title: Tracking 20 years of compound-to-target output from literature and patents.
abstract: The statistics of drug development output and declining yield of approved medicines has been the subject of many recent reviews. However, assessing research productivity that feeds development is more difficult. Here we utilise an extensive database of structure-activity relationships extracted from papers and patents. We have used this database to analyse published compounds cumulatively linked to nearly 4000 protein target identifiers from multiple species over the last 20 years. The compound output increases up to 2005 followed by a decline that parallels a fall in pharmaceutical patenting. Counts of protein targets have plateaued but not fallen. We extended these results by exploring compounds and targets for one large pharmaceutical company. In addition, we examined collective time course data for six individual protease targets, including average molecular weight of the compounds. We also tracked the PubMed profile of these targets to detect signals related to changes in compound output. Our results show that research compound output had decreased 35% by 2012. The major causative factor is likely to be a contraction in the global research base due to mergers and acquisitions across the pharmaceutical industry. However, this does not rule out an increasing stringency of compound quality filtration and/or patenting cost control. The number of proteins mapped to compounds on a yearly basis shows less decline, indicating the cumulative published target capacity of global research is being sustained in the region of 300 proteins for large companies. The tracking of six individual targets shows uniquely detailed patterns not discernible from cumulative snapshots. These are interpretable in terms of events related to validation and de-risking of targets that produce detectable follow-on surges in patenting. Further analysis of the type we present here can provide unique insights into the process of drug discovery based on the data it actually generates.
authors.affiliation: TW2Informatics, Göteborg, Sweden.
authors.forename: Christopher
authors.initials: C
authors.lastname: Southan
authors.forename: Peter
authors.initials: P
authors.lastname: Varkonyi
authors.forename: Kiran
authors.initials: K
authors.lastname: Boppana
authors.forename: Sarma A R P
authors.initials: SA
authors.lastname: Jagarlapudi
authors.forename: Sorel
authors.initials: S
authors.lastname: Muresan
mesh_terms: D003363:Cost Control; D016208:Databases, Factual; D062313:Databases, Pharmaceutical; D055808:Drug Discovery / Q000191:economics / Q000706:statistics & numerical data* / Q000639:trends; D004345:Drug Industry; D015507:Drugs, Investigational / Q000138:chemical synthesis* / Q000494:pharmacology; D004526:Efficiency; D006801:Humans; D010330:Patents as Topic; D011506:Proteins / Q000819:agonists / Q000037:antagonists & inhibitors / Q000378:metabolism*; D039781:PubMed; D012107:Research Design; D013329:Structure-Activity Relationship; D013997:Time Factors
chemical_list: D015507:Drugs, Investigational; D011506:Proteins