Novel natural and synthetic inhibitors of solute carriers SGLT1 and SGLT2.

Selective analogs of the natural glycoside phloridzin are marketed drugs that reduce hyperglycemia in diabetes by inhibiting the active sodium glucose cotransporter SGLT2 in the kidneys. In addition, intestinal SGLT1 is now recognized as a target for glycemic control. To expand available type 2 diabetes remedies, we aimed to find novel SGLT1 inhibitors beyond the chemical space of glycosides. We screened a bioactive compound library for SGLT1 inhibitors and tested primary hits and additional structurally similar molecules on SGLT1 and SGLT2 (SGLT1/2). Novel SGLT1/2 inhibitors were discovered in separate chemical clusters of natural and synthetic compounds. These have IC50-values in the 10-100 µmol/L range. The most potent identified novel inhibitors from different chemical clusters are (SGLT1-IC50 Mean ± SD, SGLT2-IC50 Mean ± SD): (+)-pteryxin (12 ± 2 µmol/L, 9 ± 4 µmol/L), (+)-ε-viniferin (58 ± 18 µmol/L, 110 µmol/L), quinidine (62 µmol/L, 56 µmol/L), cloperastine (9 ± 3 µmol/L, 9 ± 7 µmol/L), bepridil (10 ± 5 µmol/L, 14 ± 12 µmol/L), trihexyphenidyl (12 ± 1 µmol/L, 20 ± 13 µmol/L) and bupivacaine (23 ± 14 µmol/L, 43 ± 29 µmol/L). The discovered natural inhibitors may be further investigated as new potential (prophylactic) agents for controlling dietary glucose uptake. The new diverse structure activity data can provide a starting point for the optimization of novel SGLT1/2 inhibitors and support the development of virtual SGLT1/2 inhibitor screening models.

Identification of novel small molecule inhibitors for solute carrier SGLT1 using proteochemometric modeling.

Sodium-dependent glucose co-transporter 1 (SGLT1) is a solute carrier responsible for active glucose absorption. SGLT1 is present in both the renal tubules and small intestine. In contrast, the closely related sodium-dependent glucose co-transporter 2 (SGLT2), a protein that is targeted in the treatment of diabetes type II, is only expressed in the renal tubules. Although dual inhibitors for both SGLT1 and SGLT2 have been developed, no drugs on the market are targeted at decreasing dietary glucose uptake by SGLT1 in the gastrointestinal tract. Here we aim at identifying SGLT1 inhibitors in silico by applying a machine learning approach that does not require structural information, which is absent for SGLT1. We applied proteochemometrics by implementation of compound- and protein-based information into random forest models. We obtained a predictive model with a sensitivity of 0.64 ± 0.06, specificity of 0.93 ± 0.01, positive predictive value of 0.47 ± 0.07, negative predictive value of 0.96 ± 0.01, and Matthews correlation coefficient of 0.49 ± 0.05. Subsequent to model training, we applied our model in virtual screening to identify novel SGLT1 inhibitors. Of the 77 tested compounds, 30 were experimentally confirmed for SGLT1-inhibiting activity in vitro, leading to a hit rate of 39% with activities in the low micromolar range. Moreover, the hit compounds included novel molecules, which is reflected by the low similarity of these compounds with the training set (< 0.3). Conclusively, proteochemometric modeling of SGLT1 is a viable strategy for identifying active small molecules. Therefore, this method may also be applied in detection of novel small molecules for other transporter proteins.

Inulin solid dispersion technology to improve the absorption of the BCS Class IV drug TMC240.

TMC240 is a very poorly soluble and poorly permeating HIV protease inhibitor. In order to enhance its oral bioavailability, a fast dissolving inulin-based solid dispersion tablet was developed. During the dissolution test in water (0.5% or 1.0% SLS), this tablet released at least 80% of TMC240 within 30min, while a tablet with the same composition, but manufactured as physical mixture, released only 6% after 2h. In a subsequent single-dose study in dogs (200mg of TMC240), plasma concentrations of TMC240 remained below the lower limit of quantification (<1.00ng/mL) in all animals (n=3 per tested formulation), except in one dog receiving the inulin solid dispersion tablet (C(max)=1.8ng/mL, AUC(0-7 h)=3.0ngh/mL). In the latter treatment group, ritonavir co-administration (10mg/kg b.i.d.) increased TMC240 exposure more than 30-fold (mean AUC(0-7 h)=108ngh/mL; F(rel)=3588%). Exposure was also 16-fold higher than after TMC240 administration as PEG400 suspension in the presence of ritonavir (AUC(0-7 h)=6.7ngh/mL). The current data demonstrate that a solid dispersion of TMC240 in an inulin matrix allows considerable improvement in the release of poorly water-soluble TMC240, both in vitro in the presence of a surfactant and in vivo upon oral administration.

Improved bioavailability of darunavir by use of kappa-carrageenan versus microcrystalline cellulose as pelletisation aid.

The aim of this study was to produce pellet formulations containing a high drug load (80%) of the poorly soluble HIV-protease inhibitor darunavir, using wet extrusion/spheronization with kappa-carrageenan or microcrystalline cellulose (MCC) as pelletization aid. Drug release was assessed in vitro by a standardized paddle-dissolution test and in vivo by a single-dose pharmacokinetic study in dogs. Mean dissolution time (MDT) was 78.2+/-3.5 h from MCC pellets (1301+/-301 microm) and 6.1+/-0.7 min from kappa-carrageenan pellets (966+/-136 microm). In contrast to kappa-carrageenan pellets, MCC pellets did not disintegrate and showed a diffusion-controlled drug release. In line with the in vitro findings, the darunavir peak plasma levels and exposure after the administration of a 300 mg dose were more than 60-fold higher when formulated with kappa-carrageenan pellets when compared with MCC pellets, and 10-fold higher after co-administration with 10mg/kg of ritonavir. The relative bioavailability of darunavir versus the reference tablet (F(rel)) was 155% with kappa-carrageenan pellets and 2% with MCC pellets without ritonavir, while 78% and 9%, respectively, in presence of ritonavir. In conclusion, when compared with MCC pellets, the bioavailability of darunavir was substantially improved in kappa-carrageenan pellets, likely due to their better disintegration behavior.

Mechanistic model for the acute effect of fluvoxamine on 5-HT and 5-HIAA concentrations in rat frontal cortex.

A mechanistic model is proposed to predict the time course of the concentrations of 5-HT and its metabolite 5-hydroxyindolacetic acid (5-HIAA) in rat frontal cortex following acute administration of SSRIs. In the model, SSRIs increase synaptic 5-HT concentrations by reversible blockade of the SERT in a direct concentration-dependent manner, while the 5-HT response is attenuated by negative feedback via 5-HT autoreceptors. In principle, the model allows for the description of oscillatory patterns in the time course of 5-HT and 5-HIAA concentrations in brain extracellular fluid. The model was applied in a pharmacokinetic-pharmacodynamic (PK/PD) investigation on the time course of the microdialysate 5-HT and 5-HIAA response in rat frontal cortex following a 30-min intravenous infusion of 3.7 and 7.3mg/kg fluvoxamine. Directly after administration of fluvoxamine, concentrations of 5-HT were increased to approximately 450-600% of baseline values while 5-HIAA concentrations were decreased. Thereafter 5-HT and 5-HIAA concentrations gradually returned to baseline values in 6-10h, respectively. The PK/PD analysis revealed that inhibition of 5-HT reuptake was directly related to the fluvoxamine concentration in plasma, with 50% inhibition of 5-HT reuptake occurring at a plasma concentration of 1.1ng/ml (EC50). The levels of 5-HT at which 50% of the inhibition of the 5-HT response was reached (IC50) amounted to 272% of baseline. The model was unable to capture the oscillatory patterns in the individual concentration time curves, which appeared to occur randomly. The proposed mechanistic model is the first step in modeling of complex neurotransmission processes. The model constitutes a useful basis for prediction of the time course of median 5-HT and 5-HIAA concentrations in the frontal cortex in behavioral pharmacology studies in vivo.

Pharmacokinetic modeling of non-linear brain distribution of fluvoxamine in the rat.

INTRODUCTION: A pharmacokinetic (PK) model is proposed for estimation of total and free brain concentrations of fluvoxamine. MATERIALS AND METHODS: Rats with arterial and venous cannulas and a microdialysis probe in the frontal cortex received intravenous infusions of 1, 3.7 or 7.3 mg.kg(-1) of fluvoxamine. ANALYSIS: With increasing dose a disproportional increase in brain concentrations was observed. The kinetics of brain distribution was estimated by simultaneous analysis of plasma, free brain ECF and total brain tissue concentrations. The PK model consists of three compartments for fluvoxamine concentrations in plasma in combination with a catenary two compartment model for distribution into the brain. In this catenary model, the mass exchange between a shallow perfusion-limited and a deep brain compartment is described by a passive diffusion term and a saturable active efflux term. RESULTS: The model resulted in precise estimates of the parameters describing passive influx into (k in) of 0.16 min(-1) and efflux from the shallow brain compartment (k out) of 0.019 min(-1) and the fluvoxamine concentration at which 50% of the maximum active efflux (C 50) is reached of 710 ng.ml(-1). The proposed brain distribution model constitutes a basis for precise characterization of the PK-PD correlation of fluvoxamine by taking into account the non-linearity in brain distribution.

Pharmacokinetic-pharmacodynamic modeling of the effect of fluvoxamine on p-chloroamphetamine-induced behavior.

The pharmacokinetic-pharmacodynamic (PK-PD) correlation of the effect of fluvoxamine on para-chloroamphetamine (PCA)-induced behavior was determined in the rat. Rats (n=66) with permanent arterial and venous cannulas received a 30-min intravenous infusion of 1.0, 3.7 or 7.3 mg kg(-1) fluvoxamine. At various time points after the start of fluvoxamine administration, a single dose of PCA (2.5 mg kg(-1)) was injected in the tail vein and resulting behavioral effects, excitation (EXC), flat body posture (FBP) and forepaw trampling (FT), were immediately scored (scores: 0, 1, 2 or 3) over a period of 5 min. In each individual animal the time course of the fluvoxamine plasma concentration was determined up to the time of PCA administration. Observed behavioral effects were related to fluvoxamine plasma concentrations. Fluvoxamine pharmacokinetics was described by a population three-compartment pharmacokinetic model. The effects of fluvoxamine on PCA-induced behavior (probability of EXC, FBP and FT) were directly related to fluvoxamine plasma concentration on the basis of the proportional odds model. For EXC, EC(50) values for the cumulative probabilities P(Y<1), P(Y<2), P(Y<3) were 237+/-39, 174+/-28 and 100+/-20 ng ml(-1), respectively. Slightly higher EC(50) values were obtained for the corresponding effects on FBP and FT. This investigation demonstrates the feasibility of PK-PD modeling of categorical drug effects in animal behavioral pharmacology. This constitutes a basis for the future development of a mechanism-based PK-PD model for fluvoxamine in this paradigm.

Population pharmacokinetic model of fluvoxamine in rats: utility for application in animal behavioral studies.

The limitations of blood sampling in pharmacokinetic (PK)/pharmacodynamic (PD) studies in behavioral animal models could in part be overcome by a mixed effects modeling approach. This analysis characterizes and evaluates the population PK of fluvoxamine in rat plasma using nonlinear mixed effects modeling. The model is assessed for its utility in animal behavioral PK/PD studies. In six studies with a different experimental setup, study site and/or sampling design, rats received an intravenous infusion of 1, 3.7 or 7.3mg/kg fluvoxamine. A population three-compartment PK model adequately described the fluvoxamine plasma concentrations. Body weight was included as a covariate and mean population PK parameters for CL, V(1), V(2), Q(2), V(3) and Q(3) were 25.1 ml/min, 256 ml, 721 ml, 30.3 ml/min, 136 ml and 1.0 ml/min, respectively. Inter-individual variability was identified on CL (39.5%), V(1) (43.5%), V(2) (50.1%) and Q(2) (25.7%). A predictive check and bootstrap analysis confirmed the predictive ability, model stability and precision of the parameter estimates. Body weight was identified as a significant covariate of the inter-compartmental clearance Q(2). The pharmacokinetics was independent of factors such as dose, surgery (for instrumentation) and study site. The utility of the model in animal behavioral studies was demonstrated in a PK/PD analysis of the effects on REM sleep in which a sparse PK sampling design was used. By using the pertinent information from the population PK model, individual PK profiles and the PK/PD correlation could be adequately described.