A Shiny App for the Probability of Technical Success of a New Molecular Entity in the Preclinical to Clinical Translational Phase
Silvia Maria Lavezzi (1), Yauheniya Cherkas (2), Nahor Haddish-Berhane (2), Shyla Jagannatha (2), Giuseppe De Nicolao (1), Daniele Ouellet (2), Italo Poggesi (3)
(1) Università degli Studi di Pavia, Italy (2) Janssen R&D, USA, (3) Janssen R&D, Italy
Objectives: Having confidence in exposure and pharmacology [1,2] is important when drug candidates are transitioning from preclinical to clinical development. Differences between animals and humans, inherent uncertainties and inter-individual variabilities should be assessed in a quantitative manner, resorting to appropriate statistical and probabilistic models. An R-Shiny application was developed to allow the computation of the probability of technical success (PTS), defined as the probability of achieving a predefined target concentration or effect using information from preclinical experiments (e.g. estimates of PK, efficacy and/or safety). The PTS application aims to inform the transition from preclinical to clinical development and the selection of the clinically relevant doses and dosing regimens.
Methods: The tool was developed as an R-Shiny application [3] (version 0.13.2), written in R code [4] (version 3.2.4). PTS can be computed based on:
- target PK or PD endpoint to ensure drug efficacy or limit toxicity (such as: “CaverageSS > 25 ng/mL” or “CmaxSS < 50 ng/mL”);
- selection of appropriate PK or PK-PD models;
- range of dose levels and/or dosing regimens (e.g., QD vs. BID);
- relevant model parameters and respective uncertainty and inter-individual variability (with the appropriate probability distributions).
PTS for each dose is estimated as the proportion of times when the desired endpoint is achieved.
Results: To restrict computational time, only PK endpoints and 1 or 2 compartment models with 0th or 1st order input were considered. In the app, different thresholds for PK metrics can be simulated and compared simultaneously. To illustrate features and performances, a PTS computation exercise is demonstrated. The desired target was defined as “CminSS>5 ng/mL”, based on a preclinical test; a 1 compartment PK model with 1st order input was used (mean±SD values in humans for clearance, volume and absorption rate constant were 12±3.6 L/h, 150±45 L, 0.5±0.4 hr-1, respectively, based on allometric scaling). The range of doses considered was 0-20 mg (every 24 hr). With the selected choices, PTS reaches 50% when dose level is between 3 and 4 mg, and exceeds 90% for doses>8 mg.
Conclusions: An R-Shiny application for PTS computation was developed and illustrated through a case study. This tool provides a quantitative assessment of PTS that informs dose selection for a prospective FIH trial to facilitate team discussions and early decision making.
References:
[1] Morgan P, Van Der Graaf PH, Arrowsmith J, Feltner DE, Drummond KS, Wegner CD, Street SD. Can the flow of medicines be improved? Fundamental pharmacokinetic and pharmacological principles toward improving Phase II survival. Drug discovery today. 2012 May 31;17(9):419-24.
[2] Cook D, Brown D, Alexander R, March R, Morgan P, Satterthwaite G, Pangalos MN. Lessons learned from the fate of AstraZeneca’s drug pipeline: a five-dimensional framework. Nat Rev Drug Discov. 2014 Jun 1;13(6):419-31.
[3] Winston Chang, Joe Cheng, JJ Allaire, Yihui Xie and Jonathan McPherson (2016). Shiny: Web Application Framework for R. R package version 0.14.1. https://CRAN.R-project.org/package=shiny
[4] R Core Team (2016). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/