2.1. Ach Modulating Activity Scoring

Anticholinergics are a class of drugs designed to provide therapeutic benefits in a variety of disease states through inhibiting muscarinic Ach receptors in the CNS and peripheral systems. However, many medications outside the anticholinergics drug class may also elicit AC pharmacologic responses through off-target interactions. 120 medications have been classified to possess AC activity by clinicians (Hester, 2011). In the first screening step, we considered all 120 medications that encompass traditional anticholinergics/antimuscarinics as well as a large number of medications that are not regarded as traditional anticholinergics/antimuscarinics.

Our approach leverages the ever-increasing wealth of publicly available bioactivity and drug safety data. ChEMBL (Gaulton et al., 2012), the largest bioactivity database in the world, contains > 1.5 million small molecules, 10,000 receptors, and 14 million bioactivity records. We have developed TargetSearch, an in-house bioinformatics web service (http://dxulab.org/software) to mine the vast amount of ChEMBL pharmacological data for relevant drug-receptor interactions including off-target polypharmacy (Xu et al., 2017). Here we used TargetSearch to score the anticholinergicity of the 120 drugs. The molecular structures of the 120 drugs were retrieved from DrugBank (Knox et al., 2011) and used as TargetSearch queries. ChEMBL was searched for either known bioactivity between a medication and 5 muscarinic Ach receptor subtypes (M1 – M5) or unknown off-target interactions via inferred structure-bioactivity relationships. If a query medication was found to have similar structure and chemical features to a bioactive molecule in the ChEMBL database, and this bioactive molecule had known bioactivity data associated with any of M1 to M5 receptors, we could infer that the medication would share similar bioactivity on the same receptors. The widely used extended connectivity fingerprint (Morgan) algorithm (Yildirim et al., 2007) was employed in the bioinformatics screening. A 10 μM bioactivity cutoff was used to ensure a higher level of confidence in identifying known and inferred relationships. When a hit was found, the receptor-specific AC scores were calculated from the Tanimoto coefficients reported by TargetSearch (Willett, 2006), which represents the drug's Ach modulating activity. The receptor-specific AC scores are in a [0, 1] range. A receptor-specific AC score of 1 indicated a medication had known bioactivity to a muscarinic Ach receptor whereas a score of 0 meant no known or inferred interaction was found. A score between 0 and 1 indicated that an inferred interaction was identified. The individual receptor subtype AC scores were averaged to give the mean AC score of a medication. This computational approach, illustrated in Fig. 2, essentially accounts for the pharmacodynamic interactions of a drug with muscarinic Ach receptors. It is fast, systematic, and has been shown to effectively capture drug off-target polypharmacy (Keiser et al., 2009) and measure drug-induced AC toxicity burden (Xu et al., 2017).

Schematic workflow of TargetSearch AC scoring using amitriptyline as an example.