Successful and Faster Drug Development through Data Mining Dirk Belmans, Ph.D. SAS Belgium

Transcription

1 Successful and Faster Drug Development through Data Mining Dirk Belmans, Ph.D. SAS Belgium

2 The Life-Cycle of a Successful Pharmaceutical Product Sales Costs Patent Filing R&D time to launch is decreasing Launch Patent Expiry R&D costs are escalating Pressure from competitors is coming earlier With the need to manage Develop and Make and Sell and Deliver Growth to peak sales ensures return on R&D investment Sales decline quickly as generics take over globally for the first time! Years

3 Key Business Issues First-to-market is critical!!! Reengineering initiatives ongoing to reduce Develop and Make process cycle time and improve pipeline efficiency.

4 Key Process Steps Involved in Bringing a New Drug to Market Clinical Evaluation Drug Discovery Phase I - III Clinical Trials Phase IV - V Clinical Trials Process Development Commercial Manufacture Lab scale development & production Pilot scale Manuf. Scale development development & production & production Work Order EBR Launch Process Evaluation & Onward Transfer Production scheduling & capacity planning Supply chain management

5 The Birth of a New Drug Access to market controlled by FDA, EC, etc.

6 They had Many Questions! Can we learn from our data to distinguish between drug-like and nondrug-like molecules?! Can we design new active molecules by analyzing our structure/activity assay data?! We want to link chromosome structure with functional behaviour!! Can we relate variations/defects in the human genome with drug response, metabolism and diseases?! Can we use genetic info to optimize our clinical trial populations (better inclusion/exclusion criteria)?

7 And Many More Ideas...! Can we reduce the number of drop-outs in a trial by scoring each subject?! Can we use cost of treatment and quality of life data to help us decide which new drugs to develop?! We d like to set-up targeted marketing of our drugs to doctors and patients?! How can we optimize our manufacturing and validation processes (zero defects guaranteed)?

8 They had a dream...! Mountains of biochemical/clinical/marketing research data spread throughout the organization! Most of the time not integrated and difficult to access for scientists and clinicians! Well informed decisions difficult to make > Scientists felt they needed New Techniques to Consolidate and Analyze these Biomedical Databases

9 Medicinal Chemistry: CMC, ACD, MDDR High- Throughput Genomics Kinetics Toxicology Clinical Trials Outcomes Pharmacovigilance Validation

10 Strategic Value of Research Data Warehouse! Early Identification of Winning Lead Compounds (predicting successful compounds)! Leveraging Investments in Genome Research! Optimization of R&D Processes in a Cost-effective manner! Improving Submission Quality and Response Time (regulatory)! Reduction of Time to Market! Increasing Profitability

11 Data Mining is Key SAMPLE Sampling EXPLORE Visual Exploration Clustering Factor Correspondence MANIPULATE Variable Grouping Subsetting Adding or Subsetting of Records MODEL Neural Networks Tree-based Models Statistical Techniques Time Series Analysis ASSESS Data Update? New Questions?

12 Case in Outcomes Research! Large External Outcomes Database! Detailed information about patients in hospital, the treatment they get, the efficacy of the treatment, adverse events, etc.! Research Objectives! Find discriminating characteristics in patient groups using new Pain Relief Medication (Purpose: Identify patient target group for this medication)

13 Live Demo! Outcomes Research

14 Senior management say...! We must be able to - - integrate all drug research and drug outcomes external databases - Create new knowledge on existing and new drugs Enterprise Miner is THE Strategic Tool needed to perform Advanced Analysis on large, integrated research databases - Redistribute the knowledge to the different departments

15 Questions?