研究領域『科学的発見・社会的課題解決に向けた各分野のビッグデータ利活用推進のための次世代アプリケーション技術の創出・高度化』

医薬品創薬から製造までのビッグデータからの知識創出基盤の確立医薬品創薬から製造までの過程には蓄積された膨大な測定データ等が存在します。これまで異分野として個別にとらえられていた創薬の現場と製造の現場における知見および各種データを共有する仕組みを構築するとともに、創薬・製造を俯瞰的に見た医薬品開発のシステム全体の効率化および最適化を目指した研究を進めます。具体的にはこれらのデータを活用することで、大量のタンパク質 対 化合物情報からの創薬指針の抽出、大規模仮想ライブラリ創出およびそこからの新薬ターゲット発見とその合成・製造法の獲得、製造プラントの安定運転・リスク事前管理・品質安定化のための知識抽出を達成し、医薬品創薬から製造の段階を通した知識創出基盤を確立することを目標とします。

研究代表者 東京大学・工学系研究科・教授 船津 公人

Problem of Big Data analysis in chemistry and systematic application of knowledge

derived from Big Data to real world

“Development of a knowledge-generating platform driven by big data in drug

discovery through production processes”

Project Leader Prof. Kimito Funatsu(The University of Tokyo)

While massive amounts of quantitative data have accumulated across

the pipeline of a drug candidate's initial discovery up through its

production process, knowledge of and data analysis for each of the

discovery and the production processes has remained isolated.

In this project, we aim to establish a platform which allows us to unify

relevant knowledge about the different processes and their associated

data, and to advance research into improved and optimized systems that

view pharmaceutical development from a comprehensive, correlated, and

high-level perspective.

Background

2

Time-consuming, huge amount of cost

Period: 15-20 years

Success probability: 1/50000

Key point: Screening of lead compounds

and the optimization

Basic

Research

Screening of lead

compounds

and the optimization

Non-clinical test Clinical test ApprovalCommercial

production

General flow of drug development

Pilot-scaleproduction

Determination of target proteins responsible for a disease

Screening of seedsClinical trials

anddrug application

Proteins in the body100,000+

Lead optimization

Drug Discovery: An Exemplar of Big Data

Pre-clinicaltrials

Chemical library(1000s of seed chemicals)

Binding data10,000,000 pairs

Patient data22,000 entries

Side effect data5 million entries

Bioactivity data1.2 billion entries

Gene expression data1 million entries

Chemical space≥ 1060 molecules

Human genome3 billion base pairs

Background

4

Time-consuming, huge amount of cost

Period: 15-20 years

Success probability: 1/50000

Low quantity and quality of real compound library and virtual library

Low number of structures

Synthetic routes are unknown in virtual library.

Key point: Screening of lead compounds

and the optimization

Basic

Research

Screening of lead

compounds

and the optimization

Non-clinical test Clinical test ApprovalCommercial

production

General flow of drug development

No activity data for new target protein

Impossible to construct activity prediction model

Difficult to search lead compound

Pilot-scaleproduction

5

Strict check of product quality

Irregular products are scraped as waste → Serious damage

Using temperature, pressure, near infrared spectra (NIR)（Ｘ）, quality

of product(y) can be predicted and monitored on-line by Soft sensor

(statistical model：ｙ＝ｆ（Ｘ）).

But, application of soft sensor to real plants has not been realized on-

line yet because of low predictive accuracy and complex maintenance

of soft sensor.

Efficient and stable production is required, keeping high quality of chemicals

Background

Basic

Research

Screening of lead

compounds

and the optimization

Non-clinical test Clinical test ApprovalCommercial

production

General flow of drug development

Pilot-scaleproduction

In operating chemical plants, operators have to monitor

operating condition of the plants and control process variables.

But, all of them are not easy to measure online.

Process variables need to be measured online.

temperature, pressure, concentration of products, etc.

Soft sensor

concentration, ...temperature, pressure, ...

input output

measure online

X: temperature, pressure, ... y: concentration, ...

Database

BIG DATA

technical difficulties large measurement delays

estimate online

Model : y=f(X)

Soft sensor

Easy to measure Difficult to measure

Soft Sensor

Soft sensor model

time / min

Soft sensor model calculates

values of ○ with T1, T2, and P.

Temperature

1

Pressure

Temperature

2

Concentration

#Observed value. #Reduction of cost for chemical analyses. #Reported with time delay.

Objective

8

Big data

①Complicated interaction data between many proteins and many drug candidates

with other biological information.

② Large virtual library containing chemical structures to be drug candidates.

③ Plant operating data and product quality data in pharmaceutical and chemical

processes.

By utilizing the above “Big Data”, the following subjects should be realized.

Ⅰ. Construction of mathematical model derived from many proteins vs. compounds

together with other biological information, and extraction of guide for drug discovery.

Ⅱ. Automated generation of large virtual library(several billion chemical structures),

discovery of new drug, and acquisition of the synthetic routes from the library.

Ⅲ. Knowledge extraction for process monitoring and controlling contributing to stable

operation and stabilizing product quality. Development of automated construction of

soft sensor model and the model maintenance system for process monitoring.

Basic research

Screening of lead compounds

and the optimization

Non-clinical test

Clinical test

Approval

Commercial production

Flow of development

of Pharmaceuticals

Integration of chemical and Biological information

and fast processing

Analysis and prediction on interaction between chemical and biological

information

Extraction of patterns for directions in lead molecule

development based upon big data on chemical-bio

interaction化学構造・物性

結合タンパク質

遺伝子発現

パスウェイ変動

細胞活性

臨床情報

Input Output

Measured on-line

Database

Predicted on-line

Temperature, Pressure

Input Variables(X)

Concentration, Density

Output Variables(y)

Predictive ModelSoft Sensor

y = f(X)Output Variables

Easy to measure Difficult to measure

Input Variables

Process monitoringQuality control

Overview of this project

Chemical structures

Proteins

Genes

Pathways

Cellar activities

Clinical information

9

Okuno-G

Taiｊi-G & Hori-G

Funatsu-G

Pilot-Scale production

Prof. Gisbert Schneider

Framework of research project

Development of operational database of chemical plants for soft sensor Development of soft sensor construction system for plant monitoring and soft sensor maintenance method Knowledge extraction for process and quality controlling from Big Data from chemical plants

● Expansion of Huge virtual library Retrieval of useful information Addition of Synthetic routes and physical properties Visualization of contents

Integration of chemical and biological information and canonicalization of data structure Development of mathematical model for interaction between ligand and proteinExtraction of direction in lead molecule development Sharing Synthetic

routes and physical

properties of structures

Sharing interaction

information between

target protein and

candidates

TAIJI group OKUNO group

FUNATSU group

Drug candidates

Restriction of

chemical processEvaluation of feasibility of

candidate reactions is needed.

Only 3 % of reactions developed

in lab. are implemented in real

chemical plants due to reactor

size, mixing rate, dynamics,

controllability, byproducts, cost

….. 10

Rapid Evaluation of Feasibility of Synthetic Routes

HORI group

Framework of research project

Development of operational database of chemical plants for soft sensor Development of soft sensor construction system for plant monitoring and soft sensor maintenance method Knowledge extraction for process and quality controlling from Big Data from chemical plants

● Expansion of Huge virtual library Retrieval of useful information Addition of Synthetic routes and physical properties Visualization of contents

Integration of chemical and biological information and canonicalization of data structure Development of mathematical model for interaction between ligand and proteinExtraction of direction in lead molecule development Sharing Synthetic

routes and physical

properties of structures

Sharing interaction

information between

target protein and

candidates

TAIJI group OKUNO group

FUNATSU group

Drug candidates

Restriction of

chemical processEvaluation of feasibility of

candidate reactions is needed.

Only 3 % of reactions developed

in lab. are implemented in real

chemical plants due to reactor

size, mixing rate, dynamics,

controllability, byproducts, cost

….. 11

Rapid Evaluation of Feasibility of Synthetic Routes

HORI group

This research will lead to a new, collective platform

for systematic and efficient development of

pharmaceuticals from discovery through production