title: Estimation of Maximum Recommended Therapeutic Dose Using Predicted Promiscuity and Potency.
abstract: We report a simple model that predicts the maximum recommended therapeutic dose (MRTD) of small molecule drugs based on an assessment of likely protein-drug interactions. Previously, we reported methods for computational estimation of drug promiscuity and potency. We used these concepts to build a linear model derived from 238 small molecular drugs to predict MRTD. We applied this model successfully to predict MRTDs for 16 nonsteroidal antiinflammatory drugs (NSAIDs) and 14 antiretroviral drugs. Of note, based on the estimated promiscuity of low-dose drugs (and active chemicals), we identified 83 proteins as "high-risk off-targets" (HROTs) that are often associated with low doses; the evaluation of interactions with HROTs may be useful during early phases of drug discovery. Our model helps explain the MRTD for drugs with severe adverse reactions caused by interactions with HROTs.
authors.affiliation: Department of Genetics, Stanford University, Stanford, California, USA.
authors.forename: T
authors.initials: T
authors.lastname: Liu
authors.affiliation: Department of Internal Medicine, Translational Informatics Division, University of New Mexico, Albuquerque, New Mexico, USA.
authors.forename: T
authors.initials: T
authors.lastname: Oprea
authors.affiliation: Department of Internal Medicine, Translational Informatics Division, University of New Mexico, Albuquerque, New Mexico, USA.
authors.forename: O
authors.initials: O
authors.lastname: Ursu
authors.affiliation: PureInfo Discovery Corp, Albuquerque, New Mexico, USA.
authors.forename: C
authors.initials: C
authors.lastname: Hasselgren
authors.affiliation: Departments of Bioengineering and Genetics, Stanford University, Stanford, California, USA.
authors.forename: R B
authors.initials: RB
authors.lastname: Altman
mesh_terms: D004305:Dose-Response Relationship, Drug; D055808:Drug Discovery; D006801:Humans; D016014:Linear Models