Towards Safe Cholinergic Modulators: Investigating the Action of Berberine Nanoparticles

doi:10.21203/rs.3.rs-1765991/v1

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Towards Safe Cholinergic Modulators: Investigating the Action of Berberine Nanoparticles

https://doi.org/10.21203/rs.3.rs-1765991/v1

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Researchers have concentrated efforts in the search for natural-based reversible inhibitors for cholinesterase enzymes because they may play a key role in the treatment and cure of degenerative diseases like Alzheimer’s Disease. Diverse alkaloids from plants can inhibit the action of acetylcholinesterase enzyme and, among them, berberine is a promising bioactive. Although the mechanism of action of berberine still needs to be fully elucidated, the major concern lies in its poor water solubility and consequent low bioavailability. The solid dispersion technique is a well-known procedure to improve the water affinity of hydrophobic substances, however, the obtention of berberine solid dispersions is yet to be truly investigated. It is also of key importance to elucidate the safety aspects of the berberine-loaded solid dispersions and a range of tests can be applied, such as in vitro cytotoxicity in cell lines and in vivo tests using model organisms. In this work, berberine-loaded solid dispersions were obtained and evaluated as inhibitor of acetylcholinesterase enzyme (AChE). A polypropylene glycol / polyethylene glycol triblock copolymer was used in the solid dispersion formation. In a first step, the influence of berberine on AChE was investigated by docking simulation, showing the active sites in which berberine may anchor to the enzyme. The enzymatic reactions involved in the action of AChE on the acetylcholine were investigated by kinetic modeling. In vitro assays were carried out to confirm the enzymatic kinetics of AChE in the presence of berberine. berberine nanoparticles exerted an improved cytotoxic effect on tumoral cells demonstrating the improvement in water affinity. In vivo assays in Allium cepa were implemented showing that no cytotoxicity or genotoxicity is expected for the berberine solid dispersion. Berberine-loaded solid dispersions may represent a significant step towards safe nanostructures to be used in the treatment of neurodegenerative diseases.

solid dispersion

encapsulation

AChE

kinetic

bioactive

Berberis vulgaris

Berberine is an isoquinoline alkaloid found in plants of the Euphorbiaceae, Ranunculaceae, and Papaveraceae families^1,2 and also found in the roots of Berberis species, such as B. vulgaris^2,3. A wide range of pharmacological activities is already documented for berberine, including the action on cancer cells, metabolic disorders, and cardiovascular diseases^1,4.

Berberine was also reported for the treatment of neurodegenerative diseases, such as Alzheimer’s, Parkinson’s, and Huntington’s diseases^5,6. Alzheimer’s disease (AD) is a progressive brain condition that results in memory impairment, and cognitive skills decline is known to be the major cause of dementia⁷. The cholinergic hypothesis is one of the most accepted theories to understand the causes of AD, linking it to the deficiency in brain acetylcholine levels⁸. Acetylcholine concentration may be modulated by cholinesterase inhibitors, a strategy used by some drugs approved for AD treatment, such as donepezil, galantamine, and rivastigmine⁹. Berberine is a promising treatment for AD due to various mechanisms, including a significant inhibition of acetylcholinesterase^5,10,11. Several works already reported the neuroprotective activity of berberine in animal models of AD¹².

The understanding of the kinetic mechanism and the reactions between berberine and acetylcholinesterase is of key importance to develop safe and optimized therapeutic applications. Also, this approach may provide valuable information at the molecular level on the interaction of ligands with the corresponding protein¹³. One of the most common procedures used to determine the kinetic constant of inhibition is the linear transformation procedure, or double-reciprocal method of plotting kinetic data, the Lineweaver–Burk plot (reciprocal rate versus reciprocal substrate). However, the most common misuse of this method is to take a nonlinear equation, rearrange it to a linear form, and then perform linear regression to fit data without any consideration regarding the error propagation involved in the rearrangements of the original variables. In this sense, the statistical inference of kinetic parameters uncertainty is compromised, since the uncertainty of experimental variables in linear form is unknown¹⁴. The particle swarm optimization (PSO) algorithm is a heuristic optimization method, based on empirical evolutionary rules that frequently mimic successful optimization strategies found in nature, that can be successfully applied in non-linear parameter estimation procedures and computation of likelihood parameter confidence regions¹⁵.

Although presenting remarkable bioactivity, berberine shows a poor aqueous solubility that may impact membrane permeability and bioavailability¹⁶. Several nanoparticle-based delivery systems for berberine have been proposed to improve aqueous affinity and bioavailability^17–20. Solid dispersions are complex systems used in diverse applications in the pharmaceutical, chemical and food industries. In the pharmaceutical field, amorphous solid dispersions are composed of an active pharmaceutical ingredient inside an amorphous solid material, often a polymeric matrix^21,22. Excellent reviews are available in the literature demonstrating the gains in solubility, bioavailability, and water affinity of solid dispersions^21–24. Several polymers may be used as the encapsulant matrix, and poly(ethylene oxide)-poly(propylene oxide) (PEO-PPO) block copolymers are a suitable choice to facilitate the solubilization of poorly water-soluble molecules²³. Berberine-loaded solid dispersions were reported in the literature with phospholipid from soy lecithin²⁵, polyvinylpyrrolidone, polyethylene glycol, and Pluronic® F-68, a PEO-PPO-PEO block copolymer²⁶ or β-cyclodextrin²⁷.

Solid dispersions are already in use in the pharmaceutic field showing low toxicity and even a decrease in toxicity levels²⁸, but case by case studies are a demand of both academia and consumers due to increasing concerns about how nanotechnology could affect the human body in short and long terms. An initial pre-clinical toxicity analysis can be performed using cell-based in vitro cytotoxicity methodologies and the most common cell-based approaches include cell viability assays of different cell lines when exposed to different samples. Also, in vivo experiments may give insight into the toxicity of nanoengineered systems. Assays using Allium cepa L. root meristems are internationally accepted for toxicogenetic tests and the biomarkers used for the evaluation of cytotoxicity and genotoxicity are the mitotic index and the frequency of chromosome/mitotic spindle changes, respectively^29,30. These tests also correlate satisfactorily to the results observed in genetic tests carried out in other bioassays, such as those with mammals and in cell culture^29,31.

Reversible cholinergic inhibitors are in great need and nanotechnology may play a key role in improving the action of natural-based substances, however, there is a concern about the safety of such nanoparticulate systems. The present study aimed to investigate the action of berberine-loaded solid dispersion on the activity of the acetylcholinesterase enzyme, focusing on mechanistic and safety aspects. A comparison was performed between the inhibition kinetic parameters estimated by Lineweaver–Burk plot and by non-linear parameter estimation procedures and by using a hybrid optimization method combining particle swarm optimization (PSO) and Gauss–Newton algorithms. The likelihood confidence regions of kinetic parameters were computed and inhibition model discrimination was performed for the acetylcholinesterase inhibition by berberine in its free and solid dispersion forms. Furthermore, the interaction of free berberine with AChE was explored by the docking strategy.

Nanoparticles characterization. The interaction between berberine and the encapsulant Poloxamer 407 was evaluated by UV-Vis spectroscopy to find the optimal amount of berberine to be encapsulated (Figure 1). Then, the berberine solid dispersion was obtained and characterized. Figure 2 presents the Transmission Electron Microscopy image, Differential Scanning Calorimetry thermogram, and the Infrared spectra of the nanoparticles.

It is worth noting that the interaction factor is a quantitative measure of the hyperchromic effect which occurs when the chemical changes led to an increase in the molar extinction coefficient for a particular chromophore group. The increase in absorbance observed in Poloxamer:berberine solutions may be attributed to chemical interactions between the polymer and berberine, mostly by the formation of hydrogen bonds between hydroxyl and carbonyl groups. Interactions increased more sharply when the amount of Poloxamer increased, and then the value stabilized around 10:1 Poloxamer:berberine mass proportion. This value was chosen to be used in the nanoparticles obtention.

The berberine-loaded solid dispersion presented nanoparticles with irregular shapes and with sizes around 50‑100 nm. The formation of nanoparticles from solid solutions containing natural-based bioactives was reported for curcumin, lutein and betacarotene by our research group^32–38 . Based on the DSC curves, the melting peaks of P407 and berberine were 55°C and 99 °C, respectively. Berberine showed an endothermic peak at 196 °C, with another exothermic peak at 200 °C and degradation may be observed at 288 °C. Battu et al.³⁹ also reported an endothermic peak for berberine at 196 °C. An intense fusion peak for berberine was also shown at 99 °C⁴⁰. Other authors reported that the peaks at 78 °C and 126.6 °C were associated with compound dehydration, and the peaks at 192.5 °C and 205.5 °C were related to liquid-solid transition and berberine crystallization, respectively. Guo et al.⁴¹ and Zhang et al.⁴² also reported berberine peaks at 100.1 °C, 192.6 °C and 288.9 °C. Considering the absence of new transition peaks in berberine nanoparticles and physical mixture, the DSC thermograms suggested physical interaction between P-407 and berberine, so that berberine-loaded nanoparticles were probably converted to an amorphous state.

The FTIR spectrum of pure berberine showed absorption bands at 3000 cm^-1 due to aromatic vibration (C=C and C=N). Poloxamer 407 showed absorption bands at 2900 cm^‑1 (C-H) and 1110 cm^-1 (C-O). The same absorption bands were reported by other works ^39,40 which also showed N-stretching vibration at 3412 cm^-1.

In vitro cytotoxicity, anti-inflammatory and antioxidant activities. Results of the in vitro toxicity against CaCo cell line, MCF-7 (breast adenocarcinoma), NCIH460 (non-small cell lung carcinoma), VERO cells, and porcine liver cells (PLP2) are presented in Table 1. Antiinflammatory in RAW 264.7 (murine macrophages), antihaemolytic and antioxidant activities are also presented in Table 1.

Table 1. Cytotoxic activity against selected cell lines, antiinflammatory activity (IC₅₀ values) and antihemolytic (OxHLIA) and TBARS formation inhibition activity (IC₅₀ values, μg.mL^-1) of berberine and the berberine-loaded solid dispersion.

	Berberine	Berberine-loaded Solid dispersion
Cytotoxic activity (GI₅₀ values, μg.mL^-1)
CaCo	42 ± 2	252±19
MCF-7	17 ± 2	20±1
NCI-H460	7.1±0.1	16±2
PLP2	15±1	49.2±0.1
VERO	348±30	>400
Anti-inflammatory activity (IC₅₀ values, μg.mL^-1)
RAW 264.7 (murine macrophages)	8.1±0.1	26.7±0.1
Antioxidant activity (IC₅₀ values, μg.mL^-1)
OxHLIA (Δt = 60 min)	27.3±0.8	756±40
TBARS	3.08 ± 0.09	0.76 ± 0.01

Cytotoxicity was higher for free berberine than for encapsulated berberine, except for the MCF-7 cell lines, which presented similar values, and both may be considered cytotoxic for tumoral cells. Pure berberine was dispersed in DMSO, otherwise, it would not be possible to assess its properties due to the poor water solubility. This could explain the lower values of GI₅₀ of berberine when compared to the nanoparticles. On the other hand, the encapsulation allowed berberine to exert cytotoxicity on tumor cells in an aqueous medium which is important information taking into account its low water solubility and low bioavailability. The same behavior was reported by Sá et al.⁴³ with encapsulated curcuminoids using the same technique reported here.

Both samples presented anti-inflammatory action against RAW 264.7 cytokines. RAW 264.7 cells are macrophages with the known capacity of releasing pro-inflammatory cytokines as a response in several cases, such as contact with bacterial metabolites. The overexpression of these cytokines is related to the development of several diseases. Compounds with anti-inflammatory activity can inhibit cytokines expression, diminishing inflammatory responses and their effects⁴⁴.

Berberine provided the highest antioxidant activity in the OxHLIA test when compared to the nanoparticles, but nanoparticles were more efficient in the TBARS test. This could be correlated to the type of radicals generated in each method since in TBARS only hydrophobic radicals are formed, while hydrophilic and hydrophobic radicals are present in the OxHLIA test^45,46.

All tests demonstrated that berberine may present activity in water medium after encapsulation which otherwise was only observed when a good solvent was present (DMSO in this case).

Cytotoxic and genotoxic analyses in Allium cepa L. Mitotic indices (%) and chromosomal alterations indices found in root meristems of A. cepa when exposed to berberine and the berberine-loaded solid dispersion at 1, 3, and 10 wt% of berberine are presented in Table 2 and Table 3, respectively. Figure 3 presents microscopy images of the meristem cells of A. cepa after 48 h in contact with the solutions.

Table 2. Mitotic indices (%) in root meristems of A. cepa exposed to berberine, the encapsulant (Poloxamer 407), and berberine-loaded solid dispersion in 0, 24, and 48 h of exposure time.

		Mitotic Index (%)
MMS (positive control) (4x10^-4 mol.L^-1)		6.51±0.34
	Concentration (%)	0 h (Control group)	24 h	48 h
	1	28.9±0.91 ^a	34.7±1.02^a	22.1±0.90 ^a
Berberine	3	22.0±0.44^a	36.0±0.87^a	20.8±0.74^a
	10	30.8±0.57^a	29.01±1.07^a	19.42±1.89^a
	1	29.9±0.59^a	23.8±0.55^a	28.0±1.25^a
Berberine-loaded solid dispersion	3	32.1±1.02^a	24.7±0.71^a	23.0±0.91^a
	10	27.7±1.77^a	21.6±0.89^a	19.8±0.55^a
Encapsulant (Poloxamer 407)	-	29.0±0.99 ^a	19.1±1.13 ^a	17.9±1.78 ^a

MMS: methyl methane sulfonate in 24 h. Means compared by the Scott-Knott test at 0.05 significance. The same letters refer to the same means between the exposure times considered (0h, 24 h, and 48 h) within the same concentration.

Table 3. Indices of cellular changes in root meristems of A. cepa exposed to different concentrations of berberine, berberine-loaded solid dispersion, and the encapsulant (Poloxamer 407) in 0, 24, and 48 h exposure times.

		Chromosomal alterations index (%)
MMS (positive control) (4x10^-4 mol.L^-1)			4.95
	Concentration (%)	0 h (Control group)	24 h	48 h
	1	0.1±0.78^a	0.3±0.71 ^a	0.8±0.49^a
Berberine	3	0.2±0.43^a	0.5±0.60^a	0.8±0.73 ^a
	10	0.1±0.91^a	0.2±0.70^a	0.1±0.49 ^a
	1	0.1±0.81^a	0.2±0.65^a	0.1±0.44^a
Berberine-loaded solid dispersion	3	0.1±0.70^a	0.1±0.72^a	0.1±0.74 ^a
	10	0.1±0.93^a	0.1±0.80^a	0.2±0.89 ^a
Poloxamer 407	-	0.1±0.55^a	0.8± 0.74 ^a	0.4± 0.41 ^a

MMS: methyl methane sulfonate in 24 h. Means compared by the Scott-Knott test at 0.05 significance. In each treatment, equal letters mean that there was no difference indices of cellular changes between the evaluated exposure times (0, 24 and 48 h).

It is worth noting that the amount of the solid dispersion used in the experiments was adjusted to represent the same berberine concentration in the cases where free berberine was used. The mitotic indices observed for berberine (free or encapsulated) did not vary during the exposure time when compared to the control group (0 h). Also, the encapsulant used (Poloxamer 407) in the present study was not cytotoxic to meristematic cells from A. cepa roots at any exposure time. Thus, under the conditions evaluated, results showed no cytotoxicity for berberine-loaded solid dispersion.

The analyzed concentrations of encapsulated berberine, encapsulant, and encapsulated berberine did not cause a significant number of cellular alterations to the meristems in any of the analyzed periods, being non-genotoxic.

Enzyme activity assays and reaction kinetics.

In silico docking. To better understand the binding mechanism of berberine against AChE, docking studies were performed using Autodock Vina docking software. Only structures from Tetronarce californica were available for AChE and these were selected for this study (PDB: 6G1V). The predicted binding free energy value (pred∆G) obtained by AutoDock Vina was equal to -8.3 kcal/mol for AChE. These pred∆G value was translated into predicted Ki values (PredKi), and the value obtained was 877.3 nmol/L for AChE.

Along with the predicted ∆G and Ki values, usually termed SCORE analysis, it is also possible to analyze the predicted 3D binding conformation, usually termed POSE analysis. For AChE, the selected X-ray structure (PDB: 6G1V) presents an Huprine Derivative with its co-crystallized structure displayed for comparison in Figure 4⁴⁷. When analyzing berberine docked conformation against AChE, we observe that two hydrogen bonds are predicted to form with SER286 and TYR121. Overall, credible docked binding conformations of Berberine are predicted against AChE by AutoDock Vina software, with satisfactory binding site pocket occupation.

IC₅₀ and Lineweaver-Burk reciprocal plot. Berberine and the berberine-loaded solid dispersion induced a dose-dependent inhibition in the activity of the electric eel AChE (Figure 5a). Different concentrations of berberine significantly inhibited AChE in vitro, and the concentration that inhibited 50% of AChE activity (IC₅₀) was 0.32 ± 0.02 µmol/L. Similar AChE inhibition induced by berberine was reported in the literature^48–51. Berberine solid dispersion also significantly inhibited AChE activity, with an IC₅₀ value of 0.54 ± 0.06 µmol/L.

AChE enzyme kinetics was modified by incubation with berberine and berberine-loaded solid dispersion in the presence of five different substrate concentrations (ASCh; Figures 5b and 5c). In the Michaelis-Menten plot, concentrations between 0.1 to 3 µmol/L of berberine significantly decreased the AChE activity. Similarly, the increase in berberine solid dispersion concentration also reduced the AChE activity compared with the control (H₂O curve).

The AChE inhibition mechanism was determined by the Lineweaver-Burk reciprocal plot. Considering that the intersection of the lines occurred at the same point on the x-axis, the incubation with berberine and the solid dispersion inhibited AChE in a non-competitive manner. The inhibition constant Ki was estimated by Lineweaver-Burk reciprocal plots, Ki values for berberine and berberine-loaded solid dispersion were 252.9 ± 8.85 nmol/L and 424.2 ± 17.01 nmol/L, respectively. Other studies also reported that berberine induced the non-competitive AChE inhibition and similar Ki values^51–53.

Non-linear parameter estimation. The particle swarm optimization (PSO) algorithm is a heuristic optimization method, based on empirical evolutionary rules that frequently mimic successful optimization strategies found in nature¹⁵. PSO algorithm is initialized with a population of random solutions, called particles. Each particle is associated with a corresponding velocity which determines the direction and distance of its flight, like birds towards their prey. Each particle follows the optimal particle to fly through the search space at the corresponding velocity, and the velocities are updated according to their historical behaviors at each flight until the optimal solution is found. Finally, the Gauss-Newton method employs the initial value calculated by the PSO algorithm, which improves the computational efficiency and accuracy of the algorithm⁵⁴.

The likelihood confidence region obtained with the PSO for the Michaelis Menten Equation is shown in Figure 5 (A). It is possible to observe that the likelihood confidence region presents an elliptical form, indicating that the points used by the PSO during the minimization can be used effectively for the definition of the parameter confidence region¹⁵.

In Figure 5 (B) it is possible to verify the agreement between experimental and predicted data. The predicted values found under these conditions for km and Vmax are presented in Table 3 and the experimental validation is presented in Figure 6. Wetwitayaklung et al.⁵⁵ also evaluated the kinetics parameters of the catalyzed reaction of acetylthiocholine iodide (ASCh) hydrolysis into thiocholine and acetic acid by electric eel acetylcholinesterase type III (AChE), however, the Lineweaver-Burk plot was used for that. The authors determined values of km and Vmax equal to 32 µmol/L and 0.017 µmol/L ASCh/min/µg, respectively. In another work, López et al.⁵⁶ determined a km equal to 0.52 ± 0.02 mmol/L, also using the Lineweaver-Burk plot. Possibly, these differences can be attributed to different proportions of the reagents during the assay, such as enzyme concentration (0.09 U/mL in the present work and 5 U/mL used by López et al.⁵⁶) although the method used was the same⁵⁷.

Table 3. Parameters estimated and objective function values for no inhibitor added, pure berberine, and nanoencapsulated berberine as inhibitors.

Model	Inhibitor	CHI²_inf	CHI²_sup	Fobj	Estimated parameters
Michaelis Menten	-	0.216	9.348	0.804	km (nmol/L) 8970 ≤ 32520 ≤ 79030
Michaelis Menten	-	0.216	9.348	0.804	Vmax (nM ASCh/min/U) 8250 ≤ 10590 ≤ 13260
Competitive inhibition	Pure berberine	11.689	38.076	495.425	-
Competitive inhibition	Nano berberine	11.689	38.076	217.032	-
Uncompetitive inhibition	Pure berberine	10.982	36.781	131.819	-
Uncompetitive inhibition	Nano berberine	10.982	36.781	72.349	-
Non-competitive Mixed inhibition	Pure berberine	10.982	36.781	79.712	K_ic (nmol/L) 100 ≤ 190 ≤ 450
					K_iu (nmol/L) 220 ≤ 460 ≤ 1100
					a (dimensionless) 0.67 ≤ 0.92 ≤ 1.20
	Nano berberine	10.982	36.781	41.907	K_ic(nmol/L) 100 ≤ 190 ≤ 430
					K_iu(nmol/L) 420 ≤ 760 ≤ 1730
					a (dimensionless) 0.78 ≤ 0.96 ≤ 1.17
Non-competitive Pure inhibition	Pure berberine	11.689	38.076	92.459	-
Non-competitive Pure inhibition	Nano berberine	11.689	38.076	62.591	-

A model discrimination strategy was applied to verify the inhibition mechanism associated with pure berberine and nanoencapsulated berberine against AChE. Thus, the hybrid strategy was applied using Equations (11), (12), (13), and (14), as well as the previously determined km and Vmax in the absence of inhibitors. Results obtained for each case are presented in Table 3, where it is possible to observe that the kinetic data were not explained by the Competitive, Uncompetitive, and Non-competitive pure models, for both inhibitors (pure and nanoencapsulated berberine). This result is clear by the analysis of the CHI² interval and the objective function determined (Fobj). Although the Fobj determined for the data when applied to the Non-competitive mixed model still surpasses slightly the upper CHI² limit, the confidence regions between the model’s parameters present an elliptical shape, as can be observed for pure berberine in Figure 7 and nanoencapsulated berberine in Figure 8.

The average value of the estimated for AChE non-competitive mixed inhibition by berberine (Table 3) is below the result found by the Lineweaver-Burk reciprocal plots ( = 252.9 nmol/L). However, Lineweaver-Burk determined value is between the confidence interval determined for . The value estimated by the PSO is above Lineweaver-Burk , but it is also located in the confidence interval. Although and confidence intervals are not completely overlapped, there is a common region between them, in which Lineweaver-Burk can be found. In the case of the predKi determined with the molecular docking for AChE inhibition by pure berberine, the result found (877.3 nM) is in the same order of magnitude as the values obtained with the Lineweaver-Burk and PSO methods. Due to the inherent limitations of molecular docking, the predicted and experimental values are not always comparable. The fact that similar results were obtained suggests that molecular docking, using AutoDock Vina, is able to predict the inhibition order of magnitude of berberine against AChE.

Regarding nanoencapsulated berberine action to inhibit AChE, the PSO estimated was the same found for pure berberine. However, the estimated value was 4-fold higher than the result determined for free berberine. Furthermore, for both inhibitors (pure and nanoencapsulated berberine) > , a result that indicates predominantly competitive inhibition⁵⁸. Competitive inhibition occurs when inhibitors bind to the initial free enzyme form in a manner that blocks the binding of substrate molecules. On the other hand, uncompetitive inhibition occurs when the inhibitor binds to substrate-bound forms of the enzyme (preferentially or even exclusively). In this case, the binding affinities for the inhibitor and the substrate are mutually synergistic. The mixed non-competitive inhibition mechanism includes all the possibilities mentioned above ⁵⁹. Thus, in the case of free and encapsulated berberine inhibition, the main action is the attack on the binding site, impairing substrate linkage.

A polymeric solid dispersion containing berberine was evaluated as an inhibitor of the acetylcholinesterase enzyme. This extensive study included in silico simulation, kinetic modeling, and in vitro kinetic studies. Spectroscopic and thermal analyses showed satisfactory interaction between the encapsulant (Poloxamer 407) and berberine, and transmission electron microscopy showed the formation of nanometric particles when the system was redispersed in water. Taking into consideration the inherent advantages and limitations of the methods used here, in vitro, in silico and kinetic modeling pointed in the direction that the action of free and solid dispersion berberine is explained by a non-competitive mixed model. No genotoxicity and cytotoxicity were detected in the in vivo assays in Allium cepa. Also, berberine in the solid dispersion exerted an improved cytotoxic effect on tumoral cells even in water, corroborating the increase in water affinity.

Material. The following reagents were used for the solid dispersion production: berberine (90%, Sigma-Aldrich), Kolliphor® Poloxamer 407 (encapsulant, 12,000 g/mol, Sigma-Aldrich), Tween 80 (Dinâmica) and ethanol (99.8%, Neon). All chemicals were of analytical grade and purchased from common sources unless otherwise mentioned. The following reagents were used for the AChE activity assay: acetylcholinesterase (AChE; E.C. 3.1.1.7 from electric eel, Sigma-Aldrich)), 5’,5-dithiobis(2-nitrobenzoic acid) (DTNB; 98%, Sigma-Aldrich), acetylthiocholine iodide (ASCh; 99%, Sigma-Aldrich), and potassium phosphate buffer (TFK; pH 7.5, Neon). Potassium bromide (spectroscopic standard, Sigma-Aldrich) was used in the infrared spectra analyses. Trichloroacetic acid (TCA), 6-hydroxy-2,5,7,8-tetramethylchroman-2-carboxylic acid (Trolox) and 2,2′-azobis(2-methylpropionamidine) dihydrochloride (AAPH) (from Sigma-Aldrich) were used in the oxidative hemolysis inhibition assay (OxHLIA) and thiobarbituric acid reactive substances (TBARS) assays. Acetic acid, sulforhodamine B (SRB), ellipticine, dexamethasone, trypan blue, lipopolysaccharide (LPS), tris-(hydroxymethyl) aminomethane (TRIS) (from Sigma-Aldrich), dimethyl sulfoxide (DMSO) (from Fisher Scientific), Dulbecco's Modified Eagle's Medium (DMEM) and RPMI-1640 medium, fetal bovine serum (FBS), Hank's balanced salt solution (HBSS), L-glutamine, nonessential amino acid solution (2 mmol/L), penicillin/streptomycin solution (100 U.mL^− 1 and 100 mg mL^− 1, respectively), trypsin and EDTA (from Hyclone) were used in the cytotoxic and anti-inflammatory activity assays. Ethanol, acetic acid, and distilled water were used in the cytotoxic and genotoxic analyses. Methanesulfonate (MMS) was used as positive control and onion bulbs (Allium cepa L, variety beta crystal) was selected from an organic garden. The purified acetylcholinesterase was purchased directly from Sigma-Aldrich, not involving manipulation of animals or parts of animals, tissues, or other derivatives. The blood used for the OxLHIA assay was obtained randomly from the normal blood analysis of the animals and not directly for the assay.

Solid dispersion production and physicochemical characterization. The berberine-loaded solid dispersion was produced according to Sá et al.⁴³ with minor modifications. Poloxamer 407 (1.200 g), a PEO-PPO-PEO block copolymer, and Tween 80 (0.012 g), a PEO sorbitan monooleate, were added to ethanol (50 mL) and mixed under gentle stirring for 10 min. Then, berberine (0.120 g) was added and mixed for 5 min. The dispersion was sonicated (Fisher Scientific, 120W, 1/8’ probe) for 3 min under a pulse regime (30 s on and 10 s off) in an ice bath. Ethanol was evaporated in a forced air circulation oven at 40°C for 24 h and the resulting powder was stored at -10°C protected from light.

The interaction between Poloxamer and berberine was quantitatively evaluated by UV-Vis spectroscopy (OceanOptics, Red Tide USB 650 UV) as reported by Karavas et al. ⁶⁰. Ethanol was used as a solvent and different amounts of the two components were added (mass proportions from 1:1 to 20:1 Poloxamer:berberine). Solutions were obtained in triplicate and absorbance at 350 nm was used to calculate the normalized interaction coefficient (Eq. 1), where A and A₀ were the maximum absorbance at 350 nm for the solutions and an ethanolic solution of berberine alone.

$F=\frac{A-{A}_{0}}{{A}_{0}}$

(1)

The thermal properties of the solid dispersion were analyzed by Differential Scanning Calorimetry (DSC, Perkin Elmer 4000) and samples were heated in aluminum pans (0°C to 350°C at 10°C.min^-1) under nitrogen flow (100 mL.min^-1). Fourier Transform Infrared spectra (FTIR; Frontier Perkin Elmer) was performed in potassium bromide pellets with a resolution of 2 cm^-1 from 4500 to 425 cm^-1 with 32 cumulative scans. Transmission electron microscopy (TEM; JEOL model JEM 2100, 200 kV) was performed to observe the morphology of the nanoparticles in parlodium-covered copper grids (300 mesh). In addition, a mixture of berberine and Poloxamer 407 was obtained by simply mixing them in a laboratory mortar in the same mass proportion found in the solid dispersion. The objective was to compare this physical mixture with the berberine-loaded solid dispersion^43,35.

In vitro cytotoxicity, anti-inflammatory and antioxidant activity. The antihemolytic activity (OxHLIA) was assessed using the method described by Takebayashi et al.⁶¹ as fully described in previous work⁴³. Briefly, sheep blood samples were collected from healthy animals and centrifuged at 1,000 g for 5 min at 10 ºC. Plasma and buffy coats were discarded and erythrocytes were firstly washed once with NaCl solution (150 mmol/L) and three times with phosphate-buffered saline solution (PBS, pH 7.4) ⁶². The erythrocyte pellet was then resuspended in PBS at 2.8% (v/v). Using a flat-bottom 48-well microplate, 200 µL of erythrocyte solution was mixed with 400 µL of either PBS solution (control) and was dispersed in PBS, or water (for complete hemolysis). After pre-incubation at 37 ºC for 10 min with shaking, AAPH (200 µL, 160 mmol/L in PBS) was added and the optical density was measured in a microplate reader (Bio-Tek Instruments, ELX800) at 690 nm. After that, the microplate was incubated under the same conditions and the optical density was measured every 10 min at the same wavelength for approximately 300 min. The percentage of the erythrocyte population that remained intact (P) was calculated by Eq. 2 (S_t and S₀ correspond to the optical density of the sample at t and 0 min, respectively, and CH₀ is the optical density of the complete hemolysis at 0 min).

$$P\%=100 \left(\frac{{S}_{t}-{CH}_{0}}{{S}_{0}-{CH}_{0}}\right)$$

Results were expressed as the delayed time of hemolysis (Δt), calculated by Eq. 3, where Ht₅₀ is the 50% hemolytic time (min) graphically obtained from the hemolysis curve of each sample concentration. The Δt values were then correlated to the different sample concentrations ⁶¹ and the concentration able to promote a Δt hemolysis delay was calculated. The results were expressed as IC₅₀ values (µg.mL^− 1) at Δt 60 and 120 min, i.e. the sample concentration required to keep 50% of the erythrocyte population intact for 60 and 120 min. Trolox was used as a positive control.

$${\Delta }t\left(\text{min}\right)=\frac{{\text{H}\text{t}}_{50}\left(\text{s}\text{a}\text{m}\text{p}\text{l}\text{e}\right)}{{\text{H}\text{t}}_{50}\left(\text{c}\text{o}\text{n}\text{t}\text{r}\text{o}\text{l}\right)}$$

The capacity of the sample to inhibit the formation of thiobarbituric acid reactive substances (TBARS), such as malondialdehyde generated from the ex vivo decomposition of lipid peroxidation products, was evaluated using porcine brain cell homogenates, following the method described previously⁶³. Trolox was used as the positive control. The results were expressed as IC₅₀ values (µg.mL^− 1), i.e. the sample concentration providing 50% of antioxidant activity.

To assess the cytotoxicity of the sample, the sulforhodamine (SRB) assay was performed according to a procedure previously established by Abreu et al.⁶⁴) in triplicate. Berberine was dissolved in water:DMSO (1:1 vol) and berberine-loaded solid dispersion was dispersed in water, both at the same concentration of berberine (8 mg.mL¹), and this stock solution was used to prepare successive dilutions. CaCo cell line, MCF-7 (breast adenocarcinoma), NCIH460 (non-small cell lung carcinoma), and VERO cells from DSMZ (Leibniz-Institute DSMZ - German Collection of Microorganisms and Cell Cultures) were selected as human tumour cell lines. Porcine liver cells (PLP2), a primary cell culture, were prepared according to the procedure described by Abreu et al. ⁶⁴. These cells were treated for 48 h with the different sample solutions and the SRB assay was followed⁶⁵. Ellipticine was used as a positive control. The results were expressed as GI₅₀ values (concentration that inhibited 50% of the net cell growth).

For the anti-inflammatory activity determination⁶⁶, the lipopolysaccharide (LPS)-induced nitric oxide (NO) production by a murine macrophage (RAW 264.7) cell line was quantified as nitrite concentration in the culture medium. The effect of the tested compounds in the absence of LPS was also evaluated, to observe if they induced changes in NO basal levels. In negative controls, no LPS was added. For the NO determination, a Griess Reagent System kit containing sulfanilamide, N1naphthyl ethylenediamine dihydrochloride (NED), and nitrite solutions were used. Dexamethasone was used as a positive control. The results were expressed as IC₅₀ values (µg.mL^-1), i.e. compound concentration providing 50% of NO production inhibition.

Cytotoxic and genotoxic analysis of berberine inAllium cepaL. The experiments were carried out as previously described in details by Fiskesjo⁶⁷ and Sales et al.⁶⁸. For the assessment of cytotoxicity and genotoxicity of berberine and the berberine-loaded solid dispersion, the onion bulbs were placed in vials with distilled water, constantly aerated, to obtain roots of 2.0 cm in length. For analysis of berberine and Poloxamer concentrations (treatments), an experimental group with five onion bulbs was set up. Before putting the roots in contact with their respective treatments, some roots were collected and fixed to serve as a control of the bulb itself, which was identified as the time of analysis of 0 h or control of the bulb itself (Co − 0h). Then, the other roots were put in their respective treatments for 24 and 48 h, procedures called exposure times 24 and 48 h, where roots were collected every 24 h. Positive control was prepared with methyl methanesulfonate (MMS), a known cytotoxic and genotoxic substance to the A. cepa test system, at the concentration 4x10^− 4 mol.L^− 1. All roots collected during the experiment were fixed in 3:1 Carnoy solution (ethanol: acetic acid) for up to 24 h. Glass slides were prepared according to the protocol proposed by Herrero et al.⁶⁹ and analyzed under an optical microscope with a 40x objective lens. For each bulb, 1,000 cells were analyzed, totaling 5,000 cells for each control group (0 hour), each group exposure time 24 hour and each group exposure time 48 hour, totaling 15,000 cells analyzed for each concentration of tretments. For the MMS group 5,000 cells were analyzed in five independent experiments.

For estimates of the mitotic index, cells in interphase, prophase, metaphase, anaphase, and telophase were counted to determine the cytotoxic potential. The mitotic index (MI) or cell division index was calculated by Eq. 4.

$$\text{M}\text{I}=100 \text{x} \frac{\text{t}\text{o}\text{t}\text{a}\text{l} \text{n}\text{u}\text{m}\text{b}\text{e}\text{r} \text{o}\text{f} \text{d}\text{i}\text{v}\text{i}\text{d}\text{i}\text{n}\text{g} \text{c}\text{e}\text{l}\text{l}\text{s}}{\text{T}\text{o}\text{t}\text{a}\text{l} \text{n}\text{u}\text{m}\text{b}\text{e}\text{r} \text{o}\text{f} \text{c}\text{e}\text{l}\text{l}\text{s} \text{a}\text{n}\text{a}\text{l}\text{y}\text{z}\text{e}\text{d}}$$

Genotoxic potential (chromosomal alterations index, Eq. 5) was assessed by frequency of cell alterations such as micronuclei, colchicine metaphases, anaphase and telophase bridges, cells with adhesions, nuclear buds and multipolar anaphases.

$$\text{C}\text{h}\text{r}\text{o}\text{m}\text{o}\text{s}\text{o}\text{m}\text{a}\text{l} \text{a}\text{l}\text{t}\text{e}\text{r}\text{a}\text{t}\text{i}\text{o}\text{n}\text{s} \text{i}\text{n}\text{d}\text{e}\text{x}=100 \text{x} \frac{\text{t}\text{o}\text{t}\text{a}\text{l} \text{n}\text{u}\text{m}\text{b}\text{e}\text{r} \text{o}\text{f} \text{c}\text{e}\text{l}\text{l} \text{a}\text{l}\text{t}\text{e}\text{r}\text{a}\text{t}\text{i}\text{o}\text{n}\text{s} }{\text{T}\text{o}\text{t}\text{a}\text{l} \text{n}\text{u}\text{m}\text{b}\text{e}\text{r} \text{o}\text{f} \text{c}\text{e}\text{l}\text{l}\text{s} \text{a}\text{n}\text{a}\text{l}\text{y}\text{z}\text{e}\text{d}}$$

Enzyme Activity Assays

AChE activity assay and reaction kinetics. The determination of the acetylcholinesterase (AChE) activity was performed according to modifications in the methods of Ellman et al. ⁵⁷ and Pereira et al. ⁷⁰. In 96-well plate, the following reagents were pre-incubated for 10 min at 25°C: TFK (90 µL; 50 mmol/L), water (45 or 55 µL), AChE (15 µL; 0.09 U/mL) and different dilutions of (1) berberine (10 µL; 0.03, 0.1, 0.3, 1 and 3 µmol/L) or different dilutions of (2) berberine-loaded solid dispersion (10 µL; 0.03, 0.1, 0.3, 1 and 3 µmol/L). The control group was incubated with water. After incubation, DTNB (20 µL; 0.2 mmol/L) and acetylthiocholine iodide (ASCh; 20 µL; 800 µmol/L) were added. Absorbance was read at 412 nm for 4 min every 60 s using a Vis spectrophotometer (Thermoplate TP-Reader). The concentration that inhibited AChE activity in 50% (IC₅₀) compared with the control (H₂O curve) was determined by nonlinear regression analysis.

Enzyme kinetic analysis was performed according to modifications in the methods described by Miranda et al.⁷¹. As in the AChE activity assays, the following reagents were pre-incubated for 10 min at 25°C: TFK, ultrapure water, enzyme (AChE or S1), and different dilutions of (1) berberine or (2) berberine-loaded nanoparticles. The control group was incubated with water. After incubation, DTNB and different concentrations of ASCh (0, 25, 50, 100, 400 and 800 µmol/L) or BSCh (0, 25, 50, 100, 400 and 800 µmol/L) were added. Absorbance was read at 412 nm for 4 min every 60 s using a Vis spectrophotometer (Thermoplate TP-Reader).

Reaction kinetics modeling. The kinetic constants of the enzyme inhibition reactions were determined by different strategies: i) Docking simulations; ii) The Lineweaver-Burk reciprocal plot; iii) Non-linear parameter estimation using the Particle Swarm Optimization coupled with the Gauss-Newton algorithm.

i) Docking simulations. 2D and 3D optimizations of Berberine structure were performed using Marvin Sketch 16.4 software (www.chemaxon.com). AutoDockTools 1.5.2 (ADT) was then used to convert the 3D structure of Berberine to the PDBQT file format ⁷². The X-ray crystal structure of AChE (PDB: 6G1V) was obtained from the Protein Data Bank (PDB) (http://www.rcsb.org). The co-crystallized ligand was extracted from each PDB file, and ADT was used to assign polar hydrogens, add Gasteiger charges, and save the AChE structures in the required PDBQT file format ⁷¹. Autodock Vina 1.2⁷² was then used to perform molecular docking. An XYZ grid size of 30 by 30 by 30 Å was used for all structures with an exhaustiveness parameter of 16. The XYZ coordinates used for each structure were the following: 6ESY (3.6, -12.7, -12.3) and 6G1V (3.7; -4.5; 20.9). The docking conformations obtained were analyzed using the ProteinsPlus platform using the Pose View tool for 2D⁷³. The predicted Ki (inhibition equilibrium constant) was calculated as follows: Ki = exp((ΔG.1000)/(Rcal.TK)) where ΔG is the predicted binding energy (cal/mol), Rcal is 1.98719 cal/(mol.K), and TK is 298.15 K. Structure representations were prepared using PyMOL (The PyMOL Molecular Graphics System, Version 1.3, Schrödinger, LLC).

ii) The Lineweaver-Burk reciprocal plot. The Lineweaver-Burk method was applied using the classical equations described by Bisswanger ⁷⁴. This method applies a linear transformation to the inhibition kinetic models and is largely applied to enzymatic kinetic data. The following linearized models were considered for evaluation: a) Competitive inhibition (Eq. 6); b) Uncompetitive inhibition (Eq. 7); c) Mixed Non-competitive inhibition (Eq. 8); d) Pure Non-competitive inhibition (Eq. 9), where Vmax is the maximum reaction rate (µmol/L/min/U); Km is the Michaelis constant (µmol/L); [S] is the substrate concentration (µmo/L); [I] is the inhibitor (pure berberine or nanoencapsulated berberine) concentration (µmol/L); while ${K}_{ic}$ (competitive), ${K}_{iu}$ (uncompetitive) and ${K}_{i}$ are inhibition equilibrium constants (µmol/L).

$$\frac{1}{v}=\frac{1}{Vmax}+ \frac{Km . \left(1+ \frac{\left[I\right]}{{K}_{ic}}\right) }{Vmax . \left[S\right]}$$

$$\frac{1}{v}=\frac{\left(1+ \frac{\left[I\right]}{{K}_{iu}}\right)}{Vmax}+ \frac{Km}{Vmax . \left[S\right]}$$

$$\frac{1}{v}=\frac{\left(1+ \frac{\left[I\right]}{{K}_{iu}}\right)}{Vmax}+ \frac{Km . \left(1+ \frac{\left[I\right]}{{K}_{ic}}\right) }{Vmax . \left[S\right]}$$

$$\frac{1}{v}=\frac{\left(1+ \frac{\left[I\right]}{{K}_{i}}\right)}{Vmax}+ \frac{Km . \left(1+ \frac{\left[I\right]}{{K}_{i}}\right) }{Vmax . \left[S\right]}$$

iii) Non-linear parameter estimation. Non-linear parameter estimation was performed using a hybrid optimization method, which combines the particle swarm optimization (PSO) algorithm (a heuristic optimization method, based on empirical evolutionary rules that frequently mimic successful optimization strategies found in nature), and Gauss-Newton algorithms¹⁵. This approach was used for the computation of likelihood parameter confidence regions. The same kinetic models described in the previous section were considered in their non-linear form for parameter estimation, Equations (10) for Michaelis-Menten, (11) for the competitive model, (12) for the uncompetitive model, (13) for the non-competitive mixed model, and (14) for the non-competitive pure model, except for the inclusion of the Michaelis-Menten equation for analysis o kinetic data without the addition of an inhibitor. Furthermore, in this case, the parameters “a” and “b” that represent the factor with which Km and Vmax are multiplied to calculate the apparent constants in the presence of the inhibitor⁷⁵.

$$v= \frac{{V}_{max}.\left[S\right]}{Km+\left[S\right]}$$

$$v= \frac{{V}_{max} . \left[S\right]}{b.Km . \left(1+\frac{\left[I\right]}{kic}\right) + \left[S\right]}$$

$$v= \frac{a.{V}_{max} . \left[S\right]}{b.Km + \left[S\right] . \left(1+\frac{\left[I\right]}{kiu}\right) }$$

$$v= \frac{a. {V}_{max} . \left[S\right]}{Km . \left(1+\frac{\left[I\right]}{kic}\right) + \left[S\right] . \left(1+\frac{\left[I\right]}{kiu}\right) }$$

$$v= \frac{a.{V}_{max} . \left[S\right]}{Km . \left(1+\frac{\left[I\right]}{ki}\right) + \left[S\right] . \left(1+\frac{\left[I\right]}{ki}\right) }$$

The absorbance data measured in the kinetic assay was firstly used to calculate the reaction rate (v, µM/min/U) for each time interval (1, 2, 3, and, 4 min) with Eq. (15) where: ΔAbs is the absorbance variation during the evaluated time interval (Δt, min); V_W is the volume of the reaction media in the well (µL); l represents the optic path (cm); ε- represents the molar extinction coefficient of DTNB (L/mol.cm); and V_E is the volume of enzyme solution in the reaction media (µL). After that, the data was submitted to the interpolation procedure in Matlab (R2021a) by the piecewise cubic Hermite interpolating polynomials (PCHIP), which was done using MATLAB's pchip function ⁷⁶ to determine the initial reaction rate at approximately 0.2 min. These results were used in the PSO procedure.

$$v= \frac{\varDelta Abs .{ V}_{w}. l}{\varDelta t . \epsilon { . V}_{E}}$$

The interpolated reaction rate results were submitted to the hybrid optimization method using Spyder (4.2.5) with Python (3.8). This computational code is based on ESTIMA, developed by Schwaab et al.⁷⁷ originally for FORTRAN, which employs a hybrid estimation method that combines the particle swarm optimization method with a Gauss-Newton procedure, and also performs the statistical analysis of results. The following conditions were applied for parameter estimation: confidence level of 95%, 100 particles, and a maximum number of interactions equal to 100.

The weighting least squares function was considered the objective function (Fobj) to be minimized (maximum likelihood method), as described in Eq. (16):

$${F_{obj}}=\sum\limits_{{i=1}}^{{NE}} {\sum\limits_{{j=1}}^{{NY}} {\frac{{\left( {{\mathbf{y}}_{{ij}}^{e} - {\mathbf{y}}_{{ij}}^{m}} \right)\left( {{\mathbf{x}}_{i}^{m},{\mathbf{\theta }}} \right)}}{{\sigma _{{ij}}^{2}}}} }$$

where NE is the number of experiments, NY is the number of output variables, ${{y}}_{ij}^{e}$ is the vector of experimental values for the output variable, ${{y}}_{ij}^{m}$ is the vector of the predicted values for the output variables, ${{x}}_{i}^{m}$ is the vector of input variables, θ is the parameter cluster, and ${\sigma }_{ij}^{2}$ is the experimental variance.

For the model discrimination, the Chi² test was applied and the models were considered proper when the objective function was found within the Chi² range. In addition, the quality of the estimated model parameters was evaluated concerning the Likelihood confidence region and graphics comparing experimental and predicted results.

Statistical analyses of the experimental results. For the cytotoxic and genotoxic analysis in Allium cepa L., data represented in the graphs are expressed as the mean ± standard deviation of three independent experiments, and means were compared by the Scott-Knott test at 0.05 significance. In the enzymatic experiments, data represented in the graphs are expressed as the mean ± standard deviation of at least three independent experiments. Data were analyzed using one- or two-way analysis of variance (ANOVA) followed by Tukey’s post hoc test. Values of p ≤ 0.05 were considered statistically significant. The statistical analysis was performed using Prism GraphPad 5.0 software or Statistica 7.0 software.

Conflicts of Interest

The authors declare no conflict of interest.

Acknowledgements

The authors are grateful to CNPq and CAPES (Finance Code 001) for the support. The authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES to CIMO (UIDB/00690/2020). L. Barros and C. Pereira thank FCT, P.I., for the contracts through the institutional scientific employment program-contract.

Author contributions

OHG, BFO, CS and PHHA conceived the idea of the manuscript, conceptualized experiments and supervised the work. FVL conducted the kinetic modeling, and CSHS and RMA conducted the docking simulations. LBS, BPMO, BFR, PSS, and CP conducted the experimental lab work and collected data. VKF, RPI, APP, and LB supervised and interpreted experimental data. All authors contributed to the writing of the manuscript.

Data Availability Statement

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Gao, Y., Wang, F., Song, Y. & Liu, H. The status of and trends in the pharmacology of berberine: a bibliometric review [1985–2018]. Chinese Medicine 15, 7 (2020).
Sarraf, M., Beig Babaei, A. & Naji-Tabasi, S. Investigating functional properties of barberry species: an overview. Journal of the Science of Food and Agriculture 99, 5255–5269 (2019).
Imenshahidi, M. & Hosseinzadeh, H. Berberine and barberry (Berberis vulgaris): A clinical review. Phytotherapy Research 33, 504–523 (2019).
Liu, D., Meng, X., Wu, D., Qiu, Z. & Luo, H. A Natural Isoquinoline Alkaloid With Antitumor Activity: Studies of the Biological Activities of Berberine. Frontiers in Pharmacology 10, 1–12 (2019).
Fan, D., Liu, L., Wu, Z. & Cao, M. Combating Neurodegenerative Diseases with the Plant Alkaloid Berberine: Molecular Mechanisms and Therapeutic Potential. Current Neuropharmacology 17, 563–579 (2019).
Lin, X. & Zhang, N. Berberine: Pathways to protect neurons. Phytotherapy Research 32, 1501–1510 (2018).
Long, J. M. & Holtzman, D. M. Alzheimer Disease: An Update on Pathobiology and Treatment Strategies. Cell 179, 312–339 (2019).
Liu, P.-P., Xie, Y., Meng, X.-Y. & Kang, J.-S. History and progress of hypotheses and clinical trials for Alzheimer’s disease. Signal Transduction and Targeted Therapy 4, 29 (2019).
Yiannopoulou, K. G. & Papageorgiou, S. G. Current and Future Treatments in Alzheimer Disease: An Update. Journal of Central Nervous System Disease 12, 117957352090739 (2020).
Singh, A. K. et al. Berberine: A Plant-derived Alkaloid with Therapeutic Potential to Combat Alzheimer’s disease. Central Nervous System Agents in Medicinal Chemistry 19, 154–170 (2019).
Neuropsychiatric Disease and Treatment Volume 12, 2509–2520 (2016).
Yuan, N.-N. et al. Neuroprotective effects of berberine in animal models of Alzheimer’s disease: a systematic review of pre-clinical studies. BMC Complementary and Alternative Medicine 19, 109 (2019).
Rasoulzadeh, F., Jabary, H. N., Naseri, A. & Rashidi, M. R. Fluorescence quenching study of quercetin interaction with bovine milk xanthine oxidase. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 72, 190–193 (2009).
Aledo, J. C. & Juan Aledo, C. C. Enzyme kinetic parameters estimation: A tricky task? double-reciprocal. (2021) doi:10.1002/bmb.21522.
Schwaab, M., Biscaia, E. C., Monteiro, J. L. & Pinto, J. C. Nonlinear parameter estimation through particle swarm optimization. Chemical Engineering Science 63, 1542–1552 (2008).
Mirhadi, E., Rezaee, M. & Malaekeh-Nikouei, B. Nano strategies for berberine delivery, a natural alkaloid of Berberis. Biomedicine & Pharmacotherapy 104, 465–473 (2018).
Xue, M. et al. Characterization, pharmacokinetics, and hypoglycemic effect of berberine loaded solid lipid nanoparticles. International Journal of Nanomedicine 4677 (2013) doi:10.2147/IJN.S51262.
Xue, M. et al. Berberine-loaded solid lipid nanoparticles are concentrated in the liver and ameliorate hepatosteatosis in db/db mice. International Journal of Nanomedicine 10, 5049 (2015).
Chenthamara, D. et al. Therapeutic efficacy of nanoparticles and routes of administration. Biomaterials Research 23, 20 (2019).
Majidzadeh, H. et al. Nano-based delivery systems for berberine: A modern anti-cancer herbal medicine. Colloids and Surfaces B: Biointerfaces 194, 111188 (2020).
Huang, Y. & Dai, W.-G. Fundamental aspects of solid dispersion technology for poorly soluble drugs. Acta Pharmaceutica Sinica B 4, 18–25 (2014).
Schittny, A., Huwyler, J. & Puchkov, M. Mechanisms of increased bioavailability through amorphous solid dispersions: a review. Drug Delivery 27, 110–127 (2020).
Dumortier, G., Grossiord, J. L., Agnely, F. & Chaumeil, J. C. A review of poloxamer 407 pharmaceutical and pharmacological characteristics. Pharmaceutical Research vol. 23 2709–2728 (2006).
Jaskirat, S., Manpreet, W. & L, H. S. SOLUBILITY ENHANCEMENT BY SOLID DISPERSION METHOD: A REVIEW. Journal of Drug Delivery & Therapeutics 2013, 148–155 (2011).
Zhang, Z. et al. Solid dispersion of berberine-phospholipid complex/TPGS 1000/SiO 2: Preparation, characterization and in vivo studies. International Journal of Pharmaceutics 465, 306–316 (2014).
Zhaojie, M. et al. Amorphous solid dispersion of berberine with absorption enhancer demonstrates a remarkable hypoglycemic effect via improving its bioavailability. International Journal of Pharmaceutics 467, 50–59 (2014).
Zhang, Y., Cui, Y. L., Gao, L. N. & Jiang, H. L. Effects of β-cyclodextrin on the intestinal absorption of berberine hydrochloride, a P-glycoprotein substrate. International Journal of Biological Macromolecules 59, 363–371 (2013).
Bikiaris, D. N., Papageorgiou, G. Z., Papadimitriou, S. A., Karavas, E. & Avgoustakis, K. Novel Biodegradable Polyester Poly(Propylene Succinate): Synthesis and Application in the Preparation of Solid Dispersions and Nanoparticles of a Water-Soluble Drug. AAPS PharmSciTech 10, 138–146 (2009).
Herrero, O. et al. Toxicological evaluation of three contaminants of emerging concern by use of the Allium cepa test. Mutation Research/Genetic Toxicology and Environmental Mutagenesis 743, 20–24 (2012).
Silva, T. de S. et al. Cytotoxicity, Genotoxicity, and Toxicity of Plant Biostimulants Produced in Brazil: Subsidies for Determining Environmental Risk to Non-Target Species. Water, Air, & Soil Pollution 231, 233 (2020).
Sales, I. M. S. et al. Acute Toxicity of Grape, Plum and Orange Synthetic Food Flavourings Evaluated in in vivo Test Systems. Food Technology and Biotechnology 55, 131–137 (2017).
Silva, J. T. D. P. et al. Analytical validation of an ultraviolet–visible procedure for determining lutein concentration and application to lutein-loaded nanoparticles. Food Chemistry 230, (2017).
Grella Miranda, C. et al. Influence of nanoencapsulated lutein on acetylcholinesterase activity: In vitro determination, kinetic parameters, and in silico docking simulations. Food Chemistry 307, (2020).
Silva, J. T. do P. et al. Nanoencapsulation of lutein and its effect on mice’s declarative memory. Materials Science and Engineering C 76, 1005–1011 (2017).
Almeida, M. et al. Evaluation of the in vivo acute antiinflammatory response of curcumin-loaded nanoparticles. Food & Function 9, 440–449 (2018).
Rocha, F. et al. Nanodispersions of beta-carotene: effects on antioxidant enzymes and cytotoxic properties. Food and Function 9, 3698–3706 (2018).
Freitas, P. D. et al. The nanoencapsulation of curcuminoids extracted from Curcuma longa L. and an evaluation of their cytotoxic, enzymatic, antioxidant and anti-inflammatory activities †. Food and Function 10, 573–582 (2019).
Santos, P. D. D. F. et al. Endogenous antioxidant properties of curcuminoids from Curcuma longa L. obtained by a single-step extraction/nanoencapsulation approach. Journal of Food Biochemistry (2020) doi:10.1111/jfbc.13531.
Battu, S. K. et al. Physicochemical Characterization of Berberine Chloride: A Perspective in the Development of a Solution Dosage Form for Oral Delivery. AAPS PharmSciTech 11, 1466–1475 (2010).
Pinheiro, W. M. Elaboração de sistema sinérgico visando aplicações em nanobiotecnologia, baseado em microesferas de polímeros naturais contendo berberina e nanopartículas de ouro associada a doxorrubicina. Dissertação (Universidade de Brasília, 2015).
Zhang, Z. et al. Solid dispersion of berberine–phospholipid complex/TPGS 1000/SiO2: preparation, characterization and in vivo studies. International Journal of Pharmaceutics 465, 306–316 (2014).
Guo, S. et al. Solid dispersion of berberine hydrochloride and Eudragit ® S100: Formulation, physicochemical characterization and cytotoxicity evaluation. Journal of Drug Delivery Science and Technology 40, 21–27 (2017).
Silva de Sá, I. et al. In vitro and in vivo evaluation of enzymatic and antioxidant activity, cytotoxicity and genotoxicity of curcumin-loaded solid dispersions. Food and Chemical Toxicology 125, 29–37 (2019).
Zhao, C. et al. Synthesis and anti-inflammatory evaluation of novel mono-carbonyl analogues of curcumin in LPS-stimulated RAW 264.7 macrophages. European Journal of Medicinal Chemistry 45, 5773–5780 (2010).
Prieto, M. A. & Vázquez, J. A. A Time-Dose Model to Quantify the Antioxidant Responses of the Oxidative Hemolysis Inhibition Assay (OxHLIA) and Its Extension to Evaluate Other Hemolytic Effectors. BioMed Research International 2014, 1–15 (2014).
Hatia, S. et al. Evaluation of antioxidant properties of major dietary polyphenols and their protective effect on 3T3-L1 preadipocytes and red blood cells exposed to oxidative stress. Free Radical Research 48, 387–401 (2014).
Galdeano, C. et al. Increasing polarity in tacrine and huprine derivatives: Potent anticholinesterase agents for the treatment of myasthenia gravis. Molecules 23, (2018).
Huang, L. et al. Inhibition of cholinesterase activity and amyloid aggregation by berberine-phenyl-benzoheterocyclic and tacrine-phenyl-benzoheterocyclic hybrids. Bioorganic & Medicinal Chemistry 20, 3038–3048 (2012).
Jannat, S. et al. Inhibition of β-site amyloid precursor protein cleaving enzyme 1 and cholinesterases by pterosins via a specific structure – activity relationship with a strong BBB permeability. Experimental & Molecular Medicine 51, 1–18 (2019).
Jung, H. A. et al. Anti-Alzheimer and Antioxidant Activities of Coptidis Rhizoma Alkaloids. Biological and Pharmaceutical Bulletin 32, 1433–1438 (2009).
Su, T. et al. Synthesis and biological evaluation of berberine–thiophenyl hybrids as multi-functional agents: Inhibition of acetylcholinesterase, butyrylcholinesterase, and Aβ aggregation and antioxidant activity. Bioorganic & Medicinal Chemistry 21, 5830–5840 (2013).
Huang, L., Shi, A., He, F. & Li, X. Synthesis, biological evaluation, and molecular modeling of berberine derivatives as potent acetylcholinesterase inhibitors. Bioorganic & Medicinal Chemistry 18, 1244–1251 (2010).
Mak, S. et al. Synergistic Inhibition on Acetylcholinesterase by the Combination of Berberine and Palmatine Originally Isolated from Chinese Medicinal Herbs. Journal of Molecular Neuroscience 53, 511–516 (2014).
Li, W. & Zhong, K. Application of improved particle swarm optimization algorithm in solving camera extrinsic parameters. Journal of Modern Optics 66, (2019).
Wetwitayaklung, P., Limmatvapirat, C., Phaechamud, T. & Keokitichai, S. Kinetics of Acetylcholinesterase Inhibition of Quisqualis indica Linn. Flower Extract. Science, Engineering and Health Studies 1, 20–28 (2007).
López, M. D., Campoy, F. J., Pascual-Villalobos, M. J., Muñoz-Delgado, E. & Vidal, C. J. Acetylcholinesterase activity of electric eel is increased or decreased by selected monoterpenoids and phenylpropanoids in a concentration-dependent manner. Chemico-Biological Interactions 229, 36–43 (2015).
Ellman, G. L., Courtney, K. D., Andres, V. & Featherstone, R. M. A new and rapid colorimetric determination of acetylcholinesterase activity. Biochemical Pharmacology 7, 88–95 (1961).
CORTÉS, A., CASCANTE, M., CÁRDENAS, M. L. & CORNISH-BOWDEN, A. Relationships between inhibition constants, inhibitor concentrations for 50% inhibition and types of inhibition: new ways of analysing data. Biochemical Journal 357, 263–268 (2001).
Copeland, R. A., Harpel, M. R. & Tummino, P. J. Targeting enzyme inhibitors in drug discovery. Expert Opinion on Therapeutic Targets 11, 967–978 (2007).
Karavas, E., Ktistis, G., Xenakis, A. & Georgarakis, E. Effect of hydrogen bonding interactions on the release mechanism of felodipine from nanodispersions with polyvinylpyrrolidone. European Journal of Pharmaceutics and Biopharmaceutics 63, 103–114 (2006).
Takebayashi, J., Iwahashi, N., Ishimi, Y. & Tai, A. Development of a simple 96-well plate method for evaluation of antioxidant activity based on the oxidative haemolysis inhibition assay (OxHLIA). Food Chemistry 134, 606–610 (2012).
Evans, B. C. et al. Ex Vivo Red Blood Cell Hemolysis Assay for the Evaluation of pH-responsive Endosomolytic Agents for Cytosolic Delivery of Biomacromolecular Drugs. Journal of Visualized Experiments 73, 1–5 (2013).
Pinela, J., Barros, L., Carvalho, A. M. & Ferreira, I. C. F. R. Nutritional composition and antioxidant activity of four tomato (Lycopersicon esculentum L.) farmer’ varieties in Northeastern Portugal homegardens. Food and Chemical Toxicology 50, 829–834 (2012).
Abreu, R. M. V et al. Anti-hepatocellular carcinoma activity using human HepG2 cells and hepatotoxicity of 6-substituted methyl 3-aminothieno[3,2-b]pyridine-2- carboxylate derivatives: In vitro evaluation, cell cycle analysis and QSAR studies. European Journal of Medicinal Chemistry 46, 5800–5806 (2011).
Souza, A. H. P. et al. Phytochemicals and bioactive properties of Ilex paraguariensis: An in-vitro comparative study between the whole plant, leaves and stems. Food Research International 78, 286–294 (2015).
Sobral, F. et al. Chemical characterization, antioxidant, anti-inflammatory and cytotoxic properties of bee venom collected in Northeast Portugal. Food and Chemical Toxicology (2016) doi:10.1016/j.fct.2016.06.008.
Fiskesjo, G. The Allium test as a standard in environmental monitoring. Hereditas 102, 99–100 (1985).
Souza Sales, I. M. et al. Acute Toxicity of Grape, Plum and Orange Synthetic Food Flavourings Evaluated in in vivo Test Systems. Food Technology and Biotechnology 55, 131–137 (2017).
Herrero, O. et al. Toxicological evaluation of three contaminants of emerging concern by use of the Allium cepa test. Mutation Research - Genetic Toxicology and Environmental Mutagenesis 743, 20–24 (2012).
Pereira, M. E., Adams, A. I. H. & Silva, N. S. 2,5-Hexanedione inhibits rat brain acetylcholinesterase activity in vitro. Toxicology Letters 146, 269–274 (2004).
Grella Miranda, C. et al. Influence of nanoencapsulated lutein on acetylcholinesterase activity: In vitro determination, kinetic parameters, and in silico docking simulations. Food Chemistry 307, 125523 (2020).
Eberhardt, J., Santos-Martins, D., Tillack, A. F. & Forli, S. AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings. Journal of Chemical Information and Modeling 61, 3891–3898 (2021).
Schöning-Stierand, K. et al. ProteinsPlus: Interactive analysis of protein–ligand binding interfaces. Nucleic Acids Research 48, W48–W53 (2020).
Bisswanger, Hans. Enzyme kinetics: principles and methods. (Wiley-VCH, 2008).
Sharma, R. Enzyme Inhibition and Bioapplications. (BoD – Books on Demand, 2012).
Creasy, A., Barker, G., Yao, Y. & Carta, G. Systematic interpolation method predicts protein chromatographic elution from batch isotherm data without a detailed mechanistic isotherm model. Biotechnology Journal 10, 1400–1411 (2015).
Noronha, F. B., Pinto, J. C., Monteiro, J. L., Lobão, M. W. & Santos, T. J. Estima: Um pacote computacional para Estimação de Parâmetros e Projeto de Experimentos. (1993).

No competing interests reported.

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Towards Safe Cholinergic Modulators: Investigating the Action of Berberine Nanoparticles

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