Chapter 19
OpenTox Principles and Best Practices
for Trusted Reproducible In Silico
Methods Supporting Research
and Regulatory Applications
in Toxicological Science
Barry Hardy, Daniel Bachler, Joh Dokler, Thomas Exner, Connor Hardy,
Weida Tong, Daniel Burgwinkel and Richard Bergström
Abstract Our aim in this work and initiative is to establish a practice and guidance
for tracking and reporting modern in silico data analyses in a reproducible manner.
The recommended reproducible principle supports the concept that data analyses,
and more generally, scientific claims and regulatory evidence, are published with
their raw data and software code so that others may verify the findings and build
upon them. We discuss here how we are demonstrating implementations of trusted
reproducible in silico evidence workflows and are enhancing their acceptance with an
B. Hardy (B) · D. Bachler · J. Dokler · T. Exner · C. Hardy
Edelweiss Connect GmbH, Technology Park Basel, Hochbergerstrasse 60C,
4057 Basel, Switzerland
e-mail: Barry.Hardy@edelweissconnect.com
D. Bachler
e-mail: Daniel.Bachler@edelweissconnect.com
J. Dokler
e-mail: Joh.Dokler@edelweissconnect.com
T. Exner
e-mail: Thomas.Exner@edelweissconnect.com
C. Hardy
e-mail: Connor.Hardy@edelweissconnect.com
W. Tong
National Center for Toxicological Research, U.S. Food and Drug Administration,
Jefferson, AR 72079, USA
e-mail: Weida.Tong@fda.hhs.gov
D. Burgwinkel
Guardtime AS, Basel, Switzerland
e-mail: daniel.burgwinkel@guardtime.com
R. Bergström
Bergstroem Consulting GmbH, Zug, Switzerland
e-mail: richard.bergstrom.zug@gmail.com
© Springer Nature Switzerland AG 2019
H. Hong (ed.), Advances in Computational Toxicology, Challenges and Advances
in Computational Chemistry and Physics 30,
https://doi.org/10.1007/978-3-030-16443-0_19
383
OpenTox Principles and Best Practices
for Trusted Reproducible In Silico
Methods Supporting Research
and Regulatory Applications
in Toxicological Science
Barry Hardy, Daniel Bachler, Joh Dokler, Thomas Exner, Connor Hardy,
Weida Tong, Daniel Burgwinkel and Richard Bergström
Abstract Our aim in this work and initiative is to establish a practice and guidance
for tracking and reporting modern in silico data analyses in a reproducible manner.
The recommended reproducible principle supports the concept that data analyses,
and more generally, scientific claims and regulatory evidence, are published with
their raw data and software code so that others may verify the findings and build
upon them. We discuss here how we are demonstrating implementations of trusted
reproducible in silico evidence workflows and are enhancing their acceptance with an
B. Hardy (B) · D. Bachler · J. Dokler · T. Exner · C. Hardy
Edelweiss Connect GmbH, Technology Park Basel, Hochbergerstrasse 60C,
4057 Basel, Switzerland
e-mail: Barry.Hardy@edelweissconnect.com
D. Bachler
e-mail: Daniel.Bachler@edelweissconnect.com
J. Dokler
e-mail: Joh.Dokler@edelweissconnect.com
T. Exner
e-mail: Thomas.Exner@edelweissconnect.com
C. Hardy
e-mail: Connor.Hardy@edelweissconnect.com
W. Tong
National Center for Toxicological Research, U.S. Food and Drug Administration,
Jefferson, AR 72079, USA
e-mail: Weida.Tong@fda.hhs.gov
D. Burgwinkel
Guardtime AS, Basel, Switzerland
e-mail: daniel.burgwinkel@guardtime.com
R. Bergström
Bergstroem Consulting GmbH, Zug, Switzerland
e-mail: richard.bergstrom.zug@gmail.com
© Springer Nature Switzerland AG 2019
H. Hong (ed.), Advances in Computational Toxicology, Challenges and Advances
in Computational Chemistry and Physics 30,
https://doi.org/10.1007/978-3-030-16443-0_19
383