Добавил:
Sekretar
kiopkiopkiop18@yandex.ru
t.me/Prokururor I Вовсе не секретарь, но почту проверяю
Опубликованный материал нарушает ваши авторские права? Сообщите нам.
Вуз:
Предмет:
Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5338_Библиотеки_им_академика_М_И_Перельмана
.pdf
Chapter 14
https://t.me/med1917
Toxicity Prediction System for Chemical
Substances Based on Toxicity Expression
Mechanisms—AI-SHIPS
Kimito Funatsu
14.1 Introduction
In the world of data-driven chemistry, the prediction of various properties of chemicals and physical properties, as well as the design of new chemical substances, is
rapidly becoming a sub-supply. On the other hand, while there is a great need for
toxicity prediction of general chemicals, it is a technically challenging field, and
despite many years of development, a definitive system has not yet been created.
AI-SHIPS project started in June 2017 and ended in March 2022. This system has
already been completed and is gaining international recognition for its unique idea
to realize toxicity prediction based on toxicity expression mechanisms and its high
prediction accuracy. Even after the project is completed, it is important to continue
to nurture and utilize this system as a valuable asset of Japan.
14.2 Background of the AI-SHIPS Project
Under the Law Concerning the Evaluation of Chemical Substances and Regulation of Their Manufacture (Chemical Substances Control Law), general chemical
substances distributed in the world are regulated according to their properties, such
as “degradability”, “accumulation”, “long-term toxicity to humans”, or “toxicity to
animals and plants”, and their residual status in the environment. Regulations are
applied accordingly. Unlike pharmaceuticals, regulations are based on the premise
that chemicals are used on a daily basis. The Chemical Substances Control Law
imposes a 28-day repeated dose toxicity test on rats, which is a heavy burden on
K. Funatsu (B)
Data Science Center, Nara Institute of Science and Technology, 8916-5 Takayama-Cho, Ikoma,
Nara 630-0192, Japan
e-mail: funatsu@dsc.naist.jp
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024
H. Satoh et al. (eds.), Drug Development Supported by Informatics,
https://doi.org/10.1007/978-981-97-4828-0_14
247
248 K. Funatsu
https://t.me/med1917
chemical companies that develop and manufacture chemical substances. On the
other hand, social demands for toxicity prediction of general chemical substances
are expected to increase in the future. One of the reasons for this is the growing
demand for thorough implementation of the 3R principle (Replacement, Reduction,
and Refinement), which is an international animal welfare principle. Animal testing
is already banned in the European cosmetics sector, including for its raw materials.
In the future, in all fields, products containing chemicals whose safety has been
evaluated by animal testing may not be allowed to be distributed on the market.
Another point is that the time and expense involved in safety testing is a major
burden. Even if a new compound is developed, it takes about three years to evaluate its safety through animal testing, etc., and it is said that about 20% of the total
research cost is allocated for this purpose. Accelerating this safety evaluation process
is an important factor in bringing revolutionary functional chemical substances to
market at an early stage and ensuring their international competitiveness. Furthermore, the active application of materials informatics to the development of new
chemical substances in recent years has enabled multiple candidate materials to
be proposed efficiently, and the safety screening of these candidates before starting
specific development will make it possible to broaden the scope of chemical substance
development.
AI-SHIPS (an acronym for “AI-based Substances Hazardous Integrated Prediction
System”) was initiated to develop a system that can predict hepatotoxicity (cytotoxicity, lipid abnormalities, bile duct damage, and hepatomegaly) and the degree of
risk in a 28-day repeated dose study under the Chemical Substances Control Law,
plus hematological and renal toxicity for general chemical substances. The official name of the project is the Ministry of Economy, Trade and Industry’s R&D
project “Development of evaluation technology for energy-saving electronic device
materials: ‘Development of high-speed and high-efficiency safety evaluation technology supporting social implementation of functional materials—Development of
next-generation safety prediction methods using artificial intelligence with toxicityrelated big data’”, which started in June 2017 and finished this year in March 2022
1–4
].
[
The project leader is the author, and nine institutions participated in the project:
Showa Pharmaceutical University, University of Shizuoka, Meiji Pharmaceutical
University, Nagoya City University, National Institute of Advanced Industrial
Science and Technology, Chemicals Evaluation and Research Institute, Systems
Planning Institute, and Mizuho Research & Technologies. In 2020, a user consortium was formed to promote trial use of the system and to listen to requests for
improvements, with the aim of making the system more user-friendly. Sixteen companies/agencies, mainly from the private sector, participated here. In November 2021,
we participated in the OECD (Organization for Economic Cooperation and Development) QSAR Toolbox Management Group Meeting held online, together with
METI and had an opportunity to introduce AI-SHIPS. We are convinced that the
novelty of the toxicity prediction concept and the user-friendly operability, which
were highly evaluated by overseas researchers in this field, will serve as the basis for
the future development of this system. In Japan, the AI-SHIPS Symposium was held
14 Toxicity Prediction System for Chemical Substances Based on Toxicity … 249
https://t.me/med1917
on February 21, 2022, to report the final results of the project, and many people from
companies expressed their expectations for the results, including a demonstration.
14.3 Mechanism of Toxicity Prediction (3-Layers Model)
Conventional toxicity prediction based on (quantitative) structure–activity relationships ((Q)SAR) directly models the relationship between molecular structure and
toxicity, which often fails to provide reliable predictions for compounds of different
strains from the data set used for training, and the overall prediction accuracy is
not always high. In particular, it was difficult t o accept the prediction results without
reservation, because the model was a black box as to what reason (mechanism) causes
toxicity. In contrast, AI-SHIPS is innovative in that it incorporates the “three-layer
model” (see Fig.
and enables AOP (Adverse Outcome pathway)-based toxicity prediction that takes
the toxicity expression mechanism into account. AOP is a conceptual framework that
links existing knowledge on causal relationships from a molecular initiating event
(MIE), which is an action on a target molecule, through different biological hierarchies of biological responses (key events; KE) to an adverse outcome (AO), which
is the final adverse effect.
As shown in the left side of Fig. 14.1, a compound first interacts with nuclear
receptors in the cell and then through various biochemical events (MIE/KE) such
as stress response pathways, which eventually lead to toxicity expression (toxic
endpoint). However, while the relationship between compounds and MIE/KE and
14.1), which was first proposed in the world by the author Funatsu,
Fig. 14.1 Three-layers model linking compounds-MIE/KE-toxicity
250 K. Funatsu
https://t.me/med1917
between compounds and toxicity is observable, as shown in Fig. 14.1 right, the
relationship between MIE/KE and toxicity has little or no directly observable data.
To make the link from compound to toxicity, a model is needed to link this MIE/KE to
toxicity relationship. MIE/KE can be observed in vitro studies, but the final toxicity
is assessed in vivo, so the mechanism cannot be observed. Funatsu’s 3-layer model
successfully relates in vitro and in vivo information by making the unobservable
predictable through modeling. In other words, it is now possible to construct a model
that links compounds (first layer) and various intracellular events (in vitro, second
layer) in a predictive model, and finally predict toxicity in vivo (third layer) using the
predictions from the in vitro predictive model as input. This also made it possible to
examine the mechanism of toxicity expression, i.e., which intracellular events (MIE/
KE) play an important role. In fact, in the course of project execution, important
discoveries and suggestions regarding the mechanism of liver toxicity were obtained,
and other results contributing to toxicology were observed.
Another outstanding aspect of the AI-SHIPS project is that a number of in vitro
studies were conducted to collect data for building complex toxicity prediction
models. In this project, about 860 substances were listed from the toxicity data
registered in HESS, an integrated platform for toxicity evaluation support systems
operated by the National Institute of Technology and Evaluation (NITE), and a total
of 46 different tests were conducted on about 360 of these substances. Using these
vast amounts of experimental data, 130 in vitro prediction models were constructed.
In addition, in vivo prediction models were built using a wide range of in vivo data for
a total of about 2200 compounds, including the HESS registration data and additional
compounds from REACH.
Let’s look at the flow of toxicity prediction by the 3-layer model as shown in
14.2. First, structural descriptors are calculated using Mordred [5] for the struc-
Fig.
tures entered as evaluation compounds at the compound level in the first layer. This
is then input into 130 in vitro prediction models to generate positive or negative
binary predictions for each in vitro assay. This is the second layer. Using the in vitro
predictions as input parameters, the in vivo (endpoint) prediction model is used to
obtain in vivo predictions for the liver, kidney, and blood. As shown in the lower part
14.2, the predicted values obtained here are binary predictions as positive or
of Fig.
negative values with NOEL 30 mg/kg/day and 300 mg/kg/day as threshold values.
By using this three-layer model, it is possible to understand which in vitro assay
is strongly associated with each endpoint, which leads to an understanding of the
mechanism of toxicity expression.
The upper part of Fig. 14.3 shows the flow of the physiologically-based phar-
macokinetic (PBPK) prediction model. By inputting the chemical structure of the
evaluated compound, the Mordred descriptor is calculated, and by using it as input,
the gastrointestinal absorption rate, absorption rate constant, volume of distribution,
liver metabolic loss, and plasma blood cell distribution rate are calculated, which
are then input to the PBPK simulator to calculate the biokinetics: liver, renal layer,
the area under the concentration–time curve (AUC), and the highest organ concentration (C
) of the compound in the liver, renal layer, and blood are predicted by
max
entering them into the PBPK simulator. On this basis, it is also possible to predict
14 Toxicity Prediction System for Chemical Substances Based on Toxicity … 251
https://t.me/med1917
Fig. 14.2 Model linkage in AI-SHIPS (in vitro/in vivo prediction model)
the pharmacokinetics of a single or repeated 28-day dose of a chemical substance.
The lower part of Fig.
main functions available in AI-SHIPS. In physiological pharmacokinetic prediction,
if a chemical is predicted not to be absorbed, it can be considered not to be toxic and
the use of the toxicity prediction function in the lower part of Fig.
necessary.
14.3 is the same as Fig. 14.2, and these two functions are the
14.3 is no longer
14.4 Configuration of the Toxicity Prediction System
The system of AI-SHIPS is largely divided into a model and data management
system and a user system operated by users. The model data management system
allows companies to register their own substance data and toxicity data. The user
system predicts the two functions shown in Fig.
of six hepatotoxicities, renal damage, and two hematologic toxicities (anemia and
coagulation abnormalities). The accuracy of the toxicity prediction is over 80% and
at worst over 70%. This is considerably higher than conventional systems. Moreover,
by referring to the in vitro test data on which the predictions are based, it is possible to
determine which mechanism in the body is responsible for the toxicity manifestation,
which has the advantage of allowing efficient reevaluation in case the patient wishes
to reevaluate the toxicity in a new experiment.
14.3: PBPK prediction and prediction
252 K. Funatsu
https://t.me/med1917
Fig. 14.3 Physiologically based pharmacokinetic (PBPK) prediction (top half of the figure). Lower
half is the same as Fig.
For more information on the background, concept, functions, and operation of the
system, please refer to the AI-SHIPS website [
The contributions and strengths of professors from different fields of toxicology
research have been successfully combined to create a very easy-to-use and versatile
prediction system for the world’s first challenge of toxicity prediction that takes into
account the mechanism of toxicity expression. The expectations of the companies
participating in the consortium are high, and we have obtained a strategic tool that
will be useful for the chemical industry in the future.
14.2
6, 7].
14.5 Positioning of the Toxicity Prediction System
in the Overall Picture of Data-Driven Chemistry
and Expectations for the Future
Over the years, the author has promoted various studies and projects from the viewpoints of “what to make”, “how to make it in the laboratory”, “how to make it for
commercial production”, and “whether it was actually made” based on data-driven
chemistry. In the same way, the AI-SHIPS project will be asking the question, “Can
we make it?”. In this light, it can be said that the development of a chemical substance
is ultimately completed by evaluating its impact on the environment and people.
AI-SHIPS is a product of the METI project, and its use should be promoted from
the broad and lofty perspective of improving the international competitiveness of the
chemical industry by enabling the prompt introduction of highly functional chemical
14 Toxicity Prediction System for Chemical Substances Based on Toxicity … 253
https://t.me/med1917
products to the market, based on the original concept mentioned at the beginning of
this chapter. Recently, through the use of Materials Informatics (MI), it has become
possible to obtain many candidates of chemical substances with the desired functions,
and screening with this toxicity prediction system will make it possible to narrow
down the list of promising candidates in a rational manner. It is a global trend that
animal testing will be banned sooner or later, and it is desirable to utilize the toxicity
prediction system in a way that anticipates this trend, rather than following the lead of
Europe and the United States. We hope that METI and related government agencies
will continue to take an active role in chemical substance management and industry
promotion.
One of the initiatives in this project is to set up a server at the Nara Institute
of Science and Technology’s Data-driven Science Creation Center and to promote
trial use of AI-SHIPS via the Intranet. The project is also beginning to study the
management and operation system. In the near future, we would like to establish a
system to make AI-SHIPS an indispensable tool for the chemical industry.
References
1. Funatsu K (2018) Safety Prediction of Chemicals Using Toxicity-Related Big Data. MATE-
RIALSTAGE Technical Information Association co., LTD December: Preface.
utu.co.jp/doc/magazine/m_2018_12.htm. Accessed 18 February 2024
2. Funatsu K (2019) Policy and Current Status of Toxicity Prediction System Development in the
AI-SHIPS Project, Next Generation Safety Prediction Method Using Artificial Intelligence with
Toxicity-Related Big Data. Paper presented at the 32nd Annual Meeting of the Japanese Society
for Alternatives to Laboratory Animals, Tsukuba, 21 November 2019
3. Funatsu K (2018) Safety Prediction of Chemicals Using Toxicity-Related Big Data (AI-
SHIPS Project)—Substitution of Animal Experiments/Reduction of Testing Costs/Shortening
Development Time. CICSJ Bull 36(3):43–46.
4. Funatsu K (2022) Significance of the AI-SHIPS Project—Development Background, Design
Concept, and Future Development. Paper presented at the 49th Annual Meeting of the Japanese
Society of Toxicology, Sapporo, Japan, 30 June 2022
5. Moriwaki M, Tian YS, Kawashita N, Takagi T (2018) Mordred: a molecular descriptor calculator.
J Chemometrics 10:4.
6. AI SHIPS project (2021) Nara Institute of Science and Technology and Mizuho Research &
Technologies, Ltd.
7. Nara Institute of Science and Technology (NAIST) (2023) AI-SHIPS Integrated Toxicity
Prediction System: Demonstration (Video).
oads/2023/11/AI-SHIPSpvEnglish20231101.mp4. Accessed 18 February 2024
https://doi.org/10.1186/s13321-018-0258-y
http://www-dsc.naist.jp/ai-ships/en/. Accessed 18 February 2024
https://doi.org/10.11546/cicsj.36.43
http://www-dsc.naist.jp/ai-ships/wp-content/upl
https://www.gij
Chapter 15
https://t.me/med1917
Data Assimilation to Integrate
High-Speed Atomic Force Microscopy
with Biomolecular Simulations:
Characterization of Drug Target
Functions
Sotaro Fuchigami and Shoji Takada
15.1 Introduction
High-speed atomic force microscopy (HS-AFM) is a unique experimental technique
that can observe three-dimensional (3D) structure of biomolecules such as proteins
and DNA as well as its time evolution, i.e. four-dimensional structure, at the singlemolecule level. HS-AFM was largely developed by Toshio Ando and his colleagues
] and has made continuous and steady progress [2–7]. In
at Kanazawa University [
addition, HS-AFM has been used to observe the structure and dynamics of numerous
important biomolecules, contributing to the elucidation of their functional mecha-
3, 4, 6, 8–11]. However, the resolution of HS-AFM is not always sufficient
nisms [
in both space and time to reveal the details of biomolecular behavior.
To overcome this problem and obtain higher-resolution information on structure
and dynamics of biomolecules, it would be effective to integrate informatics, especially molecular dynamics (MD) simulations for four-dimensional structure analysis.
MD simulations are widely used to reveal the dynamic behavior of biomolecules and
provide very high spatiotemporal resolution information [
simulations have the problem of limited reachable time scales and some artifacts
arising from force field or methodological imperfections. The former problem can
1
]. However, MD
12, 13
S. Fuchigami
School of Pharmaceutical Sciences, University of Shizuoka, 52-1 Yada, Suruga-Ku,
Shizuoka 422-8526, Japan
e-mail: sotaro.f@u-shizuoka-ken.ac.jp
S. Takada (B)
Graduate School of Science, Kyoto University, Kitashirakawa Oiwake-Cho, Sakyo-Ku,
Kyoto 606-8502, Japan
e-mail: takada@biophys.kyoto-u.ac.jp
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024
H. Satoh et al. (eds.), Drug Development Supported by Informatics,
https://doi.org/10.1007/978-981-97-4828-0_15
255
256 S. Fuchigami and S. Takada
https://t.me/med1917
be solved to some extent by using coarse-grained (CG) models [14, 15] and Markov
state models [
incorporating experimental information.
Recently, a combination of observation data and numerical simulations based
on Bayesian statistics has made it possible to get closer to the details of observation
targets. Such a combination is called data assimilation and is actively applied in many
fields including meteorology and oceanography [
science, such data assimilation is expected to elucidate the high-resolution structural
dynamics of biomolecules [
improve the information on biomolecular behavior through data assimilation.
The information obtained in this way can reveal in real time the details of structural
changes of biomolecules and molecular mechanisms responsible for their function.
It also helps in investigating how dysfunction and hyperfunction of biomolecules are
caused by the use of functional inhibitors and mutants of biomolecules. Many such
biomolecular abnormalities are known and are often associated with disease. If HSAFM could observe them in real time, it is expected to improve our understanding
of the causes of disease, as well as aid in medical treatment and help in the drug
development process.
In this chapter, we briefly review recent research progress related to singlemolecule observation of biomolecules by high-speed atomic force microscopy. After
a brief introduction to HS-AFM, we present recently developed computational analytical methods for obtaining molecular-level information on biomolecule structure and
dynamics from HS-AFM data: computational structural analysis based on HS-AFM
images, data assimilation analysis directly from HS-AFM measurement data, and
data assimilation analysis combining HS-AFM measurement and molecular simulation. Then, recent advances in experimental instruments and the latest application research using HS-AFM to address problems in molecular cell biology are
introduced.
16, 17]. On the other hand, the latter problem can be overcome by
18, 19]. In the field of biomolecular
20]. HS-AFM data of biomolecules is also expected to
15.2 Computational Structural Analysis Based
on HS-AFM Images
The information given by HS-AFM images is limited to the surface height of an
observed biomolecule, but not the 3D structure itself. While “native” 3D structures
of the biomolecules are most often found in some database, biomolecules are dynamic
and change their conformation in the HS-ATM measurement. In addition, HS-AFM
images highly depend on the shape of the AFM probe tip. Therefore, the knowledge
of the probe shape is required for successful inference of transient biomolecule
structures from HS-AFM data. In this section, we present two recent research results
on the development of computational structural analysis methods that address these
21, 22
issues [
].
15 Data Assimilation to Integrate High-Speed Atomic Force Microscopy … 257
https://t.me/med1917
15.2.1 Flexible Fitting of Biomolecular Structures to AFM
Images via Biased Molecular Simulations
In the case when 3D structure of the target biomolecule is available, the rigid-body
fitting method is useful to determine the position and orientation of the biomolecule
]. However, the rigid-body fitting methods have
observed in an AFM image [
a limitation when an observed biomolecular structure is largely changed from a
known (normally the native-state) structure because these methods cannot change
the conformation. To overcome the problem, a flexible fitting MD simulation has
been developed by Niina et al. to change a 3D structure of biomolecule so as to fit
into an experimental AFM image [
The developed MD simulation is performed using the conventional force field
with a bias potential based on the similarity between a structural model and an
experimental AFM image used as a reference. The bias potential is defined using
the cosine similarity between the reference AFM image and a pseudo-AFM image
generated from a given structure as
23–26
21].
V
AFM
(R)
= κNk
T (1 − c.s.(R
B
))
(15.1)
where R represents the particle coordinates of the target biomolecule model, κ is a
strength parameter,
the temperature, and
N is the number of particles, kB is the Boltzmann constant, T is
c.s.(R) is the cosine similarity. A pseudo-AFM image is given
in a differentiable form with respect to particle coordinates, and thus forces acting on
particles can be calculated by differentiating the bias potential. The flexible fitting
MD simulation induces a structural change of the target biomolecule, resulting in a
structure that fits well to the given AFM image.
An appropriate value for the strength parameter κ of the bias potential was examined by a twin experiment using the flexible fitting MD simulation combined a
well-established residue-level coarse-grained (CG) protein model, AICG2+ [
27],
with the developed bias potential. The results confirmed that the optimal value of
the strength parameter was approximately 1. In this case, it was also confirmed that
the flexible fitting MD simulation can successfully sample structures with a small
deviation from the ground truth. Using the optimal value of the strength parameter, it was also demonstrated that the developed simulation works well with real
experimental AFM images for a flagellar protein FlhA
3D structures (Fig.
through the CafeMol software [
15.1). The developed simulation method is publicly available
].
28
monomer, giving plausible
c
Соседние файлы в папке Библиотека им академика М.И. Перельмана