From: Considerations and challenges for sex-aware drug repurposing
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 |