Historical Evolution and Provider Awareness of Inactive Ingredients in Oral Medications

Overview

Journal Pharm Res

Specialties Pharmacology
Pharmacy

Date 2020 Oct 30

PMID 33123783

Authors

Daniel Reker

Steven M Blum

Peter Wade

Christoph Steiger

Giovanni Traverso

Affiliations

Soon will be listed here.

Abstract

Purpose: A multitude of different versions of the same medication with different inactive ingredients are currently available. It has not been quantified how this has evolved historically. Furthermore, it is unknown whether healthcare professionals consider the inactive ingredient portion when prescribing medications to patients.

Methods: We used data mining to track the number of available formulations for the same medication over time and correlate the number of available versions in 2019 to the number of manufacturers, the years since first approval, and the number of prescriptions. A focused survey among healthcare professionals was conducted to query their consideration of the inactive ingredient portion of a medication when writing prescriptions.

Results: The number of available versions of a single medication have dramatically increased in the last 40 years. The number of available, different versions of medications are largely determined by the number of manufacturers producing this medication. Healthcare providers commonly do not consider the inactive ingredient portion when prescribing a medication.

Conclusions: A multitude of available versions of the same medications provides a potentially under-recognized opportunity to prescribe the most suitable formulation to a patient as a step towards personalized medicine and mitigate potential adverse events from inactive ingredients.

References

Harris P, Taylor R, Thielke R, Payne J, Gonzalez N, Conde J . Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2008; 42(2):377-81. PMC: 2700030. DOI: 10.1016/j.jbi.2008.08.010. View

Nagel-Edwards K, Ko J . Excipient choices for special populations. Int J Pharm Compd. 2013; 12(5):426-30. View

Gibson P . Use of the low-FODMAP diet in inflammatory bowel disease. J Gastroenterol Hepatol. 2017; 32 Suppl 1:40-42. DOI: 10.1111/jgh.13695. View

Abrantes C, Duarte D, Reis C . An Overview of Pharmaceutical Excipients: Safe or Not Safe?. J Pharm Sci. 2016; 105(7):2019-26. DOI: 10.1016/j.xphs.2016.03.019. View

Wishart D, Feunang Y, Guo A, Lo E, Marcu A, Grant J . DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 2017; 46(D1):D1074-D1082. PMC: 5753335. DOI: 10.1093/nar/gkx1037. View

Kelso J . Potential food allergens in medications. J Allergy Clin Immunol. 2014; 133(6):1509-18. DOI: 10.1016/j.jaci.2014.03.011. View

Brandstetter R, Conetta R, Glazer B . Lactose intolerance associated with Intal capsules. N Engl J Med. 1986; 315(25):1613-4. DOI: 10.1056/NEJM198612183152514. View

Reker D, Blum S, Steiger C, Anger K, Sommer J, Fanikos J . "Inactive" ingredients in oral medications. Sci Transl Med. 2019; 11(483). PMC: 7122736. DOI: 10.1126/scitranslmed.aau6753. View

Pottel J, Armstrong D, Zou L, Fekete A, Huang X, Torosyan H . The activities of drug inactive ingredients on biological targets. Science. 2020; 369(6502):403-413. PMC: 7960226. DOI: 10.1126/science.aaz9906. View

10.

Eadala P, Waud J, Matthews S, Green J, Campbell A . Quantifying the 'hidden' lactose in drugs used for the treatment of gastrointestinal conditions. Aliment Pharmacol Ther. 2008; 29(6):677-87. DOI: 10.1111/j.1365-2036.2008.03889.x. View

11.

King A . Gluten Content of the Top 200 Medications: Follow-Up to the Influence of Gluten on a Patient's Medication Choices. Hosp Pharm. 2014; 48(9):736-43. PMC: 3857120. DOI: 10.1310/hpj4809-736. View

12.

Kesselheim A, Gagne J, Eddings W, Franklin J, Ross K, Fulchino L . Prevalence and Predictors of Generic Drug Skepticism Among Physicians: Results of a National Survey. JAMA Intern Med. 2016; 176(6):845-7. DOI: 10.1001/jamainternmed.2016.1688. View

13.

Reker D, Shi Y, Kirtane A, Hess K, Zhong G, Crane E . Machine Learning Uncovers Food- and Excipient-Drug Interactions. Cell Rep. 2020; 30(11):3710-3716.e4. PMC: 7179333. DOI: 10.1016/j.celrep.2020.02.094. View

14.

Aungst B . Absorption enhancers: applications and advances. AAPS J. 2011; 14(1):10-8. PMC: 3291189. DOI: 10.1208/s12248-011-9307-4. View