The document discusses platform strategy and data-driven development in the pharmaceutical industry. It outlines the drug development process from target identification through discovery to approved product. It also discusses moving from enterprise architecture to platform strategy execution and global platform strategy as a foundation. Several use cases for data analytics are presented, including using natural language processing and machine learning to simplify compliance, optimize clinical trials, stay on top of research trends, and accelerate time to market.

1. Platform Strategy and
Data-driven development
in Pharmaceutical Industry
Lukas Ott - Domain Architect R&D
ITU - Guest Lecture - 8 april 2022

2. Agenda
1. Pharmaceutical Research & Development 101
2. From Enterprise Architecture to platform strategy execution
3. Discussion on use cases development for data analytics

3. Pharmaceutical Development in a Nutshell
Pharmaceuticals
Small
Molecule
(Chemical)
Large
Molecule
(Biologics)

4. The core of R&D in Drug Development
Target
Identification
Drug
Discovery

5. From discovery to approved product

6. From Enterprise Architecture to
Technical Architecture
Source: https://www.leanix.net/en/wiki/ea/enterprise-architect-vs-solution-architect-vs-technical-architect-whats-the-difference#form

7. IT portfolio is a balance between operative and
strategic development

8. IT Portfolio Management

9. Architecture review is a key milestone of the
analysis phase of projects

10. Key architectural principles
1. Business
2. Application
3. Information
4. Technology
5. Security & Compliance / Data Privacy

11. Core functionalities of a system in an enterprise
Data
Documents
Workflow
Business Process
Application /
Platform

12. Global Platform Strategy as Foundation
Adopt before Adapt
“A platform strategy is the mobilization of a networked business
platform to expand into and operate in a given domain. A business
platform, in turn, is a nexus of rules and infrastructure that facilitate
interactions among network users.”
In R&D Domain that means that the goal is to build foundational
platform that are supporting the business processes end-to-end.

13. R&D Domain - Background
● Use the right datasets - data quality is key!
● Regulatory Authorities
● Content Accumulation
● Leverage Available Content
● Know who does what

14. Use cases in Pharma
Simplify Compliance
● Detect relevant information in
regulatory texts
● Push information to the right
expert
Optimize Clinical Trials
● Make evidence-based decisions
based on all clinical trial data
available
Stay on Top of Research Trends
● Provides access to the latest
scientific information
Accelerate Time to Market
● Speeds up new drug application
submission
● Reduces costs for new drug
development
Connect to External Experts
● Connect to relevant experts
within external research
organizations
● Identify the most promising
collaboration partners
Connect Experts & Expertise
Internally
● Reveals networks of experts

15. Natural Language Processing (NLP) /
Machine Learning (ML) Use Cases
1. Business case
a. Create data stories
b. Main concepts
2. Build data pipelines
a. Prerequisites
b. Indexing data
c. Create service & label data

16. Questions & Open Discussion

18. Sources
https://de.slideshare.net/LukasOtt/
https://www.leanix.net/en/wiki/ea/enterprise-architect-vs-solution-architect-vs-technical-
architect-whats-the-difference#form
https://towardsdatascience.com/data-driven-isnt-dead-3230378e4c7a

19. From discovery to approved product