Comparing potential drug-drug interactions in veterinary medications using two electronic databases

doi:10.21203/rs.3.rs-23037/v2

Background: One of the most common global health issues in humans and animals is drug-drug interactions (DDIs). This issue increases the risks associated with healthcare in both human and veterinary medicine, as animals live long lives and receive many medicines to treat their illnesses. Currently, many electronic databases are being used as tools for potential DDI prediction, for example, Micromedex and Drugs.com. The purpose of this study was to examine the different abilities for the identification of potential DDIs in veterinary medicines by Micromedex and Drugs.com.

Results: A list of 140 drugs, mainly used for the treatment of disease in animal hospitals, was complied, but the Micromedex and Drugs.com databases could recognise only 96 of these drugs. After inputting the recognised drug list into the databases, Micromedex showed 429 pairs of potential DDIs, whilst Drugs.com showed 842 pairs of potential DDIs. The analysis comparing results between the two databases showed 139 pairs (12.28%) with the same severity and 993 pairs (87.72%) with different severities. Major mechanisms of contraindicated and major potential DDIs were cytochrome P450 induction-inhibition and QT interval prolongation.

Conclusion: Although Micromedex had a lower sensitivity to identify potential DDIs than Drugs.com, Micromedex provided more informative documentation. Veterinary pharmacists should evaluate potential DDIs from several databases and communicate with both the veterinarian and animal owner to ensure an appropriate drug prescription.

Large Animal Medicine

Small Animal Medicine

drug-drug interactions

electronic drug databases

veterinary medications

Multiple-drug prescriptions for the treatment of diseases and complications usually occurs in humans and animals [1, 2]. One category of adverse drug reactions (ADRs) is defined as drug-drug interactions (DDIs), in which one drug interferes with another. In particular, healthcare providers combine drugs for synergistic action and therapeutic benefit, but toxicity or adverse events may also be possible [3]. DDIs can lead to drug toxicity or decreased therapeutic effect, resulting in increase of morbidity and mortality [4–6]. The degree of severity of DDIs is categorised as follows: contraindicated, major, moderate, minor and none [7, 8]. A serious concern is focusing on the contraindicated and major severities when dispensing drugs to patients as well as animals. Recently, attention has shifted to developing online databases for detecting potential DDIs. Online DDI databases have two major types: open-access resources and subscription databases [9–11]. In general, patients usually select open-access databases, for example, Drugs.com, to access potential DDI identification. Conversely, healthcare providers prefer using a subscription database to identify potential DDIs, for example, Micromedex. Interestingly, these two databases have different features [12] and show different results in identifying potential DDIs between the prescribed drugs for oral cancer treatment [13]. Also, in veterinary medicine as well as human medicine, multidrug therapy is commonly used for the treatment of animals [14]. However, few studies focus on the competency of databases to detect potential DDIs for the management of complicated diseases in animals, for example, cardiovascular diseases, urinary diseases, metabolic diseases, skin diseases and cancers. These diseases require multiple drug use and might result in DDIs in sick animals. This study aimed to investigate the differences in performance of DDI databases for identifying potential DDIs with complicated disease treatments used in animals.

From the 96 drugs used in this study, 1132 unduplicated pairs were found by the selected databases as potential DDIs. Micromedex identified 429 pairs of potential DDIs and Drugs.com identified 842 pairs of potential DDIs. Table 1 exhibits the classification of severity for the 429 pairs identified by Micromedex as contraindicated in 18 pairs, major in 206 pairs, moderate in 143 pairs and minor in 62 pairs. Meanwhile, Drugs.com classified 842 pairs of potential DDIs as 165 pairs in major degree, 561 pairs in moderate degree and 116 pairs in minor degree. Fig. 1 demonstrates the documentation rating in each severity degree of potential DDIs identified by Micromedex, for which the summation of excellent and good scientific evidence was 63.87% (274/429).

Table 1 Collation result of the potential DDIs between the two databases.

Severity levels	Micromedex n (%)	Drugs.com n (%)
Contraindicated	18 (4.20)	N/A
Major	206 (48.02)	165 (19.59)
Moderate	143 (33.33)	561 (66.63)
Minor	62 (14.45)	116 (13.78)
Total	429 (100.00)	842 (100.00)
N/A, not available

After comparing all of the potential DDI results analysed by the two databases, 139 pairs (12.28%) showed the same severity, whilst 993 pairs (87.72%) of results showed a difference in severity. From all of the results, contraindications and major DDIs identified by Micromedex and major DDIs reported by Drugs.com were selected for determination of the type of mechanism of each potential DDI report, as shown in Table 2. Among the 86 pairs of significant potential DDIs, 15 pairs were at the contraindication degree reported by Micromedex and classified at the major degree by Drugs.com. The remaining 71 pairs were reported at the major degree by both databases.

Table 2 The significant drug pairs in potential DDIs examined by the two databases.

Micromedex

Drugs.com

List of drugs paired

PK-PD

Mechanism details

Contraindication

Major

Amiodarone—Dronedarone

Amiodarone—Ketoconazole
Ciprofloxacin—Dronedarone
Cyclosporine—Dronedarone
Dronedarone—Erythromycin
Dronedarone—Flecainide
Dronedarone—Itraconazole
Dronedarone—Ketoconazole
Dronedarone—Procainamide
Dronedarone—Sotalol
Felodipine—Itraconazole
Felodipine—Ketoconazole
Itraconazole—Nisoldipine
Ketoconazole—Nisoldipine
Praziquantel—Rifampin

PD

PK

PD

PK

PD

PK

PD

PK

Additive QT-interval prolongation

CYP3A4 inhibition by Ketoconazole

Additive QT-interval prolongation

CYP3A4 inhibition by Cyclosporine

CYP3A4 inhibition by Erythromycin

Additive QT-interval prolongation

CYP3A4 inhibition by Itraconazole

CYP3A4 inhibition by Ketoconazole

Additive QT-interval prolongation

CYP3A4 inhibition by Itraconazole

CYP3A4 inhibition by Ketoconazole

CYP3A4 inhibition by Itraconazole

CYP3A4 inhibition by Ketoconazole

CYP induction by Rifampin

Major

Amikacin—Furosemide
Aminophylline—Ciprofloxacin

Aminophylline—Mexiletine
Amiodarone—Ciprofloxacin
Amiodarone—Digoxin

Amiodarone—Erythromycin

Amiodarone—Flecainide

Amiodarone—Itraconazole
Amiodarone—Procainamide

Amiodarone—Rifampin
Amiodarone—Sotalol
Amiodarone—Verapamil
Amiodarone—Diltiazem
Amlodipine—Rifampin
Atenolol—Verapamil

Atenolol—Diltiazem

Benazepril—Spironolactone

Benazepril—Trimethoprim
Carvedilol—Verapamil

Carvedilol—Diltiazem

Ciprofloxacin—Prednisolone
Ciprofloxacin—Procainamide
Ciprofloxacin—Sotalol
Ciprofloxacin—Theophylline

Cyclosporine—Itraconazole
Cyclosporine—Rifabutin

Cyclosporine—Rifampin

Digoxin—Dronedarone

Digoxin—Itraconazole

Enalapril—Spironolactone

Enalapril—Trimethoprim
Erythromycin—Ketoconazole

Erythromycin—Procainamide
Erythromycin—Sotalol
Erythromycin—Verapamil
Erythromycin—Diltiazem
Esmolol—Verapamil

Esmolol—Diltiazem

Flecainide—Procainamide
Flecainide—Sotalol
Fluticasone—Itraconazole

Fluticasone—Ketoconazole

Furosemide—Gentamicin
Furosemide—Kanamycin
Furosemide—Streptomycin
Furosemide—Tobramycin
Itraconazole—Nifedipine
Itraconazole—Rifabutin

Itraconazole—Rifampin
Itraconazole—Sildenafil
Ketoconazole—Procainamide
Ketoconazole—Rifabutin

Ketoconazole—Rifampin
Ketoconazole—Rifapentine
Ketoconazole—Sotalol
Metoprolol—Verapamil

Metoprolol—Diltiazem

Mexiletine—Theophylline

Nifedipine—Phenobarbital
Nifedipine—Phenytoin
Nifedipine—Rifabutin
Nifedipine—Rifampin
Nifedipine—Rifapentine
Procainamide—Sotalol
Ramipril—Spironolactone

Ramipril—Trimethoprim
Salmeterol—Itraconazole

Salmeterol— Ketoconazole

Sotalol—Verapamil

Sotalol—Diltiazem

Spironolactone—Trimethoprim

PD

PK

PD

PK

PD, PK

PK

PD

PK

PD

PK

PD, PK

PD

PD, PK

PD

PK

PK, PD

PK

PD

PK

PD

PK

PD, PK

PD

PK

PD

PK

PD

PK

PD

PD, PK

PK

PD

PK

PD, PK

PD

Additive or synergistic toxicity

Decreased clearance of Theophylline by Ciprofloxacin

Decreased hepatic metabolism

Additive effects on QT interval

P-glycoprotein inhibition by Amiodarone

Additive effects on QT prolongation, CYP3A4 inhibition by Erythromycin

Antiarrhythmic inhibition by Amiodarone, CYP2D6 inhibition by Amiodarone

CYP3A4 inhibition by Itraconazole Antiarrhythmic inhibition by Amiodarone

CYP induction by Rifampin

Additive effects on refractory potential

CYP3A4 inhibition by Verapamil

CYP3A4 inhibition by Diltiazem

CYP induction by Rifampin

Additive cardiovascular effects, decreased metabolism of some beta-blockers by Verapamil

Additive cardiovascular effects, decreased metabolism of some beta-blockers by Diltiazem

Increased potassium retention secondary to lowered aldosterone levels

Additive effects of hyperkalemia

Additive cardiovascular effects, decreased metabolism of some beta-blockers by Verapamil

Additive cardiovascular effects, decreased metabolism of some beta-blockers by Diltiazem

An additive effect of risk for tendon rupture

Additive effects on the QT interval

Decreased clearance of Theophylline, CYP1A2 inhibition by Ciprofloxacin

CYP3A4 inhibition by Itraconazole

Cyclosporine metabolism induction by Rifabutin

Increased Cyclosporine clearance and decreased systemic bioavailability by Rifampin

P-glycoprotein inhibition by Dronedarone

Digoxin metabolism and clearance inhibition by Itraconazole

Increased potassium retention secondary to lowered aldosterone levels

Additive effects of hyperkalemia

CYP3A4 inhibition by Erythromycin and Ketoconazole

CYP3A4 inhibition by Erythromycin

Additive effects on QT prolongation

CYP3A4 inhibition by Erythromycin

CYP3A-mediated inhibition by Diltiazem

Additive cardiovascular effects, decreased metabolism of some beta-blockers by Verapamil

Additive cardiovascular effects, decreased metabolism of some beta-blockers by Diltiazem

Additive effects on QT prolongation

Additive effects on refractory potential

CYP3A-mediated inhibition by Itraconazole

CYP3A-mediated inhibition by Ketoconazole

Additive or synergistic toxicity

CYP3A4 inhibition by Itraconazole

CYP3A4 induction by Rifabutin, CYP3A4 inhibition by Itraconazole

CYP3A-mediated induction by Rifampin

CYP3A4 inhibition by Itraconazole

Additive QT-interval prolongation

CYP3A-mediated inhibition by Ketoconazole, CYP3A-mediated induction by Rifabutin

CYP3A-mediated induction by Rifampin

CYP3A4 induction by Rifapentine

Additive effects on the QT interval

Additive cardiovascular effects, decreased metabolism of some beta-blockers by Verapamil

Additive cardiovascular effects, decreased metabolism of some beta-blockers by Diltiazem

Decreased hepatic metabolism, CYP1A2 inhibition by Mexiletine

CYP3A4 induction by Phenobarbital

CYP3A4 induction by Phenytoin

CYP3A4 induction by Rifabutin

CYP3A4 induction by Rifampin

CYP3A4 induction by Rifapentine

Additive effects on refractory potential

Increased potassium retention secondary to lowered aldosterone levels

Additive effects of hyperkalemia

CYP3A-mediated inhibition by Itraconazole

CYP3A-mediated inhibition by Ketoconazole

Additive cardiovascular effects, decreased metabolism of some beta-blockers by Verapamil

Additive cardiovascular effects, decreased metabolism of some beta-blockers by Diltiazem

Additive effects of hyperkalemia

In terms of mechanism, 51% (44/86) were pharmacokinetics (PK)-based, 34% (29/86) were pharmacodynamics (PD)-based and 15% (13/86) were PK-PD-based. The majority of PK-based DDIs involved cytochrome P450 (CYP) induction and inhibition, whilst PD-based DDIs caused QT prolongation and potassium retention. We also found some conflict between the results of the two databases, in which one database reported potential DDIs as major but another one reported them as minor or not DDIs. For the dissimilar results as shown in Table 3, 32 pairs were identified by Micromedex as major DDIs but only minor or not DDIs by Drugs.com. Conversely, 53 pairs were specified as major DDIs by Drugs.com whilst Micromedex identified these as not DDIs.

Table 3 Different results between the two databases in the identification of the potential DDIs

Micromedex	Drugs.com	List of drugs paired
Major	Minor	Amiodarone—Sulfamethoxazole Digoxin—Gentamicin Digoxin—Spironolactone Digoxin—Trimethoprim Erythromycin—Sulfamethoxazole Flecainide—Trimethoprim Lidocaine—Phenytoin Procainamide—Sulfamethoxazole Sotalol—Sulfamethoxazole
Major	None	Amiodarone—Trimethoprim Amlodipine—Digoxin Amoxicillin—Chlortetracycline Amoxicillin/Clavulinate—Chlortetracycline Ampicillin—Chlortetracycline Chlortetracycline—Methicillin Chlortetracycline—Nafcillin Chlortetracycline—Oxacillin Chlortetracycline—Penicillin G Chlortetracycline—Penicillin V Digoxin—Felodipine Digoxin—Isradipine Digoxin—Meloxicam Digoxin—Nicardipine Erythromycin—Trimethoprim Flecainide—Lidocaine Flecainide—Trimethoprim Isradipine—Procainamide Isradipine—Sulfamethoxazole Isradipine—Trimethoprim Itraconazole—Sotalol Mexiletine—Sotalol Sotalol—Trimethoprim
None	Major	Albuterol—Carvedilol Amikacin—Polymyxin B Aminophylline—Carvedilol Amiodarone—Furosemide Amiodarone—Nafcillin Amiodarone—Phenobarbital Amiodarone—Rifabutin Amiodarone—Terbutaline Amlodipine—Rifabutin Atenolol—Aminophylline Atenolol—Theophylline Diltiazem—Flecainide Diltiazem—Itraconazole Diltiazem—Rifabutin Erythromycin—Itraconazole Erythromycin—Sildenafil Esmolol—Aminophylline Felodipine—Rifabutin Gentamicin—Polymyxin B Isradipine—Phenobarbital Isradipine—Rifabutin Itraconazole—Amlodipine Itraconazole—Isradipine Itraconazole—Nicardipine Itraconazole—Rifapentine Kanamycin—Polymyxin B Metoprolol—Aminophylline Metoprolol—Theophylline Nicardipine—Phenobarbital Nicardipine—Rifabutin Phenobarbital—Amlodipine Phenobarbital—Nisoldipine Phenytoin—Felodipine Phenytoin—Isradipine Phenytoin—Nicardipine Phenytoin—Amlodipine Procainamide—Terbutaline Rifabutin—Nisoldipine Rifampin—Cefamandole Rifampin—Felodipine Rifampin—Nicardipine Salmeterol—Carvedilol Sotalol—Albuterol Sotalol—Aminophylline Sotalol—Salmeterol Sotalol—Terbutaline Streptomycin—Polymyxin B Terbutaline—Carvedilol Theophylline—Carvedilol Theophylline—Esmolol Theophylline—Sotalol Tobramycin—Polymyxin B Verapamil—Itraconazole

The two databases used in our study could recognise animal medicines less frequently than human medicines. This may be because the databases do not include complete information about animal medicines in their DDI databases. However, Drugs.com included a list of veterinary products that covered many animal species and provided useful information, for example, dosage, administration, precautions and adverse reactions. Therefore, veterinary pharmacists should use this drug database for searching for drug information and as a source for reference documents. The potential DDI results from the drug lists were different for the two electronic databases. In this study, the results from Drugs.com exhibited a higher number of potential DDIs than Micromedex by nearly 2.0 times. This result was correlated with Lauren et al., who found that Drugs.com had higher sensitivity than Micromedex for screening DDIs in oral cancer treatment [13]. Suriyapakorn et al. compared the capability of the databases to identify potential DDIs with metabolic syndrome medications and also found that Drugs.com provided more sensitivity than the other database [15]. The reason for the high sensitivity of Drugs.com in identifying DDIs may be caused by using databases from many providers to analyse data that is contained in Micromedex. We compared the result from Micromedex between the drug list used for metabolic syndrome in human and animal diseases in identity contraindicated and major potential DDIs. The drug list for animal disease treatment identified more pairs of contraindicated and major potential DDIs; the reason might be that the drugs used in animals included many drugs related to antiarrhythmic agents, antimicrobials and antihypertensive drugs, which often show a high incidence of potential DDI when prescribed with other drugs [16–18]. Veterinary pharmacists should realise when prescribing these drug groups to avoid the severe adverse reactions.

The combination of drugs prescribed to treat canine atopic dermatitis were identified as a potential DDIs; for instance, a co-prescription of ketoconazole with cyclosporine has been suggested, which could reduce the therapeutic cost and is convenient to use. This combination appears to provide greater clinical benefits for the treatment than disadvantages. Nevertheless, an excessive number of alerts of potential DDI lacking supporting information could cause wearying to healthcare providers as well as animal owners. Many healthcare providers dislike using drug interaction databases [19] for several reasons, including alert fatigue [20–21], workflow disturbance [22] and believing that there is no clinical significance related to most DDI alerts [23]. Ideally, an applicable drug interaction database should have both high sensitivity in identifying significant interactions and high specificity in excluding insignificant interactions. As a result, healthcare providers should be sure to use more than one DDI reference for reaching the best final answer when identifying potential DDIs [24]. Apart from using several DDI databases, healthcare providers should share their decision-making with animal owners regarding any significant potential DDI pairs, to preclude animals from adverse events and minimise liability.

In multiple-drug prescriptions, drug dosages usually have a relationship with drug interactions. For example, giving high doses of some drugs may cause interactions, but if they are used at lower doses, the possibility of a DDI may decrease. Ideally, the DDI database should be able to overlook an interaction if the given drugs are at doses that will not likely develop into a DDI [25–26], which the two databases in this study were not able to do. Therefore, the input of dosage should be added to databases, so healthcare providers can select the dose of drugs under consideration. Interestingly, Micromedex and Drugs.com provide detailed information differently. Micromedex adds more information on allergy interaction, alcohol interaction, lab interaction, tobacco interaction, pregnancy interaction and lactation interaction; Drugs.com provides more information only on food interactions and presents results into two categories: consumer and professional. The diversity of information from these two databases gives many benefits to increase the confidence of healthcare providers when many health conditions are discussed with their clients.

For determination of the mechanisms of potential DDIs in contraindicated and major severity levels identified by the two databases, PK-based was the main mechanism of DDI, followed by PD-based and PK-PD-based. The PK-based DDI causes a change of drug concentration in plasma or at the targeted organ by altering the absorption, distribution, metabolism and elimination. In this study, CYP enzyme inhibition was the main result of PK-based DDI, which leads to the accumulation of co-administered drugs, for example, ciprofloxacin inhibits CYP1A2 activity and decreases the clearance of theophylline, resulting in fatal adverse drug reaction via drug toxicity. PD-based DDI is caused by one drug interfering with another drug at the target site. The main result of PD-based DDI in our study was QT prolongation, which may lead to irregular heart rhythm and sudden death, for example, co-administration of erythromycin with sotalol may result in an additive QT prolongation. As a result, healthcare providers should truly understand these DDI mechanisms to prescribe multiple drugs properly.

This study has several important limitations. The most important one is that only two drug databases were used for the evaluation, so future studies should include additional databases of both subscription and open-access type, such as Lexicomp and Epocrates Free, respectively. Changing of the drug list was also one of our concerns; each year new drugs were developed whilst old drugs disappeared. The drug list used in this study was gathered in the first quarter of 2020, so it might have changed at any time. The updating frequency of databases for their potential DDI reports might affect the results of the analysis. The potential DDI result produced by the updated version of Micromedex and Drugs.com at different time points might give different outcomes from our study, performed in the first quarter of 2020. Additionally, the two databases have no data on several drugs used in animal hospitals, resulting in incomplete potential DDI analysis. Hence, some differences may occur once all drugs are added into the databases. Finally, the results of all probable mechanisms of action in our study referred to humans, which might differ from animals due to dissimilar physiology and drug-metabolising enzyme systems [27]. Therefore, more study of potential DDIs in animals is recommended to improve medical care and decrease the possibility of DDIs as a result of multidrug therapy in animals.

Drug interaction databases showed highly variable performance in assessing the DDIs of veterinary drugs. Open-access resources, such as Drugs.com, could detect more potential DDIs. However, Micromedex, a subscription database, provided more supportive information and special features. The judgement of healthcare providers should be used, with the consent of animal owners, to determine appropriate treatments for animals and avoid potential DDIs by using several databases for the data evaluation.

Drug selection

A list of 578 drugs was taken from those used in one animal hospital in Thailand and the VetList database [28], on 9 January 2020. From the total, 140 drugs were selected as frequently prescribed for the treatment of diseases in animal hospitals. Remarkably, Micromedex and Drugs.com could not recognise 44 of these drugs, so those remaining were used for this analysis, as shown in Fig. 2 and Table 4. The unrecognised 44 items were aditroprim, afoxolaner, avoparcin, baquiloprim, carprofen, clomocycline, danofloxacin, demethylchlortetracycline, deracoxib, difloxacin, enrofloxacin, eprinomectin, fipronil, firocoxib, glutathione, ibafloxacin, imidacloprid, imidapril, ivermectin, levosimendan, limecycline, marbofloxacin, methacycline, methoprene, milbemycin oxime, moxidectin, oclacitinib, orbifloxacin, ormetoprim, pimobendan, pirlimycin, pradofloxacin, rolitetracycline, samylin, spiramycin, sulfadimethoxine, sulfadoxine, sulfamethazine, teicoplanin, tepoxalin, tilmicosin, tulathromycin, tylosin and virginiamycin.

Table 4 List of drugs used for the potential DDIs analysis

Drug class	Drug groups	Drug lists
Analgesics	Nonopioid	Meloxicam
Anthelminthics	N/A	Praziquantel
Antiarrhythmics	Antiarrhythmic agent class I	Flecainide Lidocaine Mexiletine Procainamide
	Antiarrhythmic agent class III	Amiodarone Dronedarone Sotalol
	Antiarrhythmic agent class IV	Diltiazem
	Beta-blockers	Atenolol Esmolol Metoprolol
	Cardiac glycoside	Digoxin
Antimicrobials	Aminoglycosides	Amikacin Gentamycin Kanamycin Streptomycin Tobramycin
	Carbapenems	Primaxin
	Cephalosporins	Cefamandole Cefotaxime Cephalexin Cephalothin Ceftriaxone
	Chloramphenicols	Chloramphenicol
	Fluoroquinolones	Ciprofloxacin
	Glycopeptides	Vancomycin
	Lincosamides	Clindamycin Lincomycin
	Macrolides	Erythromycin
	Monobactams	Aztreonam
	Penicillinase resistant penicillins	Methicillin
	Penicillins	Amoxicillin/Clavulinate Amoxicillin Ampicillin Carbenicillin Nafcillin Oxacillin Penicillin G Penicillin V
	Polymyxins	Polymyxin B Polymyxin E
	Rifamycins	Rifabutin Rifampin Rifapentine
	Sulfonamides	Sulfadiazine Sulfamethoxazole
	Tetracyclines	Chlortetracycline Doxycycline Minocycline Oxytetracycline
	Trimethoprims	Trimethoprim
Anticonvulsants	Barbiturates	Phenobarbital
	Hydantoin	Phenytoin
	Benzodiazepine	Diazepam
	Miscellaneous	Gabapentin Pregabalin
Antifungal agents	Azole derivatives	Itraconazole
Antifungal agents	Imidazole derivatives	Ketoconazole Miconazole
Antihistamines	H₁ receptor antagonists	Cetirizine Chlorpheniramine
Antihypertensive drugs	Angiotensin-converting enzyme inhibitors	Benazepril Enalapril Ramipril
	Beta-blockers	Carvedilol
	Calcium channel blockers	Amlodipine Felodipine Isradipine Nifedipine Nisoldipine Verapamil Nicardipine
Antitussives	N/A	Dextromethorphan
Bronchodilators	Beta-2 receptor agonists	Salmeterol Terbutaline Ventolin
Bronchodilators	Phosphodiesterase inhibitors	Aminophylline Theophylline
Corticosteroids	Systemics	Prednisolone
Corticosteroids	Topicals	Fluticasone
Diuretics	Loop diuretics	Furosemide Torsemide
Diuretics	Potassium-sparing	Spironolactone
Hormones	Thyroid products	Levothyroxine
Herbal products	N/A	Aloe vera
Immunosuppressants	Calcineurin Inhibitors	Cyclosporine
Mucolytic agents	N/A	Acetylcysteine
Expectorants	N/A	Guaifenesin
Vasodilating agents	Phosphodiesterase-5 enzyme inhibitors	Sildenafil
Miscellaneous	Amino acid supplements	Methionine
	Antioxidants	Alpha-lipoic acid
	Antiseptics	Chlorhexidine
	Liver supplements	SAM-E (S-adenosylmethionine)
	Vitamin like substances	Coenzyme Q10
N/A, not available

Drug interaction databases selection

The subscription drug interaction database Micromedex (Truven Health Analytics, USA) was selected for the analysis of potential DDI on the basis of local availability through Chulalongkorn University. The open-access drug interaction database Drugs.com (Drugsite Trust, USA) was used because this database is well known and easy to use. The results of DDI from the two databases provided the same information, as follows: severity levels, probable mechanism, clinical management, literature and references. However, Micromedex provided more supportive information about documentation levels and the onset of the interaction.

DDI categorisation

For every selected drug queries posed to the databases, the two drug interaction databases categorised potential DDIs with a few different formats and explanations. Micromedex has five severity categories to identify potential DDIs, as follows: contraindicated, major, moderate, minor and unknown. Drugs.com has no category for contraindicated. Interestingly, only Micromedex categorised the documentation level for the detected potential DDIs, at three levels, as follows: excellent – the interaction has been clearly verified from controlled studies; good – the interaction is strongly suspected but lacks well-controlled studies; fair – availability of documentation is poor but the interaction is suspected to exist on the basis of pharmacological considerations, or a pharmacologically related drug provides good documentation. All results of the potential DDIs in this study were obtained from searches in the two databases and gathered in January 2020.

ADR: Adverse drug reaction

CYP: Cytochrome P450

DDI: Drug-drug interaction

PD: Pharmacodynamics

PK: Pharmacokinetics

Ethical approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Additional material

Not applicable.

Availability of data and materials

All data are presented in the manuscript.

Competing interests

Each author declares no competing interests.

Funding

No funding was obtained for this study.

Authors’ contributions

TB collected, analysed, and interpreted the data and drafted the manuscript.
PK contributed to study design, data analysis, and revising the manuscript.
AK contributed to study design, data analysis, and revising the manuscript.
All authors have read and approved the final version of the manuscript.

Acknowledgements

This research was supported by Ratchadapisek Somphot Fund for Postdoctoral Fellowship, Chulalongkorn University.

1. Ersoy S, Engin VS. Risk factors for polypharmacy in older adults in a primary care setting: a cross-sectional study. Clin Interv Aging. 2018;13:2003–11.

2. Menditto E, Gimeno Miguel A, Moreno Juste A, Poblador Plou B, Aza Pascual-Salcedo M, Orlando V, et al.Patterns of multimorbidity and polypharmacy in young and adult population: Systematic associations among chronic diseases and drugs using factor analysis. PLoS One. 2019;14:e0210701.

3. Roblek T, Vaupotic T, Mrhar A, Lainscak M. Drug-drug interaction software in clinical practice: a systematic review.Eur J Clin Pharmacol. 2015;71:131–42.

4. Askari M, Eslami S, Louws M, Wierenga PC, Dongelmans DA, Kuiper RA, et, al. Frequency and nature of drug-drug interactions in the intensive care unit. Pharmacoepidemiol Drug Saf. 2013;22:430–7.

5. Dechanont S, Maphanta S, Butthum B, Kongkaew C.Hospital admissions/visits associated with drug-drug interactions: a systematic review and meta-analysis. Pharmacoepidemiol Drug Saf. 2014;23:489–97.

6. Reimche L, Forster AJ, van Walraven C.Incidence and contributors to potential drug-drug interactions in hospitalized patients. J Clin Pharmacol. 2011;51:1043–50.

7. Micromedex Database, Truven health analytics, Greenwood Village, Colorado, USA.http://www.micromedexsolutions.com/micromedex2/ librarian. Accessed January 9, 2020.

8. Drugs.com Database, Drugsite trust, Dallas, Texas, USA. http://www.drugs.com/drug_interactions.html. Accessed January 9, 2020.

9. Kheshti R, Aalipour M, Namazi S.A comparison of five common drug-drug interaction software programs regarding accuracy and comprehensiveness. J Res Pharm Pract. 2016;5:257–63.

10. Patel RI, Beckett RD.Evaluation of resources for analyzing drug interactions. J Med Libr Assoc. 2016;104:290–5.

11. Clauson KA, Marsh WA, Polen HH, Seamon MJ, Ortiz BI.Clinical decision support tools: analysis of online drug information databases. BMC Med Inform Decis Mak. 2007;7:7.

12. Apidi NA, Murugiah MK, Muthuveloo R, Soh YC, Caruso V, Patel R, et al. Mobile medical applications for dosage recommendation, drug adverse reaction, and drug interaction: review and comparison. Ther Innov Regul Sci.2017;51:480–5.

13. Marcath LA, Xi J, Hoylman EK, Kidwell KM, Kraft SL, Hertz DL. Comparison of nine tools for screening drug-drug interactions of oral oncolytics. J Oncol Pract.2018;14:e368–e74.

14. Sasaki K, Shimoda M. Possible drug-drug interaction in dogs and cats resulted from alteration in drug metabolism: a mini review. J Adv Res.2015;6:383–92.

15. Suriyapakorn B, Chairat P, Boonyoprakarn S, Rojanarattanangkul P, Pisetcheep W, Hunsakunachai N, et al. Comparison of potential drug-drug interactions with metabolic syndrome medications detected by two databases. PLoS ONE. 2019;14:e0225239.

16. Min JS, Bae SK. Prediction of drug-drug interaction potential using physiologically based pharmacokinetic modeling. Arch Pharm Res.2017;40:1356–

17. Kuscu F, Ulu A, Inal AS, Suntur BM, Aydemir H, Gul S, et al. Potential drug-drug interactions with antimicrobials in hospitalized patients: a multicenter point-prevalence study. Med Sci Monit. 2018;24:4240–47.

18. Carter BL. Antihypertensive drug interactions. Drugs Today (Barc).2005;41:55–63.

19. Smithburger PL, Buckley MS, Bejian S, Burenheide K, Kane-Gill SL. A critical evaluation of clinical decision support for the detection of drug-drug interactions. Expert Opin Drug Saf. 2011;10:871–82.

20. Bryant AD, Fletcher GS, Payne TH.Drug interaction alert override rates in the meaningful use era: no evidence of progress. Appl Clin Inform. 2014;5:802–13.

21. Isaac T, Weissman JS, Davis RB, Massagli M, Cyrulik A, Sands DZ, et al. Overrides of medication alerts in ambulatory care. Arch Intern Med. 2009;169:305–11.

22. Hayward J, Thomson F, Milne H, Buckingham S, Sheikh A, Fernando B, et al. Too much, too late': mixed methods multi-channel video recording study of computerized decision support systems and GP prescribing. J Am Med Inform Assoc.2013;20:e76–e84.

23. van der Sijs H, Aarts J, Vulto A, Berg M. Overriding of drug safety alerts in computerized physician order entry. J Am Med Inform Assoc. 2006;13:138–47.

24. Boyce RD, Collins C, Clayton M, Kloke J, Horn JR. Inhibitory metabolic drug interactions with newer psychotropic drugs: inclusion in package inserts and influences of concurrence in drug interaction screening software. Ann Pharmacother. 2012;46:1287–98.

25. Hansten PD, Horn JR, Hazlet TK. ORCA: operational classification of drug interactions. J Am Pharm Assoc (Wash). 2001;41:161–5.

26. Bergk V, Gasse C, Rothenbacher D, Loew M, Brenner H, Haefeli WE. Drug interactions in primary care: impact of a new algorithm on risk determination.Clin Pharmacol Ther. 2004;76:85–96.

27. Martignoni M, Groothuis GM, de Kanter R. Species differences between mouse, rat, dog, monkey and human CYP-mediated drug metabolism, inhibition and induction. Expert Opin Drug Metab Toxicol.2006;2:875–94.

28. VetList Database, Pestbox supply, Bangkok, Thailand. http://vetlist.co. Accessed January 9, 2020.

Comparing potential drug-drug interactions in veterinary medications using two electronic databases

Status:

Version 2

Abstract

Figures

Background

Results

Discussion

Conclusions

Materials And Methods

List Of Abbreviations

Declarations

References

Status:

Version 2