We investigate whether explicit syntactic features improve transformer-based biomedical relation extraction when added to typed entity marker pooling. We evaluate two augmentation strategies on top of BiomedBERT: (1) verb token augmentation, which concatenates the hidden state of the dependency root verb to the entity representations, and (2) a two-layer graph convolutional network (GCN) that refines encoder hidden states over the dependency parse before entity pooling. We experimented on three biomedical datasets: ChemProt, DDI, and AIMed with three random seeds. We found neither strategy consistently outperformed the entity-only baseline. The GCN yielded modest gains on AIMed (+0.007 F1) and ChemProt (+0.003 F1) but decreased performance on DDI (-0.013 F1). Verb token augmentation helps only on AIMed (+0.004 F1) and underperforms on the other two datasets. A syntactic characterization of the datasets reveals that DDI has substantially higher passive voice usage (50.7\% of relation-bearing sentences) than AIMed (27.0\%) or ChemProt (30.9\%), suggesting that syntactic augmentation is more effective when sentences exhibit active verbal structure with semantically informative predicates. These results suggest that corpus-level syntactic characteristics, particularly passive voice usage, may moderate the utility of explicit syntactic augmentation, though the small magnitude of observed differences warrants caution in interpretation.