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Abstract

Background:

Both polypharmacy and potential drug-drug interactions (pDDIs), especially those at the pharmacokinetic level, are common in hospitalized patients and are associated with adverse effects and failure of therapy;

Objective:

The aim of the present study is to investigate retrospectively the prevalence of polypharmacy and the risk of potential pharmacokinetic drug-drug interaction among hospitalized patients;

Methods:

The medical documentation of hospitalized patients in the unit of internal diseases at the hospital “St Marina” in Varna, Bulgaria for a period of six months (January–July 2016) was retrospectively reviewed. Lexicomp® Drug Interaction software was used for the detection of pDDI. Descriptive statistic and logistic regression were used for data analysis;

Results:

In this study, 294 patients out of 510 (57%) were selected with polypharmacy. The number of detected potential pharmacokinetic DDIs (pPKDDIs) was only 216 (or 12,4%), but almost 40% of patients with polypharmacy were exposed to at least one pPKDDIs. The most common pPKDDIs occur at the biotransformation level – 78 (36,1%), and the most common enzyme form that is involved in these interactions is cytochrome 3A4 (44 or 20,4%). The number of prescribed medications (>7) was found to increase the possibility of having pDDIs (OR 25.535, 95% CI 12.529 to 52.042; p = <0.001) and pPKDDIs (OR 5.165, 95% CI 3.430 to 7.779; p = <0.001) as well;

Conclusion and Relevance:

Caution should be taken in patients taking more than seven drugs and careful assessment of the pPKDDIs should be made. When such interactions are detected, they need to be properly evaluated and managed.

Keywords

Polypharmacy drug-drug interactions pharmacokinetic drug interactions hospitalized patients risk of interactions

Introduction

Combination pharmacotherapy is the basis of the progress made in the treatment of many socially significant diseases in areas such as cardiology, oncology, infectious diseases, etc., but also it is the reason for the occurrence of higher frequency of adverse reactions, because of the increased number of used drugs.^1–3 This complex pharmacotherapy is, in most cases, referred to as polypharmacy, and the most used definition of polypharmacy (used also for the purposes of the article), is the receiving of five or more medications from one patient.⁴

The two types of drug interactions, pharmacokinetic and pharmacodynamic, can cause significant adverse reactions if not taken into account. Most physicians are well versed and can predict potential pharmacodynamic drug interactions, while the pharmacokinetic ones are more difficult for understanding and for prediction as well as are not fully confirmed in most cases.⁵ Pharmacokinetic interactions can generally be presented as occurring at the four levels, very often presented with the following abbreviation – ADME (absorption, distribution, metabolism and excretion). Transport proteins, e.g. organic anion transporter (OAT), P-glycoprotein (P-gp), etc., and metabolizing enzymes, e.g. cytochrome P450 (CYPs), are involved in all pharmacokinetic processes, such as absorption, distribution and elimination. Drugs could be substrates or modulators (inhibitors inducers) of metabolizing enzymes and/or transport proteins that are responsible for the elimination of many drugs used in clinical practice. These interactions exactly are more significant and with greater consequences for the clinical practice.^6,7 In addition, other factors, such as genetic variation (at the level of metabolizing enzymes, drug transporters, or both), further complicate the prediction of such interactions, and must also be taken into account.⁸ That is why one of the main goals of drug research and development is to generate scientific reliable information about the potential pharmacokinetic drug-drug interactions (pPKDDIs) so that the physicians can have the necessary data to conduct safe clinical treatment when there is a danger of such interactions.

The main purpose of the present study was to review retrospectively hospitalized patients with polypharmacy for pPKDDIs, to determine the levels of interactions, their prevalence and their clinical significance.

Materials and methods

Design of the study

The medical documentation of hospitalized patients in the unit of internal diseases at the hospital “St Marina” in Varna, Bulgaria for a period of six months (January–July 2016) was retrospectively reviewed at the discharge. For the purposes of the article, the main selection criteria were – patients who received five or more drugs (polypharmacy). Prescribed pharmacotherapy in these patients has been screened for potential drug-drug interactions (pDDIs). Additionally, those patients with observed pPKDDIs were selected for a more detailed examination, such as level and main alleged mechanism of interaction, severity and risk rating of reaction and management recommendations. The Committee on Research Ethics at the Medical University “Paraskev Stoyanov” Varna, Bulgaria approved the current study. The complete patient information was kept private and was not available to the public.

Used software for detection of pDDIs

For determination of pDDIs were used Lexicomp^® Drug Interactions (Wolters Kluwer, Hudson, OH).⁹ The reason for choosing this software is that it shows high sensitivity and specificity in detecting and characterizing pDDIs.¹⁰ The software classified the interactions into five categories: A (No known interactions), B (No action needed), C (Monitor therapy), D (Consider therapy modification) and X (Avoid combination). Each interaction was also indicated by the severity of the reaction in four stages - major, moderate, minor and N/A. In the description of the interaction there was also a paragraph describing the presumed mechanism of the interaction, which for the purposes of the article emphasizes was on the different levels of pPKDDIs.

Data reporting and analysis

The reporting data of this study conforms to the STROBE guideline.¹¹ The characteristics of patients and the pDDIs were shown as numbers (n) or percentage (%) and were analyzed using descriptive methods of means and standard deviations for continuous data. We have performed logistic regression analysis to determine the presence of correlation between separate variables, such as number of prescribed drugs, hospital stay and age, and the occurrence of pDDIs and pPKDDIs as binary variables (yes/no). For each predictor odds ratio (OR) with a 95% confidence intervals (CIs) were determined. p-value <0.05 was considered as statistically significant. The data were analyzed using Excel 2010 or SigmaPlot 11.0 software.

Results

Sociodemographic and clinical characteristics of patients included in the survey

After reviewing the prescribed medications at discharge for six months (January–July 2016) of all the patients – 510, which have passed through the internal unit of the “St Marina” University Hospital in Varna, Bulgaria, 294 patients were selected, based on the polypharmacy (receiving ≥ 5 drugs). The general characteristics of the selected patients are shown in Table 1.

Table 1.

Main patient's characteristics included in the study.

Patient characteristics	Number (%); Mean ± standard deviation (SD)
Gender
Male	137 (46.6%)
Female	157 (53.4%)
Age
<40	4 (1.4%)
40–59	66 (22.5%)
>60	224 (76.2%)
Average Age	68.1 (SD ± 12.2)
Average Age (males)	68.1 (SD ± 11.9)
Average Age (females)	68.2 (SD ± 12.6)
Number of prescribed drugs (all patients were with ≥ 5 drugs)
5–7	167 (56.8%)
8–10	94 (32%)
> 10	33 (11.2%)
Average	7.5 (SD ± 2.2)
Hospital stay (days)
≤ 10	219 (74.5%)
> 10	75 (25.5%)
Average length of hospital stay	8.4 (SD ± 3.7)
Most common reason for hospitalization
Chronic heart disease	118 (40.1%)
Chronic pyelonephritis	32 (10.9%)
Chronic kidney failure	19 (6.5%)
Protracted tracheobronchitis	18 (6.1%)
Arthritis	12 (4.1%)
Gastritis	9 (3.1%)
Most common comorbidities
Hypertension	283 (56.3%)
Chronic heart failure	181 (61.6%)
NYHA 1	2 (1.1%)
NYHA 2	20 (11.1%)
NYHA 3	159 (87.9%)
Chronic kidney failure	162 (55.1%)
eGFR < 60 ml/min	107 (66.1%)
eGFR < 30 ml/min	55 (34%)
Anemia	112 (38.1%)
Diabetes	101 (34.4%)
Atrial flutter	74 (25.3%)
COPD	32 (10.9%)

Abbreviations: eGFR, estimated glomerular filtration rate; COPD, Chronic obstructive pulmonary disease.

Out of 294 selected patients, 157 (53.4%) were female and 137 (46.6%) were male. According to age, the majority of the patients were in the elderly group (>60)—224 (76.2%), as the number of those between 40 and 59 was 66 (22.5%) and under 40 – only 4 (1.4%). The average age was 68.1 (±12.22 SD).

Prevalence of pDDIs detected by Lexicomp^®

The total number of pDDIs was 1742, or approximately 5.9 (±4.9 SD) pDDIs per one person. Overall, 95% of selected patients with polypharmacy had at least one pDDI, regardless of type. The most common drug interactions with respect to the risk rating were in group C—1463 (84.0%), requiring careful monitoring of patients. One hundred and seven patients (6.1%) were in group D of their pDDIs, for which it is advisable to consider changing the therapy used. Contraindicated pDDIs (group X, 0.5%) with recommendation to avoid them, were detected in only nine patients. Based on severity of reaction, the most pDDIs were in moderate group – 1510 (86.7%), while those in major group were 98 (5.6%). All results are summarized in Table 2.

Table 2.

Classification of detected pDDIs.

Classification of pDDIs based on categories in software	Number (%) of pDDIs (total 1742)
Risk rating
A	8 (0.7%)
B	155 (8.9%)
C	1463 (84%)
D	107 (6.2%)
X	9 (0.5%)
Severity of reactions
N/A	14 (0.8%)
Minor	120 (6.9%)
Moderate	1510 (86.7%)
Major	98 (5.6%)

A (No known interactions); B (No action needed); C (Monitor therapy); D (Consider therapy modification); X (Avoid combination).

Potential pharmacokinetic drug-drug interactions (pPKDDIs)

Most of the detected interactions (>85%) are pharmacodynamic in nature or have an unclear mechanism. pPKDDIs cover a relatively small proportion of all detected interactions – 216 (12.4%). However, the studied medical cards of the selected patients were 294, 113 of them showed at least one pPKDDIs, or approximately 40 percent (38.4%). The data are summarized in Table 3.

Table 3.

Potential pharmacokinetic drug-drug interactions (pPKDDIs) detected in patients included in the study.

Potential pharmacokinetic drug interactions (pPKDDIs)	Number (%)
Absorption interactions	52 (24.1%)
Changes in pH	36 (16.7%)
Changes in AUC	14 (6.5%)
Formation of complexes	2 (0.9%)
Distribution interactions	36 (16.7%)
PPB interactions	8 (3.7%)
OATP1B1 interactions	1 (0.5%)
P-gp interactions	20 (9.3%)
BCRP interactions	7 (3.3%)
Metabolism interactions	78 (36.1%)
Changes in hepatic blood flow	2 (0.9%)
CYP1A2	2 (0.9%)
CYP2C9	12 (5.6%)
CYP2C19	11 (5.1%)
CYP2D6	7 (3.3%)
CYP3A4	44 (20.4%)
Excretion interactions	50 (23.2%)
OAT	49 (22.7%)
OCT2/MATE-1	1 (0.5%)

Abbreviations: AUC, area under the curve; PPB, plasma protein binding; OATP, organic anion transporting polypeptides; BCRP, breast cancer resistance protein; P-gp, P-glycoprotein; CYP, Cytochrome P450; OAT, organic anion transporter; OCT, organic cation transporter; MATE, multidrug and toxic compound extrusion.

As can be seen from the table, the most common pPKDDIs occur at biotransformation level – 78 (36.1%), and the most common enzyme form that is involved in these interactions is cytochrome 3A4 (44 or 20.4%). Interactions associated with altered absorption showed the next higher prevalence among pharmacokinetic interactions – 52 (24.1%), followed by altered excretion – 50 (23.1%) and distribution – 36 (16.7%) respectively.

The most common pairs of individual pharmacokinetic levels of interactions and their management are presented in Table 4. As most pPKDDIs are highly dependent on the used dose and the chosen dosing regimen, the table shows those of the interacting drugs, as well as a comment on their effect in the given case.

Table 4.

pPKDDIs, their putative mechanism and management.

pPKDDIs/number (%)/Severity and risk rating	Putative pharmacokinetic mechanism of interaction	Dosage regimen of concomitantly administered drugs	Commentary/Management guidelines
Absorption interactions
Cefuroxime axetil + Omeprazole /(1; 0.5%)/Moderate X	Omeprazole increases gastric pH and therefore decreases the solubility of cefuroxime axetil, which may lead to reduced bioavailability of the last one	C – 250 mg bid (10 days) O – 20 mg bid (1 month)	The effect of omeprazole lasts about 12–24 h; therefore it cannot be avoided by changing the dosage regimen of cefuroxime/Avoid concomitant use
Levofloxacin + Calcium salts /(1; 0.5%)/Moderate D	Formation of chelate complexes; decrease absorption of quinolones	L – 500 mg daily (7 days) C – 200 mg bid (10 days)	In contrast to the above example, this interaction can be easily avoided/Avoid concurrent administration (at least 2 h before or 2 h after) of quinolones and calcium salts to minimize the impact of this interaction.
Rosuvastatin + Clopidogrel /(2, 0.9%)/Moderate C	Clopidogrel increases AUC of Rosuvastatin, mechanism of interaction remains unclear, metabolizing enzymes may also be involved	R – 20 mg once daily C – 75 mg once daily	Interaction is more pronounced when rosuvastatin is administered at 40 mg; in addition, the interaction is considered to be more beneficial with respect to cardiac biomarkers/Monitor for increased toxicity of rosuvastatin (e.g. myalgia, rhabdomyolysis, and liver function) in patients receiving clopidogrel
Distribution interactions
Levothyroxine (T4) + Furosemide /(4; 1.9%)/Moderate C	Furosemide inhibits binding of T4 to albumin and TBG, increasing transient free thyroid hormone concentrations	L – 50–125 mcg daily F – 40–160 mg daily	Тhe interaction was observed only when furosemide was administered in doses above 80 mg; only one of the patients in the study had a dose above 80 mg/Close monitoring of thyroid hormone levels in patients concomitant receiving furosemide (more than 80 mg)
Co-trimoxazole + Acenocoumarol /(2; 0.9%)/Moderate C	Dual mechanism: sulfonamide inhibits metabolism (CYP2C9) and induces displacement of acenocoumarol from plasma proteins. The influence of sulfonamides on vitamin K-producing GI flora may also alter the effect of acenocoumarol	CT – 960 mg bid (7–10 days) A – 1–2 mg daily (INR-control)	The complex mechanisms of interaction require adjustment of the dose of acenocoumarol when co-administering sulphonamides/Monitor for increased/decreased anticoagulant effects (e.g. increased/decreased INR) of vitamin K antagonists if a sulfonamide is initiated
Azithromycin + Atorvastatin /(1; 0.5%)/Moderate C	Unclear mechanism; presumably involving OATP1B1 or CYP3A4	Az – 500 mg daily (5 days) At – 10 mg at bedtime	Azithromycin is not a significant inhibitor of OATP1B1 or CYP3A4, however, the software offers caution when prescribing both drugs/Monitor patients more closely for evidence of atorvastatin toxicity (e.g. muscle aches or pains, renal dysfunction, etc.).
Dabigatran etexilate + Amiodarone /(1; 0.5%)/Moderate C	Amiodarone inhibits P-gp, a transporter responsible for dabigatran disposition.	DE – 210 mg bid A – 200 mg daily	The use of dabigatran with P-gp inhibitors is contraindicated at CrCL less than 50 ml/min/ Monitor closely for evidence of an excessive clinical response to dabigatran (e.g. bruising, bleeding) if dabigatran etexilate is combined with a P-gp inhibitor.
Telmisartan + Rosuvastatin /(7; 3.2%)/Minor B	Telmisartan inhibits ABCG2- (also known as BCRP), a transporter responsible for rosuvastatin efflux.	T – 80–160 mg daily R – 10–20 mg daily	Increased attention to muscle toxicity when co-administering both drugs; switching to another statin reduces the risk/Be cautious if both drugs are administered concomitantly.
Metabolism interactions
Ciprofloxacin + Pentoxifylline /(1; 0.5%)/Minor C	Ciprofloxacin is a moderate inhibitor of CYP1A2 – it increases the serum concentration of pentoxifylline and probably its active metabolite (M1)	C – 500 mg daily P – 600 mg daily	Slight increase in concentrations without significant clinical manifestations/Monitor for increased pentoxifylline effects during concomitant treatment with moderate CYP1A2 inhibitors.
Acenocoumarol + Amiodarone /(1; 0.5%)/Major D	The primary mechanism of these interactions is likely to be related to the ability of amiodarone or its active metabolite (desethylamiodarone) to inhibit CYP2C9	Ac – 1–2 mg daily Am – 200 mg daily	The dose of acenocoumarol should be reduced (∼30%–50%) when amiodarone is present/Monitor patients more closely for evidence of increased anticoagulant effects
Acenocoumarol + Allopurinol /(1; 0.5%)/Moderate D	The mechanism of this interaction is not clear, but might be related to allopurinol's ability to inhibit the CYP2C9	Ac – 1–2 mg daily All – 100 mg bid	No direct clinical studies have been performed on the interaction between acenocoumarol and allopurinol/Monitor INR more frequently when allopurinol is initiated or discontinued
Clopidogrel + Omeprazole /(4; 1.9%)/Major D	Omeprazole may diminish the antiplatelet effect of clopidogrel by inhibiting its activation by CYP2C19	C – 75 mg once daily O – 20 mg bid (14–28 days)	Rabeprazole, lansoprazole or pantoprazole may be lower-risk alternatives to omeprazole/Avoiding concurrent use of clopidogrel with omeprazole.
Tramadol + Tolperisone /(2; 0.9%)/Minor B	Tolperisone may increase the plasma concentration of CYP2D6 substrates	Tr – 37.5 mg tid To – 150 mg bid	Tolperisone is not expected to have a clinically relevant effect on CYP2D6, therefore the interaction is considered to be insignificant/No action required.
Ticagrelor + Atorvastatin /(2; 0.9%)/Moderate C	Ticagrelor is a weak inhibitor of CYP3A4 and may increase plasma concentrations of atorvastatin	T – 90 mg bid A – 40 mg at bedtime	Serious side effects have been reported with the high-dose regimen of atorvastatin (80 mg), while at low doses the interaction is considered to be negligible/Monitor patients closely for evidence of atorvastatin toxicities.
Amlodipine + Simvastatin /(2; 0.9%)/Moderate C	Amlodipine may increase the serum concentration of Simvastatin by inhibiting CYP3A4	A – 5–10 mg daily S – 10–40 mg daily	One of the detected patients was prescribed a high dose of simvastatin - 40 mg, despite the recommendations/Dose of simvastatin should not exceed 20 mg daily if co-administering amlodipine.
Fluconazole + Apixaban /(1; 0.5%)/Major D	Fluconazole inhibits moderately CYP3A4 and increases concentrations of Apixaban	F – 100 mg daily (5 days) A – 2,5 mg bid	Although fluconazole is a moderate inhibitor of CYP3A4, concomitant use with CYP3A4 substrates requires caution/Monitor patients receiving apixaban with inhibitors of CYP3A4 for evidence of bleeding
Excretion interactions
Methotrexate + NSAIDs (Dexketoprofen, Etoricoxib and Etodolac) /(6; 2.8%)/Moderate C	NSAIDs may decrease renal excretion of methotrexate by inhibiting renal OATs	M – 7.5–25 mg weekly D – 25 mg PRN Etor – 90 mg daily PRN Etod – 400 mg bid PRN	Only two patients in the study were on low doses of methotrexate (below 15 mg), while the other four were on doses above 15 mg/Due to the severity of this potential interaction, consider avoiding the concomitant use of NSAIDs and methotrexate, especially if methotrexate is being used in higher doses.
Metformin + Co-trimoxazole /(1; 0.5%)/Moderate C	The suspected primary mechanism of this interaction is trimethoprim inhibition of active renal tubular secretion of metformin (e.g. via OCT2, MATE1)	M – 500 mg bid C – 960 bid (5 days)	Slight increases in metformin concentration and AUC were observed; clinical significance – low/Monitoring for increased systemic effects of metformin during concomitant treatment with Co-trimoxazole.

Abbreviations: PRN, as needed; BID, twice daily; TID, three times daily; NSAID, nonsteroidal anti-inflammatory drug.

Factors associated with pDDIs

After performing the analysis, the results have shown positive correlation of the number of the prescribed drugs and the occurrence of the pDDIs – OR 25.535 (95%CI 12.529 to 52.042; p = <0001). For the other predictors, such as age and hospital stay, no statistically significant difference was observed – 1.234 (95% CI 0.692–2.199, p = 0.570) and 1.035 (95% CI 0.585–1.831, p = 0.978), respectively (Table 5). The occurrence of pPKDDIs has shown positive correlation with all studied factors – number of prescribed drugs (OR 5.165; 95% CI 3.430–7.779; p = <0.001), hospital stay (OR 2.165; 95% CI 1.094 to 4.284; p = 0.036) and age (OR 2.375; 95% CI 1.354 to 4.166; p = 0.004) (Table 5).

Table 5.

Predictors associated with pDDIs and pPKDDIs.

Independent variable	Dependent variable: pDDI (yes, no)		Dependent variable: pPKDDI (yes, no)
Independent variable	OR (95% CI)	p-value	OR (95% CI)	p-value
Number of drugs
>7 ≤7	25.535 (12.529 to 52.042)	p = <0.001	5.165 (3.430 to 7.779)	p = <0.001
Hospital stay
>10 ≤10	1.035 (0.585 to 1.831)	p = 0.978	2.165 (1.094 to 4.284)	p = 0.036
Age
>60 ≤60	1.234 (0.692 to 2.199)	p = 0.570	2.375 (1.354 to 4.166)	p = 0.004

Discussion

Potential drug-drug interactions are common in hospitalized patients. Knowledge of these interactions and how to manage them can reduce the frequency of side effects and increase the degree of effectiveness. Therefore, the purpose of this study is to provide information on the most commonly observed pDDIs (with an emphasis on pPKDDIs) in prescriptions of hospitalized patients, their prevalence and type depending on the severity of the reaction and the pharmacological mechanism of the interaction and how to manage them.

In the present study, prevalence of the polypharmacy (≥5 drugs) was detected in 294 medical charts (selected for the present study) out of 510 examined, which means that every second patient had a prescribed polypharmacy. Excessive polypharmacy (≥10 drugs) was identified in 55 medical charts (∼11%). Out of these selected patients with polypharmacy, at least one pDDIs was detected in 280 of them (95%) or approximately 5.9 (±4.9 SD) pDDIs per one person. According to the polypharmacy, these results are close to those reported by recently published data conducted in elderly patients with cardiovascular disease in a hospital in Ethiopia by Assefa et al.¹² and by other authors as well.^13–15 The frequency of pDDIs among patients with polypharmacy was quite identical to the data reported by Nusair et al.¹⁶ with slight differences about mean values – 4.2 ± 3.0 vs. 5.9 ± 4.9 (in the present study) per patient. In general, it can be concluded that polypharmacy is a common phenomenon in hospitalized patients and should be taken into account by physicians, because of its direct relation to the possibility of developing pDDIs.

The most common detected types of pDDIs were in the risk category C (84.0%) and based on the severity of the reaction – in the moderate group (86.7%). These interactions do not require, in most of the cases, any special action, but rather close monitoring of the patients. Those results correspond to our previously published article¹⁷ and also to that from other authors,^18,19 with slight change in the percentage, but in the same range. Major pDDIs, in the risk categories D and X, were relatively rare – 6.1% and 0.5% respectively (Table 2), but they deserve special attention because in most cases they are associated with drugs with low therapeutic index (e.g. statins, anticoagulants, cardiac glycosides, antithrombotics, methotrexate etc.) and can significantly harm the patient's health.^17,20,21

Pharmacokinetic drug interactions occur when a drug alters absorption, distribution or elimination (disposition) of a simultaneously administered drug, leading to changes in its plasma concentration levels.⁵ As mentioned in the result section, relatively small amount of pPKDDIs (216 or 12,4% of all pDDIs) were recognized by the software program. The involved pharmacokinetic levels are represented in Table 3. Interactions occurring at the level of metabolizing enzymes and transport proteins are considered to be with the greatest clinical significance. Inhibition or induction of these enzyme systems and transport proteins is highly dependent on the doses and the dosing regimens used.⁶ In our study, these interactions were predominant and almost equally prevalent – those of them involving metabolizing cytochrome enzymes (CYPs) and transport proteins were – 76 (4.4%) and 78 (4.5%) respectively. Other authors report a significantly higher frequency of pPKDDIs (CYPs or transporter). In the study published in 2018 by Bajracharya et al. reported 36.9% pharmacokinetic interactions out of 657 DDIs.²² Doan et al. detected 80% of CYP-mediated DDIs among patients with polypharmacy, as this risk increases as a function of the number of medications dispensed.²³ Zakrzewski-Jakubiak et al. revealed the same incidence of CYP-mediated DDIs as the most involved CYPs are 3A4, 2D6 and 2C9 with 70.2%, 22.7% and 3.4% respectively.²⁴

Usually, pharmacokinetic interactions are described as involving one of the levels, but sometimes the mechanisms are complex – they may affect both the distribution and elimination processes of the influenced drug or involve both pharmacokinetic and pharmacodynamic mechanisms. Of the pharmacokinetic interactions presented in Table 4 (an example of such complex) interaction is acenocoumarol and co-trimoxazole (sulfamethoxazole/trimethoprim). Оn the one hand, displacement of acenocoumarol from the plasma proteins binding sites and on the other hand inhibition of metabolism (via CYP2C9) by sulfonamide (sulfamethoxazole) are the putative pharmacokinetic mechanisms. Alteration of GI flora responsible for production of vitamin K by co-trimoxazole may be involved in pharmacodynamic mechanisms. Lexicomp software classifies the reaction as D and based on severity as major, and recommends reducing the dose of coumarin anticoagulant by 10% to 20% prior to starting the sulfonamide antibiotic and then monitoring INR closely to further guide dosing. Some authors are more critical and recommend the use of alternative antibiotics instead of adjusting the dosage regimens of the two drugs.^25,26 Another similar complex interaction (not shown in Table 4) is between acenocoumarol and levothyroxine, which was detected in one of the patients. Several mechanisms have been proposed: (1) binding of coumarin anticoagulants to plasma proteins may be decreased in hyperthyroid patients (PK); (2) thyroid hormone increases the affinity of vitamin K-dependent epoxide reductase for coumarins (PD); (3) more pronounced metabolism of vitamin K-dependent blood clotting factors in patients with increased thyroid function (PD). The software program classifies the reaction as C and based on severity as moderate, and recommends monitoring by the physicians for increased anticoagulant effects of vitamin K antagonists and regularly review the patient's thyroid status.²⁷ In some cases, both drugs may interact with each other, affecting their pharmacokinetics, therefore the final result may be difficult to predict. An example of such interaction is rosuvastatin and telmisartan. Telmisartan inhibits ABCG2-efflux transporter (also known as BCRP), which takes part in the disposition of rosuvastatin, thereby increasing plasma concentration of the last one. In turn, rosuvastatin could increase exposure of telmisartan, by competition for the hepatic uptake transporter – OATP1B3 and glucuronidation by UGT1A3. The software characterizes it as insignificant (B/minor), which has been confirmed in clinical trials.^28,29 The given examples were intended to present the complexity and difficulties in predicting, evaluating, and managing pPKDDIs. In most cases, they require a special approach. On the one hand, the use of appropriate software for their detection, while on the other hand, a specialist, such as a clinical pharmacist, is needed, with experience in the field of drug interactions, to suggest a course of action. As mentioned above, dose and dosing regimen play an important role in interactions. In our study, several patients with doses above the recommended and concomitantly using interacting drug were detected – one patient taking amlodipine and a high dose of simvastatin – 40 mg (recommendations are no more than 20 mg dose) and four patients taking high doses of methotrexate - >15 mg weekly with NSAIDs. In the latter case, NSAIDs are prescribed only when necessary, which reduces the risk of serious interactions.

After performing the analysis, we haven't found statistically significant association between age, hospital stay and pDDIs (Table 5). However, we found a significant one between the number of drugs taken (>7) and the pDDIs. The frequency of pPKDDIs increases with all analyzed factors (number of taken drugs, hospital stay and age) with statistical significance. In addition, almost every second patient (38.4%) with polypharmacy is with an increased risk of pPKDDIs, which is an indication that there is a need to know the mechanism of pharmacokinetic interactions and the ways to manage them. In our previously cited study in patients with heart failure, no correlation was found between age and pDDIs, while such was observed between the number of drugs taken and pDDIs.¹⁷ Moura et al.³⁰ have demonstrated a link between the length of hospital stay in intensive care units and the risk of developing potential drug interactions. Other authors confirm this relation, as well as that between age, number of taken drugs and the risk of developing pDDIs.^19,31,32 A study conducted in patients over 45 years of age in Primary Care, Southern Brazil by Teixeira et al.,³³ have found no statistically significant association regarding age, but a significant one when the patients take three or more drugs and risk of pDDIs.

Limitations of the study

A single software was used to detect pDDIs and pPKDDIs. The reasons for this were that the software has one of the largest databases and that it has a high sensitivity.¹⁰ Also, the assessment of pPKDDIs has not been evaluated in terms of timing of administration since we had no information about time window.

Conclusions

In the present study, a high incidence of polypharmacy and pDDIs observed in hospitalized patients in the unit of internal diseases was shown. Therefore, caution should be taken in patients with more than seven prescribed drugs and careful assessment of the risk of the possible pDDIs. pPKDDIs generally comprise a small proportion of all interactions, but almost 40% of patients with polypharmacy were exposed to at least one pPKDDIs. Their risk increases with the number of drugs taken, and significantly less with the hospital stay and age, and should be taken into account. They are difficult to predict and therefore require a specific approach – the use of appropriate software or a clinical pharmacist with experience in the field of drug interactions.

Footnotes

Abbreviations

Acknowledgments

The authors would like to thank the colleagues from the Internal Disease Unit at St Marina University Hospital in Varna, Bulgaria for the provided data.

Declaration of conflicting interests

The Autors declare that there is no conflict of interest

Ethics approval

Ethical approval to report this case was obtained from ethics committee of medical university “Prof. Dr Paraskev Stoyanov” – Varna, ethics approval number 105/18.08.2021.

Informed consent

Informed consent for patient information to be published in this article was not obtained because this is a retrospective study and all patient details are de-identified.

ORCID iD

Nadezhda Hvarchanova

Author biographies

Kaloyan D Georgiev. In 2003, associate professor Kaloyan D Georgiev graduated from the Faculty of Pharmacy, Sofia with a diploma in pharmacology “Potentiation of the Antileukemic Effects of Bendamustine in Combination With Imatinib and Erufosine.” In 2005, he was appointed as an assistant professor to the Department of Pharmacology at Medical University “Prof. Dr. Paraskev Stoyanov” Varna. After developing a dissertation in the section “Pharmaceutical Design and Biochemical Pharmacology” at the Bulgarian Academy of Sciences (BAS) in Sofia with the topic “Design, Synthesis and Pharmacological Characterization of Peptide Mimetics of Endomorphin-2, Morphicetin and RGD with Analgesic and Antitumor Activity” he has received the educational and scientific degree “Doctor” in 2013. He has acquired a specialty in Pharmacology in 2010 and Clinical Pharmacy in 2018. From 2015 until now, he holds the academic position of associate professor and is the head of the Department of Pharmacology, toxicology, and pharmacotherapy, Faculty of Pharmacy, Medical University “Prof. Dr. Paraskev Stoyanov”—Varna. Since 2020, he has received the educational and scientific degree “Doctor of Science” in pharmacology identification, analysis, and evaluation of pharmacokinetic and pharmacodynamics drug interactions.

Nadezhda Hvarchanova. In 2011, chief assistant professor Nadezhda Hvarchanova graduated from the Faculty of Pharmacy, Plovdiv. Since 2013, she has been an assistant professor and chief assistant professor in the Department of Pharmacology, Toxicology, and Pharmacotherapy, Faculty of Pharmacy, Medical University “Prof. Dr. Paraskev Stoyanov”—Varna. After developing a dissertation at the Department of Pharmacology, Toxicology, and Pharmacotherapy with the topic “Pharmacotherapeutic Study, Potential Side and Toxic Effects of Cardioactive Drugs in the Treatment of Hospitalized Patients with Chronic Heart Failure,” she received the educational and scientific degree “Doctor” in 2018 and in the same year she acquired a specialty in “toxicology and toxicological analysis.”

Elitsa Stoychev. In 2009, assistant professor Elitsa Stoychev graduated from the Faculty of Pharmacy, Eberhard Karls Universitaet, Tubingen, Germany. In 2012, she won a scholarship from the School of Pharmacy at the University of Otago, Dunedin, New Zealand, and works as a visiting researcher to the Department of Social Pharmacy. Since 2018, she has been an assistant professor in the Department of Pharmacology, Toxicology, and Pharmacotherapy, Faculty of Pharmacy, Medical University “Prof. Dr. Paraskev Stoyanov”—Varna.

Branimir Kanazirev. In 1979, Professor Dr. Branimir Kanazirev graduated from the Medical University “Prof. Dr. Paraskev Stoyanov” in Varna. He was an assistant professor and chief assistant professor in the Department of Propaedeutics of Internal Diseases in the years between 1979 and 2012. After developing a dissertation with the topic “Survival and prognostics of patients with heart failure and impaired left ventricular systolic and segmental function,” he received the educational and scientific degree “Doctor” in 2012. He held the academic position of associate professor in 2013 and the academic position of professor in the scientific specialty “internal diseases” in 2018. He has acquired a specialty in “internal diseases” in 1984 and “rheumocardiology” in 1986. In 1986, he specialized in non-invasive and invasive cardiology at the National Center for Cardiovascular Diseases in Sofia. In 1994, he acquired a diploma from the Educational Commission of Foreign Graduates—ECFMG. In 2000, he specialized “Transesophageal echocardiography and stress echocardiography” at Inselspital, Bern, Switzerland, in 2010, he received a diploma of “European Cardiologist,” in 2012, “EACVI—Certificate for Adult Transthoracic Echocardiography,” in 2016, he received a certificate as a “Heart Failure” specialist from ESC and in 2017, he was honored as a “Fellow of ESC for Heart Failure.” Branimir Kanazirev currently is a professor at the Department of Internal Medicine, Medical University of Varna.

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Prevalence of polypharmacy and risk of potential drug-drug interactions among hospitalized patients with emphasis on the pharmacokinetics

Abstract

Background:

Objective:

Methods:

Results:

Conclusion and Relevance:

Keywords

Introduction

Materials and methods

Design of the study

Used software for detection of pDDIs

Data reporting and analysis

Results

Sociodemographic and clinical characteristics of patients included in the survey

Prevalence of pDDIs detected by Lexicomp®

Potential pharmacokinetic drug-drug interactions (pPKDDIs)

Factors associated with pDDIs

Discussion

Limitations of the study

Conclusions

Footnotes

Abbreviations

Acknowledgments

Declaration of conflicting interests

Ethics approval

Informed consent

ORCID iD

Author biographies

References

Prevalence of pDDIs detected by Lexicomp^®