Fig 1.
Overview of drug effect modeling.
(1) Drugs, drug targets, genes, and gene interactions are curated from DrugBank and Reactome to form a gene-drug network. (2) The expression of each gene is modeled as a linear regression of incoming nodes, where the coefficient parameters are learned from the Connectivity Map. (3) IBD data is curated from the Gene Expression Omnibus (GEO), and for each sample (4) a drugged IBD sample is created using the network. (5) The healthy samples are averaged to create a healthy patient representation, and (6) each drugged IBD sample is compared to the healthy sample using Euclidean distance to create a personalized ranked drug list.
Fig 2.
Excerpts from dendrogram shown in S1 Fig.
Clusters of drugs emerge which share similar mechanisms, such as steroids and immunosuppressants (A), topoisomerase blockers used as antibiotics and chemotherapy (B), adrenergic and dopamine receptor drugs (C), and anti-inflammatory and immunomodulating drugs (D). Dendrogram branches are colored by the first level of the anatomic therapeutic chemical classification system (see S1 Fig for legend).
Fig 3.
Principal components analysis showing drug responders and nonresponders before and after infliximab treatment in patients with CD (A) and UC (B) from GSE16879. Responder samples after treatment appear to migrate towards the control healthy samples.
Fig 4.
(A) PCA showing untreated, methotrexate treated, and simulated methotrexate treated samples generated by NetPTP for samples in GSE45867. Samples were further visualized along PC1 (B) and PC2 (C) separately, showing that simulated methotrexate samples are located between untreated and measured methotrexate samples along PC2. Our method seems conservative in that the simulated treatment samples tend to remain closer to the untreated samples as compared to the treated samples.
Table 1.
Average drug rankings for each GEO study for CD samples.
Table 2.
Average drug rankings for each GEO study for UC samples.
Table 3.
Overall average drug rankings for CD and UC human colonic samples.
Fig 5.
Heatmaps showing drug ranks for IBD related drugs for patients with CD (A) and UC (B) from GSE16879, GSE10616, GSE36807, and GSE9686. Blue indicates a drug with higher predicted efficacy and orange indicates one with lower predicted efficacy.
Fig 6.
Heatmap showing drug ranks for IBD related drugs for two mouse IBD colonic sample studies from GEO.
Blue indicates a drug with higher predicted efficacy and orange indicates one with lower predicted efficacy.
Fig 7.
Sulfasalazine rank significantly decreases (p = 0.01) from day 2 to day 6 of DSS administration for mice colonic samples from GSE22307.
The mean is indicated by the black bars.
Table 4.
Overall average drug rankings for DSS and TNBS mouse colonic samples.
Table 5.
Logistic regression model assessing fibroplasia in TNBS mice.