Voorzitter Raad van Bestuur; projectleider Patiëntveiligheid; hoogleraar 'Patiëntveiligheid', Vrije Universiteit / Amsterdam UMC
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Metabolic drug interactions - the impact of prescribed drug regimens on the medication safety.
Fialova, D., Vrbensky, K., Topinkova, E., Vlcek, J., Soerbye, L.W., Wagner, C., Bernabei, R. Metabolic drug interactions - the impact of prescribed drug regimens on the medication safety. Pharmacy World & Science: 2005, 27(5) A23-A24. Abstract, Proceedings of the 5th Spring Conference on Clinical Pharmacy Integrating Research, Education & Clinical Practice, Stockholm, Sweden, 25-28 May 2005.
Background and objective: Risk/benefit profile of prescribed drug regimens is unkown. Over 60% of
commonly used medications interact on metabolic pathways (cytochrom P450 (CYP450), uridyl-glucuronyl
tranferasis (UGT I, II) and P-glycoprotein (PGP) transport). Using an up-to-date knowledge on metabolic drug interactions, we aimed the study to determine the medication risk of commonly prescribed drug combinations. Design: A mathematical model was created to estimate the medication risk in various drug combinations considering (1) metabolic properties of medications prescribed in the sample, (2) medications' affinity to major and alternate metabolic pathways, (3) metabolic capacity of the enzymatic systems and their feasibility to inhibition or induction (CYP2C19, CYP3A4, CYP2D6, CYP2C8/9, CYP1A2, UGT1 and 2, PGP) and (4) medication potential to induct or inhibit metabolic pathways. The dataset of the EU ADHOC project (Aged in Home Care, 2001, 2003) was used, especially comprehensive medication data on the representative samples of home care elderly in the Czech Republic (CZ), Norway (NO) and Netherlands (NL); 1015 patients 65 (CZ=430, NO=388 and NL=197). Main Outcome Measures: 1. Proportion of drug combinations with potentially harmful methabolic interactions; 2. medications having high potential to frequently reachtnigher plasmatic levels in various drug combinations (in o ver 90% fo drug combinations); 3. medications having high potential to seriously increase drug level (about 95% and more of expected level) in prescribed drug combinations.Results: Potentially harmful metabolics interaction were identified in 40% (n=460) of prescribed drug combinations (CZ 65.8%, NL 41,1%, NO 35.6%) mostly on CYP3A$ or CYP2D2 metabolic pathways. High potential to frequently increase drug levels in various combinations was documented for omeprazol, glibenclamide, blimepiride, metoprolol, ibuprofen, dilthiazem, tolbutamide,
flunitrazepam and zolpidem. Among medications which seriously increase drug levels in different drug
combinations were determined: acetylsalicylic acidm, glipizid, nifedipin, metoprolol, omeprazol, amlopidin, ibuprofen, alprazolam, oxybutynin, flunitrazepam, glimepirid, thiaprofenic, acid, ciniarizin, verapamil and digoxin. Conclusions: Several commonly used medications were documented to frequently or seriously increase drug levels in prescribed drug combinations. Further studies are needed to support our findings. (aut.ref.)
commonly used medications interact on metabolic pathways (cytochrom P450 (CYP450), uridyl-glucuronyl
tranferasis (UGT I, II) and P-glycoprotein (PGP) transport). Using an up-to-date knowledge on metabolic drug interactions, we aimed the study to determine the medication risk of commonly prescribed drug combinations. Design: A mathematical model was created to estimate the medication risk in various drug combinations considering (1) metabolic properties of medications prescribed in the sample, (2) medications' affinity to major and alternate metabolic pathways, (3) metabolic capacity of the enzymatic systems and their feasibility to inhibition or induction (CYP2C19, CYP3A4, CYP2D6, CYP2C8/9, CYP1A2, UGT1 and 2, PGP) and (4) medication potential to induct or inhibit metabolic pathways. The dataset of the EU ADHOC project (Aged in Home Care, 2001, 2003) was used, especially comprehensive medication data on the representative samples of home care elderly in the Czech Republic (CZ), Norway (NO) and Netherlands (NL); 1015 patients 65 (CZ=430, NO=388 and NL=197). Main Outcome Measures: 1. Proportion of drug combinations with potentially harmful methabolic interactions; 2. medications having high potential to frequently reachtnigher plasmatic levels in various drug combinations (in o ver 90% fo drug combinations); 3. medications having high potential to seriously increase drug level (about 95% and more of expected level) in prescribed drug combinations.Results: Potentially harmful metabolics interaction were identified in 40% (n=460) of prescribed drug combinations (CZ 65.8%, NL 41,1%, NO 35.6%) mostly on CYP3A$ or CYP2D2 metabolic pathways. High potential to frequently increase drug levels in various combinations was documented for omeprazol, glibenclamide, blimepiride, metoprolol, ibuprofen, dilthiazem, tolbutamide,
flunitrazepam and zolpidem. Among medications which seriously increase drug levels in different drug
combinations were determined: acetylsalicylic acidm, glipizid, nifedipin, metoprolol, omeprazol, amlopidin, ibuprofen, alprazolam, oxybutynin, flunitrazepam, glimepirid, thiaprofenic, acid, ciniarizin, verapamil and digoxin. Conclusions: Several commonly used medications were documented to frequently or seriously increase drug levels in prescribed drug combinations. Further studies are needed to support our findings. (aut.ref.)
Background and objective: Risk/benefit profile of prescribed drug regimens is unkown. Over 60% of
commonly used medications interact on metabolic pathways (cytochrom P450 (CYP450), uridyl-glucuronyl
tranferasis (UGT I, II) and P-glycoprotein (PGP) transport). Using an up-to-date knowledge on metabolic drug interactions, we aimed the study to determine the medication risk of commonly prescribed drug combinations. Design: A mathematical model was created to estimate the medication risk in various drug combinations considering (1) metabolic properties of medications prescribed in the sample, (2) medications' affinity to major and alternate metabolic pathways, (3) metabolic capacity of the enzymatic systems and their feasibility to inhibition or induction (CYP2C19, CYP3A4, CYP2D6, CYP2C8/9, CYP1A2, UGT1 and 2, PGP) and (4) medication potential to induct or inhibit metabolic pathways. The dataset of the EU ADHOC project (Aged in Home Care, 2001, 2003) was used, especially comprehensive medication data on the representative samples of home care elderly in the Czech Republic (CZ), Norway (NO) and Netherlands (NL); 1015 patients 65 (CZ=430, NO=388 and NL=197). Main Outcome Measures: 1. Proportion of drug combinations with potentially harmful methabolic interactions; 2. medications having high potential to frequently reachtnigher plasmatic levels in various drug combinations (in o ver 90% fo drug combinations); 3. medications having high potential to seriously increase drug level (about 95% and more of expected level) in prescribed drug combinations.Results: Potentially harmful metabolics interaction were identified in 40% (n=460) of prescribed drug combinations (CZ 65.8%, NL 41,1%, NO 35.6%) mostly on CYP3A$ or CYP2D2 metabolic pathways. High potential to frequently increase drug levels in various combinations was documented for omeprazol, glibenclamide, blimepiride, metoprolol, ibuprofen, dilthiazem, tolbutamide,
flunitrazepam and zolpidem. Among medications which seriously increase drug levels in different drug
combinations were determined: acetylsalicylic acidm, glipizid, nifedipin, metoprolol, omeprazol, amlopidin, ibuprofen, alprazolam, oxybutynin, flunitrazepam, glimepirid, thiaprofenic, acid, ciniarizin, verapamil and digoxin. Conclusions: Several commonly used medications were documented to frequently or seriously increase drug levels in prescribed drug combinations. Further studies are needed to support our findings. (aut.ref.)
commonly used medications interact on metabolic pathways (cytochrom P450 (CYP450), uridyl-glucuronyl
tranferasis (UGT I, II) and P-glycoprotein (PGP) transport). Using an up-to-date knowledge on metabolic drug interactions, we aimed the study to determine the medication risk of commonly prescribed drug combinations. Design: A mathematical model was created to estimate the medication risk in various drug combinations considering (1) metabolic properties of medications prescribed in the sample, (2) medications' affinity to major and alternate metabolic pathways, (3) metabolic capacity of the enzymatic systems and their feasibility to inhibition or induction (CYP2C19, CYP3A4, CYP2D6, CYP2C8/9, CYP1A2, UGT1 and 2, PGP) and (4) medication potential to induct or inhibit metabolic pathways. The dataset of the EU ADHOC project (Aged in Home Care, 2001, 2003) was used, especially comprehensive medication data on the representative samples of home care elderly in the Czech Republic (CZ), Norway (NO) and Netherlands (NL); 1015 patients 65 (CZ=430, NO=388 and NL=197). Main Outcome Measures: 1. Proportion of drug combinations with potentially harmful methabolic interactions; 2. medications having high potential to frequently reachtnigher plasmatic levels in various drug combinations (in o ver 90% fo drug combinations); 3. medications having high potential to seriously increase drug level (about 95% and more of expected level) in prescribed drug combinations.Results: Potentially harmful metabolics interaction were identified in 40% (n=460) of prescribed drug combinations (CZ 65.8%, NL 41,1%, NO 35.6%) mostly on CYP3A$ or CYP2D2 metabolic pathways. High potential to frequently increase drug levels in various combinations was documented for omeprazol, glibenclamide, blimepiride, metoprolol, ibuprofen, dilthiazem, tolbutamide,
flunitrazepam and zolpidem. Among medications which seriously increase drug levels in different drug
combinations were determined: acetylsalicylic acidm, glipizid, nifedipin, metoprolol, omeprazol, amlopidin, ibuprofen, alprazolam, oxybutynin, flunitrazepam, glimepirid, thiaprofenic, acid, ciniarizin, verapamil and digoxin. Conclusions: Several commonly used medications were documented to frequently or seriously increase drug levels in prescribed drug combinations. Further studies are needed to support our findings. (aut.ref.)