Introduction Translating findings from cell and animal models to the clinic is difficult because these systems insufficiently capture human disease biology. Computational approaches, notably pathways modeling, have overcome interspecies differences by mapping shared signaling pathways between species. We considered whether this approach could be generalized to bridge animal and virtual drug screening data to effects measured in patients. We emphasized translational psychiatry datasets because of the unmet need for effective therapies. Methods We conducted three parallel analyses using published drug screening data in zebrafish, structure-function computational screening data, and clinically-reported efficacy data for experimental and approved antipsychotic drugs and predicted drug pathways using the PathFX algorithm Results Despite choosing screens developed for unique aspects of psychiatry and without careful curation of shared drugs, we found pathways associated with drugs that performed “well” across these distinct platforms—specifically, among drugs measured as favorable (expected to improve disease) or unfavorable (expected to aggravate disease) across screens, these drugs connected to distinct pathway proteins; however, these drugs are connected to similar protein families and shared Gene Ontology functional terms. By comparing screens, we discovered that favorable drugs may influence G-proteins, solute carrier family proteins, adrenoceptors, and steroid hydroxylase activity and that unfavorable drugs affect serotonin receptors and phosphodiesterase activity. Discussion This suggests that predictive platforms could emphasize functional information as features that could overcome differences in distinct screening platforms to eventually improve translational approaches.
Su et al. (Wed,) studied this question.