Artificial Intelligence in the Pharmaceutical Industry

The Emerging Technologies Lab is currently looking for students who want to write their bachelor or master thesis at the Institute of Technology Management on the role of artificial intelligence in the pharmaceutical industry.

AI start-ups entering the pharmaceutical industry

Starts-ups entering the pharma arena transform the sector by artificial intelligence (AI). Wearables, virtual assistants, AI in research, drug discovery, mental health, genetics/genomics, patient data management or risk management are only some of the examples of new entrants that impact the pharma business.


Scope of the thesis:
In your project and based on a previous work, you will systematically characterize top 50 AI start-ups, such as Atomwise, Cyclica, DeepMind Health, Exscientia, Insilico Medicine, Numerate, and Recursion Pharmaceuticals. Your project involves but is not limited to the analysis and evaluation of business models, technologies, products, services, and collaborations the companies have as well as a rating of the impact the companies may have on the future of pharma R&D.


Your profile:
• Theoretical background of the pharma business
• Organized, conscientious and result-oriented working attitude
• Very good communication and written skills in English

COVID-19 R&D ecosystem

Discovering and developing a new drug/vaccine is a risky, costly and long-term endeavour. Generally, pharmaceutical companies need to invest around USD 3 billion and up to 14 years per new drug.


In January 2020, a new and threatening disease, COVID-19, emerged and SARS-CoV2 was identified as the originator of a later emerging global pandemic. By today, around 88 million people have been detected to be infected by SARS-CoV2 and around 1.9 million people died in course of a COVID-19 disease so far. Very early in the pandemic, politicians and scientists promised that a vaccine might be available by end of 2020. In parallel, numerous initiatives were started to repurpose existing drugs or to use existing drug candidates for the development of a COVID-19 treatment under the FDAs Emergency Use Authorization. At once, governments, the WHO and non-for-profit organizations started to actively support and/or control the timely provision of a COVID-19 treatment and various unnormal initiatives were started, such as a major investment of the German government in Curevac. By today, two vaccines BNT162b2 (Pfizer/BioNTech) and mRNA-1273 (Moderna) have been approved in the US and EU and other projects are in the final process of vaccine/drug approval.

 

Scope of the thesis:
In your project and based on a previous work, you will systematically analyse the R&D ecosystems of BioNTech, Moderna and other key players in the COVID-19 combat. Your work involves but is not limited to the analysis and evaluation of business models, technologies, products, services, and collaborations to answer the question of why is it possible to discover and develop a new drug in 1 year while the process usually takes more than 10 years.


Your profile:
• Theoretical background of the pharma business
• Organized, conscientious and result-oriented working attitude
• Very good communication and written skills in English

Economies of scope in pharma R&D

Pharmaceutical companies, such as Novartis or Roche, are among the top investors in research & development (R&D) worldwide. On average, they invest up to USD 10 billion annually to discover and develop new drugs/vaccines. Next to technologies or management processes, R&D strategy is a major differentiator in the market success of these research-driven companies.


Scope of the thesis:
In your project, you will analyse the R&D strategies and focus points (therapeutic indications) of top 20 pharma companies (and parent companies) for the last 20 years and try to correlate your finding/results with the corporate outputs (viz. number of new drugs launched) in that time period.


Your profile:
• Practical experience in managing and analysing big data sets
• Organized, conscientious and result-oriented working attitude
• Very good communication and written skills in English

Pharma R&D risks and artificial intelligence

Research and development (R&D) is a risky endeavour for pharma companies. The average success rate of pharmaceutical R&D is around 4%, while 96% of R&D projects fail due to lack of efficacy, toxicity or other reasons. Pharma companies use new technologies, such as AI, to reduce R&D-associated risks and to increase the efficiencies of their R&D organizations.


Scope of the thesis:
Based on previous results, you will redesign a risk matrix for pharma R&D and use this matrix to evaluate the impact of AI on pharma R&D efficiency. Concrete you will answer the questions: What are the risks associated with pharma R&D? Can machine learning, deep learning or other AI application reduce those risks and, thus, increase the R&D efficiency? Can the impact of AI on pharma R&D be quantified?


Your profile:
• Theoretical background of the pharma business
• Organized, conscientious and result-oriented working attitude
• Very good communication and written skills in English

Originator of pharma innovation

Big pharma companies leverage internal and external innovation to bring new drugs to market. Next to discovery research and technology providers as well as clinical research organization, companies, such as Roche and Pfizer, have licensed or acquired more than half of their product portfolios and R&D project pipelines from peers and biotech companies.


Scope of the thesis:
In your project, you will analyse the current product portfolios and R&D pipelines of top 10 pharma companies and evaluate the sources of innovation (the originator of the drug compound). In doing so, you will analyse annual reports, patent information and other legally relevant information to make an industry portfolio analysis.


Your profile:
• Practical experience in managing and analysing big data sets
• Organized, conscientious and result-oriented working attitude
• Very good communication and written skills in English

Open innovation models in pharma

In view on numerous challenges with respect to R&D efficiency and effectiveness, big pharma companies have changed their research & development (R&D) models from predominantly closed to more open for external innovation.


Scope of the thesis:
Based on a previous work, you will analyse the R&D models of top 20 pharma companies by evaluating R&D models, current product and R&D pipelines (Phase 1 to launched) and open innovation initiatives. You will set up a frame of how to classify the various R&D models to illustrate the state-of-openness in the industry.


Your profile:
• Practical experience in managing and analysing big data sets
• Organized, conscientious and result-oriented working attitude
• Very good communication and written skills in English

The regulatory framework for artificial intelligence in pharma/biotech

The FDA is moving to encourage the use of artificial intelligence (AI) in health care. For example, AI could be a tool that is integrated into smartphones or wearable devices for a variety of early detection applications in clinical trials, reducing the need for expensive specialist visits while providing more reliable data earlier. Or is could be used for evidence-based disease subtyping or stratifying treatments.


Scope of the thesis:
In your project, you will analyse the regulatory framework of the U.S. Food and Drug Administration (FDA) and the European Medicines Agency (EMA) in context of AI and conclude scope of applications, drivers and hurdles for the use AI in the pharma/biotech sector. This involves an elaboration of new technologies that have already reviewed by the FDA as well as assessment of new regulatory initiatives that are planned to encourage the use of AI.


Your profile:
• Theoretical background of the pharma business
• Organized, conscientious and result-oriented working attitude
• Very good communication and written skills in English

Contact and application

Interested students should submit their CV, grade transcripts, and a short motivation statement to:

NHaefner
Prof. Dr. Naomi Haefner
Assistant Professor of Technology Management
AlexanderSchuhmacher
Alexander Schuhmacher
Reutlingen University Research Fellow