Drug–Disease Association and Drug-Repositioning Predictions in Complex Diseases Using Causal Inference–Probabilistic Matrix Factorization

The high incidence of complex diseases
has become a worldwide threat
to human health. Multiple targets and pathways are perturbed during
the pathological process of complex diseases. Systematic investigation
of complex relationship between drugs and diseases is necessary for
new association discovery and drug repurposing. For this purpose,
three causal networks were constructed herein for cardiovascular diseases,
diabetes mellitus, and neoplasms, respectively. A causal inference-probabilistic
matrix factorization (CI-PMF) approach was proposed to predict and
classify drug–disease associations, and further used for drug-repositioning
predictions. First, multilevel systematic relations between drugs
and diseases were integrated from heterogeneous databases to construct
causal networks connecting drug–target–pathway–gene–disease.
Then, the association scores between drugs and diseases were assessed
by evaluating a drug’s effects on multiple targets and pathways.
Furthermore, PMF models were learned based on known interactions,
and associations were then classified into three types by trained
models. Finally, therapeutic associations were predicted based upon
the ranking of association scores and predicted association types.
In terms of drug–disease association prediction, modified causal
inference included in CI-PMF outperformed existing causal inference
with a higher AUC (area under receiver operating characteristic curve)
score and greater precision. Moreover, CI-PMF performed better than
single modified causal inference in predicting therapeutic drug–disease
associations. In the top 30% of predicted associations, 58.6% (136/232),
50.8% (31/61), and 39.8% (140/352) hit known therapeutic associations,
while precisions obtained by the latter were only 10.2% (231/2264),
8.8% (36/411), and 9.7% (189/1948). Clinical verifications were further
conducted for the top 100 newly predicted therapeutic associations.
As a result, 21, 12, and 32 associations have been studied and many
treatment effects of drugs on diseases were investigated for cardiovascular
diseases, diabetes mellitus, and neoplasms, respectively. Related
chains in causal networks were extracted for these 65 clinical-verified
associations, and we further illustrated the therapeutic role of etodolac
in breast cancer by inferred chains. Overall, CI-PMF is a useful approach
for associating drugs with complex diseases and provides potential
values for drug repositioning.