The document discusses how artificial intelligence can help address challenges in the pharmaceutical industry related to high drug development costs and failure rates. It describes how AI is being used to analyze chemical, pharmacological, genomic and clinical trial data to help discover new drug candidates, optimize drug properties, improve clinical trial design and patient recruitment, and make the drug development process more efficient overall. The outcomes could include quicker drug discovery, more efficient clinical trials, and higher drug approval rates, helping to increase efficiency and reduce costs across the pharmaceutical industry.

1. Case Study
Artificial Intelligence in the
Life Sciences Industry
Emily Lewis, MS, CCRP

2. Background

3. Industry Challenges
● High rate of failure/low success rate in drug discovery
and development
● Clinical trials are plagued with inefficiencies in trial
design, patient recruitment, and data management
● Leads to high cost of running the business and of
marketed drugs

4. Status Quo
● Research/Discovery - Rely heavily on trial-and-error,
high-throughput screening, and time-consuming
laboratory experiments
● Development - Trials are designed and analyzed using
manual processes which cause lengthy clinical
development timelines

5. Problem Statement
The industry needs more precise and
effective drugs

6. Applications in Drug
Discovery

7. Training Models for Drug Discovery
Chemical Libraries
● Structure, properties, and biological activities for small
molecules
● Peptide libraries
● Biological pathway-specific libraries

8. Training Models for Drug Discovery
Pharmacological Data
● Drug properties and mechanisms of action
● Pharmacokinetics (how the drug is absorbed, distributed,
metabolized, and excreted)
● Pharmacodynamics (the effects of the drug on the body)

9. Training Models for Drug Discovery
Biological and Genomic Data
● Omics - Proteomics, metabolomics, genomics,
transcriptomics, pharmacogenomics
● Biomarkers to help you identify target populations for
treatments who would be likely to have a favorable drug
response

10. Key Applications in Drug Discovery
→ Molecular Generation:
Generate novel molecular structures with desired properties that
could lead to more effective drugs
→ Predictive Modeling:
Predict the effectiveness of molecules as potential drugs
→Optimization:
Help us determine how to make drugs work better (be more
efficacious) and reduce side effects (be more safe)

11. Applications in Drug
Development

12. Training Models for Drug Development
● Historical Clinical Trial Data
○ Trial designs, patient demographics, outcomes, adverse
events reported
● Real World Evidence (RWE)
○ insurance claims, patient registries, and electronic health
records
● Regulatory Guidelines and Previous Submissions
● Scientific Publications
○ Peer-reviewed literature reflecting medical research and
emerging trends

13. Training Models for Drug Development
● Clinical Study Reports (CSRs)
● Operational Data
○ Site performance, patient recruitment rates, resource
utilization)
● Patient Data
○ Medical history, patient reported outcomes (PROs), social
determinants of health (SDOH), treatment responses
*Ensure this data is as diverse and inclusive as possible

14. Key Applications in Drug Development
→Trial Design Optimization:
AI models can propose efficient clinical trial designs, predicting optimal dosing,
duration, and cohort sizes using historical preclinical data
→Document Creation and Customization:
GenAI can be used to write protocol templates, regulatory submission
documentation, draft informed consent forms, and patient recruitment materials
→Patient Recruitment:
Predictive models can identify ideal patient populations, enhancing recruitment
efficiency and diversity

15. Results
● Discovery of novel compounds for complex diseases that
were previously difficult to target
● Decreased operational costs due to streamlined trial
recruitment and optimized processes and therefore reduced
overall trial duration
● Quicker, more informed decision making
● More representative clinical trials

16. Potential Outcomes
“A clinical trial proceeded ahead of schedule with high-
quality data.”
“A drug had promising results in advanced clinical trials,
leading to successful regulatory approval.”

17. Impact
Increased Efficiency Reduced time for initial drug discovery from
years to months, reducing time to market for new drugs
Cost Reduction Significant decrease in the cost of the drug
discovery phase
Better Success Rate Higher rate of successful drug candidates
moving from the lab into clinical trials in humans