title: Use of genetic technologies to compare medicines.
abstract: In order to ensure that patients receive the safest and most effective medicines possible, it is often necessary to compare medicines and assess the extent to which they are similar in their clinical impact. Full clinical trials with appropriate endpoints remain the only method to compare the clinical impact of two medicines with absolute certainty. Other available methods (including physicochemical analysis, genomics, and transcriptomics) can provide partial information about certain aspects of a medicine's biological impact, with possible clinical implications. Especially for biologics and non-biological complex drugs, which are more difficult to characterize by physicochemical means than small molecules, genomics and transciptomic studies can yield valuable insights for physicians, regulators, and drug developers. In this review, we cite and summarize a variety of studies that exemplify the emerging science of applying genomics and transcriptomics technologies to compare medicines. We discuss key aspects of experimental design, conduct of genetic assays, and advanced data analysis, all of which are critical for the successful execution of such studies. Finally, we propose new areas for which such studies can be applied to maximize patient benefit and reduce safety issues.
authors.affiliation: Immuneering Corporation, Cambridge, MA, USA.
authors.forename: S E
authors.initials: SE
authors.lastname: Kolitz
authors.forename: F
authors.initials: F
authors.lastname: Towfic
authors.forename: I
authors.initials: I
authors.lastname: Grossman
authors.forename: M R
authors.initials: MR
authors.lastname: Hayden
authors.forename: B
authors.initials: B
authors.lastname: Zeskind
mesh_terms: D000818:Animals; D001688:Biological Products / Q000009:adverse effects; D005821:Genetic Techniques; D006801:Humans; D004364:Pharmaceutical Preparations / Q000032:analysis*; D016896:Treatment Outcome
chemical_list: D001688:Biological Products; D004364:Pharmaceutical Preparations
keywords: biosimilars; computational biology; gene expression; generics; microarray