Biopartitioning micellar electrokinetic chromatography – Concept study of cationic analytes

Biopartitioning micellar electrokinetic chromatography – Concept study of cationic analytes

Journal Pre-proof Biopartitioning micellar electrokinetic chromatography - concept study of cationic analytes Krzesimir Ciura ConceptualizationWriti...

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Biopartitioning micellar electrokinetic chromatography - concept study of cationic analytes

Krzesimir Ciura ConceptualizationWriting- Original draft preparationMethodologySupervisionProject administration Hanna Kapica , Szymon Dziomba InvestigationWriting- Reviewing and Editing , Piotr Kawczak , Mariusz Belka SoftwareData curationVisualization , Tomasz Baczek Î PII: DOI: Reference:

S0026-265X(19)32443-9 https://doi.org/10.1016/j.microc.2019.104518 MICROC 104518

To appear in:

Microchemical Journal

Received date: Revised date: Accepted date:

9 September 2019 29 November 2019 9 December 2019

Please cite this article as: Krzesimir Ciura ConceptualizationWriting- Original draft preparationMethodologySupervis Hanna Kapica , Szymon Dziomba InvestigationWriting- Reviewing and Editing , Piotr Kawczak , Mariusz Belka SoftwareData curationVisualization , Tomasz Baczek , Biopartitioning micellar electrokinetic chromatography - concept study of cationic analytes,Î Microchemical Journal (2019), doi: https://doi.org/10.1016/j.microc.2019.104518

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Highlights   

The concept of biopartitioning micellar electrokinetic chromatography (BMEKC) was presented QSRAR model for predicting the blood–brain barrier (BBB) permeability was proposed Application of BMEKC retention parameters to plasma protein binding assessment.

Biopartitioning micellar electrokinetic chromatography - concept study of cationic analytes Krzesimir Ciura1*, Hanna Kapica1, Szymon Dziomba2, Piotr Kawczak3, Mariusz Belka3, Tomasz Bączek3 1-

2-

3-

Department of Physical Chemistry, Faculty of Pharmacy, Medical University of Gdansk, 107 J. Hallera Avenue, 80-416, Gdansk, Poland Department of Toxicology, Faculty of Pharmacy, Medical University of Gdansk, 107 J. Hallera Avenue, 80-416, Gdansk, Poland Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Medical University of Gdansk, 107 J. Hallera Avenue, 80-416 Gdansk, Poland

(*) Author to whom correspondence should be addressed: Krzesimir Ciura Email: [email protected]

Key words: micellar electrokinetic chromatography, biomimetic chromatography, blood brain barrier, protein binding

Abstract: In the early stages of drug discovery, beyond the biological activity screening, the physicochemical properties including lipophilicity, ionization, solubility as well as metabolic profiling and protein-binding affinity should be determined for each drug candidate. The aim of the presented investigation was the transfer of biopartitioning micellar chromatography (BMC) proposed by Medina-Hernández and co-workers to micellar electrokinetic chromatography (MEKC). Therefore, biopartitioning micellar electrokinetic chromatography (BMEKC) was demonstrated as an alternative tool for estimation of biological properties of drug candidates. To simplify the data processing of developed approach and increase the assay throughput, the application of apparent retention factor (kapp) was proposed. Based on introduced kapp, simple molecular descriptors, protein binding constant and

biological

properties like blood-brain barrier (BBB) permeability, quantitative retention–activity relationships (QRARs) models were established. These results indicated usefulness of BMEKC method. In comparison to BMC, the proposed BMEKC method offers significant reduction of reagents consumption which meet green analytical chemistry principles.

1 Introduction: Application of chromatographic profiling of drug candidates can significantly accelerate the drug discovery pipeline. Chromatographic characterization of solutes can support measurement of nonspecific binding to proteins and lipids. Hence, chromatographically determines physicochemical properties of compounds improves selection of structures with desired pharmacokinetic properties and reduced toxicity [1,2]. Briefly, the theoretical assumptions of biomimetic chromatography are based on equilibrium state during the chromatographic process where molecules are distributed between the mobile and the stationary phases, and migrate through the chromatographic system. The distribution of each xenobiotic between the generic circulation and tissues compartment is determined by analogical equilibrium processes [1,3]. Nowadays, various commercially available stationary phases can be used to mimic biological systems like immobilized artificial membrane (IAM) and protein binding stationary phases. However, the high cost of these sorbents significantly limits their use. As an alternative for the above-mentioned columns, Medina-Hernández and co-workers proposed biopartitioning micellar chromatography (BMC) [4]. BMC is a special mode of micellar liquid chromatography (MLC) where Brij35, above its critical micellar concentration (CMC), is added into mobile phase. In such chromatographic system the conditions are similar to biological barriers and extracellular fluids. Monomers of Brij35 molecules are adsorbed on the surface of hydrophobic stationary phase. As a consequence, monolayer of Brij35 coat the surface of the stationary phase which mimics the ordered array of the membranous hydrocarbon chains. It should be emphasized that typical C18 column might be used in BMC. Several reports indicated usefulness of BMC in drug discovery process. It was successfully applied to predict blood-tissue partition [5], gastrointestinal absorption [6], skin

permeability [7,8], protein binding [9] and many other biological and toxicological properties [10]. Alternatively to chromatographic methods, electrophoretic techniques like micellar electrokinetic chromatography (MEKC) can be considered for biomimeting. MEKC employees an aqueous solution of surfactant that forms pseudostationary phase in background electrolyte (BGE). Under such conditions, lipophilicity of solutes is reflected in their retention. Furthermore, molecular size and hydrogen-bonding descriptors are determined as other most important factors responsible for analytes migration in MEKC [11]. On this basis the similarity between analytes-micelles interaction and analytes-biological membranes is stated which was evidenced in the numerous works on the application of MEKC in the estimation of chemicals biological properties like antibacterial activity [12], tadpole narcosis [13] or skin permeation [14]. The main aim of the present study was the transfer of BMC to MEKC conditions using nonionic Brij35 surfactant. To simplify the experimental procedure, the apparent retention factor (kapp) parameter was introduced. As a result, biopartitioning micellar electrokinetic chromatography (BMEKC) method was proposed as an alternative tool for estimation of the biological properties of drug candidates. This idea meet the concept of green analytical chemistry, since implementation of BMEKC requires much lower amounts of solvents as compared to BMC. A set of model pharmaceuticals, with recognized pharmacological and pharmacokinetic prosperities, was used. Obtained kapp of model solutes were used to estimate blood-brain barrier permeability (express as log BB) and plasma protein binding (PPB) using QRAR models.

2 Experimental: 2.1 Materials Methanol (grade for HPLC purity was ≥ 99.8%), polyoxyethylene (23) lauryl ether (Brij 35), 4-(2-hydroxyethyl)piperazine-1-ethanesulfonic

acid

(HEPES)

and

tris(hydroxymethyl)aminomethane (TRIS) were supplied from Sigma-Aldrich (Steinheim, Germany). Sodium hydroxide (NaOH) was purchased from POCH (Gliwice, Poland). Water for chromatography (LC-MS Grade) LiChrosolv® was bought from Merck (Darmstadt, Germany). 2.2 Set of model compounds The set of structurally diverse 36 drugs, listed in Table 1, were used during the study. All substances of appropriate purity were bought from different sources: Sigma–Aldrich, (Steinheim, Germany); Acros Organics (Massachusetts, United States); Boc Sciences (New York, United States); DC Chemicals (Shanghai, China) – the details are given in Table 1. Stock solutions of target drugs were prepared in methanol at the concentration of 1 mg/mL and further diluted with BGE to working concentration of 100 μg/mL. The stock solutions of analytes were stored at 8 °C between the analyses. 2.3 MEKC analysis All CE experiments were carried out with a P/ACE MDQ plus system (Sciex, Framingham, MA, USA) equipped with a photodiode array detector (PDA) and controlled by 32 Karat software (version 10.2). MEKC analysis were conducted in uncoated fused-silica capillaries (Polymicro, West Yorkshire, UK) of 50 cm total length and 50 μm i.d. The capillary was rinsed before use with 0.1 M NaOH (30 min), ultrapure water (10 min) and background electrolyte BGE (10 min). Between each single run, the capillaries was rinsed by BGE (2

min). All rinsing procedures were carried out using 345 kPa pressure. The separations were performed by applying voltage of 30 kV with positive polarity (0.5 min of ramp time). The capillary temperature was keep constant at 36.5 ± 0.1°C. The BGE consisted of aqueous solution of 10 mM Brij35, 60 mM Tris and 50 mM HEPES (pH=7.4). Thiourea was used as a marker of EOF. 2.4 Dynamic light scattering (DLS) analysis The DLS analysis was performed with Litesizer 500 (Anton Paar, Graz, Austria). The measurements were conducted in standard quartz cuvettes at 36 °C using back scattering angle (175°) and 658 nm laser. Five subsequent measurements were performed for every analyzed sample. The refractive index and viscosity of solvent were set at 1.329 and 0.705 mPas while the refractive index and absorbance of the material was 1.35 and 0.01, respectively. 2.5 Molecular modeling The ―hin‖ files for each compound were obtained based on the SMILES (Simplified Molecular Input Line Entry Specification) notation by using OpenBabel 2.3.3 software. Afterward, two steps of calculations were carried out by HyperChem 8.08 software (Hypercube, Waterloo, Canada). In the first step, the chemical structures were pre-optimized using the molecular mechanics calculations (MM+) to reduce time of further calculations. In the second step, semi-empirical calculation method Austin Model 1 (AM1) was implemented. Finally, Dragon 7.0 (Talete, Milan, Italy) software was applied to calculate 3028 descriptors. 2.6 MLR In order to design the QRAR models, multiple linear regression (MLR) was applied. The QRAR models were built using the retention data and the calculated descriptors as dependent

and biological data as independent variables. Variable selection algorithm implemented in Statistica software (Statistica 12, Statsoft, USA) was used for selection of the best predictor. This algorithm calculates variance of descriptors and dependent variable, as well as creates list of the best predictors based on F test. Coefficients of correlation (R) and determination (R2), value of Snedecor’s F-distribution test, standard deviation and standard estimation error were used for statistical assessment of established QRAR models. Q2 was calculated using leave-one-out cross validation technique and root-mean-squared error of cross-validation (RMSECV) was obtained. 3. Results and discussion 3.1 Concept of transfer BMC to proposed BMEKC BMC was proposed as a chromatographic system able to mimic the interaction of chemical with biological environment. By simple measurement of analyte retention in BMC, permeability of molecules though biological barriers might be predicted [15–17]. Brij35 is a nonionic surfactant that is used in BMC. It is a commercial name for dodecyl-polyethylene-oxide-ether (CH3(CH2)11(OCH2CH2)23-OH). The poly-ethylene-oxide chains is the hydrophilic part of the molecule whereas n-alkyl chains form hydrophobic region and interior of micelles in aqueous environment [18]. BMC mobile phase typically consists of phosphate buffer saline (PBS) to mimic the pH and osmotic pressure of biological fluids. Direct transfer of such condition to CE is impossible since Na+ and Cl- ions feature high electrophoretic mobility which results in high buffer conductivity and unacceptable heat generation during separation process. For this reason the BGE was composed of 60 mM TRIS and 50 mM HEPES solution. Since the ionic strength of

the applied BGE was estimated to be 3-fold lower as compared to PBS, the DLS analysis was performed to compare the micelles of Brij35 formed in both buffers. The DLS measurements revealed the presence of two types of particles (Figure 1). The presence of bigger particles was assumed to originate from micelles aggregation. The measurements done by particles number (red trace in Figure 1) proved that this fraction was low abundant as compared to monodispersed micelles. Thus, the role of aggregates in pseudostationary phase formation was considered to be negligible. Mean hydrodynamic diameter of micelles in Tris/HEPES buffer was found to be 11.5 ± 0.2 nm while in PBS buffer it was 13.5 ± 0.5 nm (n = 3; polydispersity index < 25%) which might be attributed to the ionic strength difference. The solution containing Tris/HEPES buffer featured also apparently broader size distribution (4.2 – 19.5 nm) of micelles then PBS solution (5.4 – 11.1 nm) when measured by intensity. However, more than 98% of particles (measured be number) were found in the 4.2 – 9.4 nm size range in the case of Tris/HEPES buffer which confirmed the equivalence of both tested solutions.

Figure 1. The size distribution by intensity (black trace) and by particles number (red trace) measured with DLS technique for 10 mM Brij35 solution in (A) 60 mM Tris/100 mM HEPES buffer and (B) PBS (pH 7.4). Determination of retention factor in MEKC (logkMECK) is done using formula established by Terabe and coworkers [19]:

(

)

(1)

This equation includes micelles mobility. However the non-ionic micelles such as these formed with Brij35 do not feature electrophoretic mobility and migrate with cathodic EOF velocity. The Terabe equation is applicable whenever tEOF < tR < tMC, so it could not have been used in presented study. Determination of solutes retention in nonionic EKC includes measurements of their mobility not only in EKC but also in CZE using the BGE devoid of surfactant. Such approach enables to estimate electrophoretic mobility of analyte and calculate its retention in the presence of micelles [20]. Considering limited solubility of analytes in surfactant-depleted BGE, we decided to simplify the methodology of retention assessment and proposed the use of apparent retention factor (kapp):

(2) Considering the throughput of the elaborated BMEKC method, introduction of kapp is advantageous as compared to classical retention factor as the latter parameter estimation is twice-more laborious. All collected kapp were summarized in Table 1. The obtained kapp values ranged from -0.011 to -0.407. Similarly to apparent electrophoretic mobility often used in CZE, kapp values might be negative. For cationic and anionic analytes featuring high affinity to pseudostationary phase kapp tends to zero. Taking into account that charge of analytes (cations and anions) impacts their migration velocity, the model set of analytes was limited to cations. It should be emphasized that the concept of BMEKC will be tested for anions and neutral compounds in our future works. During our experimental work, a difficulty with EOF repeatability was encountered. A migration time shifts of EOF marker were apparent (Figure 2). It was assumed to be a result of differences in capillary surface saturation with surfactant as a consequence of relatively long sequences (up to 16 hours per day). Despite various approaches to capillary conditioning

were tested, this problem was not solved and EOF velocity shifts were noticeable. The attention should be paid that such shifts are an exhaustively described problem in CE. However, it was shown that fluctuations of EOF and analytes migration are proportional. Thus, the use of relative migration time of analyte in respective to internal standard or EOF marker is sufficient to address this issue [21]. Interestingly, the application of kapp enables to overcome the observed drifts. To verify this assumption, the precision of kapp determination for famotidine as model substance was investigated (average kapp = - 0.111). The intra-day (n = 6) and inter-day (n = 6; the assay was performed throughout three consecutive days) coefficients of variation of kapp were < 3.6% and < 4.6%, respectively. The deviations were considered acceptable. Representative electropherograms of selected BMEKC analyses were presented in Figure 2.

Figure 2. BMEKC electropherograms achieved for the analytes (A) olanzapine and (B) risperidone. The detailed experimental conditions were reported in Section 2. 3.2 Application to PB assessment Usually drug molecules interact with plasma proteins in the blood stream. The serum proteins, mainly human serum albumin (HSA) and alpha-1-acid glycoprotein (AAG), play the main role in distribution and accumulation of drugs. Considering drug-protein interaction, it should

be highlighted that only free drug fraction is able to reach the receptor and release pharmacological response. Strong PPB effect (more than 95%) affects the pharmacotherapy safeness and cause several adverse effects including: low clearance and brain penetration, increased risk of drug–drug interaction as well as loss of efficacy [22]. Several studies indicated usefulness of chromatographic methods for assessment of drug affinity to plasma protein. Despite HSA and AAG stationary phases are commercially available [22], the economic aspects and fast degradation of chemically bonded proteins limit its popularity. As a consequence, cost effective methods are highly desired. Application of BMC seems to be an alternative, since it was successfully used for prediction of PPB affinity of anticonvulsant drugs [5]. Based on the referred study, we examined the BMEKC to predict PPB affinity. The proposed model for prediction of % PPB couples in silico strategies and experimentally measured chromatographic retention data. Such approach is named quantitative retention activity relationships (QRAR). It is commonly used for prediction of biological properties [23–26]. The multiple linear regression (MLR) was chosen as the regression method. The superiority of MLR method is an ease of interpretation of established models and results are directly related to original data. The logarithm of apparent affinity constant (logK), that converts % PPB to the linear free energy information, were calculated using formula proposed by Valko and co-workers [22]: (2)

The logK was obtained from the data collected in the literature and was used as predictor variable. Values of % PPB and logK were listed in Table 1. The obtained model was based on k and polar surface area (PSA) parameters.

LogK = 1.916 (±0.165) -0.011(±0.002) PSA + 3.402 (±0.564)kapp R = 0.878; R2 = 0.771; Q2 = 0.703; RMSECV = 0.286; F = 48.708; p< 0.0001; s = 0.393 N = 32 Only one outlier was detected by 2.5 sigma-rule (metoprolol). The theory of BMC retention mechanism assumes that mainly hydrophobic and electronic properties govern retention of analytes. The sterical properties of molecules are considered to feature lesser effect on retention in BMC. For this reason addition of the information on PSA of molecules improved the prediction ability of developed model. According to Comprehensive Medicinal Chemistry dictionary, PSA is defined as the amount of molecular surface arising from polar atoms (nitrogen and oxygen atoms together with their attached hydrogen atom) [27]. PSA was listed as one of vital descriptors which governed retention on AGP stationary phase [28]. The structures with smaller PSA showed higher PB affinity. The obtained model indicated that molecules with higher affinity to Brij35 micelles feature higher value of % PPB. Similar results has been noticed by Medina-Hernández an co-workers [5]. The statistical figures of obtained models meet the Tropsha et al. criteria (R2 > 0.6 and Q2 > 0.5) [29]. Although, human serum albumin-mimetic chromatography, which employees hexadecyltrimethylammonium bromide (CTAB) as surfactants (R2 = 0.89 [30]) or sodium dodecyl sulfate (SDS) (R2 = 0.88 [31]) showed better predictive accuracy, the results should be compared with due caution since the group of compounds used in referred studies was significantly smaller (N = 17 [30]; N = 14 [31] vs. N = 32 in presented study). 3.3 Application to blood- brain barrier permeability assessment The optimization of structures of the drugs candidates targeting the central nervous system (CNS) is problematic task. These molecules must permeate through the blood–brain barrier (BBB). The physiological role of BBB, which protects the CNS from pathogens and toxins, makes it a natural obstacle to central nervous system active (CNS+) drugs candidates. In

contrary to drug candidates with a peripheral activity, the penetration across the BBB might lead to side effects. Consequently, the assessment of BBB permeability of each drug candidate is very important in the drug discovery pipeline. Medina-Hernández’s group [4] and De Vrieze with co-workers [25] proved the utility of BMC for prediction of BBB permeability of model substances. These reports combined computational and experimental approaches. The proposed strategy is rational, because nowadays it is obvious that no descriptor could predict the blood–brain distribution alone. In our established model two descriptors were used: LogBB = 1.120 (±0.415) + 0.746 (±0.239) Mor31s + 3.243 (±1.459) kapp R = 0.853; R2 = 0.727; Q2 = 0.563; RMSECV = 0.571; F=16.017; p< 0.00041; s = 0.49641; N=15 The developed model was based on kapp and a descriptor belonging to 3D-MoRSE (Molecular Representation of Structures based on Electronic diffraction) class. These group of molecular descriptor encoding 3D structure of a molecule by a fixed number of variables [32,33]. 3DMoRSE descriptors depends on Euclidean distance between atoms and total number of atoms, and are weighted by atomic properties. The applied descriptor (Mor31) was computational with scattering parameter s = 30 Å−1 and weighted by atomic intrinsic state (I-state). Weighted descriptors are sensitive to the presence of specific molecular fragments, especially in the case of I-state. The weighting procedure depends on local vertex invariant that is affected by electronic properties (principal quantum number, number of valence electrons and the number of sigma electrons). The increase in both used descriptors resulted in an increase in the value of log BB. Comparing the predictive abilities of the obtained BMEKC models with previously published BMC model similar value of determination coefficient (R2 = 0.73 vs. R2 = 0.75 [4]) or correlation coefficient (R = 0.85 vs. R = 0.79 [25] ) has been found. The presented results

indicate that the proposed model based on BMEKC approach can provide very similar information about BBB permeability as previously reported BMC models. In Figure 3 the relationship between observed and predicted log BB and logK vaules was presented.

Figure 3. Scatterplots comparing observed log BB (green) and logK (blue) value with predicted value. 3.4 Economical and environmental benefits Nowadays, the environmental and economic aspects are considered in every field of life including analytical chemistry [34,35]. The proposed concept of green analytical chemistry includes reduction of chemical reagents [36]. Develop BMEKC method can significantly decrease the amount of Brij35 consumption as compared to BMC which was presented in Figure 4 on the exemplary analysis of nine β-blockers [23]. In the case of the capillary electrophoresis system used in the presented study, the typical vial volume was 1.4 mL and the analysis required the use of three vials (one for capillary conditioning and two for

electrophoretic separation) that can be usually used for up to 8 analyses. Considering a typical flow in BMC method (1 mL min−1), the differences in reagents consumption is enormous when compared to BMEKC. Furthermore, BMEKC offers possibilities to diminish analysis time. Comparison of cost of mobile and pseudostationary phases indicates the superiority of BMEKC over BMC. Based on the price lists of Merck company, the estimated cost of PB prediction of nine β-blockers, using BMEKC was about four times lower than with BMC. However, it should be mentioned that the cost of purchasing of each device equipped with the same type of detector, is two to three times higher in case of CE than HPLC.

Figure 4. The comparison of mobile and pseudostationary phases consumption for analysis of nine β-blockers. Protocol of BMC analysis was describes previously [23]. The capillary rinsing procedure was not included because conditioning of both devices before use is obligatory.

4. Conclusion During the early phase of drug discovery it is important to evaluate physicochemical profile of drug candidates. Chromatographic and electromigration techniques are cost-effective platforms for this purpose [37]. Currently HPLC and CE devices are automated, efficient and highly repeatable. Application of proposed BMEKC offered possibilities to downsizing BMC. In consequence the noticeable lower consumption of reagents can be reached. Moreover, BMEKC can shorten the analysis time. The established QRAR models met the Tropsha et al. criteria [29]. The above observation justifies the fact that the proposed BMEKC methods can be successfully used for PPB assessment and estimation of BBB permeability. The usefulness of BMEKC for assessment of other biological properties of xenobiotics will be investigation in our laboratory in the future.

Acknowledgment This work was supported by the National Science Centre of Poland (grant number 2018/02/X/ST4/02564). The authors express their appreciation to Anton Paar company for providing a DLS instrument for presented experiments.

Table 1: Calculated molecular descriptors, determines apparent retention factor k BEMC, PB data and calculated affinity constant logK

No 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36

Name PB* % acebutolol 26 acyclovir 15 atenolol 11 benzylimidazole bisoprolol 30 celiprolol 28 chlorpheniramine 72 clozapine 97 diazepam 99 diltiazem 78 esmolol 55 famotidine 18 fluoxetine 95 haloperidol 92 indoramin 90 metoprolol 11 nadolol 30 nebivolol 98 nizatidine 35 olanzapine 93 ondansetron 73 oxprenolol 80 perphenazine 95 pindolol 40 prochlorperazine 95 promethazine 93 propranolol 90 ranitidine 15 risperidone 88 salbutamol sertraline 98 sotalol 0 sulpiride 40 trazodone 93 zolpidem 93 zopiclone 45

logK -0.460 -0.758 -0.913 -0.374 -0.416 0.395 1.385 1.695 0.530 0.078 -0.664 1.200 1.010 0.913 -0.374 1.514 -0.275 1.065 0.416 0.581 1.200 -0.183 1.200 1.065 0.913 -0.758 0.831 1.514 -0.183 1.065 1.065 -0.095

kapp -0.369 -0.354 -0.344 -0.113 -0.321 -0.363 -0.284 -0.041 -0.186 -0.172 -0.347 -0.111 -0.146 -0.192 -0.190 -0.344 -0.366 -0.059 -0.116 -0.155 -0.203 -0.366 -0.045 -0.365 -0.035 -0.291 -0.290 -0.344 -0.258 -0.407 -0.074 -0.399 -0.373 -0.034 -0.011 -0.087

logBB -0.15 -0.5 -0.87 0.39 0.52 1.34 1.15 0.78 -0.15 0.64 -1.23 -0.02 -1.17 1.6 -0.28 -

PSA 88 115 85 18 60 91 16 31 33 84 68 238 21 41 48 51 82 71 140 59 40 51 55 57 35 32 41 112 62 73 12 87 110 42 38 92

* - data collected from DrugBank (https://www.drugbank.ca/)

Mor31s -1.093 -0.939 -0.624 0.105 1.060 -1.369 0.786 -0.617 -0.646 -0.413 0.517 -1.705 2.993 0.848 1.213 0.885 -1.974 0.293 0.177 0.462 0.537 0.729 0.042 -0.141 0.538 0.031 0.211 -0.816 0.363 -0.339 0.709 -0.509 -1.020 -0.650 -0.221 -0.397

Supplier Sigma-Aldrich Acros Organics Sigma-Aldrich Sigma-Aldrich Sigma-Aldrich Sigma-Aldrich Boc Sciences Sigma-Aldrich Sigma-Aldrich Sigma-Aldrich Sigma-Aldrich Sigma-Aldrich Sigma-Aldrich Sigma-Aldrich Sigma-Aldrich Sigma-Aldrich Sigma-Aldrich Sigma-Aldrich Sigma-Aldrich DC-Chemicals Boc Sciences Sigma-Aldrich Sigma-Aldrich Sigma-Aldrich Sigma-Aldrich Sigma-Aldrich Sigma-Aldrich Sigma-Aldrich Boc Sciences Sigma-Aldrich Sigma-Aldrich Sigma-Aldrich Sigma-Aldrich Sigma-Aldrich Sigma-Aldrich Sigma-Aldrich

Author Statement: Krzesimir Ciura: Conceptualization, Writing- Original draft preparation, Methodology, Supervision, Project administration, Funding acquisition, Software, Formal analysis, Investigation Hanna Kapica: Investigation, Szymon Dziomba: Investigation, Writing- Reviewing and Editing Piotr Kawczak: Software Mariusz Belka: Software, Data curation, Visualization Tomasz Bączek: Software

Declaration of Competing Interest The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

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