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A Modeling Approach towards Identifying Potential Bivalent Sensitizers of Neuromuscular Blocking Agents

Marc Pudlo1*, Lucie Javot2, Paul Michel Mertes3, Philippe Trechot4, Pierre Gillet4 and Nadine Petitpain2

1FDE, EA4267, Univ. Bourgogne Franche-Comte, F-25000 Besancon, France

2Regional Pharmacovigilance Centre, University Hospital of Nancy, Nancy, France

3Department of Anesthesia, University Hospital of Strasbourg, Strasbourg, France

4Department of Clinical Pharmacology and Toxicology, University Hospital of Nancy, France

Corresponding Author:
Dr Marc Pudlo
FDE, EA-4267, UFR SMP 19 rue Ambroise Pare University de Franche Comte-25030 Besancon CEDEX, France
Tel:
+33 3 81 66 55 42
E-mail: marc.pudlo@univ-fcomte.fr

Received date: 25/04/2016 Accepted date: 16/05/2016 Published date: 21/05/2016

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Abstract

Objective: Anaphylactic reactions induced by neuromuscular blocking agents (NMBAs) can occur at first contact and might be due to cross-sensitization by other drugs or chemicals. Our aim was to investigate whether divalent molecules sharing chemical features with NMBAs might potentially cause cross-sensitization. Methods: We constructed a pharmacophore key from chemical features common to all NMBAs (two positive or ionizable features 1.0807 nm apart) and used the key to screen FDA-approved small drug molecules of the Drug Bank® database (1541 molecules). The selected molecules were categorized on the basis of the values for three main parameters (fit value, relative energy and mean polar surface area). Results: Screening from the pharmacophore key selected 13 NMBAs and 88 non-NMBA drugs. Of these 88 drugs, 42 had high-ranking parameter values and were considered preferential cross-sensitizers. These included the dopamine D2 receptor ligands aripiprazole and domperidone. Pholcodine, as well as nizatidine, ranitidine, antrafenine, cabergoline and, to some extent, chlorhexidine best fulfilled the required criteria of apolar character, bioavailability and ionization rate. Conclusion: Our data support the hypothesis that pholcodine might be a potential NMBA cross-sensitizer. They confirmed the results of inhibition tests on patient serum suggesting that dopamine D2 receptor ligands might be cross-sensitizers. They also identified chlorhexidine, a widely used disinfectant incriminated in several cases of immediate hypersensitivity reactions, as a potential cross-sensitizer. Pharmacophore modelling is an inexpensive, straightforward approach that can be used to identify potential NMBA cross-sensitizing agents.

Keywords

Neuromuscular blocking agents, Sensitizers, Cross-sensitizers, Molecular modelling, Pharmacophore, Pholcodine, Chlorhexidine.

Introduction

Anaphylactic reactions during anesthesia are an issue of much concern and are caused mainly by neuromuscular blocking agents (NMBAs) through an immunoglobulin E (IgE)-mediated mechanism [1].

Anaphylactic reactions induced by NMBAs can occur at first contact and might be due to sensitization by contact with other drugs or chemicals. This problem is not specific to NMBAs since reactions to radio contrast media frequently occur on first exposure suggesting previous sensitization through an unknown molecule [2].

NMBAs belong to a variety of chemical families, all of which displaying two ammonium groups separated by a distance of 1-1.45 nm. These ammonium groups, which are either two quaternary ammonium ions (QAIs) or one QAI and a tertiary protonated amine, account for the ability to induce neuromuscular blockade [3] but are also responsible for NMBA binding to IgE. Two binding sites 1.5 nm apart may enable cross-linking of two mast cell-bound IgE molecules [4].

However, unlike most IgE-mediated allergic events, many NMBA-elicited reactions occur at first contact [4,5]. In addition, the NMBA-reactive IgE antibodies do not recognize just one NMBA but can recognize other NMBAs as well. This prompted the idea that some drugs and environmental agents might act as sensitizers and as inducers of IgE molecules able to bind NMBAs. Attempts to identify sensitizers having quaternary ammonium ions were initiated back in the 1980s in studies of the inhibition of binding to NMBA-specific IgE molecules using serum from allergic patients [6]. This approach identified mainly molecules with one (monovalent), two (divalent) or more quaternary ammonium groups but was limited by serum availability from allergic patients.

In our search for potential sensitizers in NMBA anaphylaxis we used a pharmacophore-oriented strategy which defines the chemical features constituting the common denominator of a set of molecules and which is widely used in drug discovery when no structural information on the biological target is available [7] and recently introduced to improve early detection of unexpected adverse effect [8]. A pharmacophore was generated from a representative set of NMBAs and a pool of small molecules was screened to identify putative NMBA sensitizing drugs.

Methods

The pharmacophore generation protocol was developed using the Catalyst® algorithm based on the generalized CHARMm force field implemented in Accelrys Discovery Studio 2.5. Figure 1 shows steps A or A’ to D of ligand preparation, pharmacophore key generation and drug screening.

pharmacy-pharmaceutical-sciences-drug-screening

Figure 1: Pharmacophore key construction and drug screening procedure.

Ligand Preparation

Ligand preparation (step A or A’) whether for pharmacophore key generation or drug screening involved: (i) ionization state attribution and generation of a 3-dimensional coordinate using the ‘Prepare Ligands protocol’. Ionization state was predicted for a pH range of 5.5-9.5 using the estimated pKa values of the Drug Bank® database, (ii) generation of conformers using the ‘Best’ option of the corresponding protocol for complete coverage of the conformational space [9].

Pharmacophore Generation

To design the pharmacophore, we superimposed four NMBAs (step B), each representing an NMBA class (Figure 2): tubocurarine and atracurium, a rigid and flexible bistetrahydroisoquinoleine, respectively, succinylcholine, a flexible dimer of acetylcholine, and pancuronium, an amino-steroid. The ‘HipHop’ algorithm [10] of the ‘Pharmacophore Generation protocol’ produced a series of molecular alignments and identified the configuration of positive ionizable features or charges that were common to the molecules. The resulting pharmacophore key was composed of two positively charged or ionizable moieties with a mean between-centre distance of 1.081 nm (tolerance of 0.741-1.421 nm).

pharmacy-pharmaceutical-sciences-construct-pharmacophore

Figure 2: NMBAs used to construct the pharmacophore key.

Database Screening

We screened the Food and Drug Administration (FDA)-approved small molecule drugs of the Drug Bank® database (1541 drugs) [11] and inserted pholcodine to take into account current information involving this drug (pKa1=8.38; pKa2=6.67) [12] in the genesis of anaphylactic reactions [13,14]. We used the ‘Pharmacophore Mapping protocol’ (step D) with the ‘flexible fitting’ option to minimize the distances between the features of the pharmacophore key and to map atoms in the molecule. The matching parameters were fit value (FV), relative energy (RE) and mean polar surface area (MPSA). FV is a dimensionless parameter (range 0-2) describing the overlap between the chemical features of the pharmacophore key and conformational model. It is given by the equation FV=Σ [1- Σ (D/T)2] where D is the displacement of the feature from the centre of the location constraint and T is the radius of the location constraint sphere for the feature (aka tolerance). RE is the difference between the total energy and lowest available energy of a conformational model (range 0 to ∞, kcal.mol-1). RE is related to the requirement for a sufficiently large population of conformers, which means that the relative population for the energy level is a function of RE. Molecules with a FV threshold above 1.5 and an RE threshold below 20 kcal.mol-1 were considered to have high-ranking parameter values. MPSA is the surface sum over all polar atoms of the molecule (Å2) and was measured for each molecule to account for the apolar character of NMBAs. The inclusion of hydrophobic parameters which, unlike positive ionizable features or charges, are not well defined geometrically, could have given rise to false positives.

Results

Figure 1 summarizes screening of the 1542 small molecule drugs of our database using the pharmacophore key that selected 101 drugs: 13 were NMBAs and 88 non-NMBAs. As an example, Figure 3 shows pholcodine fitting in the pharmacophore key.

pharmacy-pharmaceutical-sciences-positive-ionizable

Figure 3: Pholcodine fit within the pharmacophore key: positive ionizable features or charges (red sphere) pholcodine presented as sticks with carbon (grey), hydrogen (white), oxygen (red) and nitrogen (blue).

Two entries are given for each NMBA in Table 1: i- the best FV and associated RE, ii- the lowest RE and associated FV. All 13 NMBAs had either a high FV or low RE. The large number of NMBAs, selected with our pharmacophore key, including hexafluoronium (a nicotinic acetylcholine receptor antagonist), indicated that this key was indeed the common denominator of this class of drugs. Our pharmacophore key robustness was verified by the following: (i) we twice randomly selected four NMBAs other than those of Figure 2 to obtain two other pharmacophore whose moieties were separated by a distance of 0.9094 and 1.2293 nm. Of the three pharmacophore, our initial pharmacophore key had the best average FV for all NMBAs (1.95 versus 1.88 and 1.78 for the two others); (ii) we changed one NMBA among our four chosen NMBAs with an NMBA of the same class. The distance between the two moieties was invariable (1.081 nm). The only NMBA of our database that was not selected was pipecuronium because of its particular ionization profile.

  By best FV By lowest RE MPSAc
FVa REb FVa REb  
Alcuronium 1.828 12.575 1.594 0.192 46.94
Atracurium 1.999 14.71 1.998 10.55 126.4
Decamethonium 2 7.138 1.999 2.737 0
Doxacurium 1.990 11.50 1.924 1.600 163.4
Gallamine 1.990 6.824 1.999 1.714 41.01
Hexafluoronium 1.58 12.0149 1.5703 5.8691 0
Metocurine 1.998 3.128 1.991 1.291 55.38
Mivacurium 1.999 0.1314 1.999 0.08426 144.9
Pancuronium 1.959 8.291 1.958 2.159 52.60
Rocuronium 1.999 3.202 1.975 1.016 60.20
Succinylcholine 2 3.832 1.999 3.597 52.60
Tubocurarine 1.999 13.01 1.995 1.39 10-14 84.65
Vecuronium 1.999 5.707 1.990 1.39 10-14 57.04

Table 1: Parameter values for NMBA fit in the Pharmacophore.

Among the 88 selected non-NMBA drugs, 42 were considered to be “preferential sensitizers” because of their high-ranking FV and RE matching parameter values (Table 2).

    By best FV By lowest RE MPSAc
FVa REb FVa REb Å2
Cough suppressant Pholcodinee 1.991 8.875 1.983 5.739 56.80
Acetylcholinesterase inhibitors Ambenomium 1.999 3.972 1.999 2.189 58.20
Demecarium 1.999 3.682 1.999 3.682 59.08
NMBA potentiators (Antibiotics)
    Aminoglycosides Amikacin 1.999 6.794 1.999 4.60 10-4 336.8
Arbekacin 2 11.77 1.999 0.04728 302.1
Framycetin 2 3.367 1.999 8.6 10-4 362.8
Gentamicin 2 9.091 1.999 7.2 10-3 212.1
Kanamycin 1.999 15.73 0.5330 2.2 10-13 289.1
Netilmicin 1.999 20.90 1.999 0.7686 213.7
Paromomycin 2 21.06 1.999 0.1086 355.4
Tobramycin 1.999 21.31 1.907 1.0 10-14 274.6
  Polypeptides Colistimethate 1.999 10.62 0.8889 5.39E-14 771.5
Colistin 2 5.47E-05 0.5713 -7.30E-14 498.7
Polymyxin B 1.999 8.812 1.611 -2.09E-14 495.5
Tetracyclines Lymecycline 2 8.042 0.3496 3.840 261.7
Tigecycline 1.999 16.22 1.530 1.39 10-14 215.6
Other categories
Macrolides (antibiotics) Azithromycin 1.999 17.26 1.999 4.327 182.5
Dirithromycin 1.999 7.30.10-06 1.999 7.3 10-06 202.1
Other antibiotics Quinupristin 1.999 20.79 1.983 2.07E+00 258.9
Vancomycin 1.641 8.566 1.641 8.566 539.5
  Antineoplastics Imatinib 2 8.326 1.999 5.598 92.06
Mitoxantrone 1.999 7.037 1.999 1.445 172.3
Porfimer 1.999 4.787 1.999 4.787 316.9
  Vinorelbine 1.995 11.20 1.868 2.987 136.3
H2 antagonists Nizatidine 2 6.581 1.999 3.056 149.9
Ranitidine 2 11.42 1.999 4.004 136.5
Antipsychotics Pipazethate 1.882 15.71 1.831 2.248 82.60
dD2 ligand Aripiprazole 1.999 1.925 50.59 1.729 50.59
D2 ligand Domperidone 2 4.976 2 4.976 74.90
Beta blockers Carteolol 1.964 8.272 1.860 0.5167 79.75
Carvedilol 1.871 18.32 1.850 8.278 81.14
    Miscellaneous Antrafenine 1.999 30.02 1.998 5.486 64.68
Cabergoline 1.911 22.02 1.793 6.195 74.08
Caspofungin 1.999 16.74 1.998 2.361 418.2
Chlorhexidine 1.999 11.77 1.999 6.094 178.5
Colestipol 2 2.221 1.999 1.780 105.1
Deferoxamine 1.999 1.207 2 5.546 211.1
Plerixafor 2 2.543 1.999 3.1 10-4 108.5
Spermine 1.999 3.273 1.999 2.022 88.50
Degarelix 1.999 14.34 1.120 0 526.6
Terlipressin 1.999 7.218 1.338 0.07420 566.7
Vapreotide 1.999 16.44 1.999 1.157 404.5

Table 2: Parameter values for the 42 non-NMBA drugs considered to be preferential sensitizers.

Many of these 42 drugs were to be expected on account of their pharmacologic properties (the acetylcholinesterase inhibitors ambenonium and demecarium) or on account of their action as NMBA potentiators (8 aminoglycosides, 3 polypeptides and 2 tetracycline’s). In addition, there were 4 antibiotics (2 macrolides, 1 streptogramin (quinupristin), 1 glycopeptide (vancomycin)), 4 antineoplastic, 2H2 antagonists, 3 antipsychotics of which 2 were ligands of the dopamine D2 receptor (aripiprazole and domperidone), 2 beta blockers and 11 miscellaneous drugs. Some were less likely “preferential sensitizers” because they had a higher MPSA than the NMBAs. Selected drugs with a high MPSA were all the antibiotics, including those known as NMBA potentiators, and 4 miscellaneous drugs (deferoxamine, degarelix, terlipressin and vapreotide). The drugs with the best fitting parameters in terms of FV, RE, MPSA and ionization state for our pharmacophore were nizatidine, ranitidine, antrafenine, cabergoline and pholcodine. The remaining 46 drugs were denoted “non-preferential sensitizers” (Table 3). They included 22 iminium compounds with a planar structure in stark contrast to the tetrahedral structure of a QAI, 16 drugs with low matching parameters (FV<1.5 and/or a RE>20 kcal.mol-1), 5 ligands of gadolinium the conformers of which had been generated without gadolinium and 3 ubiquitous amino-acids (L-Arg is also an iminium compound).

  By best FV By lowest RE
FVa REb FVa REb
Iminiums
Azathioprine 0.9447 0.9438 0.6047 -7.133 E-04
Bacitracin 1.999 22.53 1.062 -2.25E-04
Capreomycin 2 14.22 1.999 4.68E+00
Cimetidine 1.981 12.78 1.901 8.778
Chloroquine 1.999 7.880 1.986 5.951
Eszopiclone 1.832 23.94 1.715 6.816
Famotidine 1.999 7.13 1.999 3.994
Gefitinib 1.997 12.53 1.982 3.612
Gonadorelin 1.999 11.19 1.982 4.909
Icatibant 2 13.73 1.999 0.4263
Irinotecan 1.999 8.599 1.999 4.655
Hydroxychloroquine 1.999 12.42 1.995 7.288
Hydroxystilbamidine 1.999 5.191 1.999 2.560
Nafarelin 1.999 30.44 0.8733 12.37
Pentamidine 1.999 6.914 1.999 6.246
Proflavine 1.865 0 1.865 0
Quinacrine 1.996 5.446 1.9618 4.163
Streptomycin 1.999 13.57 1.333 -4.17E-14
Valganciclovir 1.999 12.04 1.999 5.935
Valaciclovir 1.999 7.825 1.999 6.200
Vardenafil 2 5.168 1.999 3.381
Zopiclone 1.882 32.54 1.739 12.14
Drugs with low matching parameter values    
Acetophenazine 0.781821 16.29881 0.677224 4.30114
Bleomycin 1.993296 24.58758 1.18941 24.5544
Carphenazine 0.732206 11.04386 0.701733 6.762175
Dimethindene 1.017 20.8 0.9254 10.20
Fluphenazine 0.702513 11.46592 0.6800079981 3.49333
Lisdexamfetamine 0.5941 9.593 0.4764 7.183
Lisinopril 0.466323 11.67692 0.4337678 5.053228
Pentolinium 0.847707 12.504957 0.686721 5.4655
Perphenazine 0.700605 3.378792 1.00329 14.18785
Prochlorperazine 0.732011 3.51842 0.732011 3.51842
Rolitetracycline 1.3435 14.807999 1.27541 6.36621
Ropinirole 0.366417 14.65474 0.2801067 11.172
Sitagliptine 0.6601 8.654 0.5431 5.182
Thioproperazine 0.7542167 14.3766 0.698052 5.4121
Triethylperazine 0.77373 16.33269 0.675515 4.77848
Trifluoperazine 0.766536 9.2605956 0.697729 3.40221
Gadolinium contrast agents      
GadobenateDemiglumine 0.3514 31.49 0.3250 11.90
Gadodiamide 0.4107 10.94 0.3822 9.465
GadofosvesetTrisodium 0.3916 28.77 0.2435 5.304
GadopentateMeglumine 0.5390 22.24 0.4187 9.280
Gadoversetamide 0.4373 18.35 0.3247 12.27
Aminoacids
L-arginine 1.423272 9.116566 1.30116 3.84341
L-cystine 1.867698 10.49869 1.83217 7.21531
L-lysine 0.795507 9.87638 0.464607 1.95225

Table 3: Parameter values for the 46 non-NMBA drugs considered to be non-preferential sensitizers.

Discussion

Screening using our pharmacophore key identified 13 NMBAs among 14 NMBAs included small molecule drugs of our data base, thereby confirming that the key was the common denominator of the NMBA structure. The key also identified 88 non-NMBAs. Among these were 42 “preferential sensitizers”, selected on the basis of either a high drug-key overlap (FV>1.5) or a low energy needed to fit the key (RE <20 kcal.mol-1). The drug’s MPSA was also considered in order to account for the apolar character of NMBAs apart from the two ammonium centers. The 42 “preferential sensitizers” included antibiotics, antineoplastic, H2 antagonists, antipsychotics, beta blockers, as well as pholcodine, chlorhexidine, antrafenine, cabergoline, caspofungin, colestipol, deferoxamisome, plerixafor and even spermine.

The presence of chlorhexidine and pholcodine among the 42 non-NMBAs drugs is worthy of interest. Regarding pholcodine, because it could contribute to the hypothesis of its role as a sensitizer in anaphylactic reactions during anesthesia with NMBAs [13-15] and chlorhexidine, because this drug is both a disinfectant for external use as an antiseptic coated in central venous catheter [16].

Among the selected drugs with high bioavailability, some had two basic amines (e.g. nizatidine, ranitidine, antrafenine, cabergoline and pholcodine) and consequently a significant ionization rate [12], thereby meeting all criteria. The anti-psychotics pipazethate and the two dopamine D2 receptor ligands aripiprazole and domperidone, as well as the beta-blockers carteolol and carvedilol, with at least one weak basic function, have a reduced ionization rate and are therefore less likely to bind.

Some of our findings are quite close to those of Baldo [4] who performed inhibition assays in two subjects with anaphylactic reaction to alcuronium and identified pentolinium and the dopamine D2 receptor ligands, promethazine and chlorpromazine, as cross-reacting molecules. We also identified two dopamine D2 receptor ligands (aripiprazole and domperidone) as well as pentolinium, but it was low ranking.

In conclusion, this inexpensive, straightforward molecular approach proved to be an effective tool in the preliminary identification of potential NMBA sensitizing agents. Our pharmacophore key identified 88 putative NMBA sensitizers of which 42 had high-ranking parameter values and 5/42 fulfilled all criteria, namely, pholcodine, nizatidine, ranitidine, antrafenine and cabergoline. Chlorhexidine, also listed among the 42 is of special interest.

There is no in vitro method to identify cross sensitizers and until now cross sensitizers identification are based on clinical observations. Screening for candidate sensitizing drugs by computational analysis may be a first step and help clinicians toward identifying drugs with strong sensitizing potential.

References