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Accelerated Predictive Healthcare Analytics with Pumas, a High Performance Pharmaceutical Modeling and Simulation Platform

Rackauckas, Chris and Ma, Yingbo and Noack, Andreas and Dixit, Vaibhav and Mogensen, Patrick Kofod and Byrne, Simon and Maddhashiya, Shubham and Santiago Calderón, José Bayoán and Nyberg, Joakim and Gobburu, Jogarao V. S. and Ivaturi, Vijay (2020) Accelerated Predictive Healthcare Analytics with Pumas, a High Performance Pharmaceutical Modeling and Simulation Platform. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20201207-122822501

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Abstract

Pharmacometric modeling establishes causal quantitative relationship between administered dose, tissue exposures, desired and undesired effects and patient’s risk factors. These models are employed to de-risk drug development and guide precision medicine decisions. Recent technological advances rendered collecting real-time and detailed data easy. However, the pharmacometric tools have not been designed to handle heterogeneous, big data and complex models. The estimation methods are outdated to solve modern healthcare challenges. We set out to design a platform that facilitates domain specific modeling and its integration with modern analytics to foster innovation and readiness to data deluge in healthcare. New specialized estimation methodologies have been developed that allow dramatic performance advances in areas that have not seen major improvements in decades. New ODE solver algorithms, such as coefficient-optimized higher order integrators and new automatic stiffness detecting algorithms which are robust to frequent discontinuities, give rise to up to 4x performance improvements across a wide range of stiff and non-stiff systems seen in pharmacometric applications. These methods combine with JIT compiler techniques and further specialize the solution process on the individual systems, allowing statically-sized optimizations and discrete sensitivity analysis via forward-mode automatic differentiation, to further enhance the accuracy and performance of the solving and parameter estimation process. We demonstrate that when all of these techniques are combined with a validated clinical trial dosing mechanism and non-compartmental analysis (NCA) suite, real applications like NLME parameter estimation see run times halved while retaining the same accuracy. Meanwhile in areas with less prior optimization of software, like optimal experimental design, we see orders of magnitude performance enhancements. Together we show a fast and modern domain specific modeling framework which lays a platform for innovation via upcoming integrations with modern analytics.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
https://doi.org/10.1101/2020.11.28.402297DOIDiscussion Paper
ORCID:
AuthorORCID
Mogensen, Patrick Kofod0000-0001-5850-0663
Additional Information:The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license. This version posted November 30, 2020. The Pumas project was started in 2017 with the support of the University of Maryland Baltimore School of Pharmacy (UMB). Professor Jill Morgan, the chair of Pharmacy Practice and Science (PPS) and Dean Natalie Eddington were instrumental in their support. The staff, faculty, students and researchers at the Center for Translational Medicine in UMB drove a lot of the initial testing and we are extremely thankful for their patience while adopting a new language and tool. Brian Corrigan from Pfizer who encouraged us throughout the journey and nudged the team to achieve new heights. All the early adopters and believers. Viral Shah and Deepak Vinchii from Julia Computing for being our trusted technology collaborators. Last but the most important thank you goes out to the members of the Julia Language community who develop world class scientific computing packages and create an environment that is conducive to newcomers. Competing Interest Statement: Pumas is a proprietary software developed by Pumas-AI Inc. Authors Rackauckas, Ma, Noack, Dixit, Mogensen, Byrne, Maddhashiya, Calderon, Nyberg, Gobburu, and Ivaturi all are or have been affiliated with Pumas-AI Inc. in the past 36 months.
Funders:
Funding AgencyGrant Number
University of MarylandUNSPECIFIED
Subject Keywords:pharmacometrics, nonlinear mixed effects models, pharmacokinetics, pharmacodynamics, differential equations, high-performance computing, parallelism, optimal design, noncompartmental analysis, stiffness, just-in-time compilation, Julia
Record Number:CaltechAUTHORS:20201207-122822501
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20201207-122822501
Official Citation:Accelerated Predictive Healthcare Analytics with Pumas, a High Performance Pharmaceutical Modeling and Simulation Platform. Chris Rackauckas, Yingbo Ma, Andreas Noack, Vaibhav Dixit, Patrick Kofod Mogensen, Simon Byrne, Shubham Maddhashiya, José Bayoán Santiago Calderón, Joakim Nyberg, Jogarao V.S. Gobburu, Vijay Ivaturi. bioRxiv 2020.11.28.402297; doi: https://doi.org/10.1101/2020.11.28.402297
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:106945
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:07 Dec 2020 20:50
Last Modified:07 Dec 2020 20:50

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