# A simulation trial evaluating ABiMed, a clinical decision support system for medication reviews and polypharmacy management

Abdelmalek Mouazer, Sophie Dubois, Romain Léguillon, Nada Boudegzdame, Thibaud Levrard, Yoann Le Bars, Christian Simon, Brigitte Séroussi, Julien Grosjean, Romain Lelong, et al.

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## To cite this version:

Abdelmalek Mouazer, Sophie Dubois, Romain Léguillon, Nada Boudegzdame, Thibaud Levrard, et al. A simulation trial evaluating ABiMed, a clinical decision support system for medication reviews and polypharmacy management. Research in Social and Administrative Pharmacy, 2025, 21 (11), pp.904-913. 10.1016/j.sapharm.2025.06.006. hal-05130379

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Submitted on 23 Jan 2026

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# A simulation trial evaluating ABiMed, a clinical decision support system for medication reviews and polypharmacy management

Abdelmalek Mouazer<sup>a,b</sup>, Sophie Dubois<sup>e</sup>, Romain Léguillon<sup>a,c</sup>, Nada Boudegzdame<sup>a,f</sup>, Thibaud Levrard<sup>d</sup>, Yoann Le Bars<sup>d</sup>, Christian Simon<sup>d</sup>, Brigitte Séroussi<sup>a,b</sup>, Julien Grosjean<sup>a,c</sup>, Romain Lelong<sup>a,c</sup>, Catherine Letord<sup>a,c</sup>, Stéfan Darmoni<sup>a,c</sup>, Karima Sedki<sup>a</sup>, Pierre Meneton<sup>a</sup>, Rosy Tsopra<sup>f,g</sup>, Hector Falcoff<sup>e</sup>, Jean-Baptiste Lamy<sup>a,* ID</sup>

## ARTICLE INFO

**Keywords:**

Clinical decision support systems

Polypharmacy management

Medication review

Visual analytics

STOPP/START

Simulation trial

## ABSTRACT

**Background:** Medication review is a structured interview of the patient, performed by the pharmacist and aimed at optimizing drug treatments. In practice, medication review is a long and cognitively-demanding task that requires specific knowledge. Various tools and criteria have been proposed, but their application is tedious.

**Methods:** We designed ABiMed, a clinical decision support system for medication reviews. The design was supported by literature reviews and clinicians focus groups. ABiMed supports the pharmacist by implementing the STOPP/START v2 criteria and by presenting aggregated drug knowledge, such as dosage, interactions or adverse effects, visually using tables, graphs and flower glyphs. We evaluated ABiMed with 39 community pharmacists during a simulation trial, each pharmacist performing a medication review for two fictitious patients without ABiMed, and two others with ABiMed. We recorded the problems identified by the pharmacist, the interventions proposed, the response time, the perceived usability and the comments. Pharmacists' medication reviews were compared to an expert-designed gold standard.

**Results:** With ABiMed, pharmacists found 1.6 times more relevant drug-related problems during the medication review (p < 0.0001) and proposed better interventions (p < 0.0001), without needing more time (p = 0.56). The System Usability Scale score is 82.7, which is ranked "excellent". In their comments, pharmacists appreciated the visual aspect of ABiMed and its ability to compare the current treatment with the proposed one. A multifactor analysis showed no difference in the support offered by ABiMed according to the pharmacist's age or sex, in terms of percentage of problems identified or quality of the proposed interventions.

**Conclusions:** The use of an intelligent and visual clinical decision support system can help pharmacists when they perform medication reviews. Our main perspective is the evaluation of the system with real patients, and its diffusion among pharmacists.

## 1. Introduction

The worldwide population of people aged 65 or over is expected to double, rising from 761 million to 1.6 billion by 2050.¹ Many of them are exposed to polypharmacy (commonly defined as taking 5+

long-term drugs<sup>2,3</sup>), which is associated with potentially inappropriate medications (PIMs) and drug-related problem (DRP) including adverse drug events (ADE).⁴ In France, inappropriate prescriptions concern more than one in two elderly patients and their direct cost is 507 million € per year.⁵

<footnote><sup>a</sup> INSERM, Sorbonne Université, Université Sorbonne Paris-Nord, Limics, 75006, Paris, France</footnote>

<footnote><sup>b</sup> Assistance Publique - Hôpitaux de Paris (AP-HP), Paris, France</footnote>

<footnote><sup>c</sup> Department of Biomedical Informatics, Rouen University Hospital, France</footnote>

<footnote><sup>d</sup> EIG SAS, 92400, Courbevoie, France</footnote>

<footnote><sup>e</sup> SFTG Recherche (Société de Formation Thérapeutique du Généraliste), Paris, France</footnote>

<footnote><sup>f</sup> Université Paris Cité, Sorbonne Université, Inserm, Centre de Recherche des Cordeliers, F-75006, Paris, France</footnote>

<footnote><sup>g</sup> Department of Medical Informatics, AP-HP, Hôpitaux Européen Georges-Pompidou, F-75015, Paris, France</footnote>

<footnote>* Corresponding author.
E-mail addresses: jibalamy@free.fr, jiba@lesfleursdunormal.fr (J.-B. Lamy).</footnote>
General practitioners (GPs) are open to deprescribing inappropriate drugs,⁶ but often lack time and pharmacological knowledge. Pharmacists could play a key role,⁷ by conducting Medication Review (MR), "a structured evaluation of a patient's medicines with the aim of optimizing medicines use and improving health outcomes. This entails detecting drug-related problems and recommending interventions".⁸ MR can change practices,⁹ solve about 1 DRP out of 2¹⁰, reduce hospitalizations in emergency departments by about one third¹¹,¹² and saves costs (up to 273 € per patient per year, including drug cost but also the cost of management of the associated adverse events¹³,¹⁴). However, MR is a complex task, requiring efficient collaboration with the GP, but also strong skills in clinical pharmacy.¹⁵,¹⁶

To perform MR, pharmacists can be assisted by drug databases offering a pharmaceutical description of all marketed drugs, and by tools that aid prescribing or deprescribing,¹⁷,¹⁸ e.g. STOPP/START.¹⁹,²⁰ It includes STOPP rules for detecting PIMs, and START rules for detecting potential omissions. Studies demonstrated its ability to improve prescribing appropriateness.²¹ However, drug databases provide one page per drug, which is tedious when the patient takes many drugs, and criteria like STOPP/START, in their paper format, are time-consuming and difficult to apply in clinical routine.²²

Clinical Decision Support Systems (CDSSs) have been designed to overcome these limitations.²³ A recent scoping review²⁴ found 19 CDSSs for managing polypharmacy in the elderly. Most of them implement paper criteria by automating their recommendations, or give access to drug knowledge. But the review highlighted limits: the targeted user is often the physician instead of the pharmacist, and the output is often displayed in a non-friendly format (e.g. long text).

ABiMed²⁵,²⁶ aims at overcoming these limitations. It is a CDSS for helping community pharmacists to perform type-3 MR (i.e. medication review that includes patient interview and clinical data⁵⁸). ABiMed associates two approaches: it implements STOPP/START rules but also provides aggregated drug knowledge from the Theriaque drug database and from drug SPCs (Summary of Product Characteristics), using a graphical presentation, based on visual analytics.

The present study aims at evaluating the usability of ABiMed and its impact when used by pharmacists performing MRs. The study is a simulation trial,²⁷ i.e. clinical case data were used but no real patients were involved. We recruited community pharmacists with a clinical activity, and they performed MRs on clinical cases, according to a sequential group protocol. Two interventions were compared: ABiMed (test) vs the tools and resources the pharmacist commonly uses for MRs (control). The resulting MRs were matched with a gold standard defined by experts. We considered three outcomes: (1) the identification of problems (PIMs and DRP) in the patient drug order (primary outcome), (2) the quality of the propositions for solving these problems, and (3) the time required for performing the MR.

# 2. Materials and methods

## 2.1. Brief description of ABiMed

ABiMed is a CDSS for medication review and polypharmacy management.²⁵,²⁶ The development of ABiMed followed a user-centered approach. Pharmacists and physicians were consulted through questionnaires and focus group sessions at the beginning of the project, for collecting their needs and expectations. A first prototype was presented during focus groups and workshops, and then refined according to the clinician feedback. An ergonomic assessment was also performed.

ABiMed helps at three levels. First, it facilitates communication between pharmacists and GPs, including the transfer of the patient data from the GP's EHR to the pharmacist. Second, it provides drug knowledge contextualized for the patient, using a graphical presentation. The drug knowledge being extracted from the Theriaque drug database²⁸ (for drug-drug interactions, drug-disease interactions and dosage) and directly from drug SPCs (Summary of Product Characteristics, for

adverse events) using Natural Language Processing (NLP) tools we developed in a previous project.²⁹ Dosages are presented in a table, showing the dosage of all drugs at a time and highlighting potential dose or timing errors, and taking into account patient age, sex, renal and hepatic status, drug indication and associated drugs (Fig. 1-A). Potential adverse effects are summarized with flower glyphs; the surface of each petal is proportional to the risk of adverse effect in a given anatomical category, e.g. cardiac or neurological (Fig. 1-B). Contraindications and drug-drug interactions are shown on a radial graph, where each drug is represented by a circle, the presence of contraindications is indicated by the drug's circle color, and drug-drug interactions are represented by lines connecting two drugs (Fig. 1-C). Third, it implements the STOPP/START v2 criteria¹⁹ through a rule-based system that takes into account drugs (ATC classes but also indications and doses), clinical conditions (ICD10 codes) and biology (LOINC codes and associated values) (Fig. 1-D).

ABiMed also proposes a comparative mode. It compares the current treatment with the treatment after the interventions proposed during MR, in order to check that these interventions solve the problems identified without introducing new problems.

The current evaluation does not focus on the pharmacist-GP collaboration (point 1 above), but only on the two other points (2 and 3). For more details on ABiMed, its design, its functions, its ergonomic assessment and the preliminary qualitative evaluations, please refer to Ref. 26.

## 2.2. Recruitment

We recruited community pharmacists having a clinical practice in France. We excluded pharmacists refusing to participate or without Internet access. Pharmacists were recruited by emails, via lists of pharmacists from associations and territorial professional health communities, on a voluntary basis. They were compensated for their participation. Pharmacists followed an online training session via webinar (about 1 h). We presented to them the project and its goal, the ABiMed software and the evaluation principles.¶

## 2.3. Clinical cases and gold standard

An expert committee including two pharmacists (S Dubois, R Léguillon), a GP (HF) and three researchers in health informatics with MD or PharmD (AM, RT, JBL) designed four clinical cases, labeled A, B, C, D, based on realistic situations (Table 1). Each consisted of an elderly patient with polypharmacy, consulting his/her pharmacist for a problem leading to a MR. All clinical data was filled in the case; thus, during the study, pharmacists did not have to enter any data.

For each case, a gold standard was devised. It consisted of a list of drug-related problems and, for each problem, a pharmaceutical intervention. Interventions consisted of deprescribing a drug, prescribing a new drug, replacing a drug, changing the dose, or prescribing lab tests. Each intervention was assigned a score using the CLEO v3 clinical scale (CLinical, Economic and Organizational impacts of pharmacists' interventions),³⁰ from 1 (minor) to 4 (vital).

## 2.4. Protocol

The study followed a sequential group protocol, in which each pharmacist first carried out two cases without ABiMed (i.e. control cases) online, and then two with ABiMed (i.e. test cases). As there is no real patients, the actual subject of the study are the pharmacists. They were asked to use their usual resources when not having ABiMed.

This protocol permits all pharmacists to test ABiMed, which allows collecting the qualitative opinion of all pharmacists, and increases the statistical power, each participant being its own control. Moreover, in previous studies that used a parallel group protocol, we found that participants in the control group lacked motivation and were more likely to give up, compromising the entire study.
<figure><img src="image_4.png" /><figcaption>A: dosage tab</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>C: drug interaction tab</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>B: adverse effect tab</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>D: STOPP/START tab</figcaption></figure>

<caption>**Fig. 1.** Screenshots of ABiMed's tabs for decision support. A: table summarizing current dosages and recommended ones, taking into account age, renal and hepatic status, indications and drug associations; B: adverse effects profile expected with the entire drug order, presented as flower glyphs; C: drug interactions, presented as a radial graph; D: STOPP/START rules detected.</caption>

<table><caption>**Table 1**
Metrics of the four clinical cases.</caption><thead><tr><th>Case</th><th>#drugs</th><th>#conditions</th><th>#problem</th><th>total CLEO score</th></tr></thead><tbody><tr><td>A</td><td>8</td><td>8</td><td>6</td><td>10</td></tr><tr><td>B</td><td>7</td><td>7</td><td>5</td><td>7</td></tr><tr><td>C</td><td>5</td><td>6</td><td>6</td><td>7</td></tr><tr><td>D</td><td>5</td><td>5</td><td>5</td><td>11</td></tr></tbody></table>

## 2.5. Comparison groups¶

Pharmacists were randomized into 4 groups, labeled G1-4. Each group of pharmacists included the same clinical cases, but with a different order of passage, as shown in Table 2. In each group, the first two cases were treated by the pharmacist without ABiMed (control cases) and the last two cases with ABiMed (test cases). Control cases were carried out before test cases, to avoid any learning phenomenon when using ABiMed.

## 2.6. Ethics

The INSERM ethics evaluation committee (IRB00003888) reviewed and approved the study.¶

## 2.7. Number of subjects

In the literature, the mean number of problems identified during MRs

<table><caption>**Table 2**
Definition of the four randomization groups.</caption><thead><tr><th>Intervention</th><th>without ABiMed</th><th>with ABiMed</th></tr></thead><tbody><tr><td>Order of passage</td><td>#1</td><td>#2</td></tr><tr><td>Group G1</td><td>case A</td><td>case B</td></tr><tr><td>Group G2</td><td>case C</td><td>case D</td></tr><tr><td>Group G3</td><td>case B</td><td>case A</td></tr><tr><td>Group G4</td><td>case D</td><td>case C</td></tr></tbody></table>

ranges from 2.7 to 4.6³¹, with or without support, with a standard deviation up to 1.8. Considering a risk $α = 5 \%$, a power $1 - β = 0.9$ and bilateral tests, this translates to 38 subjects needed. Here, a “subject” is a fictitious patient, there are thus theoretically 4 “subjects” per community pharmacists. Nevertheless, we aimed at recruited 40 pharmacists, and not 10, in order to have a sufficient population size for collecting their personal opinions.

## 2.8. Data collected

For each pharmacist, we collected:

(1) demographic data (online questionnaire): age group, gender (male, female, other), number of MRs carried out the last year, previous knowledge of STOPP/START (yes or no). Age was collected in 10-year age classes, to prevent possible re-identification of pharmacists.

(2) for each case (automatic collection): problems identified and pharmaceutical interventions proposed by the pharmacist, response time.

(3) satisfaction questionnaire: SUS scale (System Usability Scale³²) comprising 10 questions with 5 possible answers.

(4) free-text comments.

Clinical cases were corrected semi-automatically. Interventions were rated using CLEO scores, the maximum value was the value defined in the gold standard, and lower values were given for suboptimal interventions. The minimum value was -1, for harmful interventions. Interventions proposed by pharmacists were automatically matched with the gold standard. In case of match, we considered the problem as identified and the intervention as having its maximum value. Then, cases were manually and blindly reviewed by S Dubois, HF and JBL. They verified problems and interventions, taking into account textual comments, and manually rated interventions that did not match the gold standard.
## 2.9. Outcomes and criteria

The outcomes of the study includes three dimensions, efficacy: the proportion of drug-related problems identified by the pharmacist and the quality of the proposed interventions, efficiency: the amount of resources required, here the main resource is the time of the pharmacist, and the usability perceived by the pharmacist.

Thus, the evaluation criteria were:

(1, primary criteria) the percentage of problems identified in the MR, obtained by dividing the number of problems in the gold standard that the pharmacist successfully identified, by the total number of problems in the gold standard.

(2) the CLEO score ratio, obtained by dividing the sum of the pharmacist's interventions CLEO scores by the sum of the gold standard interventions CLEO scores,

(3) the overall time spent to perform a MR,

(4) the pharmacist perceived usability, measured by SUS.

For example, a pharmacist identified 2 problems in case #A and proposed 2 interventions, of CLEO score 1 and 2; the percentage of problems identified is 33.3 % (since case #A has 6 problems) and the CLEO score ratio is 30 % (since case #A has a total of 10 CLEO score and only 1 + 2 = 3 were proposed).

## 2.10. Evaluation website

The evaluation website for the pharmacists was based on ABiMed. It included two interfaces for MRs. The "with ABiMed" interface included 8 tabs: (1) the clinical case presentation, (2) the patient data (drugs taken, clinical conditions and lab tests), (3) the patient interview, (4-7) the decision support tabs (corresponding to the tabs shown in Fig. 1), (8) the interface for the pharmacist to enter his interventions and comments. The "without ABiMed" interface was identical but without tabs #4-7.

## 2.11. Statistical analysis

Statistical analysis was performed blindly using R, with a risk $\alpha = 5 \%$ and bilateral tests. The unit of analysis is the MR, i.e. a given clinical case solved by a given pharmacist.

Criteria #1-3 were compared using Welch two-Sample t-test. Times were logged before comparison, to reduce the impact of long durations. Linear Mixed Models (LMM) were used for finer analysis; they were preferred from ANOVA because they permit the distinction between fixed and random factors, and their results is independent from the order in which factors are tested. Two models were tested for each criterion #1-3: a simple model considering three fixed factors, ABiMed, Case and Group, and a complex model considering ABiMed, Case, Age class of the pharmacist, Sex, Previous knowledge of STOPP/START and Number of MRs performed the last year, as well as their interactions with ABiMed. For each, the random effect was modeled according to the pharmacist ID. Type-III ANOVA was used for computing p-values.

The possible carryover effect was evaluated by comparing criteria #1-3 between the first case treated by pharmacists and the second (both without ABiMed), and the third and the fourth (both with ABiMed), respectively.

Supplementary file #1 and #2 contain the datasets, and #3 the R sources.

## 3. Results

### 3.1. Recruited participants

We recruited 39 community pharmacists in the study. Table 3 shows their demographic data.

### 3.2. Impact on problem identification

The left part of Table 4 shows the percentage of problems identified by pharmacists without and with ABiMed. The overall percentage is 45.0 % without ABiMed vs 71.9 % with ABiMed, the difference is highly significant ($p = 1.1 \times 10^{-12}$). This shows that ABiMed had a strong positive impact: using ABiMed, pharmacists identified 1.6 times more problems (95 % confidence interval: 1.35-1.91).

The left part of Table 5 shows the LMM analysis with the simple and complex models. The simple model confirms the impact of ABiMed, and shows that there is a significant effect of clinical cases, i.e. some cases are simpler and some other more difficult. The randomization group has no significant effect.

The complex model tests several characteristics of pharmacists, and their interaction with ABiMed. Three factors are significant: ABiMed, cases and age. Fig. 2 shows that the percentage of problems identified slightly decreases with age. This is expected because, in France, only younger pharmacists were trained in MR during their studies.

### 3.3. Impact on proposed interventions

The center part of Table 4 shows the CLEO score ratio of the interventions proposed by pharmacists, without and with ABiMed. The difference is highly significant, showing that pharmacists proposed clinically better pharmaceutical interventions with ABiMed.

The complex LMM model (Table 5, center part) identified three significant factors: ABiMed, clinical case, age, and one interaction between ABiMed and case. Effect of age is similar to its effect on the percentage of problems identified (see above). Fig. 3 analyses the case-ABiMed interaction. For case A, B and C, the use of ABiMed increased the CLEO score ratio, but not for case D.

### 3.4. Response times

The right part of Table 4 shows mean and median response times, without and with ABiMed. There is no significant difference. Thus, the

<table><caption>**Table 3**
Demographic characteristics of the recruited pharmacists.</caption><thead><tr><th rowspan="2">Characteristic</th><th rowspan="2">Type</th><th colspan="2">Modalities/Aggregation</th></tr><tr><th>Male</th><th>17 (44 %)</th></tr></thead><tbody><tr><td rowspan="3">Sex</td><td rowspan="3">nominal</td><td>Female</td><td>22 (56 %)</td></tr><tr><td>Other</td><td>0</td></tr><tr><td>20-29</td><td>6 (15 %)</td></tr><tr><td rowspan="3">Age</td><td rowspan="3">integer</td><td>30-39</td><td>13 (33 %)</td></tr><tr><td>40-49</td><td>10 (26 %)</td></tr><tr><td>50-59</td><td>7 (18 %)</td></tr><tr><td rowspan="2">Previous knowledge of STOPP/START</td><td rowspan="2">nominal</td><td>60+</td><td>3 (8 %)</td></tr><tr><td>Yes</td><td>29 (74 %)</td></tr><tr><td rowspan="2"># MR in the last year</td><td rowspan="2">integer</td><td>No</td><td>10 (26 %)</td></tr><tr><td>Min</td><td>0 (for 19 pharm.)</td></tr><tr><td rowspan="3">Group</td><td rowspan="3">nominal</td><td>Max</td><td>50 (for 1 pharm.)</td></tr><tr><td>Mean</td><td>4.4</td></tr><tr><td>Median</td><td>1</td></tr><tr><td rowspan="4"></td><td rowspan="4"></td><td>G1</td><td>10</td></tr><tr><td>G2</td><td>9</td></tr><tr><td>G3</td><td>11</td></tr><tr><td>G4</td><td>9</td></tr></tbody></table>
<caption>**Table 4**</caption>

Mean percentages of problems that have been identified by pharmacists, mean interventions CLEO score ratios, and mean and median response time, without or with ABiMed, for each clinical case, and overall, with 95 % confidence intervals (CI). *: difference is statistically significant when comparing without vs with ABiMed.

<table><caption>**Table 5**</caption><thead><tr><th rowspan="2"></th><th colspan="2">% problems identified</th><th colspan="2">interventions CLEO score ratio</th><th colspan="2">mean/median time (minutes)</th></tr><tr><th>without ABiMed</th><th>with ABiMed</th><th>without ABiMed</th><th>with ABiMed</th><th>without ABiMed</th><th>with ABiMed</th></tr></thead><tbody><tr><td>Case A</td><td>46.8 %</td><td>78.7 %</td><td>31.0 %</td><td>62.2 %</td><td>30.5/28.3</td><td>35.0/21.9</td></tr><tr><td>Case B</td><td>43.8 %</td><td>84.4 %</td><td>40.8 %</td><td>81.0 %</td><td>22.9/24.7</td><td>41.4/25.1</td></tr><tr><td>Case C</td><td>43.5 %</td><td>67.9 %</td><td>40.5 %</td><td>70.4 %</td><td>25.1/24.1</td><td>16.0/15.1</td></tr><tr><td>Case D</td><td>45.6 %</td><td>59.5 %</td><td>49.2 %</td><td>48.3 %</td><td>31.0/22.5</td><td>27.4/23.9</td></tr><tr><td><strong>Overall</strong></td><td><strong>45.0 %</strong></td><td><strong>71.9 %</strong></td><td><strong>40.0 %</strong></td><td><strong>65.0 %</strong></td><td><strong>27.3/25.4</strong></td><td><strong>29.3/20.2</strong></td></tr><tr><td>95 % CI</td><td>40.3–49.6 %</td><td>66.8–77.0 %</td><td>34.1–46.0 %</td><td>59.4–70.6 %</td><td>22.8–35.9</td><td>23.6–31.0</td></tr><tr><td>p-value</td><td colspan="2">p = 1.1 × 10<sup>-12</sup> *</td><td colspan="2">p = 9.8 × 10<sup>-9</sup> *</td><td colspan="2">p = 0.56</td></tr></tbody></table>

p-values obtained with Linear Mixed Models (LMM) for each factor, and for their interactions with ABiMed (i.e. inter. p-value) for the complex model, for each of the three variables of interest (% problems identified, CLEO score ratio, response time). *: difference is statistically significant.

<table><thead><tr><th rowspan="2">Model</th><th rowspan="2">Factor</th><th colspan="2">% problems identified</th><th colspan="2">interventions CLEO score ratio</th><th colspan="2">response time</th></tr><tr><th>p-value</th><th>inter. p-value</th><th>p-value</th><th>inter. p-value</th><th>p-value</th><th>inter. p-value</th></tr></thead><tbody><tr><td rowspan="3">Simple</td><td>ABiMed</td><td>&lt; 2.2 × 10<sup>-16</sup> *</td><td></td><td>1.6 × 10<sup>-12</sup> *</td><td></td><td>0.52</td><td></td></tr><tr><td>Case</td><td>0.0099 *</td><td></td><td>0.015 *</td><td></td><td>0.018 *</td><td></td></tr><tr><td>Group</td><td>0.14</td><td></td><td>0.16</td><td></td><td>0.16</td><td></td></tr><tr><td rowspan="6">Complex</td><td>ABiMed</td><td>0.0028 *</td><td>–</td><td>0.0061 *</td><td>–</td><td>0.058</td><td>–</td></tr><tr><td>Case</td><td>0.024 *</td><td>0.31</td><td>0.00016 *</td><td>0.0058 * (Fig. 3)</td><td>0.022 *</td><td>0.11</td></tr><tr><td>Age class</td><td>0.035 * (Fig. 2)</td><td>0.79</td><td>0.044 *</td><td>0.92</td><td>0.34</td><td>0.0098 * (Fig. 4)</td></tr><tr><td>Sex</td><td>0.98</td><td>0.70</td><td>0.46</td><td>0.56</td><td>0.089</td><td>0.089</td></tr><tr><td>STOPP/START known</td><td>0.76</td><td>0.14</td><td>0.89</td><td>0.45</td><td>0.75</td><td>0.81</td></tr><tr><td>#MRs in the last year</td><td>0.16</td><td>0.95</td><td>0.73</td><td>0.40</td><td>0.58</td><td>0.58</td></tr></tbody></table>

<figure><img src="image_8.png" /><figcaption>**Fig. 2.** Bar plot showing the percentage of problems identified, for each age class. Bar width is proportional to the number of pharmacists.</figcaption></figure>

use of ABiMed did not require additional time for pharmacists, but did not allow gaining time either. However, as pharmacists identified and treated more problems with ABiMed, the per-problem response time is lower.

The complex LMM model (Table 5, right part) identified one significant factor and one interaction: cases and ABiMed-age interaction. Fig. 4 suggests that, with ABiMed, younger pharmacists were slightly faster and older pharmacists slightly slower. Younger pharmacists might be more comfortable with computerized CDSSs. However, the difference is limited and the size of subgroups is small, so it should be interpreted cautiously.

<figure><img src="image_9.png" /><figcaption>**Fig. 3.** Bar plot showing the CLEO score ratio, without or with ABiMed (left and right), for each clinical case (A–D).</figcaption></figure>

### 3.5. Carryover effect

Fig. 5 shows the impact of the order of passage of the four cases on the primary criteria. No significant differences were found when comparing the percentage of problems identified between cases #1 and #2 (p = 1), and between cases #3 and #4 (p = 0.76), the CLEO score ratio (p = 0.37 and 0.37, respectively) and the log(time) (p = 0.51 and 0.18, respectively). Thus, there was no carryover effect. This was expected, the four cases covering distinct clinical problems. On the contrary, the important difference between cases #1–2 and #3–4 was caused by the intervention.

### 3.6. Perceived usability

The mean SUS score is 82.7 (55–100 depending on the pharmacists,
<figure><img src="image_10.png" /><figcaption>Fig. 4. Bar plot showing the response time, without or with ABiMed, for each age class. Bar width is proportional to the number of pharmacists.</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>Fig. 5. Bar plot showing the mean percentage of problems identified according to the order of passage of the four clinical cases.</figcaption></figure>

Fig. 6). This is ranked as "excellent" according to the SUS adjective rating scale.33 The less positive answers were obtained for the last question, "I need to learn a lot of things before I can start using ABiMed".

### 3.7. Pharmacist comments

The pharmacist comments were enthusiastic, e.g. "the software is really great, it is very reassuring" or "ABiMed greatly facilitates the work of the pharmacist who performs a medication review and allows him/her to quickly decide on the therapeutic decision to take."

The pharmacists appreciated the visual aspects of ABiMed, including tables ("the summary tables are very readable and allow us to have an overview. Well done!"), the graph showing drug interactions ("I find that the interaction tab is very practical") and the flower glyphs showing adverse effects ("the adverse effects tab is very useful and visual: it could be used on a daily basis at the counter, even outside of medication reviews! Well done"). They also appreciated the comparative mode of ABiMed ("a good way to train and learn by adding and testing several molecules together according to different patient profiles.").

Several pharmacists mentioned that ABiMed was very useful for identifying problems, but more limited when it comes to proposing interventions and suggesting replacement drugs. Thus, they proposed the implementation of clinical practice guidelines targeting the main chronic disorders for elderly patients.

### 3.8. Per-problem analysis¶

Table 6 shows the list of problems in each case, the elements of information that can be found about them (e.g. STOPP/START criteria, known adverse effects, drug-drug interactions, etc), either in ABiMed or in other tools and databases. For each of the 22 problems, the percentage of pharmacists that identified it is given, with and without ABiMed.¶

For most problems, the percentage is higher with ABiMed. This is particularly true for many STOPP/START criteria, including pneumococcal vaccine (note that flu vaccine was not considered, because it was not the appropriate season), but also for dosage problems, such as the adjustment of the dose of allopurinol according to renal function (case B). For a few problems, the percentage is lower with ABiMed. Sometimes, there might be some kind of competition between problems: e.g. for the diarrhea adverse event (case B), slightly fewer pharmacists considered reducing or stopping metformin when used ABiMed, however, there were more numerous to suggest the deprescription of fluoxetine (involved in a severe interaction), and fluoxetine could also be responsible for diarrhea. Consequently, pharmacists possibly considered that remove fluoxetine was sufficient. In case D, the problem on domperidone has been less identified with ABiMed; the corresponding alert was missing in ABiMed due to a bug. In the same case, the cramp adverse event was not present in ABiMed on the flower glyph because it is not present in the drug SPC; here, cramps were caused by hypernatremia (which was present as an adverse effect). As previously, competition between problems may also occur, as escitalopram may also

<figure><img src="image_12.png" /><figcaption>Fig. 6. Distribution of answers to each question of the SUS usability test, in percent (colors were reversed for even questions, for which an approbation corresponds to a negative opinion about ABiMed: here, "pro" and "cons" mean "pro-ABiMed" and "cons-ABiMed"). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>
<caption>**Table 6**</caption>

List of the problems present in each case. # is the number of the problem. Note that some problems span over several drugs. Control is without ABiMed and test with ABiMed. DDI stands for drug interaction.

<table><thead><tr><th rowspan="2">Case</th><th rowspan="2">Drug</th><th rowspan="2">Problem</th><th rowspan="2">Details</th><th rowspan="2">#</th><th colspan="2">% identified</th><th rowspan="2">CLEO</th></tr><tr><th>Control</th><th>Test</th></tr></thead><tbody><tr><td rowspan="10">A</td><td rowspan="2">Ketoprofen LP 200 cpr</td><td>STOPP E4</td><td>Stop NSAID's if eGFR &lt;50 ml/min/1.73m2 (risk of deterioration in renal function)</td><td>1</td><td>78.6</td><td><strong>100.0</strong></td><td>2</td></tr><tr><td>STOPP H2</td><td>Stop NSAID with severe hypertension (risk of exacerbation of hypertension) or severe heart failure (risk of exacerbation of heart failure)</td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="2">Spironolactone altizide cpr</td><td>DDI</td><td>Caution for use with gout</td><td>2</td><td><strong>33.3</strong></td><td><strong>33.3</strong></td><td>2</td></tr><tr><td>STOPP L2</td><td>Stop use of regular (as distinct from PRN) opioids without concomitant laxative (risk of severe constipation)</td><td>3</td><td>42.9</td><td><strong>77.8</strong></td><td>2</td></tr><tr><td rowspan="2">Lamaline (acetaminophen + Opium + caffeine)</td><td>START H2</td><td>Start laxatives in patients receiving opioids regularly</td><td></td><td></td><td></td><td></td></tr><tr><td>Dosage</td><td>Overdose, due to cumulated dose of acetaminophen, also present below</td><td></td><td></td><td></td><td></td></tr><tr><td>Acetaminophen 1 g cpr</td><td>Dosage</td><td>Overdose, due to cumulated dose of acetaminophen, also present in Lamaline</td><td></td><td></td><td></td><td></td></tr><tr><td>Flecainide 20 mg gelule</td><td>Adverse events</td><td>Visual troubles, reported by the patient</td><td>4</td><td>66.7</td><td><strong>94.4</strong></td><td>2</td></tr><tr><td rowspan="3">Allopurinol 300 mg cpr (none)</td><td>Indication</td><td>No clear indications</td><td></td><td></td><td></td><td></td></tr><tr><td>Dosage</td><td>Overdose, due to chronic renal failure</td><td>5</td><td>52.4</td><td><strong>100.0</strong></td><td>1</td></tr><tr><td>START I2</td><td>Consider pneumococcal vaccine at least once after age 65 according to national guidelines</td><td>6</td><td>7.1</td><td><strong>66.7</strong></td><td>1</td></tr><tr><td rowspan="4">B</td><td rowspan="2">Tamoxifen 10 mg cpr</td><td>DDI</td><td>Contraindicated with fluoxetine</td><td>1</td><td>57.1</td><td><strong>83.3</strong></td><td>3</td></tr><tr><td>Fluoxetine 20 mg gelule</td><td>Contraindicated with tamoxifene</td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="2">Metformin chl 500 mg cpr</td><td>Adverse events</td><td>Diarrhea, reported by the patient</td><td>2</td><td><strong>90.5</strong></td><td>83.3</td><td>1</td></tr><tr><td>Adverse events</td><td>Diarrhea, reported by the patient</td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="3">C</td><td rowspan="2">Oxomemazine syrup (none)</td><td>STOPP D14</td><td>Stop first-generation antihistamines (safer, less toxic antihistamines now widely available)</td><td>3</td><td>61.9</td><td><strong>94.4</strong></td><td>1</td></tr><tr><td>START A4</td><td>Start antihypertensive therapy where systolic blood pressure consistently &gt;160 mmHg and/or diastolic blood pressure consistently &gt;90 mmHg; if systolic blood pressure &gt;140 mmHg and/or diastolic blood pressure &gt;90 mmHg, if diabetic</td><td>4</td><td>9.5</td><td><strong>72.2</strong></td><td>1</td></tr><tr><td>START I2</td><td>Consider pneumococcal vaccine at least once after age 65 according to national guidelines</td><td>5</td><td>0.0</td><td><strong>88.9</strong></td><td>1</td></tr><tr><td rowspan="5">D</td><td rowspan="2">Lorazepam 1 mg cpr</td><td>STOPP D5</td><td>Stop benzodiazepines for ≥4 weeks (no indication for longer treatment; risk of prolonged sedation, confusion, impaired balance, falls, road traffic accidents; all benzodiazepines should be withdrawn gradually if taken for more than 4 weeks as there is a risk of causing a benzodiazepine withdrawal syndrome if stopped abruptly)</td><td>1</td><td>88.9</td><td><strong>100.0</strong></td><td>2</td></tr><tr><td>STOPP K1</td><td>Stop benzodiazepines (sedative, may cause reduced sensorium, impair balance)</td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="2">Esomeprazole 40 mg gelule</td><td>STOPP F2</td><td>Stop PPI for uncomplicated peptic ulcer disease or erosive peptic oesophagitis at full therapeutic dosage for &gt;8 weeks (dose reduction or earlier discontinuation indicated)</td><td>2</td><td>61.1</td><td><strong>90.5</strong></td><td>1</td></tr><tr><td>STOPP B6</td><td>Loop diuretic as first-line treatment for hypertension (safer, more effective alternatives available)</td><td>3</td><td><strong>55.6</strong></td><td>52.4</td><td>1</td></tr><tr><td rowspan="2">Macrogol (none)</td><td>Indication</td><td>Should not be prescribed in monotherapy for hypertension</td><td></td><td></td><td></td><td></td></tr><tr><td>Dosage</td><td>Not taken in the morning as recommended</td><td>4</td><td><strong>22.2</strong></td><td>14.3</td><td>1</td></tr><tr><td rowspan="2">Escitalopram 10 mg cpr</td><td>START E5</td><td>Start vitamin D supplement in older people who are housebound or experiencing falls or with osteopenia (Bone Mineral Density T-score is &gt; -2.5 but &lt; -1.0 in multiple sites)</td><td>5</td><td>22.2</td><td><strong>73.8</strong></td><td>1</td></tr><tr><td>START I2</td><td>Consider pneumococcal vaccine at least once after age 65 according to national guidelines</td><td>6</td><td>11.1</td><td><strong>76.2</strong></td><td>1</td></tr><tr><td rowspan="5">E</td><td rowspan="2">Escitalopram 10 mg cpr</td><td>STOPP D4</td><td>Stop selective serotonin re-uptake inhibitors (SSRI's) with current or recent significant hyponatraemia i.e. serum Na+ &lt; 130 mmol/l (risk of exacerbating or precipitating hyponatraemia)</td><td>1</td><td>77.8</td><td><strong>97.6</strong></td><td>2</td></tr><tr><td>DDI</td><td>Contraindicated with domperidone</td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="2">Spironolactone altizide cpr</td><td>Adverse events</td><td>Risk of torsades de pointes</td><td></td><td></td><td></td><td></td></tr><tr><td>Adverse events</td><td>Cramp, reported by the patient</td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="2">Calcium vitamin D3 (none)</td><td>DDI</td><td>Contraindicated with escitalopram</td><td>2</td><td><strong>66.7</strong></td><td>47.6</td><td>4</td></tr><tr><td>Adverse events</td><td>Risk of torsades de pointes</td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="2">Calcium vitamin D3 (none)</td><td>Indication</td><td>Cramp, reported by the patient</td><td>3</td><td><strong>61.1</strong></td><td>38.1</td><td>2</td></tr><tr><td>START E4</td><td>Insufficient as the patient has osteoporosis with fracture</td><td>4</td><td>11.1</td><td><strong>52.4</strong></td><td>2</td></tr><tr><td rowspan="2">Calcium vitamin D3 (none)</td><td>START I2</td><td>Consider pneumococcal vaccine at least once after age 65 according to national guidelines</td><td>5</td><td>11.1</td><td><strong>61.9</strong></td><td>1</td></tr></tbody></table>

cause hyponatraemia.

## 4. Discussion

In this paper, we presented a simulation trial evaluating ABiMed, an innovative CDSS for supporting MR via implementation of STOPP/

START criteria and visual presentation of drug knowledge. Results show that, with ABiMed, pharmacists identified significantly more problems and proposed better interventions, without spending more time.

The participants were community pharmacists with a clinical activity, and not students, contrary to many other studies evaluating CDSS. Recruiting professionals is much more difficult, as they have less time
available. Professional pharmacists are also older than students, and thus they may not be as open to innovation and digital tools. However, they are more representative of the target users of CDSS.

Sex had no significant impact, as well as the sex-ABiMed interaction. It means that the support provided by ABiMed is the same for males and females. The rationale for testing the sex factor was that there could be a difference, because (a) our teams including mostly male researchers, and (b) cognitive capacities of males and females sometimes differ, especially when color perception is involved,³⁴ and ABiMed relies heavily on colors.

Surprisingly, the previous knowledge of STOPP/START and the number of MRs performed in the last year had no significant impact. Possibly, the knowledge of STOPP/START is not enough to execute the numerous and complex rules manually, and many pharmacists did zero or one MR, thus the impact of this factor may be difficult to analyze.

The main limit of the study is that it is a simulation trial²⁷: pharmacists may not have acted as they would with real patients. However, simulation trials are often used for CDSSs because of their simplicity to set up, e.g. Ref. 35. They also permit a better comparison, since the same fictitious patients can be treated both with and without the CDSS.

Another limit is that all patient data was already entered in the system, thus pharmacists did not have to enter any additional data and just focused on the analysis. This does not correspond to reality: pharmacists often have limited access to patient data³⁶ (usually stored in electronic health records owned by GPs or hospitals), and some data is of questionable quality, or still in textual format, that machines cannot interpret.³⁷ Data entry has been a huge barrier when testing the PRIMA-eDS CDSS for managing polypharmacy.³⁸ To overcome this problem, we proposed two solutions in ABiMed: (1) the transfer of the data available in the GP's EHR, which the approval of the GP and provided its software is compatible, and (2) the use of an adaptive questionnaire for facilitating the entry of patient clinical conditions for the automatic execution of STOPP/START rules.³⁹ Nevertheless, the integration of ABiMed into the medical workflow remains a matter of future research.

Additionally, pharmacists were paid volunteers, which might bias the results.

During the study, some limits were found in the ABiMed CDSS. Results suggest that the support offered by ABiMed may vary according to the patient profile: for problem identification, the mean is the same for all cases without ABiMed (about 45 %) but differs with ABiMed (60-85 %); for interventions (CLEO score ratio), no difference is found for case D. This was related to the missing alert for domperidone; the associated intervention was ranked 4. Actually, pharmacists performed better with ABiMed for other problems of case D, but worse for the domperidone, possibly because they were relying on ABiMed guidance. This is known as automation bias.⁴⁰ Thus, we should take a particular care to missing alerts.

Such problems could limit the generalizability of the system. Thus, after the study, we fixed the problem on domperidone, but we also set up a complete test system for ABiMed, which simulates keyboard and mouse input and verifies the obtained output (including graphics). The test system includes more than 650 test cases covering all functionalities of the system. In particular, 200 test cases were generated from the drug database by considering random drugs and disorders, for testing drug-drug and drug-disease interactions and limiting the risk of missing alerts.

The implementation of ABiMed in clinical practice remains a challenge. Several barriers and facilitators have been identified for medication reviews. A systematic review⁴¹ mostly identified barriers and facilitators directly related to the system (e.g. information quality, system quality) and to the associated expected outcomes, in terms of patient safety, error reduction, but also increased communication between pharmacists and GPs. A Qualitative Study⁴² identified four main themes: (1) correctly assessing and incorporating the patient viewpoint, (2) finding the optimal level of comprehensiveness of the MR, (3)

inclinations of healthcare providers and (4) inter-professional collaboration. Another study,⁴³ focused on community pharmacists, identified poor motivation and difficulties in communication with GPs and patients, lack of time (patient interviews being mentioned as the most time-consuming task in MR) and lack of self-confidence as the main barriers. Facilitators included workflow organization techniques, training and adequate remuneration. Increased workload is a barrier frequently mentioned for CDSS in primary care¹⁴; consequently the connection of ABiMed with EHR and/or dispensation software is an important point to consider in the future.

Nevertheless, improved MR could lead to many patient outcomes: a recent Cochrane review⁴⁵ concluded that MR is likely to reduce hospital readmissions and may reduce emergency department contacts, but may have little to no effect on mortality, and the effect quality of life is uncertain. Beyond clinical outcomes, MRs can have positive impacts in terms of communication between pharmacists, GPs and patients, and reduction of healthcare costs.¹³,¹⁴

In the literature, several CDSSs were clinically evaluated for the support of MR, and were reviewed in Ref. 46. However, most were less sophisticated than ABiMed, being limited to triggering criteria-based alerts or detecting adverse reactions, or focused on hospital rather than primary care. Hospital CDSSs for polypharmacy obtain better results,⁴⁷ possibly due to the use of EHR. Outside hospital, examples include: the detection of outlier prescriptions via machine-learning for outpatients,⁴⁸ the use of PHARAO, a system evaluating risk for common side effects in hospital and primary care,⁴⁹ the execution of STOPP rules in nursing homes,¹⁴ and the triggering of alerts from an electronic expert support system in geriatric clinics and primary care.⁵⁰ An interview-based evaluation of G-MEDSS (Goal-directed Medication review Electronic Decision Support System), a CDSS for MR, suggests that the CDSS can effectively support the pharmacist, but that some improvements are needed, e.g. for structuring report or for proposing replacement drugs.⁵¹ The usability and usefulness of a CDSS is also a question of matter, a recent study⁵² on a commercial hospital CDSS for MR showed the presence of usability problems. Many problems were specific to hospital, though (e.g. related to the display of alerts from several patients, or to the connection with the EHR).

Contrary to ABiMed, few visual approaches were proposed, e.g. the use of colored tables to present interactions,⁵³ and most systems targeted physicians rather than pharmacists. Exceptions include: a limited 5-rule alert system,⁵⁴ and a more substantial alert system but associated with a low sensitivity.⁵⁵ In that context, the visual presentation of drug knowledge can be a complementary approach, as it is less intrusive than alerts, and not limited to a few situations but can include all drug interactions.

ABiMed associates STOPP/START v3 criteria with the visual presentation of drug properties (e.g. interactions, adverse effects and dosage). This allows the pharmacist to identify additional problems that are not considered by STOPP/START criteria (e.g. wrong dose), to help in various situations (e.g. to identify the drug responsible for a given adverse event reported by a patient) but also to complement some STOPP/START criteria with additional information (for instance, criteria STOPP B13 related to the association of an aldosterone antagonist with a potassium-conserving drug is also associated with a drug-drug interaction between these two drugs, providing additional information, e.g. the fact that it is of particular importance in case of renal failure). In terms of workflow, ABiMed is intended to be used during medication reviews, although some pharmacists expressed their desire to use it also during drug dispensation. Of course, such usage would require a connection with the dispensation software used by the pharmacy. However, limited data is available in dispensation software, and in particular, diagnoses and lab test results are not present. In the literature, only one system devoted to community pharmacists, TRIM,⁵⁶ was connected to EHR, in a very specific and centralized situation: US Veterans Affairs, which operates centers integrating GPs and pharmacists.
Various methodologies exist for evaluating and improving the usability of CDSSs. Mixed method evaluations<sup>44</sup> associate quantitative (e.g. questionnaires such as SUS) and qualitative (e.g. think aloud interviews analyzed by experts) studies. In comparison, the present study focuses on quantitative aspects, including performance aspects in terms of the number of problems identified and time spent by the pharmacist. Many simulation trials are focused on performance, because organizational problems and difficulties are more difficulty to identify on virtual patients than in real life. Co-design<sup>57</sup> consists of associating patients and clinicians to the design of CDSSs. During the design of ABiMed, we consulted clinicians through questionnaires and focus groups, and we presented them early prototypes. However, we did not fully engage in co-design, the main reason being that we planned to introduce advanced visual analytics techniques, such as flower glyphs, which were not known by pharmacists or patients.

## 5. Conclusion

In conclusion, a simulation trial showed that ABiMed, a innovative CDSS that associates STOPP/START criteria implementation and visual presentation of drug knowledge for helping pharmacists carrying MR, enabled them to identify 1.6 times more problems than with their usual tools, and to propose better interventions, without requiring more time. Pharmacists appreciated ABiMed and gave it a high perceived usability, ranked "excellent". Our main perspectives are the improvement of ABiMed e.g. integrating STOP/START v3 and clinical practice guidelines for common disorders to better support the proposition of pharmaceutical interventions, the use of formal argumentation to structure the discussion between pharmacists and GPs, and the evaluation of ABiMed in clinical situations with real patients, including the pharmacist-GP collaboration aspect.

## 6. Summary table

### 6.1. What was already known on the topic

* Medication review reduces hospitalizations and saves costs, but is a long and tedious task for pharmacists.

* Paper tools, such as STOPP/START criteria, are complex to use.

* Most clinical decision support systems for polypharmacy focus on hospital, physicians, and alert triggering.

* On the contrary, ABiMed focuses on pharmacists, and combines the STOPP/START criteria with visual drug knowledge using tables, graphs and glyphs.

### 6.2. What this study added to our knowledge

* We conducted a randomized simulated trial comparing ABiMed to usual resources used by pharmacists, on clinical cases.

* Results show that, with ABiMed, pharmacists identify 1.6 times more problems and proposed better interventions, without requiring more time.

* The perceived usability was ranked as "excellent".

## CRediT authorship contribution statement

**Abdelmalek Mouazer:** Visualization, Software, Methodology, Conceptualization.
**Sophie Dubois:** Validation, Resources, Methodology, Investigation, Conceptualization.
**Romain Léguillon:** Validation, Resources, Methodology.
**Nada Boudegzdame:** Methodology.
**Thibaud Levrard:** Software.
**Yoann Le Bars:** Software, Methodology.
**Christian Simon:** Supervision, Software.
**Brigitte Séroussi:** Methodology.
**Julien Grosjean:** Methodology.
**Romain Lelong:** Methodology.
**Catherine Letord:** Methodology.
**Stéfan Darmoni:** Supervision.
**Karima Sedki:** Methodology.
**Pierre Meneton:** Writing – review & editing, Formal analysis.
**Rosy Tsopra:** Writing – original draft, Validation,

**Methodology.** **Hector Falcoff:** Validation, Resources, Methodology, Investigation, Conceptualization.
**Jean-Baptiste Lamy:** Writing – original draft, Visualization, Validation, Supervision, Software, Project administration, Methodology, Funding acquisition, Formal analysis, Conceptualization.

## Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Lamy Jean-Baptiste reports financial support was provided by French National Research Agency. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgments

This work was funded by the French Research Agency (ANR) through the ABiMed project [Grant No. ANR-20-CE19-0017].

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.sapharm.2025.06.006.

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