**Title:** Drug clustering to anticipate new aspects of drug safety profile: application to gabapentinoids and other voltage-gated calcium channel ligand drugs

**Short title:** Drug clustering to anticipate drug safety profile

Thibault Viguier¹, PharmD resident; Marie-Sara Agier¹,², PharmD, Annie-Pierre Jonville-Béra¹,², MD; Bruno Giraudeau²,³; PhD; Bérenger Largeau¹*, PharmD

**Affiliations:**

¹CHU de Tours, Service de Pharmacosurveillance, Centre Régional de Pharmacovigilance
Centre-Val de Loire, Tours, France

²Université de Tours, Université de Nantes, INSERM, methodS in Patients-centered outcomes and Health Research (SPHERE) – UMR 1246, Tours, 37044, France

³CHU de Tours, Centre d'investigation clinique - CIC INSERM 1415, Tours, France

Correspondence to: Bérenger Largeau - berengerlargeau@univ-tours.fr ; ORCID: 0000-0002-6824-7283

CHRU de Tours Service de Pharmacosurveillance, Centre Régional de Pharmacovigilance
Centre-Val de Loire; 2 boulevard Tonnellé, 37044, Tours, France

Telephone: +33(0) 2 47 47 80 29; Fax: +33(0) 2 47 47 38 26

T Viguier: thibaultpointviguier@gmail.com; MS Agier: marie-sara.marchand@chu-tours.fr; AP Jonville-Béra: jonville-bera@chu-tours.fr; B Giraudeau: bruno.giraudeau@univ-tours.fr

**Keywords:** Gabapentinoids, Calcium Channel Blockers, Anticonvulsants, Pharmacovigilance, Clustering

**Original research**

Manuscript word count: 2892 words; References: 42; Abstract word count: 249 words

Table: 0; Figures: 3

Supplementary information: 3 tables, 4 figures

**Statements and declarations**

**Data availability:** Data other than that provided in the supplementary material can be requested from the corresponding author (BL).

**Funding:** This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

**Conflict of interest:** The authors have stated explicitly that there are no conflicts of interest in connection with this article.

**Ethics approval:** Not applicable. Per the Jardé law in France regarding research involving human participants, no ethical review or informed consent was required because an existing anonymized database was used.

**Patient consent to participate:** Not applicable

**Permission to reproduce material from other sources:** obtained

**Consent to publish:** Not applicable

**Acknowledgments:** None

**Clinical trial registration:** not applicable
# BULLET POINT SUMMARY

**What is already known about this subject:**

- Gabapentin and pregabalin bind to α2-δ subunit of voltage-gated calcium channels (Ca$_{v}$).

- Other drugs targeting Ca$_{v}$ include cardiovascular calcium channel blockers (CCBs) and anticonvulsants (levetiracetam, ethosuximide, zonisamide).

- In addition to pharmacodynamics, the safety profile of gabapentinoids seems to overlap with cardiovascular CCBs (edema) and Ca$_{v}$-blocking anticonvulsants (suicide, ataxia).

**What this study adds:**

- The safety profile of gabapentinoids overlaps with the one of some dihydropyridine CCBs and Ca$_{v}$-blocking anticonvulsants.

- These results could be used to anticipate some unidentified adverse drug reactions of gabapentinoids from information accumulated with older drugs and sharing a common molecular target and adverse drug reactions signature.
ABSTRACT

AIM: Gabapentin and pregabalin bind to α2-δ subunit of voltage-gated calcium channels (CaV). Other drugs targeting CaV include cardiovascular calcium channel blockers (CCBs) and anticonvulsants (levetiracetam, ethosuximide, zonisamide). In addition to pharmacodynamics, the safety profile of gabapentinoids seems to overlap with the one of cardiovascular CCBs (edema) and CaV-blocking anticonvulsants (suicide, ataxia). The objective of this study was to cluster the safety profile of different CaV-ligand drugs by focusing on whether gabapentinoids present a distinct adverse drug reactions (ADR) signature from cardiovascular CCBs and anticonvulsants.

METHODS: We extracted all ADRs with at least one significant disproportionate reporting (ROR) related to gabapentinoids, CCBs or anticonvulsants in Vigibase. After principal component analysis preprocessing, a hierarchical ascendent classification was performed to cluster gabapentinoids and other CaV-ligand drugs that share a similar ADR signature. Robustness of the results was performed through four sensitivity analyses, varying on the dataset or the clustering method.

RESULTS: A total of 16 drugs and 65 ADRs were included. Gabapentinoids were in the cluster #1 including 8 other drugs (isradipine, nicardipine, lacidipine, lercanidipine, ethosuximide, levetiracetam, zonisamide and nimodipine). Cluster #2 contained two drugs (diltiazem, verapamil), and cluster #3 contained four drugs (amlodipine, felodipine, nifedipine and nitrendipine). The clustering results were consistent in all sensitivity analyses.

CONCLUSION: The safety profile of gabapentinoids overlaps with the one of some dihydropyridine CCBs and CaV-blocking anticonvulsants. These results could be used to anticipate some unidentified ADRs of gabapentinoids from information accumulated with older drugs and sharing a common molecular target and ADR signature.
# 1. INTRODUCTION

The receptogram of a drug can be used to anticipate some of its adverse drug reactions (ADRs) and the aggregation of pharmacovigilance data from older drugs sharing a common molecular target can be a powerful tool for predicting its overall safety profile [1]. While gabapentinoids (gabapentin, pregabalin) were synthesized as GABA-mimetics, the discovery of their pharmacological target followed their approval by several years [2]. The increasing use and misuse of gabapentinoids [3,4] has led to emerging safety signals with cardiovascular (e.g., heart failure [5]) and neuropsychiatric concerns (e.g., suicidal behavior [6]).

Gabapentin and pregabalin bind with a strong affinity to α2-δ subtypes 1 and 2 of voltage-gated calcium channels (Ca$_{v}$). The auxiliary α2-δ proteins increase currents and cell surface expression of the pore-forming α1-subunit of Ca$_{v}$1, Ca$_{v}$2 and Ca$_{v}$3 [2]. The auxiliary α2-δ1 protein has ubiquitous distribution, being present in central and peripheral nervous system, endocrine tissue and within Ca$_{v}$1.2 of vascular smooth muscle and cardiac tissue [2]. The pore-forming α1-subunit of Ca$_{v}$1.2 is the pharmacological target of calcium channel blockers (CCBs) (Fig. 1, from Doan *Tech Reg Anesth Pain Manag* 2010 [7]), responsible, according to their tissue selectivity, for vascular ADRs (e.g., vasodilatory peripheral edema) and cardiac ADRs (sinus bradycardia, atrioventricular block, left ventricular dysfunction) [8]. These cardiovascular ADRs are also reported with gabapentinoids [8]. Interestingly, quite specific off-target ADRs of CCBs such as parkinsonism and gynecomastia are also reported with gabapentinoids [8].

The α2-δ2 subunit is highly expressed in central nervous system, especially in the cerebellum. Gabapentinoid binding to this subunit is thought to cause ataxia, a particularly prevalent ADR of these drugs [9]. Similarly, ataxia is a shared ADR with Ca$_{v}$-blocking anticonvulsants such as levetiracetam [10] and zonisamide [11]. This could be because the α2-δ2 subunit also affects the electrophysiological properties of Ca$_{v}$ expressed in the central nervous system that are the target of levetiracetam (Ca$_{v}$2.1, Ca$_{v}$2.2) [12], ethosuximide (Ca$_{v}$3.1, Ca$_{v}$3.2) [13], and zonisamide (Ca$_{v}$3.2) [13,14] (Fig. 1). For these anticonvulsants, many other ADRs are also common with gabapentinoids such as suicide [15,16], parkinsonism [17,18], and gynecomastia [19]. Taken together, the safety profile and the pharmacodynamics properties of gabapentinoids appear to overlap with other drugs targeting Ca$_{v}$ such as cardiovascular CCBs and Ca$_{v}$-blocking anticonvulsants.

In addition to its pharmacokinetic and pharmacodynamic characteristics, a drug is also defined by its safety profile. As a result, a particular drug or pharmacological class may be grouped together in a group of drugs with a well-known safety profile, in order to anticipate some potential unidentified ADRs. The objective of this study was to cluster the safety profile of different Ca$_{v}$-ligand drugs by focusing on whether gabapentinoids present a distinct adverse drug reactions (ADR) signature from cardiovascular CCBs and Ca$_{v}$-blocking anticonvulsants.

# 2. METHODS

## The WHO pharmacovigilance database, Vigibase

VigiBase, the WHO global pharmacovigilance database, gathers reports of suspected ADRs since 1968, including more than 30 million of individual case safety reports (ICSRs) from over 150 countries worldwide at the time of extraction. For each ICSR, data available included administrative information (qualification reporter, country of primary source), patient demographics, exposure characteristics (drug name, rechallenge) and ADR features (time to onset, seriousness, outcome, recurrence). In Vigibase, the Anatomical Therapeutic
50 Chemical (ATC) classification are used for drug coding and ADR are reported according to the Medical Dictionary for Regulatory Activities (MedDRA) terminology.
51

52

53 Drug and ADR selection to capture ADR signature

54 Drug selection

55 All drugs with affinity for at least one type of Ca<sub>v</sub> expressing an α2-δ subunit available on the French market were considered, including cardiovascular CCBs (ATC C08C, D and E), gabapentinoids (gabapentin, pregabalin) and Ca<sub>v</sub>-blocking anticonvulsants (levetiracetam, ethosuximide, zonisamide). Although flunarizine is a Ca<sub>v</sub>3 blocker [20], this drug has been excluded because of a broader pharmacodynamic profile related to its H1-antihistamine, antidopaminergic, and anticholinergic effects that accounts for some of these ADRs [21]. Drug combinations (e.g., amlodipine/losartan) were not considered. Drugs with relatively low numbers of ICSRs (≤ 1,500 in the whole database) were also excluded to limit the potential for clustering to be driven by the level of drug use.

64

65 ADR selection

66 We extracted all ADRs related to the included drugs to performed a disproportionality analysis at the MedDRA ‘Preferred term’ level. Database extraction was conducted on December 01, 2022. The association between each drug-ADR combination was examined using the reporting odds ratio (ROR) method [22] (with all other drugs as comparator) to select all ADRs with a significant disproportionality signal for at least one of the included drugs. As conventionally defined [22], a signal of disproportionate reporting was considered significant if both conditions were met: lower boundary of the 95% confidence interval of ROR >1 and the number of observed ICSRs for a drug-ADR combination ≥3 [22]. We anticipated the fact that the number of ADRs associated with a disproportionality signal for at least one of the drugs studied could potentially be very large, which could negatively impact the quality of clustering, even with principal component analysis (PCA) preprocessing. Thus, the disproportionality analysis performed for ADR selection was limited to the subset of ICSRs reported in France by health care professionals (physician or pharmacist) where each of the drugs studied was the only drug suspected in the occurrence of the ADR. Furthermore, in France, drug causality assessment is carried out by regional pharmacovigilance centers, which work as a network and therefore have a more standardized approach to analysis than if the cases considered for ADR selection included different countries. Of these ADRs, we excluded those related to drug indications (e.g., epilepsy, anxiety disorder, angina pectoris) and those related to very specific aspects of the safety profile (e.g., pregnancy and perinatal conditions, product misuse, neoplasms). All excluded ADRs with reasons for exclusion are listed in eTable S1, Online Resource. After selecting the ADRs to capture the ADR signature, we extracted the data from Vigibase to calculate each ROR value across the whole database.

89

90 Cluster analysis

91 After dimensionality reduction by PCA, a hierarchical ascendent classification was performed to cluster gabapentinoids and other Ca<sub>v</sub>-ligand drugs that share a similar ADR signature (dendogram). The number of principal components used was the smallest that achieved at least 85% inertia (cumulative percentage of total variation) [23] to increase the stability of clustering. No scaling of ADR variables was performed, as this would have reduced the information provided by the high variance variables. No-centered PCA is a well-established technique when the data are such that origin is an important point of reference [23]. This is the case for pharmacovigilance datasets, because if there are no ADR reports for a drug, it is reasonable to assume that the drug (if old) is not significantly associated with that ADR. As
previously described in clustering approaches on pharmacovigilance data, similarity between clusters was calculated by Euclidean distance [24] and cluster analysis was performed with Ward's linkage method [25,26]. The results of the hierarchical ascendent classification analysis were plotted as a dendrogram, and the numbers of clusters were visually estimated.

Robustness of the clustering analysis results was assessed by the following sensitivity analyses: 1/ clustering of ADRs at the high-level term (HLT) level, 2/ restriction of ADRs with at least 5 ICSRs for the drug-ADR combination, 3/ use of the k-means method for clustering and 4/ use of gabapentinoids as supplementary individuals for both, PCA and clustering by K-means.

In the sensitivity analyses, a PCA preprocessing was also performed and the same criteria were applied as in the main analysis (i.e., similarity metric, linkage rule). As with the K-means algorithm, the number of clusters has to be predetermined, it has been set at three. This threshold was chosen because the drugs belong to three distinct pharmacological classes (gabapentinoids, cardiovascular CCBs, and Ca<sub>v</sub>-blocking anticonvulsants).

Hierarchical ascendent classification was also performed without the gabapentinoids to investigate their impact on partitioning. A total of 500 iterations of the clustering process was performed in randomly resample of the original dataset, and estimated the cluster-wise stability with the Jaccard coefficient. A Jaccard similarity index >0.5 indicates a stable and reproducible cluster [25].

Per the Jardé law in France regarding research involving human participants, no ethical review or informed consent was required because an existing anonymized database was used. The statistical analysis was performed with R, version 4.1.3 (R Foundation). Key targets and ligands in this article are hyperlinked to corresponding entries in http://www.guidetopharmacology.org, the common portal for data from the IUPHAR/BPS Guide to PHARMACOLOGY, and are permanently archived in the Concise Guide to PHARMACOLOGY [27,28].

## 3. RESULTS

A total of 16 drugs and 65 ADRs were included. Manidipine, cilnidipine, barnidipine, nisoldipine, benidipine, bepridil, nilvadipine, perhexiline, clevidipine, azelnidipine, gallopamil, efonidipine, fendiline and lidoflazine were excluded based on their insufficient number of ICSRs and/or because they were not marketed in France. The 65 ADRs with a signal of disproportionate reporting were distributed in 12 system organ classes (SOCs) (eTable S2, Online Resource). The most represented SOCs were psychiatric disorders (14/65, 22%), cardiac and vascular disorders (14/65, 22%) and neurological disorders (10/65, 15%). Of these ADRs, 33 were shown without significant disproportionality (e.g., orthostatic hypotension, gingival hypertrophy, atrioventricular block) for gabapentin (n=30) or pregabalin (n=32). The ROR values for each drug-ADR combination are provided in eTable S3, Online Resource.

Drug clustering obtained with the primary analysis is shown in Fig. 2. The selection of the first three principal components retained 89% of the total inertia. We visually selected three clusters for analysis. Gabapentinoids were in the cluster #1, including 8 others drugs (isradipine, nicardipine, lacidipine, lercanidipine, ethosuximide, levetiracetam, zonisamide and nimodipine). Cluster #2 contained two drugs (diltiazem, verapamil), and cluster #3 contained four drugs (amlodipine, felodipine, nifedipine and nitrendipine). Jaccard index pointed out a high cluster-wise stability for cluster #1 (95%), #2 (90%) and #3 (93%).
149 In sensitivity analyses, ADRs were grouped into 50 different HLTs (eTable S2, Online Resource). Restriction of ADRs with a significant disproportionality signal with at least 5 ICSRs for the drug-ADR combination led to the selection of 53 distinct ADRs. The results of hierarchical ascendent classification clustering were consistent in all sensitivity analyses (eFigures S2 and S3, Online Resource). Hierarchical ascendant classification incorporating the gabapentinoid data did not significantly affect the construction of the clusters (eFigure S1, Online Resource). The K-means algorithm found the same categorization as in the primary analysis (Fig. 3) even when gabapentinoids were used as supplementary individuals (eFigure S4 Online Resource).

158

## 4. DISCUSSION

159 The originality of this work was to develop a new approach to evaluate and cluster the safety profile of drugs sharing a common pharmacological target. Using a clustering method on data from the WHO pharmacovigilance database, the safety profile of gabapentinoids showed a strong overlap with the one of cardiovascular CCBs (e.g., nimodipine, lercanidipine, nicardipine) and Ca<sub>v</sub>-blocking anticonvulsants (ethosuximide, levetiracetam and zonisamide). These results suggest that in terms of ADR signatures, gabapentinoids are more like chimeric drugs at the interface between dihydropyridine CCBs and Ca<sub>v</sub>-blocking anticonvulsants, rather than a pharmacological class in their own right. Bradycardic CCBs and dihydropyridine CCBs indicated for chronic stable angina pectoris were found in two other clusters. In other words, gabapentinoids showed greater similarity in ADR signature to some dihydropyridines and Ca<sub>v</sub>-blocking anticonvulsants than bradycardic CCBs showed similarity to dihydropyridine used in angina pectoris. In addition, Fig. 2 shows that the ADR signature of gabapentinoids is about as close to nimodipine as diltiazem is to verapamil (i.e., same distance or dissimilarity on the dendrogram y-axis). These results may have two different implications, either anticipating new potential ADRs or generating hypotheses for drug repurposing.

176

177 Information accumulated with older drugs sharing a common pharmacological target and ADR signature with gabapentinoids could be used to anticipate some of the novel and unlabeled ADRs of gabapentinoids. For atrioventricular block, one of the ADRs included in the signature studied (eTable S3, Online Resource), our disproportionality analysis like others [29] showed no significant association for either pregabalin or gabapentin. If this conventional method of identifying potential ADRs by a disproportionality approach had been applied, it would have been reasonable to assume that this cardiac conduction disorder is probably not an ADR of gabapentinoids. The cluster of gabapentinoids includes some CCBs that are known to cause atrioventricular block. Although this is mainly described with bradycardic CCBs, it is also reported with some dihydropyridines such as nimodipine [30], especially in overexposure situations, which exposes to a risk of loss of selectivity on Ca<sub>v</sub>. Furthermore, this is a well-documented ADR for pregabalin [31] that is now included in its European summary of product characteristics [32]. These findings prompt attention to this potential risk with gabapentin, which may be explained by the negative dromotropic effect of gabapentinoids mediated by cardiac Ca<sub>v</sub>1.2 blockade [8]. Similarly, there was no significant association between pregabalin or gabapentin and gingival hyperplasia in Vigibase. Gingival hyperplasia is not labelled in the European summary of product characteristics for pregabalin and gabapentin. When examining this association for gabapentinoids nearest neighbors based on the dendrogram results (Fig. 2), the risk of gingival overgrowth in patients taking nimodipine or Ca<sub>v</sub>-blocking anticonvulsants (levetiracetam, zonisamide, ethosuximide) [33,34] is well established. This may suggest a common ADR with gabapentinoids. Interestingly, Ca<sub>v</sub> blockade within gingival fibroblasts is assumed to be a central

198
pathophysiological component in the genesis of drug-induced gingival overgrowth [35], and there is growing experimental [36,37] and pharmacovigilance [34] evidences which support that gabapentin can induce gingival overgrowth. When questioning the safety profile of gabapentinoids, it seems important to keep in mind that it is a modulator of the α2-δ subunit of Ca_v that is ubiquitous and affects its electrophysiological properties. Many of the well-known ADRs of cardiovascular CCBs (vasodilation, peripheral edema, heart failure) and Ca_v-blocking anticonvulsants (suicide, ataxia, gynecomastia) are also reported with gabapentinoids. Taken together, the clustering approach developed in this study seems to be an interesting tool to raise awareness in the pharmacology community of the potential risk for occurrence of previously unidentified ADRs for newly marketed drugs.

In the area of safety signal detection, recent work has shown the critical importance of sensitivity analyses in reducing the rate of false discoveries that are not true ADRs. Indeed, Khouri et al. [22] showed that for the ROR method, the positive predictive value increased from approximately 30% to 80% depending on whether the disproportionality signal was robust from 0 to 6 predefined sensitivity analyses, respectively. On the other hand, there is no effective method to explore the risk of false negatives except lowering the significance threshold or by considering competing drugs or events, which *de facto* leads to an exponential increase in the false positive rate. This is a problem mainly for orphan drugs and newly marketed drugs [38]. In this case, the clustering method used appears to be an interesting tool to use information from older drugs that have a similar ADR signature to generate hypotheses and to be particularly attentive to potential safety signals that might emerge in the future. This would complement the shortcomings of current detection methods with respect to the risk of false negatives, which arises mainly from situations where the level of exposure of the drug is low in the population, as well as the notoriety bias of preferentially reporting ADRs that are labeled in the summary of product characteristics.

Another ancillary benefit that could be discussed is the repositioning of gabapentinoids in the validated therapeutic indications of closely related drugs such as nimodipine. Meta-analyses of randomized controlled trials of prophylactic nimodipine administration for cerebral vasospasm following subarachnoid hemorrhage have noted a benefit to reduce the risk of poor outcome and secondary ischemia after aneurysmal subarachnoid hemorrhage [39]. In addition, acute seizures occur in 6-18% of patients with subarachnoid hemorrhage [40]. Thus, the vasodilatory properties of gabapentinoids, notably demonstrated on rat cerebral arteries [41], associated with their anticonvulsant effect, could be the rationale for evaluating their potential efficacy in controlling both vasospasm and seizures in patients with subarachnoid hemorrhage.

The cluster analysis yielded consistent results, given that the bradycardic CCBs were classified in the same cluster, as well as the dihydropyridine CCBs used in chronic stable angina. Furthermore, the results were robust to our multiple sensitivity analyses varying both the selected ADRs and the clustering method. However, there are limitations inherent to the nature of the database. Indeed, under-reporting and selective reporting (e.g., notoriety bias due to media alert, Weber effect related to novelty of the drug, severity of the ADR) are well described in the field of pharmacovigilance and therefore distort knowledge about drug safety profiles [22,42]. Furthermore, the clustering results were based on the ROR value (point estimate) between each drug-ADR combination, which implies a degree of caution in interpretation. First, the ROR value reflects the strength of the disproportionality signal and not the magnitude of the "risk" of ADR occurrence. Second, its use in cluster analysis could
248 not take into account the precision (confidence interval) of this point estimate. Finally, as this
249 approach is purely exploratory, it should only be considered as hypothesis generating.

250
251 **5. CONCLUSION**
252 The ADR signature of gabapentinoids appears to have a strong overlap with the one of some
253 dihydropyridine CCBs and Ca<sub>v</sub>-blocking anticonvulsants. These results suggest that in terms
254 of safety profile, gabapentinoids are more like chimeric drugs at the interface between
255 dihydropyridine CCBs and Ca<sub>v</sub>-blocking anticonvulsants, rather than a pharmacological class
256 in their own right. This information could be used to anticipate some new and unidentified
257 ADRs, and the review of the non-significant ADRs of gabapentinoids highlighted gingival
258 hypertrophy and atrioventricular block as a potential ADR of gabapentin.
**ACKNOWLEDGMENTS:** None

**STATEMENTS AND DECLARATIONS**

**Conflict of interest:** The authors have stated explicitly that there are no conflicts of interest in connection with this article.

**Funding information:** This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

**Data availability:** Data other than that provided in the supplementary material can be requested from the corresponding author (BL).

**Ethics approval:** Not applicable. Per the Jardé law in France regarding research involving human participants, no ethical review or informed consent was required because an existing anonymized database was used.

**Consent to participate:** Not applicable

**Consent to publish:** Not applicable

**AUTHOR CONTRIBUTIONS**

**T Viguier:** Investigation, Software, Formal Analysis, Writing – Original Draft

**MS Agier:** Writing – Review & Editing, Supervision

**AP Jonville-Béra:** Writing – Review & Editing, Project administration

**B Giraudeau:** Methodology, Writing – Review & Editing, Supervision

**B Largeau:** Conceptualization, Methodology, Investigation, Software, Formal Analysis, Visualization, Writing – Original Draft
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FIGURE LEGENDS

**Fig. 1** | Schematic representation of the different subunits of a voltage-gated calcium channels (Ca<sub>v</sub>) with the binding site of gabapentinoids (α2-δ subunit) and other Ca<sub>v</sub>-targeting drug ligands (pore-forming α1-subunit). Reprinted and modified from L Doan *Tech Reg Anesth Pain Manag* 2010, with permission from Elsevier [7]

DHP, dihydropyridines

**Fig. 2** | Dendrogram representing the results of the hierarchical clustering analysis in the primary analysis.

**Fig. 3** | Cluster plot of partitioning with the K-means algorithm in the sensitivity analysis.