Drug administration via feeding tubes—a procedure that carries risks: systematic identification of critical factors based on commonly administered drugs in a cohort of stroke patients

Abstract

Abstract Purpose Drug administration via feeding tubes is considered a process with many uncertainties. This review aimed to give a comprehensive overview of data available on feeding tube application and to carry out risk assessments for drug substances commonly administered to stroke patients. Methods Drugs frequently administered via feeding tubes were identified through a retrospective analysis of discharge letters from a stroke unit. Physicochemical, pharmacokinetic, and stability properties of these drugs and data on drug-enteral nutrition interactions were systematically searched for in the European Pharmacopoeia, Hagers Handbook of Pharmaceutical Practice, Birchers clinical-pharmacological data compilation, and the Martindale Complete Drug Reference, as well as from databases including DrugBank, DrugDex, PubChem, Google Scholar, and PubMed. Results Of the drugs most commonly administered via feeding tubes in the present stroke patient cohort, bisoprolol, candesartan, and ramipril could be considered the least critical due to their overall favourable properties. Acetylsalicylic acid, amlodipine, hydrochlorothiazide, omeprazole and esomeprazole, simvastatin, and torasemide pose risks based on pH or light-dependent instability or proposed food effects. The most critical drugs to be administered via feeding tubes are considered to be furosemide, levodopa, and levothyroxine as they show relevant instabilities under administration conditions and substantial food effects; the latter two even possess a narrow therapeutic index. However, little information is available on drug-tube and drug-formula interactions. Conclusion Feeding tube administration of medications turned out to be a highly complex process with several unmet risks. Therefore, investigations that systematically assess these risk factors using clinically relevant model systems are urgently needed.