diff --git a/joss.05925/10.21105.joss.05925.crossref.xml b/joss.05925/10.21105.joss.05925.crossref.xml new file mode 100644 index 0000000000..641f6ef190 --- /dev/null +++ b/joss.05925/10.21105.joss.05925.crossref.xml @@ -0,0 +1,302 @@ + + + + 20240207T222426-05ab3deab7d4338db1b2375a3451d2166fd485d8 + 20240207222426 + + JOSS Admin + admin@theoj.org + + The Open Journal + + + + + Journal of Open Source Software + JOSS + 2475-9066 + + 10.21105/joss + https://joss.theoj.org + + + + + 02 + 2024 + + + 9 + + 94 + + + + Chi: A Python package for treatment response +modelling + + + + David + Augustin + https://orcid.org/0000-0002-4885-1088 + + + + 02 + 07 + 2024 + + + 5925 + + + 10.21105/joss.05925 + + + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + + + + Software archive + 10.5281/zenodo.10510572 + + + GitHub review issue + https://github.com/openjournals/joss-reviews/issues/5925 + + + + 10.21105/joss.05925 + https://joss.theoj.org/papers/10.21105/joss.05925 + + + https://joss.theoj.org/papers/10.21105/joss.05925.pdf + + + + + + The systems biology markup language (SBML): A +medium for representation and exchange of biochemical network +models + Hucka + Bioinformatics + 4 + 19 + 10.1093/bioinformatics/btg015 + 2003 + Hucka, M., Finney, A., Sauro, H. M., +Bolouri, H., Doyle, J. C., Kitano, H., Arkin, A. P., Bornstein, B. J., +Bray, D., Cornish-Bowden, A., & others. (2003). The systems biology +markup language (SBML): A medium for representation and exchange of +biochemical network models. Bioinformatics, 19(4), 524–531. +https://doi.org/10.1093/bioinformatics/btg015 + + + Filter inference: A scalable nonlinear mixed +effects inference approach for snapshot time series data + Augustin + PLOS Computational Biology + 5 + 19 + 10.1371/journal.pcbi.1011135 + 2023 + Augustin, D., Lambert, B., Wang, K., +Walz, A.-C., Robinson, M., & Gavaghan, D. (2023). Filter inference: +A scalable nonlinear mixed effects inference approach for snapshot time +series data. PLOS Computational Biology, 19(5), 1–29. +https://doi.org/10.1371/journal.pcbi.1011135 + + + Preclinical pharmacokinetic / pharmacodynamic +modeling and simulation in the pharmaceutical industry: An IQ consortium +survey examining the current landscape + Schuck + The AAPS journal + 2 + 17 + 10.1208/s12248-014-9716-2 + 2015 + Schuck, E., Bohnert, T., Chakravarty, +A., Damian-Iordache, V., Gibson, C., Hsu, C.-P., Heimbach, T., +Krishnatry, A. S., Liederer, B. M., Lin, J., Maurer, T., Mettetal, J. +T., Mudra, D. R., Nijsen, M. J., Raybon, J., Schroeder, P., Schuck, V., +Suryawanshi, S., Su, Y., … Wong, H. (2015). Preclinical pharmacokinetic +/ pharmacodynamic modeling and simulation in the pharmaceutical +industry: An IQ consortium survey examining the current landscape. The +AAPS Journal, 17(2), 462–473. +https://doi.org/10.1208/s12248-014-9716-2 + + + Impact of a five-dimensional framework on +r&d productivity at AstraZeneca + Morgan + Nature Reviews Drug Discovery + 3 + 17 + 10.1038/nrd.2017.244 + 2018 + Morgan, P., Brown, D. G., Lennard, +S., Anderton, M. J., Barrett, J. C., Eriksson, U., Fidock, M., Hamrén, +B., Johnson, A., March, R. E., Matcham, J., Mettetal, J., Nicholls, D. +J., Platz, S., Rees, S., Snowden, M. A., & Pangalos, M. N. (2018). +Impact of a five-dimensional framework on r&d productivity at +AstraZeneca. Nature Reviews Drug Discovery, 17(3), 167–181. +https://doi.org/10.1038/nrd.2017.244 + + + Translational PK/PD modeling to increase +probability of success in drug discovery and early +development + Lavé + Drug Discovery Today: +Technologies + 21-22 + 10.1016/j.ddtec.2016.11.005 + 1740-6749 + 2016 + Lavé, T., Caruso, A., Parrott, N., +& Walz, A. (2016). Translational PK/PD modeling to increase +probability of success in drug discovery and early development. Drug +Discovery Today: Technologies, 21-22, 27–34. +https://doi.org/10.1016/j.ddtec.2016.11.005 + + + Simulating clinical trials for model-informed +precision dosing: Using warfarin treatment as a use case + Augustin + Frontiers in Pharmacology + 14 + 10.3389/fphar.2023.1270443 + 1663-9812 + 2023 + Augustin, D., Lambert, B., Robinson, +M., Wang, K., & Gavaghan, D. (2023). Simulating clinical trials for +model-informed precision dosing: Using warfarin treatment as a use case. +Frontiers in Pharmacology, 14. +https://doi.org/10.3389/fphar.2023.1270443 + + + Probabilistic inference on noisy time series +(PINTS) + Clerx + Journal of Open Research +Software + 10.5334/jors.252 + 2019 + Clerx, M., Robinson, M., Lambert, B., +Lei, C. L., Ghosh, S., Mirams, G. R., & Gavaghan, D. J. (2019). +Probabilistic inference on noisy time series (PINTS). Journal of Open +Research Software. +https://doi.org/10.5334/jors.252 + + + Modeling and simulation workbench for NONMEM: +Tutorial on pirana, PsN, and xpose + Keizer + CPT: pharmacometrics & systems +pharmacology + 6 + 2 + 10.1038/psp.2013.24 + 2013 + Keizer, R. J., Karlsson, M., & +Hooker, A. (2013). Modeling and simulation workbench for NONMEM: +Tutorial on pirana, PsN, and xpose. CPT: Pharmacometrics & Systems +Pharmacology, 2(6), 1–9. +https://doi.org/10.1038/psp.2013.24 + + + gPKPDSim: A SimBiology-based GUI application +for PKPD modeling in drug development + Hosseini + Journal of pharmacokinetics and +pharmacodynamics + 45 + 10.1007/s10928-017-9562-9 + 2018 + Hosseini, I., Gajjala, A., Bumbaca +Yadav, D., Sukumaran, S., Ramanujan, S., Paxson, R., & Gadkar, K. +(2018). gPKPDSim: A SimBiology-based GUI application for PKPD modeling +in drug development. Journal of Pharmacokinetics and Pharmacodynamics, +45, 259–275. +https://doi.org/10.1007/s10928-017-9562-9 + + + Scipion PKPD: An open-source platform for +biopharmaceutics, pharmacokinetics and pharmacodynamics data +analysis + Sorzano + Pharmaceutical Research + 7 + 38 + 10.1007/s11095-021-03065-1 + 2021 + Sorzano, C., Fonseca-Reyna, Y., & +Cruz-Moreno, M. P. de la. (2021). Scipion PKPD: An open-source platform +for biopharmaceutics, pharmacokinetics and pharmacodynamics data +analysis. Pharmaceutical Research, 38(7), 1169–1178. +https://doi.org/10.1007/s11095-021-03065-1 + + + Accelerated predictive healthcare analytics +with pumas, a high performance pharmaceutical modeling and simulation +platform + Rackauckas + BioRxiv + 10.1101/2020.11.28.402297 + 2020 + Rackauckas, C., Ma, Y., Noack, A., +Dixit, V., Mogensen, P. K., Byrne, S., Maddhashiya, S., Santiago +Calderón, J. B., Nyberg, J., Gobburu, J. V., & others. (2020). +Accelerated predictive healthcare analytics with pumas, a high +performance pharmaceutical modeling and simulation platform. BioRxiv, +2020–2011. +https://doi.org/10.1101/2020.11.28.402297 + + + Myokit: A simple interface to cardiac +cellular electrophysiology + Clerx + Progress in biophysics and molecular +biology + 1-3 + 120 + 10.1016/j.pbiomolbio.2015.12.008 + 2016 + Clerx, M., Collins, P., De Lange, E., +& Volders, P. G. (2016). Myokit: A simple interface to cardiac +cellular electrophysiology. Progress in Biophysics and Molecular +Biology, 120(1-3), 100–114. +https://doi.org/10.1016/j.pbiomolbio.2015.12.008 + + + SciPy 1.0: Fundamental Algorithms for +Scientific Computing in Python + Virtanen + Nature Methods + 17 + 10.1038/s41592-019-0686-2 + 2020 + Virtanen, P., Gommers, R., Oliphant, +T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, +P., Weckesser, W., Bright, J., van der Walt, S. J., Brett, M., Wilson, +J., Millman, K. J., Mayorov, N., Nelson, A. R. J., Jones, E., Kern, R., +Larson, E., … SciPy 1.0 Contributors. (2020). SciPy 1.0: Fundamental +Algorithms for Scientific Computing in Python. Nature Methods, 17, +261–272. +https://doi.org/10.1038/s41592-019-0686-2 + + + + + + diff --git a/joss.05925/10.21105.joss.05925.jats b/joss.05925/10.21105.joss.05925.jats new file mode 100644 index 0000000000..ee032809b5 --- /dev/null +++ b/joss.05925/10.21105.joss.05925.jats @@ -0,0 +1,514 @@ + + +
+ + + + +Journal of Open Source Software +JOSS + +2475-9066 + +Open Journals + + + +5925 +10.21105/joss.05925 + +Chi: A Python package for treatment response +modelling + + + +https://orcid.org/0000-0002-4885-1088 + +Augustin +David + + +* + + + +Department of Computer Science, University of Oxford, +Oxford, United Kingdom + + + + +* E-mail: + + +5 +8 +2023 + +9 +94 +5925 + +Authors of papers retain copyright and release the +work under a Creative Commons Attribution 4.0 International License (CC +BY 4.0) +2022 +The article authors + +Authors of papers retain copyright and release the work under +a Creative Commons Attribution 4.0 International License (CC BY +4.0) + + + +Python +pkpd +treatment planning +inference +Bayesian inference + + + + + + Summary +

Chi + is an open source Python package for treatment response modelling with + support for implementation, simulation and parameter estimation. + Supported treatment response models include pharmacokinetic & + pharmacodynamic (PKPD) models, physiology-based pharmacokinetic (PBPK) + models, quantitative systems pharmacology (QSP) models, and nonlinear + mixed effects (NLME) models. The package provides two interfaces to + implement single-individual treatment response models: 1. an SBML + interface, which implements models based on SBML file specifications + (Hucka + et al., 2003); and 2. a general purpose interface that allows + users to implement their own, custom models using Python code. Models + implemented using SBML files automatically provide routines to + administer dosing regimens and to evaluate parameter sensitivities + using the simulation engine + Myokit + (Clerx + et al., 2016). These single-individual treatment response + models can be extended to NLME models, making the simulation of + inter-individual variability of treatment responses possible.

+

In + Chi, + model parameters can be estimated from data using Bayesian inference. + We provide a simple interface to infer posterior distributions of + model parameters from single-patient data or from population data. For + the extreme case where the available population data only contains a + single measurement for each individual, the package also implements + filter inference, enabling the inference of NLME model parameters from + such snapshot time series data + (Augustin, + Lambert, Wang, et al., 2023). For the purpose of model-informed + precision dosing (MIPD), + Chi + can be used to find individual-specific dosing regimens that optimise + treatment responses with respect to a target treatment outcome.

+

To sample from posterior distributions, + Chi + uses Markov chain Monte Carlo (MCMC) algorithms implemented in the + Python package + PINTS + (Clerx + et al., 2019). To optimise dosing regimens, different + optimisation algorithms can be used, including optimisers implemented + in + SciPy + (Virtanen + et al., 2020) or in + PINTS + (Clerx + et al., 2019).

+

Documentation, tutorials and install instructions are available at + https://chi.readthedocs.io.

+ + + Statement of need +

Treatment response modelling has become an integral part of + pharmaceutical research + (Morgan + et al., 2018; + Schuck + et al., 2015). In the early phase of drug development, + treatment response models help with target and lead identification, + and contribute to a mechanistic understanding of the relevant + pharmacological processes. In the transition to the clinical + development phase, these models provide guidance and help to identify + safe and efficacious dosing regimens + (Lavé + et al., 2016). During clinical trials, treatment response + models further facilitate the assessment of safety, efficacy and + treatment response variability. More recently, treatment response + models are also being used in the context of MIPD, where models help + to predict individualised dosing regimens for otherwise + difficult-to-administer drugs + (Augustin, + Lambert, Robinson, et al., 2023).

+

The most widely used software packages and computer programs for + treatment response modelling include NONMEM + (Keizer + et al., 2013), + Monolix, + and Matlab Simbiology + (Hosseini + et al., 2018). Other software packages include Scipion PKPD + (Sorzano + et al., 2021), + PoPy, + Pumas + (Rackauckas + et al., 2020), and a number of + R + libraries. These packages provide an extensive toolkit for + PKPD modelling. However, most of these solutions are difficult to use + for research as their source code is neither publicly distributed nor + subject to open-source licenses, which conceals the algorithmic + details, limits the transparency of the modelling results, and hinders + the methological development. Notable exceptions are Scipion PKPD and + the R libraries, which make their source code publicly available on + GitHub.

+

Chi + is an easy-to-use, open-source Python package for treatment response + modelling. It is targeted at modellers on all levels of programming + expertise. Modellers with a primary focus on the pharmacology can use + Chi + to quickly implement models and estimate their model parameters from + data. Modellers with an interest in methodological research can use + Chi’s + modular, open source framework to study the advantages and limitations + of different modelling choices, as well as research new approaches for + treatment response modelling. We hope that the open-source nature of + this package will increase the transparency of treatment response + models and facilitate a community effort to further develop their + methodology.

+ + + + + + + + HuckaMichael + FinneyAndrew + SauroHerbert M + BolouriHamid + DoyleJohn C + KitanoHiroaki + ArkinAdam P + BornsteinBenjamin J + BrayDennis + Cornish-BowdenAthel + others + + The systems biology markup language (SBML): A medium for representation and exchange of biochemical network models + Bioinformatics + Oxford University Press + 2003 + 19 + 4 + 10.1093/bioinformatics/btg015 + 524 + 531 + + + + + + AugustinDavid + LambertBen + WangKen + WalzAntje-Christine + RobinsonMartin + GavaghanDavid + + Filter inference: A scalable nonlinear mixed effects inference approach for snapshot time series data + PLOS Computational Biology + Public Library of Science + 202305 + 19 + 5 + https://doi.org/10.1371/journal.pcbi.1011135 + 10.1371/journal.pcbi.1011135 + 1 + 29 + + + + + + SchuckEdgar + BohnertTonika + ChakravartyArijit + Damian-IordacheValeriu + GibsonChristopher + HsuCheng-Pang + HeimbachTycho + KrishnatryAnu Shilpa + LiedererBianca M + LinJing + MaurerTristan + MettetalJerome T + MudraDaniel R + NijsenMarjoleen Jma + RaybonJoseph + SchroederPatricia + SchuckVirna + SuryawanshiSatyendra + SuYaming + TrapaPatrick + TsaiAlice + VakilynejadMajid + WangShining + WongHarvey + + Preclinical pharmacokinetic / pharmacodynamic modeling and simulation in the pharmaceutical industry: An IQ consortium survey examining the current landscape + The AAPS journal + 2015 + 17 + 2 + 10.1208/s12248-014-9716-2 + 462 + 473 + + + + + + MorganPaul + BrownDean G. + LennardSimon + AndertonMark J. + BarrettJ. Carl + ErikssonUlf + FidockMark + HamrénBengt + JohnsonAnthony + MarchRuth E. + MatchamJames + MettetalJerome + NichollsDavid J. + PlatzStefan + ReesSteve + SnowdenMichael A. + PangalosMenelas N. + + Impact of a five-dimensional framework on r&d productivity at AstraZeneca + Nature Reviews Drug Discovery + 2018 + 17 + 3 + https://doi.org/10.1038/nrd.2017.244 + 10.1038/nrd.2017.244 + 167 + 181 + + + + + + LavéThierry + CarusoAntonello + ParrottNeil + WalzAntje + + Translational PK/PD modeling to increase probability of success in drug discovery and early development + Drug Discovery Today: Technologies + 2016 + 21-22 + 1740-6749 + https://www.sciencedirect.com/science/article/pii/S1740674916300439 + 10.1016/j.ddtec.2016.11.005 + 27 + 34 + + + + + + AugustinDavid + LambertBen + RobinsonMartin + WangKen + GavaghanDavid + + Simulating clinical trials for model-informed precision dosing: Using warfarin treatment as a use case + Frontiers in Pharmacology + 2023 + 14 + 1663-9812 + https://www.frontiersin.org/articles/10.3389/fphar.2023.1270443 + 10.3389/fphar.2023.1270443 + + + + + + ClerxMichael + RobinsonMartin + LambertBen + LeiChon Lok + GhoshSanmitra + MiramsGary R. + GavaghanDavid J. + + Probabilistic inference on noisy time series (PINTS) + Journal of Open Research Software + 201907 + 10.5334/jors.252 + + + + + + KeizerRon J + KarlssonMO + HookerA + + Modeling and simulation workbench for NONMEM: Tutorial on pirana, PsN, and xpose + CPT: pharmacometrics & systems pharmacology + Wiley Online Library + 2013 + 2 + 6 + 10.1038/psp.2013.24 + 1 + 9 + + + + + + HosseiniIraj + GajjalaAnita + Bumbaca YadavDaniela + SukumaranSiddharth + RamanujanSaroja + PaxsonRicardo + GadkarKapil + + gPKPDSim: A SimBiology-based GUI application for PKPD modeling in drug development + Journal of pharmacokinetics and pharmacodynamics + Springer + 2018 + 45 + 10.1007/s10928-017-9562-9 + 259 + 275 + + + + + + SorzanoCOS + Fonseca-ReynaY + Cruz-MorenoMA Pérez de la + + Scipion PKPD: An open-source platform for biopharmaceutics, pharmacokinetics and pharmacodynamics data analysis + Pharmaceutical Research + Springer + 2021 + 38 + 7 + 10.1007/s11095-021-03065-1 + 1169 + 1178 + + + + + + RackauckasChris + MaYingbo + NoackAndreas + DixitVaibhav + MogensenPatrick Kofod + ByrneSimon + MaddhashiyaShubham + Santiago CalderónJosé Bayoán + NybergJoakim + GobburuJogarao VS + others + + Accelerated predictive healthcare analytics with pumas, a high performance pharmaceutical modeling and simulation platform + BioRxiv + Cold Spring Harbor Laboratory + 2020 + 10.1101/2020.11.28.402297 + 2020 + 11 + + + + + + ClerxMichael + CollinsPieter + De LangeEnno + VoldersPaul GA + + Myokit: A simple interface to cardiac cellular electrophysiology + Progress in biophysics and molecular biology + Elsevier + 2016 + 120 + 1-3 + 10.1016/j.pbiomolbio.2015.12.008 + 100 + 114 + + + + + + VirtanenPauli + GommersRalf + OliphantTravis E. + HaberlandMatt + ReddyTyler + CournapeauDavid + BurovskiEvgeni + PetersonPearu + WeckesserWarren + BrightJonathan + van der WaltStéfan J. + BrettMatthew + WilsonJoshua + MillmanK. Jarrod + MayorovNikolay + NelsonAndrew R. J. + JonesEric + KernRobert + LarsonEric + CareyC J + Polatİlhan + FengYu + MooreEric W. + VanderPlasJake + LaxaldeDenis + PerktoldJosef + CimrmanRobert + HenriksenIan + QuinteroE. A. + HarrisCharles R. + ArchibaldAnne M. + RibeiroAntônio H. + PedregosaFabian + van MulbregtPaul + SciPy 1.0 Contributors + + SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python + Nature Methods + 2020 + 17 + 10.1038/s41592-019-0686-2 + 261 + 272 + + + + +
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