title: Quantifying and Communicating Uncertainty in Preclinical Human Dose-Prediction.
abstract: Human dose-prediction is fundamental for ranking lead-optimization compounds in drug discovery and to inform design of early clinical trials. This tutorial describes how uncertainty in such predictions can be quantified and efficiently communicated to facilitate decision-making. Using three drug-discovery case studies, we show how several uncertain pieces of input information can be integrated into one single uncomplicated plot with key predictions, including their uncertainties, for many compounds or for many scenarios, or both.
authors.affiliation: CVMD iMed DMPK, AstraZeneca R&D SE-431, 83, Mölndal, Sweden.
authors.forename: M
authors.initials: M
authors.lastname: Sundqvist
authors.affiliation: CVMD iMed DMPK, AstraZeneca R&D SE-431, 83, Mölndal, Sweden.
authors.forename: A
authors.initials: A
authors.lastname: Lundahl
authors.affiliation: CVMD iMed DMPK, AstraZeneca R&D SE-431, 83, Mölndal, Sweden.
authors.forename: M B
authors.initials: MB
authors.lastname: Någård
authors.affiliation: CVMD iMed DMPK, AstraZeneca R&D SE-431, 83, Mölndal, Sweden.
authors.forename: U
authors.initials: U
authors.lastname: Bredberg
authors.affiliation: CVMD iMed DMPK, AstraZeneca R&D SE-431, 83, Mölndal, Sweden.
authors.forename: P
authors.initials: P
authors.lastname: Gennemark