Gender differences in drug therapy

Abstract

Therapeutics has steadily evolved over the last two decades, from a broad system designed to target multiple patients with one management approach to a personalized model that focusses on the characteristics of each individual patient and tailors treatment accordingly. In order to achieve the maximum benefit and minimum risk, personalized medicine needs to address multiple individual parameters including pathology, physiology, genetics, comorbidity, sex and gender. While considerable personalized pharmacotherapeutic advances have been made in all fields, sex and gender have somewhat lagged behind. This has possibly been the result of a long-standing premise that females of childbearing age should be generally omitted from clinical trials. This is now changing. For several years, drug regulatory agencies have been advocating the inclusion of both sexes in preclinical and clinical trial design, ensuring adequate statistical power to discriminate sexgender differences. This has led to a gradual but steady increase in published data, which highlights sexually dimorphic differences in the pharmacokinetics, pharmacodynamics, and pharmacogenetics of several drugs. Male and female drug metabolism patterns, gene expression profiles, drug interaction pathways, and adverse drug reaction reporting have all contributed to a corpus of knowledge that needs to be constantly expanded and solidified. Furthermore, this information needs to be understood within the context of gender-bias in adherence to therapy and adverse effect reporting. Adherence to medication is a complex matter based on multiple factors including socioeconomic, health system, and patient-specific factors.

Further sex-gender research is required to consolidate and expand current data, within bench-to-bedside translational application frameworks. This needs to be incorporated into all commercial drug development stages. The inclusion of sex-gender differences in independent research needs to be incentivized through peer-review guidelines, grant application priorities and institutional review boards. This drives prospective study design towards research models capable of generating information that contributes towards the inclusion of sex-gender differences in personalized medicine treatment algorithms.