Table 2.
Sex-aware drug repurposing examples
Method | Examples | Development | Sex-Aware Approach |
---|---|---|---|
Data Mining | Drug Central [149] | Database | Drug Database compilation using FDA, EMA, and PMDA; information includes active ingredients, MOA’s, indivations, pharmacological actions, regulatory data, chemical structure, and adverse drug events separated by sex to help correct for sex-bias |
AwareDX [7] | Study/Analysis | Pharmacovigilance algorithm that predicts sex-bias adverse events from FAERS data and found 20,817 sex-specific drug risks | |
“Sex differences in pharmacokinetics predict adverse drug reactions in women” ([14]) | Study/Analysis | Pharmacokinetic differences by sex are linked to sex-specific adverse drug reactions using data procured from ISI Web of Science and PubMed | |
Molecular Association | “Gender differences in the effects of cardiovascular drugs” [18] | Study/Analysis | Sex influences on pharmacokinetics, pharmacodynamics, and other physiological factors are reviewed for cardiovascular drug response |
“Brd4-bound enhancers drive cell-intrinsic sex differences in glioblastoma” [150] | Study/Analysis | Sex-specific epigenetic signatures are identified in GBM mouse astrocytes and human glioblastoma stem cells | |
“Sex-Dependent Gene Co-Expression in the Human Body” [25] | Study/Analysis | Across-tissue RNAseq analysis finds co-expression to be highly sex-dependent | |
Networks | “Population-scale identification of differential adverse events before and during a pandemic” [9] | Study/Analysis | Sex-specific desparities are presented in network analysis of adverse drug events before and during COVID-19 pandemic |
“Gene regulatory network analysis identifies sex-linked differences in colon cancer drug metabolism” [17] | Analysis using PANDA and LIONESS | Molecular differences investigated using sex-specific networks to uncover role in metabolism of drugs in colon cancer | |
“Sex Differences in Gene Expression and Regulatory Networks across 29 Human Tissues” [151] | Analysis using LIONESS | Sex biases are found in patient-specific networks in every tissue and by disease | |
“Detecting phenotype-driven transitions in regulatory network structure” [152] | Analysis using ALPACA | Sexual dimorphism are investigated in human breast tissue gene expression networks | |
Ligand-Binding Prediction | “3D pharmacophoric similarity improves multi adverse drug event identification in pharmacovigilance” [165] | Study/Analysis | Pharmaceutical 3D structure similarity predictions are combined with adverse drug events as a method that may be applied for comparing safety by sex-aware reporting |
Experimental | “Sexual differentiation of central vasopressin and vasotocin systems in vertebrates: different mechanisms, similar endpoints” [153] | Study/Analysis | Rat model is used in comparison with human model to compare sex-bias of common neuropsychiatric drug targets |
Studies, tools, and databases that have taken sex into account for drug repurposing are described here in this table. The main method is listed (as described in Table 1) as well as examples and a short explanation of how the method integrated sex-specific awareness