QT time prolongation
Adverse drug events
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Explanations of the substances for patients
The changes in exposure mentioned relate to changes in the plasma concentration-time curve [AUC]. Alprazolam exposure increases to 176%, when combined with itraconazole (174%) and fluoxetine (101%). This can lead to increased side effects. Itraconazole exposure increases to 101%, when combined with alprazolam (100%) and fluoxetine (101%). We did not detect any change in exposure to fluoxetine, when combined with alprazolam (100%). We cannot currently estimate the influence of itraconazole.
The pharmacokinetic parameters of the average population are used as the starting point for calculating the individual changes in exposure due to the interactions.
Alprazolam has a high oral bioavailability [ F ] of 88%, which is why the maximum plasma levels [Cmax] tend to change little during an interaction. The terminal half-life [ t12 ] is 11.7 hours and constant plasma levels [ Css ] are reached after approximately 46.8 hours. The protein binding [ Pb ] is moderately strong at 70.2% and the volume of distribution [ Vd ] is 50 liters in the middle range, Since the substance has a low hepatic extraction rate of 0.04, displacement from protein binding [Pb] in the context of an interaction can increase exposure. The metabolism mainly takes place via CYP3A4.
Itraconazole has a mean oral bioavailability [ F ] of 55%, which is why the maximum plasma levels [Cmax] tend to change with an interaction. The terminal half-life [ t12 ] is 21 hours and constant plasma levels [ Css ] are reached after approximately 84 hours. The protein binding [ Pb ] is very strong at 99.8% and the volume of distribution [ Vd ] is very large at 796 liters, which is why, with a mean hepatic extraction rate of 0.44, both liver blood flow [Q] and a change in protein binding [Pb] are relevant. The metabolism mainly takes place via CYP3A4 and the active transport takes place in particular via PGP.
Fluoxetine has a mean oral bioavailability [ F ] of 60%, which is why the maximum plasma levels [Cmax] tend to change with an interaction. The terminal half-life [ t12 ] is 24 hours and constant plasma levels [ Css ] are reached after approximately 96 hours. The protein binding [ Pb ] is moderately strong at 94.5% and the volume of distribution [ Vd ] is very large at 2275 liters, which is why, with a mean hepatic extraction rate of 0.33, both liver blood flow [Q] and a change in protein binding [Pb] are relevant. The metabolism takes place via CYP2C19, CYP2C9, CYP2D6 and CYP3A4, among others.
|Serotonergic Effects a||2||Ø||Ø||++|
Recommendation: As a precautionary measure, symptoms of serotonergic overstimulation should be taken into account, especially after increasing the dose and at doses in the upper therapeutic range.
Rating: Fluoxetine modulates the serotonergic system to a moderate extent. The risk of a serotonergic syndrome can be classified as low with this medication if the dosage is in the usual range. According to our knowledge, neither alprazolam nor itraconazole increase serotonergic activity.
Recommendation: The risk of anticholinergic side effects such as blurred vision, confusion and tremor is increased with this therapy. If possible, the therapy should be switched or the patient should be closely monitored for other symptoms Constipation, mydriasis and reduced vigilance are monitored.
Rating: Together, fluoxetine (moderate) and alprazolam (mild) increase anticholinergic activity. According to our findings, itraconazole does not increase anticholinergic activity.
QT time prolongation
Rating: In combination, itraconazole and fluoxetine can potentially trigger ventricular arrhythmias of the torsades de pointes type. We do not know of any QT-prolonging potential for alprazolam.
General adverse effects
|Side effects||∑ frequency||alp||itr||flu|
|Coordination problem||24.8 %||24.8||n.a.||n.a.|
|Memory impairment||24.3 %||24.3||n.a.||n.a.|
|Increased appetite||19.9 %||19.9||n.a.||n.a.|
Xerostomia (19.5%): alprazolam, fluoxetine
Constipation (17.1%): alprazolam
Diarrhea (15.5%): fluoxetine, itraconazole
Loss of appetite (10.4%): fluoxetine
Dyspepsis (8%): fluoxetine
Vomiting (5%): itraconazole
Abdominal pain (2.9%): itraconazole
Dysarthria (17.1%): alprazolam
Asthenia (14%): fluoxetine
Tremor (8%): fluoxetine
Headache (6.1%): itraconazole
Confusion (6%): alprazolam
Weight gain (14.9%): alprazolam
Depression (11.7%): alprazolam
Anxiety (9%): fluoxetine
Feeling nervous (8.5%): fluoxetine
Rebound effect: alprazolam
Reduced libido (10.2%): alprazolam
Upper respiratory infection (8%): itraconazole
Sinusitis (4.5%): itraconazole
Pulmonary edema: itraconazole
Rash (6%): itraconazole
Pruritus (4%): itraconazole
Stevens johnson syndrome: alprazolam
Erythema multiforme: fluoxetine
Peripheral edema (4%): itraconazole
Hypertension (3%): itraconazole
Heart failure: itraconazole
Fever (2.5%): itraconazole
Blurred vision: fluoxetine
Liver failure: alprazolam
Prolonged bleeding time: fluoxetine
Anaphylactic reaction: fluoxetine
Hypersensitivity reaction: itraconazole
Hearing loss: itraconazole
Based on your
Abstract: Alprazolam is a short-acting triazolobenzodiazepine with anxiolytic and antidepressant properties. It has a half-life of 10-15 hours after multiple oral doses. Approximately 20% of an oral dose is excreted unchanged in the urine. The major urinary metabolites are alpha-OH alprazolam glucuronide and 3-HMB benzophenone glucuronide. The objective of this study was to characterize the reactivity of alprazolam and three metabolites in the Abbott ADx and TDx urinary benzodiazepine assays compared with the EMIT d.a.u. benzodiazepine assay. Alprazolam (at 300 ng/mL) gave an equivalent response as the 300 ng/mL low control (nordiazepam). alpha-OH alprazolam gave an equivalent response to this control between 300-500 ng/mL and 4-OH alprazolam between 500-1000 ng/mL. The 3-HMB benzophenone was not positive even at 10,000 ng/mL. The ADx screening assay was positive in 26 of 31 urine specimens collected from alprazolam-treated patients. All 31 of these specimens were confirmed positive for alpha-OH alprazolam by GC/MS after enzymatic hydrolysis and formation of a TMS derivative. For the TDx, 27 of 31 specimens were positive for benzodiazepines and all 31 were confirmed by GC/MS. All 5 of the negative ADx specimens and 4 of 5 TDx specimens contained 150-400 ng/mL of alpha-OH alprazolam. In conclusion, both the ADx and TDx urine benzodiazepine assays are acceptable screening assays for alprazolam use when the alpha-OH alprazolam concentration is greater than 400 ng/mL.
Abstract: Alprazolam, a triazolobenzodiazepine, is the first of this new class of benzodiazepine drugs to be marketed in the United States and Canada. It achieves peak serum levels in 0.7 to 2.1 hours and has a serum half-life of 12 to 15 hours. When given in the recommended daily dosage of 0.5 to 4.0 mg, it is as effective as diazepam and chlordiazepoxide as an anxiolytic agent. Its currently approved indication is for the treatment of anxiety disorders and symptoms of anxiety, including anxiety associated with depression. Although currently not approved for the treatment of depressive disorders, studies published to date have demonstrated that alprazolam compares favorably with standard tricyclic antidepressants. Also undergoing investigation is the potential role of alprazolam in the treatment of panic disorders. Alprazolam has been used in elderly patients with beneficial results and a low frequency of adverse reactions. Its primary side effect, drowsiness, is less than that produced by diazepam at comparable doses. Data on toxicity, tolerance, and withdrawal profile are limited, but alprazolam seems to be at least comparable to other benzodiazepines. Drug interaction data are also limited, and care should be exercised when prescribing alprazolam for patients taking other psychotropic drugs because of potential additive depressant effects.
Abstract: Six fasting male subjects (20-32 years of age) received an oral tablet and an IV 1.0-mg dose of alprazolam in a crossover-design study. Alprazolam plasma concentration in multiple samples during 36 h after dosing was determined by electron-capture gas-liquid chromatography. Psychomotor performance tests, digit-symbol substitution (DSS), and perceptual speed (PS) were administered at 0, 1.25, 2.25, 5.0, and 12.5 h. Sedation was assessed by the subjects and by an observer using the Stanford Sleepiness Scale and a Nurse Rating Sedation Scale (NRSS), respectively. Mean kinetic parameters after IV and oral alprazolam were as follows: volume of distribution (Vd) 0.72 and 0.84 l/kg; elimination half-life (t1/2) 11.7 and 11.8 h; clearance (Cl) 0.74 and 0.89 ml/min/kg. There were no significant differences between IV and oral alprazolam in Vd, t1/2, or area under the curve. The mean fraction absorbed after oral administration was 0.92. Performance on PS and DSS tests was impaired at 1.25 and 2.5 h, but had returned to baseline at 5.0 h for both treatments. Onset of sedation was rapid after IV administration and the average time of peak sedation was 0.48 h. Sedation scores were significantly lower during hour 1 after oral administration than after IV, but were not significantly different at later times. Alprazolam is fully available after oral administration and kinetic parameters are not affected by route of administration. With the exception of rapidity of onset, the pharmacodynamic profiles of IV and oral alprazolam are very similar after a 1.0-mg dose.
Abstract: A 48-year-old man presented to the emergency department with confusion, agitation, diaphoresis, and muscle rigidity after beginning treatment with fluoxetine, a serotonin reuptake inhibitor. He had discontinued treatment with tranylcypromine, a monoamine oxidase inhibitor, 2 weeks earlier. The constellation of findings was diagnostic of the serotonin syndrome.
Abstract: Fluoxetine is well absorbed after oral intake, is highly protein bound, and has a large volume of distribution. The elimination half-life of fluoxetine is about 1 to 4 days, while that of its metabolite norfluoxetine ranges from 7 to 15 days. Fluoxetine has a nonlinear pharmacokinetic profile. Therefore, the drug should be used with caution in patients with a reduced metabolic capability (i.e. hepatic dysfunction). In contrast with its effect on the pharmacokinetics of other antidepressants, age does not affect fluoxetine pharmacokinetics. This finding together with the better tolerability profile of fluoxetine (compared with tricyclic antidepressants) makes this drug particularly suitable for use in elderly patients with depression. Furthermore, the pharmacokinetics of fluoxetine are not affected by either obesity or renal impairment. On the basis of results of plasma concentration-clinical response relationship studies, there appears to be a therapeutic window for fluoxetine. Concentrations of fluoxetine plus norfluoxetine above 500 micrograms/L appear to be associated with a poorer clinical response than lower concentrations. Fluoxetine interacts with some other drugs. Concomitant administration of fluoxetine increased the blood concentrations of antipsychotics or antidepressants. The interactions between fluoxetine and lithium, tryptophan and monoamine oxidase inhibitors, in particular, are potentially serious, and can lead to the 'serotonergic syndrome'. This is because of synergistic pharmacodynamic effects and the influence of fluoxetine on the bioavailability of these compounds.
Abstract: No Abstract available
Abstract: BACKGROUND: Substrates and inhibitors of the cytochrome P450 isozyme CYP2D6 have overlapping structural characteristics. Two prototype serotonin uptake inhibitors, sertraline and fluoxetine, share these structural criteria and have been identified as potent inhibitors of CYP2D6 in vitro. The current study was undertaken to investigate whether genetically determined CYP2D6 activity alters the disposition of sertraline or fluoxetine or both. METHODS: Single doses of sertraline (50 mg) and fluoxetine (20 mg) were administered successively to 20 young men with high (extensive metabolizers; n = 10) and low (poor metabolizers; n = 10) CYP2D6 activity. Blood and urine samples were collected for 5 to 7 half-lives and sertraline, desmethylsertraline, fluoxetine, and norfluoxetine were determined by GC and HPLC techniques. RESULTS: Poor metabolizers had significantly greater fluoxetine peak plasma concentrations (Cmax; increases 57%), area under the concentration versus time curve (AUCzero-->infinity; increases 290%), and terminal elimination half-life (increases 216%) compared with extensive metabolizers. The total amount of fluoxetine excreted in the urine during 8 days was almost three times higher in poor metabolizers than in extensive metabolizers (719 versus 225 micrograms; p < 0.05), whereas the total amount of norfluoxetine excreted in urine of poor metabolizers was about half of that of extensive metabolizers (524 versus 1047 micrograms; p < 0.05). Norfluoxetine Cmax and AUCzero-->t were significantly smaller in poor metabolizers (decreases 55% and decreases 53%, respectively), and the partial metabolic clearance of fluoxetine into norfluoxetine was 10 times smaller in this group (4.3 +/- 1.9 versus 0.4 +/- 0.1 L/hr; p < 0.05). No significant differences between extensive and poor metabolizers were found for sertraline and desmethylsertraline pharmacokinetics. CONCLUSION: These data indicate that poor metabolizers accumulate fluoxetine but not sertraline and that CYP2D6 plays an important role in the demethylation of fluoxetine but not of sertraline.
Abstract: No Abstract available
Abstract: AIMS: The study was designed to investigate whether genetically determined CYP2C19 activity affects the metabolism of fluoxetine in healthy subjects. METHODS: A single oral dose of fluoxetine (40 mg) was administrated successively to 14 healthy young men with high (extensive metabolizers, n=8) and low (poor metabolizers, n = 6) CYP2C19 activity. Blood samples were collected for 5-7 half-lives and fluoxetine, and norfluoxetine were determined by reversed-phase high performance liquid chromatography. RESULTS: Poor metabolizers (PMs) showed a mean 46% increase in fluoxetine peak plasma concentrations (Cmax, P < 0.001), 128% increase in area under the concentration vs time curve (AUC(0, infinity), P < 0.001), 113% increase in terminal elimination half-life (t(1/2)) (P < 0.001), and 55% decrease in CLo (P < 0.001) compared with extensive metabolizers (EMs). Mean +/- (s.d) norfluoxetine AUC(0, 192 h) was significantly lower in PMs than that in EMs (1343 +/- 277 vs 2935 +/- 311, P < 0.001). Mean fluoxetine Cmax and AUC(0, infinity) in wild-type homozygotes (CYP2C19*1/CYP2C19*1) were significantly lower than that in PMs (22.4 +/- 3.9 vs 36.7 +/- 8.9, P < 0.001; 732 +/- 42 vs 2152 +/- 492, P < 0.001, respectively). Mean oral clearance in individuals with the wild type homozygous genotype was significantly higher than that in heterozygotes and that in PMs (54.7 +/- 3.4 vs 36.0 +/- 8.7, P < 0.01; 54.7 +/- 3.4 vs 20.6 +/- 6.2, P < 0.001, respectively). Mean norfluoxetine AUC(0, 192 h) in PMs was significantly lower than that in wild type homozygotes (1343 +/- 277 vs 3163 +/- 121, P < 0.05) and that in heterozygotes (1343 +/- 277 vs 2706 +/- 273, P < 0.001), respectively. CONCLUSIONS: The results indicated that CYP2C19 appears to play a major role in the metabolism of fluoxetine, and in particular its N-demethylation among Chinese healthy subjects.
Abstract: Itraconazole (ITZ) is a potent inhibitor of CYP3A in vivo. However, unbound plasma concentrations of ITZ are much lower than its reported in vitro Ki, and no clinically significant interactions would be expected based on a reversible mechanism of inhibition. The purpose of this study was to evaluate the reasons for the in vitro-in vivo discrepancy. The metabolism of ITZ by CYP3A4 was studied. Three metabolites were detected: hydroxy-itraconazole (OH-ITZ), a known in vivo metabolite of ITZ, and two new metabolites: keto-itraconazole (keto-ITZ) and N-desalkyl-itraconazole (ND-ITZ). OHITZ and keto-ITZ were also substrates of CYP3A4. Using a substrate depletion kinetic approach for parameter determination, ITZ exhibited an unbound K(m) of 3.9 nM and an intrinsic clearance (CLint) of 69.3 ml.min(-1).nmol CYP3A4(-1). The respective unbound Km values for OH-ITZ and keto-ITZ were 27 nM and 1.4 nM and the CLint values were 19.8 and 62.5 ml.min(-1).nmol CYP3A4(-1). Inhibition of CYP3A4 by ITZ, OH-ITZ, keto-ITZ, and ND-ITZ was evaluated using hydroxylation of midazolam as a probe reaction. Both ITZ and OH-ITZ were competitive inhibitors of CYP3A4, with unbound Ki (1.3 nM for ITZ and 14.4 nM for OH-ITZ) close to their respective Km. ITZ, OH-ITZ, keto-ITZ and ND-ITZ exhibited unbound IC50 values of 6.1 nM, 4.6 nM, 7.0 nM, and 0.4 nM, respectively, when coincubated with human liver microsomes and midazolam (substrate concentration < Km). These findings demonstrate that ITZ metabolites are as potent as or more potent CYP3A4 inhibitors than ITZ itself, and thus may contribute to the inhibition of CYP3A4 observed in vivo after ITZ dosing.
Abstract: OBJECTIVE: Our objective was to evaluate the effect of the CYP3A5 genotype on the pharmacokinetics and pharmacodynamics of alprazolam in healthy volunteers. METHODS: Nineteen healthy male volunteers were divided into 3 groups on the basis of the genetic polymorphism of CYP3A5. The groups comprised subjects with CYP3A5*1/*1 (n=5), CYP3A5*1/*3 (n=7), or CYP3A5*3/*3 (n=7). After a single oral 1-mg dose of alprazolam, plasma concentrations of alprazolam were measured up to 72 hours, together with assessment of psychomotor function by use of the Digit Symbol Substitution Test, according to CYP3A5 genotype. RESULTS: The area under the plasma concentration-time curve for alprazolam was significantly greater in subjects with CYP3A5*3/*3 (830.5+/-160.4 ng . h/mL [mean+/-SD]) than in those with CYP3A5*1/*1 (599.9+/-141.0 ng . h/mL) (P=.030). The oral clearance of alprazolam was also significantly different between the CYP3A5*1/*1 group (3.5+/-0.8 L/h) and CYP3A5*3/*3 group (2.5+/-0.5 L/h) (P=.036). Although a trend was noted for the area under the Digit Symbol Substitution Test score change-time curve (area under the effect curve) to be greater in subjects with CYP3A5*3/*3 (177.2+/-84.6) than in those with CYP3A5*1/*1 (107.5+/-44), the difference did not reach statistical significance (P=.148). CONCLUSIONS: The CYP3A5*3 genotype affects the disposition of alprazolam and thus influences the plasma levels of alprazolam.
Abstract: Itraconazole (ITZ) is metabolized in vitro to three inhibitory metabolites: hydroxy-itraconazole (OH-ITZ), keto-itraconazole (keto-ITZ), and N-desalkyl-itraconazole (ND-ITZ). The goal of this study was to determine the contribution of these metabolites to drug-drug interactions caused by ITZ. Six healthy volunteers received 100 mg ITZ orally for 7 days, and pharmacokinetic analysis was conducted at days 1 and 7 of the study. The extent of CYP3A4 inhibition by ITZ and its metabolites was predicted using this data. ITZ, OH-ITZ, keto-ITZ, and ND-ITZ were detected in plasma samples of all volunteers. A 3.9-fold decrease in the hepatic intrinsic clearance of a CYP3A4 substrate was predicted using the average unbound steady-state concentrations (C(ss,ave,u)) and liver microsomal inhibition constants for ITZ, OH-ITZ, keto-ITZ, and ND-ITZ. Accounting for circulating metabolites of ITZ significantly improved the in vitro to in vivo extrapolation of CYP3A4 inhibition compared to a consideration of ITZ exposure alone.
Abstract: PURPOSE: The objective is to confirm if the prediction of the drug-drug interaction using a physiologically based pharmacokinetic (PBPK) model is more accurate. In vivo Ki values were estimated using PBPK model to confirm whether in vitro Ki values are suitable. METHOD: The plasma concentration-time profiles for the substrate with coadministration of an inhibitor were collected from the literature and were fitted to the PBPK model to estimate the in vivo Ki values. The AUC ratios predicted by the PBPK model using in vivo Ki values were compared with those by the conventional method assuming constant inhibitor concentration. RESULTS: The in vivo Ki values of 11 inhibitors were estimated. When the in vivo Ki values became relatively lower, the in vitro Ki values were overestimated. This discrepancy between in vitro and in vivo Ki values became larger with an increase in lipophilicity. The prediction from the PBPK model involving the time profile of the inhibitor concentration was more accurate than the prediction by the conventional methods. CONCLUSION: A discrepancy between the in vivo and in vitro Ki values was observed. The prediction using in vivo Ki values and the PBPK model was more accurate than the conventional methods.
Abstract: BACKGROUND: Serotonin syndrome is a rare but serious complication of treatment with serotonergic agents. In its severe manifestations, death can ensue. Early recognition and aggressive management are crucial to mitigating the syndrome. Often the presentation can be subtle and easy to miss. CASE REPORTS: We present 2 cases of serotonin syndrome seen in the psychiatric consultation service of a busy academic hospital. Both patients had favorable outcomes because of early recognition and aggressive management. CONCLUSION: Physicians should carefully consider and rule out the clinical diagnosis of serotonin syndrome when presented with an agitated or confused patient who is taking serotonergic agents.
Abstract: Transporters in proximal renal tubules contribute to the disposition of numerous drugs. Furthermore, the molecular mechanisms of tubular secretion have been progressively elucidated during the past decades. Organic anions tend to be secreted by the transport proteins OAT1, OAT3 and OATP4C1 on the basolateral side of tubular cells, and multidrug resistance protein (MRP) 2, MRP4, OATP1A2 and breast cancer resistance protein (BCRP) on the apical side. Organic cations are secreted by organic cation transporter (OCT) 2 on the basolateral side, and multidrug and toxic compound extrusion (MATE) proteins MATE1, MATE2/2-K, P-glycoprotein, organic cation and carnitine transporter (OCTN) 1 and OCTN2 on the apical side. Significant drug-drug interactions (DDIs) may affect any of these transporters, altering the clearance and, consequently, the efficacy and/or toxicity of substrate drugs. Interactions at the level of basolateral transporters typically decrease the clearance of the victim drug, causing higher systemic exposure. Interactions at the apical level can also lower drug clearance, but may be associated with higher renal toxicity, due to intracellular accumulation. Whereas the importance of glomerular filtration in drug disposition is largely appreciated among clinicians, DDIs involving renal transporters are less well recognized. This review summarizes current knowledge on the roles, quantitative importance and clinical relevance of these transporters in drug therapy. It proposes an approach based on substrate-inhibitor associations for predicting potential tubular-based DDIs and preventing their adverse consequences. We provide a comprehensive list of known drug interactions with renally-expressed transporters. While many of these interactions have limited clinical consequences, some involving high-risk drugs (e.g. methotrexate) definitely deserve the attention of prescribers.
Abstract: The accurate estimation of "in vivo" inhibition constants () of inhibitors and fraction metabolized () of substrates is highly important for drug-drug interaction (DDI) prediction based on physiologically based pharmacokinetic (PBPK) models. We hypothesized that analysis of the pharmacokinetic alterations of substrate metabolites in addition to the parent drug would enable accurate estimation of in vivoandTwenty-four pharmacokinetic DDIs caused by P450 inhibition were analyzed with PBPK models using an emerging parameter estimation method, the cluster Newton method, which enables efficient estimation of a large number of parameters to describe the pharmacokinetics of parent and metabolized drugs. For each DDI, two analyses were conducted (with or without substrate metabolite data), and the parameter estimates were compared with each other. In 17 out of 24 cases, inclusion of substrate metabolite information in PBPK analysis improved the reliability of bothandImportantly, the estimatedfor the same inhibitor from different DDI studies was generally consistent, suggesting that the estimatedfrom one study can be reliably used for the prediction of untested DDI cases with different victim drugs. Furthermore, a large discrepancy was observed between the reported in vitroand the in vitro estimates for some inhibitors, and the current in vivoestimates might be used as reference values when optimizing in vitro-in vivo extrapolation strategies. These results demonstrated that better use of substrate metabolite information in PBPK analysis of clinical DDI data can improve reliability of top-down parameter estimation and prediction of untested DDIs.
Abstract: BACKGROUND: Anticholinergic drugs put elderly patients at a higher risk for falls, cognitive decline, and delirium as well as peripheral adverse reactions like dry mouth or constipation. Prescribers are often unaware of the drug-based anticholinergic burden (ACB) of their patients. This study aimed to develop an anticholinergic burden score for drugs licensed in Germany to be used by clinicians at prescribing level. METHODS: A systematic literature search in pubmed assessed previously published ACB tools. Quantitative grading scores were extracted, reduced to drugs available in Germany, and reevaluated by expert discussion. Drugs were scored as having no, weak, moderate, or strong anticholinergic effects. Further drugs were identified in clinical routine and included as well. RESULTS: The literature search identified 692 different drugs, with 548 drugs available in Germany. After exclusion of drugs due to no systemic effect or scoring of drug combinations (n = 67) and evaluation of 26 additional identified drugs in clinical routine, 504 drugs were scored. Of those, 356 drugs were categorised as having no, 104 drugs were scored as weak, 18 as moderate and 29 as having strong anticholinergic effects. CONCLUSIONS: The newly created ACB score for drugs authorized in Germany can be used in daily clinical practice to reduce potentially inappropriate medications for elderly patients. Further clinical studies investigating its effect on reducing anticholinergic side effects are necessary for validation.