with resting states (Figure 1e).

To determine, whether CBG lacks structural selectivity (similar to CBD; Ghovanloo, Shuart, et al., 2018; Sait et al., 2020), we voltage-clamped isolated rat DRG neurons (held at −100 mV and pulsed to −40 or −55 to elicit maximal current). The bath solutions contained 300-nM tetrodotoxin (TTX), to block all the TTX-sensitive Na_v channels (IC₅₀ of TTX-sensitive channels is ~10–30 nM; Ghovanloo et al., 2021; Hille, 2001). Then, we perfused 10-μM CBG for ~3 min (time-matched to the experiments shown in Na_v1.7-HEK cells in Figure 1b) and measured peak amplitude differences of the TTX-resistant Na_v current in response to CBG (Figure 1f). Our results suggested that 10-μM CBG, on average, inhibited ~50% of the TTX-resistant current in rat DRG, which is in close agreement with the data in Figure 1b. This suggests that, like CBD, CBG lacks structural selectivity among Na_v channels (Ghovanloo & Ruben, 2021; Ghovanloo, Shuart, et al., 2018). However, similar to CBD, it is likely that CBG has state selectivity for channels that tend to occupy deeper inactivated states (Zhang & Bean, 2021) (The degree of inactivation is related to the time course of a depolarization pulse (Gawali & Todt, 2016)).

3.2 |. CBG prevents Na_v channel opening, but does not affect activation voltage dependence

We next examined the effects of CBG on Na_v channel activation by measuring peak channel conductance at membrane potentials between −120 and +30 mV. We show the effects of 15-μM CBG on peak conductance as a function of membrane potential (Figure 2a). About ~90% of the sodium conductance was inhibited at 15-μM CBG. Next, we plotted the maximal sodium conductance (G_max) from cells exposed to 1- to 30-μM CBG at −25 mV (potential at which maximal current was elicited). Our results indicate that CBG imparts a concentration-dependent decrease in G_max (Figure 2b). This decrease started to become statistically significant compared with vehicle at 4 μM.

FIGURE 2.

CBG does not alter activation and inhibits conductance in Na_v1.7 channels. (a) Conductance difference in Na_v1.7 channels after exposure to vehicle or 15-μM CBG, as a function of membrane potential. The holding potential was −120 mV (refer to inset in panel d). (b) Quantification of peak macroscopic conductance at −25 mV across different CBG concentrations. Data shown as individual values with means ± SEM (n = 8–17). (c) Average current density of hNa_v1.7 channels after exposure to vehicle or 15-μM CBG. Data shown as means ± SEM. (d) Voltage dependence of activation as normalized conductance plotted against membrane potential. Data shown as means ± SEM (vehicle: V_1/2 = −48.6 ± 1.6 mV, slope = 7.7 ± 1.4, n = 10; CBG: V_1/2 = −46.4 ± 5.6 mV, slope = 5.2 ± 2.2, n = 10). (e) Normalized current density, further displaying unaltered activation. Data shown as means ± SEM. (f) Midpoints of activation (V_1/2) across concentrations (in mV). Data shown as individual values with means ± SEM (n = 8–17)

In Figure 2c, we show a plot of sodium current density as peak I_Na divided by membrane capacitance (nA/pF) as a function of membrane potential, which consistent with the G_max, shows a ~90% reduction in magnitude at 15 μM. The normalized conductance is plotted against membrane potential (Figure 2d), showing that neither 15 μM nor any of the other CBG concentrations induce large changes in the midpoint (V_1/2) or apparent valence (slope, k) of activation of the available Na_v channels (Figures 2d–f and S3A). Therefore, exposure to CBG concentration-dependently prevented Na_v channels from conducting, although such exposure did not alter the voltage dependence of activation. This effect is similar to that reported for CBD (Ghovanloo, Shuart, et al., 2018).

3.3 |. CBG concentration-dependently hyperpolarizes the inactivation curve, but does not alter open-state inactivation

We next measured the voltage dependence of inactivation from a 500-ms pre-pulse duration. Generally, durations that are in the range of low hundreds of milliseconds are considered more indicative of fast inactivation than slow inactivation. However, 500 ms may be considered to trigger an intermediate amount of inactivation. Indeed, measuring inactivation using longer pre-pulses would be more physiologically relevant for Na_v1.7 channels which are predominantly found in cells where the resting membrane potential (RMP) is by in large depolarized compared with the channel availability V_1/2 (RMP = ~−60 mV; Amir et al., 1999). The normalized current amplitudes at the test-pulse are shown as a function of 500-ms pre-pulse voltages (Figure 3a–c). Our results suggested that while there is a concentration-dependent shift in the inactivation curves, the shifts started to become statistically significant from vehicle at 15 μM (Figure 3d). We also found that the slopes of the inactivation curves were not significantly different from vehicle, at any of the concentrations used (Figure S3B). The current at the test pulse was inhibited by ~90% at 15 μM; however, unlike activation, the voltage dependence of SSI of the remaining current was significantly hyperpolarized, by ~12 mV. This indicates CBG increased the propensity for channels to inactivate over the 500-ms pre-pulse in channels that were not inhibited from opening from rest, further suggesting that CBG stabilizes the inactivated states of Nav channels. Thus, the overall effect of hyperpolarizing the inactivation curves by CBG is similar to that reported for CBD (Ghovanloo, Shuart, et al., 2018).

FIGURE 3.

CBG hyperpolarizes 500-ms inactivation curve in Na_v1.7 channels and does not alter open-state fast inactivation. (a, b) Sample macroscopic current traces after exposure to vehicle (Veh) or 15-μM CBG. (c) Voltage dependence of 500-ms inactivation as normalized current plotted against membrane potential fit with Boltzmann. (d) Quantification of SSI midpoints (in mV). Data shown as individual values with means ± SEM (n = 10–33). * P<0.05, significantly different from vehicle. (e) Mean open-state fast inactivation time constants (ms). Data shown as individual values with means ± SEM (n = 5–21). (f) Protocol used for experiments to obtain data in (e)

To ensure that CBG’s hyperpolarizing effect on the inactivation curve is reproducible during shorter pre-pulse durations, we performed another set of experiments at 200 ms (Figure S4). As expected, Na_v1.7 channels displayed a more depolarized inactivation curve at 200 ms than 500 ms (Figure S4A), which is consistent with channels accumulating more inactivation with longer depolarization durations at each step (at 500 ms). Furthermore, our results indicate that even at 200 ms, CBG displays similar effects on the inactivation curve as it does after 500 ms, with the shift in the SSI curve becoming significantly different from vehicle at 15 μM (Figure S4B).

We also measured the rate time constant of open-state inactivation at −25 mV (also known as true fast inactivation; Ahern, 2013) using trace fitting by an exponential function, which did not differ significantly at any of the CBG concentrations (1–15 μM, the current amplitudes were too small for reliable curve fitting at 30 μM, as shown in Figure 2b) compared with vehicle (Figure 3e,f).

3.4 |. CBG stabilizes inactivated states of Na_v channels by slowing recovery

To assess the time dependence and degree of stabilization of the inactivated state, we then measured the recovery from inactivation of Na_v1.7 channels, at different CBG concentrations (1–30 μM). This was done after depolarizing pre-pulse durations of 20 ms (fast inactivated), 500 ms (intermediate inactivated), or 5 s (slow inactivated), from a holding potential of −120 mV. To measure recovery from inactivation, we held the channels at −120 mV to ensure that the channels were fully available, then pulsed the channels to −20 mV at one of the above durations and allowed different time intervals at −120 mV to measure recovery as a function of time (Figure 4a). The mean normalized recovery following the pre-pulse in vehicle and various CBG concentrations were plotted and fit with a bi-exponential function (Figure 4b–d).

FIGURE 4.

CBG slows recovery from inactivation in Na_v1.7 channels. (a) The protocol that was used to measure CBG effect on channel recovery from duration pre-pulses. (b) Recovery from inactivation in the presence of 0 (Veh)-30 μM CBG, from 20 ms (Veh: τ_Fast = 4.5 ± 0.2 ms, τ_Slow = 69.7 ± 22 ms; 1 μM: τ_Fast = 5.0 ± 0.1 ms; τ_Slow = 110.9 ± 39 ms; 4 μM: τ_Fast = 5.1 ± 0.3 ms, τ_Slow = 74.3 ± 36 ms; 7 μM: τ_Fast = 6.5 ± 0.7 ms, τ_Slow = 56.6 ± 15 ms; 15 μM: τ_Fast = 9.9 ± 1.7 ms, τ_Slow = 78 ± 12 ms; 30 μM: τ_Fast = 11.6 ± 2.0 ms, τ_Slow = 99 ± 13 ms; n = 14–24). (c) 500 ms (Veh: τ_Fast = 5.0 ± 0.3 ms, τ_Slow = 154 ± 16 ms; 1 μM: τ_Fast = 5.6 ± 0.2 ms; τ_Slow = 201 ± 16 ms; 4 μM: τ_Fast = 6.7 ± 0.5 ms, τ_Slow = 217 ± 21 ms; 7 μM: τ_Fast = 14 ± 1.6 ms, τ_Slow = 305 ± 35 ms; 15 μM: τ_Fast = 60.4 ± 4.7 ms, τ_Slow = 936 ± 173 ms; 30 μM: τ_Fast = 18.3 ± 3.8 ms, τ_Slow = 349 ± 25 ms; n = 13–28). (d) 5 s (Veh: τ_Fast = 5.5 ± 0.6 ms, τ_Slow = 280 ± 16 ms; 1 μM: τ_Fast = 6.8 ± 0.8 ms; τ_Slow = 590 ± 27 ms; 4 μM: τ_Fast = 4.3 ± 0.9 ms, τ_Slow = 832 ± 58 ms; 7 μM: τ_Fast = 7.2 ± 3.0 ms, τ_Slow = 850 ± 52 ms; 15 μM: τ_Fast = 87 ± 26 ms, τ_Slow = 1066 ± 115 ms; 30 μM: τ_Fast = 4.6 ± 3.6 ms, τ_Slow = 674.5 ± 41 ms; n = 5–25). (e, f) The slow components of recovery from inactivation in vehicle and CBG at 20 ms, 500 ms, and 5 s are shown on the left Y-axis, and the fraction of slow to fast component of recovery from inactivation is shown on the right Y-axis

Our results suggested that, when the channels are fast-inactivated, the lower concentrations of 1–4 μM do not alter the time course of recovery compared with vehicle, and only at 7 μM can a slowing of recovery become detectable. However, at the higher CBG concentrations of 15–30 μM, even 20 ms of inactivation accumulation is sufficient to significantly slow channel recovery (Figure 4b). When the pulse duration is increased to 500 ms, we see that both of lower concentrations, notably 4 μM, begin to display a slowing of the recovery (Figure 4c). As expected, after 5 s of inactivation accumulation, even the lowest concentration of 1 μM causes a major slowing of recovery (Figure 4d). This is an important finding, as the inactivation V_1/2 of Na_v1.7 channels is hyperpolarized relative to the RMP of DRG neurons, and therefore, at RMP (when there is no AP firing), much of the membrane-bound Na_v1.7 channels are in the inactivated state, which would be closer to the data shown in Figure 4d than those in Figure 4b, which suggests that under physiological conditions, relatively lower concentrations of CBG would prevent Na_v1.7 channels from opening. To further illustrate this point, we show the τ_Slow and fraction of the recovery fit with τ_Slow plotted in Figure 4e,f, which show that CBG increases the fraction of recovery that is slow and the time constant of the slow component of recovery from inactivation from all three pre-pulse durations. This further indicates that CBG slows the recovery from inactivation, supporting the idea that CBG stabilizes the inactivated states of the channel. However, it does so at lower concentrations (e.g., 4 vs. 30 μM) than would be expected, based on the steady-state measurements shown in Figure 3c,d. This could have major physiological implications.

3.5 |. CBG accelerates onset of closed-state fast inactivation

To assess the effect of CBG on entry into closed-state fast inactivation, we performed a series of experiments in which we maintained channels at −120 mV to ensure all channels are available. Then, we pulsed the channels to −60 mV, which is approximately the RMP of DRG (Amir et al., 1999) neurons at increasing intervals of time, up to 1 s, followed by a 10-ms test pulse at −20 mV to measure current amplitude. Our results indicate that CBG concentration-dependently increases closed-state fast inactivation onset, and that even at the highest concentration of 30 μM, ~10 ms of a depolarizing pulse is needed to speed up onset (Figure S5). This further supports our previous findings that suggested that CBG did not alter open-state fast inactivation, as true open-state fast inactivation occurs over time course of only a couple of milliseconds, substantially less than 10 ms.

3.6 |. CBG hyperpolarizes steady-state slow inactivation

Next, we measured the effect of CBG on steady-state slow inactivation at 5 and 10 s durations (Figure S6). To do this, we held channels at −120 mV, followed by depolarizing pulses for either 5 or 10 s, which was followed by a hyperpolarizing pulse back to −120 mV for 100 ms to recover the channels that were fast-inactivated (the selection of 100 ms was based on the recovery data shown in Figure 4b). Finally, the current amplitude was measured by a test pulse to −20 mV (Figure S6). Consistent with our previous findings (i.e., recovery from 5 s of inactivation in Figure 4d), we found that CBG concentration-dependently also stabilizes the steady-state slow inactivated states of Nav1.7, and that this effect becomes more pronounced as the channels enter deeper inactivated states (i.e., more so after 10 s than 5 s; Gawali & Todt, 2016) (Figure S6). Additionally, CBG’s effect on slow inactivation became statically significant at 7 μM, unlike the 200 ms and 500 ms data where this effect occurred at 15 μM. This further supports the idea that as channels accumulate more inactivation, lower concentrations of CBG induce more pronounced effects on the gating of Na_v channels.

3.7 |. CBG inhibits conductance more potently than it hyperpolarizes inactivation

The fundamental differences between reducing conductance and left-shifting availability have important mechanistic pharmacological consequences. A compound that reduces G_max reduces the number of available channels from opening; conversely, a compound that left-shifts inactivation increases the likelihood of the channels that are available to open, to have an increased propensity to inactivate. Although both mechanisms contribute to a loss of excitability, a reduced G_max may be preferable to prevent AP firing in the first place. As shown in Figures 2 and 3, the significant effects of CBG on G_max and inactivation V_1/2 started to occur at different concentrations. To quantify this difference between the two effects, we first subtracted mean number across CBG concentrations from the mean number from vehicle from Figure 2b (G_max) and Figure 3d (Inactivation V_1/2). This calculation provided the difference between CBG effects at a given concentration, compared with vehicle. Then, we divided the difference for each of G_max and V_1/2 by the corresponding data for vehicle, to obtain the the CBG effect as a percentage of the vehicle effect. The results from these calculations are plotted in Figure 5a, which show that CBG alters the Na_v channel G_max at lower concentrations, compared to the inactivation V_1/2. Interestingly, by the time 15-μM CBG is reached, only ~10% of the vehicle G_max is left.

FIGURE 5.

CBG effects on conductance versus inactivation. (a) Comparison of concentration-dependent effects of CBD on G_max with effects on inactivation, as a percentage of vehicle effects. (b) Normalized relationship of the data from (a) and fit with the Hill equation (inactivation = 13.3 ± 1.0, G_max = 3.4 ± 1.0). (c) Diagram of the concentration-dependent modality of CBG effects on Nav channels. Given than CBG is highly hydrophobic, it readily partitions into the membrane. Once inside the membrane it interacts with both the (1) resting state and (2) inactivated states of the Nav channel, but with a greater affinity for the inactivated state. At lower concentrations, CBG predominantly prevents channels from opening, and hence (3) G_max block. At higher concentrations, in addition to G_max, CBG also (4) enhances inactivation

Next, to obtain an approximate potency difference between the two effects, we normalized the numbers in Figure 5a, to the maximal values, which are plotted in Figure 5b. This normalization was based on a logical assumption of physiological pharmacology. Although the percentage shift of V_1/2 at the maximal concentration of 30 μM is only at about 25% of vehicle, because 15–30 μM almost completely abolish G_max, then any further shifts in V_1/2 would be physiologically inconsequential. In other words, if no sodium current is left, an increased likelihood of inactivation would be irrelevant. When we fitted the data with the Hill equation, we found that CBG’s IC₅₀ in inhibiting G_max is approximated to 3.5 μM, with the IC₅₀ for V_1/2 being estimated to 13.2 μM. As higher concentrations of CBG are likely to culminate in modulation of diverse targets, we suggest that CBG’s potential therapeutic value in reducing Na_v channel activity with respect to pain is more likely stem from inhibiting G_max, and not from hyperpolarization of the inactivation curves.

Overall, our results on the empirical effects of CBG on Na_v channels suggest that, due to its high hydrophobicity (a higher partitioning/distribution of CBG in lipid vs. water indicates a greater preference for the membrane phase at equilibrium), CBG readily enters the membrane, from where it interacts with the resting and inactivated states of Na_v channels, though with a greater affinity for the channels that are in the inactivated conformations. If the RMP is such that a given Na_v channel is chronically accumulating inactivation, such as Na_v1.7 channels in DRG, then lower levels of CBG in the membrane would be sufficient to keep channels from opening, which would culminate in cellular reduction of excitability (Figure 5c).

3.8 |. CBG reduces neuronal excitability in a Hodgkin–Huxley model of DRG neurons

To test the effects of CBG on neuronal excitability, we used a modified version of the Hodgkin–Huxley model to simulate a DRG neuron’s excitability, which includes both TTX-R and TTX-S currents (Hodgkin & Huxley, 1952; Verma et al., 2020). We ran two simulations of the CBG condition, at 2 and 15 μM. As the RMP in the DRG is estimated to ~−60 mV, based on the 4-state model in Figure 1e, we determined that 2-μM CBG would prevent opening of ~30% of Na_v channels. Therefore, in the 2-μM simulation, we only reduced the overall sodium conductance, by about 30%. In the 15-μM simulation, we reduced the sodium conductance by 87% and hyperpolarized the inactivation curve by 12 mV, which is based on Figure 3c,d. In both sets of compound simulations, we assumed CBG to be non-selective in modulating both TTX-R and TTX-S currents. This assumption was based on the data in Figure 1f, along with the reports of the non-selectivity of CBD in modulating the gating of Na_v channels (Ghovanloo et al., 2021; Ghovanloo, Shuart, et al., 2018). In our simulations, the channels were given a series of step-wise current injections with increasing intensities at each step for 100 ms. Each 100 ms step was followed by a 50 ms recovery period in which no current injection was applied (Figure 6a,b). Our results suggest that the peak amplitude of the first AP in 2-μM CBG is smaller than vehicle. This is consistent with the reduction of peak sodium conductance caused by CBG. Consistent with the marked effects of 15 μM on conductance and inactivation, the resulting simulations displayed large reductions of excitability across stimulus injection intensities.

FIGURE 6.

CBG reduces excitability in an action potential model. (a) Simulation of the effects of CBG on action potential morphology over a series of increasing current injection intensities. (b) Zoomed-in simulation of action potentials from the first interval shown in (a). (c) Number of peaks in CBG divided by number of peaks in vehicle (Veh = 100%; 2 μM = 64%; 15 μM = 9%)

The overall effect of CBG on the AP electrogenesis is that at all current injection intensities, CBG reduces the number of APs, leading to a net loss of excitability. To further visualize and quantify these simulation predictions, we normalized the number of spikes in CBG conditions to vehicle values, which suggested that CBG at 2 μM reduced APs by 36% and at 15 μM reduced APs by 91% (Figure 6c).

3.9 |. CBG reduces spontaneous steady-state excitability in rat DRG neurons

To assess whether the predicted CBG-mediated reduction in neuronal excitability was expressed in whole cells, we measured the excitability of rat DRG neurons using multielectrode array recordings (MEA). We measured spontaneous firing over a 10-min period in MEA wells exposed to vehicle only, 2 μM CBG or 15 μM CBG (Figure 7a,b). The activity of all wells was compared before vehicle or compound addition, and there was no significant difference among the wells. Our results suggested that CBG concentration-dependently reduced neuronal firing. Normalization of the mean reduced excitability after exposure to CBG, relative to the excitability after exposure to vehicle, suggested that 2- and 15-μM CBG reduced firing by 32% and 89%, respectively (Figure 7c). While these numbers are in close agreement with the simulation results (and support our assumption regarding CBG’s non-selective nature that reflected in AP modelling), CBG most likely hits other targets besides Na_v channels, particularly at 15 μM, which collectively result in reduced DRG firing. These findings suggest that CBG at low micromolar concentrations, at least in part via Na_v channels, can reduce DRG firing, which can potentially be used against pain.

FIGURE 7.

CBG reduces excitability of primary rat DRG neurons in MEA. (a) Representative images of MEA recordings of AP firing at vehicle, 2- and 15-μM CBG. The firing frequency of each active electrode is colour coded: white/red mean high, and blue/black mean low frequencies. (b) Quantification of MEA data showing firing rate (left-hand graph) and steady-state spontaneous activity (right hand graph) of the primary neurons. Data shown as individual values with mean ± SEM (n = 3–5). (c) The DRG excitability after exposure to CBG (2 μM or 15 μM) was normalized to excitability after vehicle (Veh)

4 |. DISCUSSION

Cannabis derivatives have a long history of being used as therapeutic agents (Ghovanloo & Ruben, 2021; Morales et al., 2017; Nachnani et al., 2021). Although in recent years, CBD has been the subject of many studies, the related compound, CBG, is still far from being thoroughly understood. Despite having a slightly higher affinity for the CB receptors than CBD, CBG’s interactions at these sites are not sufficiently strong to impart psychoactivity. This merits further investigations into this compound. Although CBD’s interactions with Na_v channels as a mechanism of clinical efficacy against convulsions remains speculative, the crucial role of Na_v channels in excitability, and the potency at which CBD inhibits these channels suggests an important relationship between the two effects (Ghovanloo & Ruben, 2021). Of course, the relationship between CBD and Na_v channels, though likely to be vital, is not the only important relationship (Ghovanloo et al., 2021; Ghovanloo, Shuart, et al., 2018; Kaplan et al., 2017; Orvos et al., 2020; Pumroy et al., 2019; Ross et al., 2008; Zhang & Bean, 2021). Furthermore, the relationship between CBD and Na_v channels provides a critical blueprint to gain potential understanding of how CBG could produce therapeutic effects, such as analgesia (Evans, 1991).

4.1 |. CBG compared with CBD from a Na_v channel perspective

Potent endocannabinoid receptor agonists are suggested to alleviate chemotherapy-induced neuropathy (Pascual et al., 2005; Rahn et al., 2007). However, agonism at CB receptors using THC (or synthetic analogues of THC) may exacerbate symptoms (Johnson et al., 2010; Lynch et al., 2004; Pinsger et al., 2006; Skrabek et al., 2008; Ware et al., 2010). Conversely, in vivo treatment with CBD has robustly reduced chemotherapy-induced neuropathic symptoms.

Sativex is composed of a 1:1 ratio of CBD and THC and has shown some clinical efficacy in alleviating neuropathic pain, though the THC content exacerbates symptoms in some patients (Johnson et al., 2010; Mechoulam et al., 2007; Russo & Guy, 2006). This suggests that both CB receptor-dependent and -independent pathways could contribute to cannabinoid-mediated effects on pain. However, too much activity at CB receptors, as shown by THC, could cause unwanted effects. CBG could theoretically be ideal in that it possesses the favourable properties of both THC and CBD, without their unwanted effects, pertaining to CB receptors and Na_v channels.

Here, we set out to investigate mechanisms that could attenuate the excitability of DRG neurons and thus contribute to the action of CBG as an analgesic. As Na_v channels are the initiators of excitability in DRGs (and other excitable cells) (Hille, 2001), we first characterized CBG’s effects on the Na_v1.7 channel, a threshold channel and a major contributor to pain (Bennett et al., 2019; Dib-Hajj et al., 2013). Our findings suggest that the effects of CBG on Na_v channels are very similar to those of CBD, but with some key differences. For instance, although both CBD and CBG display ~10-fold state dependence, CBG inhibits Na_v channels about 2-fold less potently than CBD, from both resting and inactivated-states. This indicates that CBG has a higher ratio of concentration per fold state dependence than CBD (Table 1). It is worth mentioning that at high micromolar concentrations of either compound, much more pronounced cytotoxicity is likely to be caused by the compound’s effects on several other targets, before other Na_v channels would be affected (Ghovanloo & Ruben, 2021). A potential advantage for CBG is that it is more hydrophobic than CBD and may thus be more bioavailable, could be absorbed into bio-membrane of fatty tissues more readily, and possibly have a longer lasting effect than CBD. This, in theory, suggests that at the same submicromolar concentrations, CBG would produce a less strong anti-excitatory tonic effect than CBD but, as it may persist for longer in the neurons, it may be a more effective Na_v channel inhibitor for chronic hyperexcitability.

TABLE 1.

The comparison of CBG to CBD with respect to Na_v channels

Parameter	CBG	CBD
LogD	7.04	6.32
State dependence	~10	~10a
IC50 on Nav channels (μM)	~2–22	~1–12a
Concentration/fold state dependence (ratio)	2	1.1

^aData from Ghovanloo, Shuart, et al. (2018).

Our voltage-clamp data in rat DRG neurons suggest that, like CBD, CBG most likely lacks structural selectivity among Na_v channels. However, given that CBG is a state-dependent inhibitor (along with its high hydrophobicity), we suggest that DRG neurons could be among its high affinity cellular targets (e.g., it is suggested that 37% of the dry weight of neuronal soma of cultured DRG neurons is made up of lipid, Calderon et al., 1995, whereas this number could be as low as 10% in muscle in some animals, Listrat et al., 2016). This is important because, despite having only nine members (excluding Na_v2.1 channels), the Na_v superfamily orchestrates diverse and complex patterns of AP generation in different excitable cells (Ghovanloo et al., 2016; Ghovanloo & Ruben, 2021). The activity of Na_v channels is determined by the local membrane potential they are subjected to. Each one of the 9 members of the family has a characteristic V_1/2 for channel availability. If the V_1/2 of each Na_v channel is compared with the RMP of the cell where it is predominantly found, a rough estimate of Na_v channel inactivation at RMP can be determined. In Figure 8, we show a comparison of 5 general tissue-specific cell types with RMPs and V_1/2 of their respective predominantly-expressed Na_v channels, which are taken from published data (Amir et al., 1999; Cannon et al., 1993; Espinosa & Kavalali, 2009; Ghovanloo et al., 2020; Ghovanloo et al., 2021; Ghovanloo, Peters, & Ruben, 2018; He & Soderlund, 2014; Howard et al., 2007; Huang et al., 2017; Li et al., 2015; Mason & Cummins, 2020). These comparisons suggest that in the five cell types (other cells and tissue must be taken into consideration on an individual basis) that are shown, Na_v1.7 channels in DRG neurons are the most inactivated of the nine subtypes (Figure 8a–e). This would suggest that any state-dependent, Na_v channel inhibitor that lacks structural subtype selectivity would have a higher functional selectivity for Na_v1.7 channels at lower concentrations, in the DRG. This could, in theory, suggest a reduction in the probability of Na_v channel-induced toxicity in other tissues, such as the heart and skeletal muscle. With respect to CBG, this general principle could work in concert with its high hydrophobicity to attenuate pain (Figure 8f). The potential analgesic effect of CBG is further supported by CBG’s preferential effect in blocking G_max over stabilizing inactivation. This could explain the reduction of spontaneous firing of DRG neurons in our MEA data. However, if CBG is to be considered as a viable analgesic agent, mode of administration, bioavailability, and tissue distribution must also be considered. Finally, future studies must compare CBD with CBG in pain models to determine the superiority of either compound.

FIGURE 8.

Comparison of the availabilities of Na_v channels in some native tissues, where they are predominantly found. (a) GABAergic. (b) Glutamatergic. (c) DRG. (d) Skeletal muscle. (e) Cardiac muscle. (f) Na_v channel availability relative to the resting membrane potential (RMP) of the tissues described in this figure. The square brackets around “high” and “low” around the two-sided arrow indicate compound concentration. CBG has a greater affinity for Na_v1.7 channels in DRG than other Na_v channels in the indicated cell types due to relative amount of inactivation due to local RMP at the given cell type. The RMP and availability numbers are taken from published data. Please note that the numbers come from different groups who used different expression systems, and therefore should be regarded as estimates. The numbers that were used to generate the plots are indicated inside the bars in each of the graphs

4.2 |. CBG—Possible mechanism of Na_v channel inhibition

A summary of our results with a proposed mechanism of action is shown in the diagram in Figure 5c. The similarities in the general effects of CBG and CBD on Na_v channel gating and kinetics prompted us to suggest that CBG could bind at a similar location within the Nav fenestration-central cavity site where CBD binds (Ghovanloo et al., 2021; Sait et al., 2020). Additionally, the steepness of the concentration–response curves along with the physicochemical properties of CBG would also suggest a possible modulation of the membrane elasticity, which could contribute to the hyperpolarization of the inactivation curve at higher than clinically achievable concentrations (e.g., >15 μM), without altering voltage dependence of activation. This effect would be similar to that suggested for amphiphilic compounds (Lundbæk et al., 2004; Lundbæk et al., 2010), and CBD (Ghovanloo et al., 2021), which are thought to modulate the biophysical properties of the membrane, at concentrations that are many times over what is required to modulate high-affinity targets. CBG’s lack of interaction with the open state of the channel is also similar to CBD (Ghovanloo, Shuart, et al., 2018), and is consistent with the proposed blocking scheme for an ultra-hydrophobic compound, which suggests that as the drug becomes more hydrophobic, it tends to interact more with resting and inactivated states of the channel (Ghovanloo & Ruben, 2021). Indeed, CBG’s potency across the described targets in the literature suggests that its high affinity targets are modulated within submicromolar to low micromolar ranges (De Petrocellis et al., 2011; Muller et al., 2019; Pollastro et al., 2011). This is another reason why we suggest CBG’s analgesic effects via the blocking of Na_v channels in nociceptors would also be in the low micromolar range.

A recent paper showed that CBD might produce some of its analgesic effects on neuronal excitability via binding to the slow-inactivated states of Na_v channels, thereby reducing neuronal firing (Zhang & Bean, 2021). It is likely that similar effects would be induced after administration of CBG.

In summary, our results show that CBG is a state-dependent Na_v channel inhibitor, that inhibits G_max more potently than it causes a hyperpolarizing shift of inactivation. Because it is a state-dependent inhibitor, CBG would display a preference for the Na_v channels that occupy deeper inactivated states, which could be a primary result of the biophysical properties of the channel itself (i.e., its gating voltage dependence and kinetics (Zhang & Bean, 2021)) or secondary to local cellular RMP. Finally, the general similarities we observed between CBG and CBD (Ghovanloo & Ruben, 2021) lead us to suggest that CBG also interacts at the Na_v channel pore/fenestration interface and modulates membrane elasticity. Collectively, our findings suggest that these interactions could be crucial to CBG-mediated analgesia.

Supplementary Material

SUPPORTING INFORMATION

Additional supporting information may be found in the online version of the article at the publisher’s website.

What is already known

• Cannabigerol (CBG) is a non-psychoactive active constituent of cannabis and is suggested as an analgesic.

• Previous work has shown that 10-μM cannabigerol inhibits voltage-gated sodium (Na_v) channels.

What does this study adds

• Cannabigerol is a state-dependent Na_v inhibitor that reduces G_max more potently than it hyperpolarizes inactivation.

• Cannabigerol’s action on Na_v1.7 in dorsal root ganglion (DRG) neurons reduces their neuronal excitability.

What is the clinical significance

• Targeting the “threshold” peripheral Na_v channel expressed in DRGs, Na_v1.7, is promising to reduce pain.

• Cannabigerol’s inhibitory effect on Na_v1.7 may provide a therapeutic approach to treat pain syndromes.

Abbreviations

AP

action potential

CBD

cannabidiol

CBG

cannabigerol

DRG

dorsal root ganglion

G_max

maximal conductance

HEK

human embryonic kidney

HEPES

N-2-hydroxyethylpiperazine-N′−2-ethanesulfonic acid

MEA

multi-electrode array

Na_v

voltage-gated sodium channel

RMP

resting membrane potential

SSI

steady-state inactivation

THC

Δ⁹-tetrahydrocannabinol

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available from the corresponding author upon reasonable request.