Large-scale prediction and testing of drug activity on side-effect targets

Abstract

Discovering the unintended 'off-targets' that predict adverse drug reactions is daunting by empirical methods alone. Drugs can act on several protein targets, some of which can be unrelated by conventional molecular metrics, and hundreds of proteins have been implicated in side effects. Here we use a computational strategy to predict the activity of 656 marketed drugs on 73 unintended 'side-effect' targets. Approximately half of the predictions were confirmed, either from proprietary databases unknown to the method or by new experimental assays. Affinities for these new off-targets ranged from 1 nM to 30 μM. To explore relevance, we developed an association metric to prioritize those new off-targets that explained side effects better than any known target of a given drug, creating a drug-target-adverse drug reaction network. Among these new associations was the prediction that the abdominal pain side effect of the synthetic oestrogen chlorotrianisene was mediated through its newly discovered inhibition of the enzyme cyclooxygenase-1. The clinical relevance of this inhibition was borne out in whole human blood platelet aggregation assays. This approach may have wide application to de-risking toxicological liabilities in drug discovery.

Figure 1. Predicting off-targets, and their novelty

(a) Success of SEA predictions. Known: predicted off-targets confirmed via proprietary databases. Confirmed: predictions tested in vitro achieving IC₅₀ <30 μM; Ambiguous: predictions with 25–50% activity at 30 μM. Inactive: <25% activity. (b) SEA enriched for non-trivial similarity. Drugs were binned (grey) by lowest Tc yielding valid SEA predictions. Hit rates of SEA (red) and 1NN (blue) with s.d. (c) Non-intuitive (chlorotrianisene) and straightforward (medrysone) SEA predictions, with Tc to closest references. Chlorotrianisene is only marginally similar to indomethacin, but is (correctly) predicted for COX-1. (d) Sequence similarities of each confirmed drug off-target to drug’s closest known targets.

Figure 2. Off-target networks

(a–c) Off-target networks for three drugs. Known targets of the drugs are grey while newly predicted targets are blue; the adverse events associated with each are orange and red, respectively. Red adverse events are significantly (ef > 1, q-Value < 0.05) associated with the new off-targets. Targets related by sequence are connected by grey edges. (d) Chlorotrianisene inhibits platelet aggregation. Two independent experiments (red and blue) shown for chlorotrianisene and indomethacin. Vehicle: negative control; ASA: Acetylsalicylic acid (positive control). Asterisks indicate significant (*: paired student t-test p-Value < 0.05) and highly significant (**: p-Value < 0.01) differences to vehicle control with s.d.

Figure 3. Target and drug promiscuity

(a) Target promiscuity. Targets are sorted based on the percentage of drugs hitting the target below 30 μM (indicated by numbers next to target names). Colors code for seven distinct target classes. (b) Promiscuous drugs are often hydrophobic and cationic. Each point represents one drug. Ionization at pH 7.4 and ALogP values were calculated from drug structures. Hit rate: percentage of targets the drug binds below 30 μM.