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Table 1 Drug repurposing methods overview

From: Considerations and challenges for sex-aware drug repurposing

Method Description Advantages Disadvantages Examples
Data Mining Analysis of data from various sources (including peer-reviewed published experimental data, databases, screens, pharmaceutical information, EHR’s, etc.) - Crowdsource data
- Multiomic data accessible
- Reuse of previously analyzed data
- Limited data for rare diseases and understudied drugs, and dependent on large sample sizes
- Inconsistency of data structure
- Ethics/privacy (for EHR data)
- Mastermind [89]
- Pharos [90]
- Iwata H et al. 2015 [91]
- Duffy Á et al. 2020 [16]
Ligand-Binding Prediction Interactions between ligands and targets are predicted to determine suitable candidates through binding by structural and chemical simulation - Identify novel drug targets
- Identify novel compound structures
- Prior knowledge of protein function not required
- Detect possible side effects by off-target binding
- Requires target’s tertiary structure
- Experimental binding affinities often not recapitulated
- Disregards downstream effects
- Computationally expensive
- Missing biological context to allow tissue or sex-specificity
- Chupakhin V et al. 2013 [92]
- Napolitano F et al. 2013 [93]
- Vilar S et al. 2014 [94]
- Cao R et al. 2014 [95]
- Cheng F et al. 2013 [96]
Molecular Associations Molecular perturbations are associated with disease, therapeutic outcomes, or drug candidates - Elucidate drug/disease mechanisms
- Compatible with multiomic data
- Detect druggable pathways
- Exposes off-target drug effects
- High signal-to-noise ratio inhibits deconvolution of signatures
- Disregards physiological interactions
- Associations may not convey direct causations
- Dr. Insight [97]
- signatureSearch [98]
- Sanseau P et al. 2012 [99]
- Grover MP et al. 2015 [100]
Networks The relationship of genes within and between pathways provide insight for upstream and downstream drug targets that may infer treatment for a disease phenotype and/or show drug interactions within a biological system - Multiomic data
- Reveals relationships
- Determine mechanistic pathways
- Exposes off-target drug effects
- Statistically complex
- Computationally expensive
- Requires strong signal-to-noise or large datasets to deconvolute signal
- Drug2Ways [101]
- Green CS et al. 2015 [102]
Experimental—Perturbation Screens Cultured cells are treated with a variety of drugs and screened for phenotypic response - Shows gene expression as a result of perturbation
- Displays consociation between cell receptors and pharmaceuticals
- Non-predicted, in-vitro results
- Immortalized cells
- Lacks heterogeneity
- Limited microenvironment
- Costly
- LINCS L1000 profiles [103]
- Iljin K et al. 2009 [104]
- Shen M et al. 2018 [105]
Experimental—Binding Assays The chemical engagement of targets and ligands are tested in vitro to divulge repurposed candidates based on disease-target matching via affinity/thermal stabilization and structures - Physically measured drug-target binding activity
- Captures biophysical features
- Reveals promiscuous drug-target interactions
- Disregards downstream effects
- Selection of drugs and targets are much more restricted than in silico approaches due to feasibility (cost, time, and accessibility)
- Cellular ThermoStability Assay (CETSA) [106]
- Miettinen TP et al. 2014 [107]
Experimental—Animal Models Organisms are treated with drugs to model patient response and patient-specific disease-causing genetic variants can be introduced to provide more pertinent system - Recapitulates full physiological system
- Resource for multiomic data collection
- In-vivo results
- Patient-specific models allow for precision medicine
- Significant financial and time expense
- Requires narrowed-down list of candidates
- Results frequently do not translate to patient response
- Orthologous targets may vary greatly from human target structure
- UAB C-PAM [108]
- JAX Center for Precision Genetics [109]
- BCM Center for Precision Medicine Models [110]
- vivoChip [111]
- The Hollow Fiber Model [112]
  1. This table describes the methods of drug repurposing with advantages and disadvantages for each. Examples listed were methods used in studies or by consortiums and research centers