Student research poster, Leaders for Global Operations (LGO

Heyman, Jeff

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Conclusions Simulation Modeling to Predict Drug Pipeline Throughput Project Background Internship Objectives Results* Company Background Approach Model Structure: Driving strategic decision-making: (examples) Jeff Heyman, LGO 2010 http://www.novartis.com http://lgo.mit.edu Novartis Supervisors: Project Supervisor:  Tim Smith, Associate Director of Business Intelligence Project Champion: Detlev Biniszkiewicz, Global Head of Portfolio Management Faculty Supervisors: Deborah Nightingale, Professor of the Practice of Aeronautics and Astronautics and Engineering Systems Roy Welsch, Professor of Statistics and Management Science and Engineering Systems Model Assessment: •  Accuracy: Validation against historical data shows good predictability for aggregate forecasts, but weaker predictability for finer metrics; questionable historical data represents greatest source of error •  Usability: Iterative design and frequent user feedback led to extremely user-friendly design;  Fast simulation time (e.g., 1-3 minutes) allows real-time scenario analysis •  Leadership buy-in: Strong response from research management; will present new capability to broader NIBR leadership in February 2010; trained an internal technical expert and champion to allow future model growth. Pharmaceutical R&D involves considerable uncertainty, including: •  High attrition – a large number of projects must be undertaken to yield a relatively small number of compounds that enter clinical development; the likelihood of commercial success for any single discovery project is extremely low.  •  High project-to-project variability in time and resources required to reach subsequent phases of development  •  Long concept-to-commercialization time (~8-12 years) Senior leaders must make decisions today about pipeline size and balance, the impact of which will not be observable for many years. NIBR’s Portfolio Management Group (PMG) aims to create tools that aid senior management in understanding pipeline status and progress. The PMG cites forward projection as a desired capability not currently achieved in a systematic, aggregated fashion. Key questions include: •  Given the current pipeline, how many successful proof-of-concept (POC) studies can we expect in x-years? To achieve a desired number of POCs in x-years, what should our pipeline look like?  •  How can various pipeline levers affect pipeline productivity? Primary goal: To create a forward-looking pipeline model that •  Accounts for system complexity and uncertainty •  Incorporates historical performance data  •  Is easy to use to support real-time managerial decisions Expected benefits: Bridging strategy and execution by facilitating •  Scenario Analysis •  Resource Planning •  Portfolio Gap Identification and Resolution Novartis AG is a global leader in pharmaceuticals, vaccines, generics, and consumer health products. Its mission is to discover, develop and successfully market innovative products to prevent and cure diseases, to ease suffering and to enhance the quality of life. •  Headquartered in Basel, Switzerland •  100,000 associates in 140 countries Novartis Institutes for Biomedical Research (NIBR) is the global research organization of Novartis. Its research approach prioritizes patient need and disease understanding, and emphasizes proof-of-concept trials – small-scale studies used to get an early read on the safety and efficacy of drug candidates. •  Headquartered in Cambridge, MA •  2008 R&D investment = $7.2B, or 17.4% of sales Understanding the Real-World: The Drug Pipeline Modeling the Real-World: Scoping and Data Collection Input Parameters: •  Phase Durations •  Phase Transition Rates Data Sources: •  Historical •  Benchmarking •  Interview-based Predictive Pipeline Model Planning Model Development: Iterative, Incremental Design Output data: •  Probabilistic forecasts of pipeline status Capabilities: •  Scenario Analysis •  Resource Planning •  Portfolio Gap ID Source:  Novartis Company Website; www.novartis.com Baseline model •  Mock data •  Project transitioning (durations, transition rates) •  Monte Carlo Simulation Iteration 1 •  Historical data •  Multiple project types (e.g., small-molecule versus biologics) •  “Expansion points” (e.g., backup projects) Iteration 2 •  More historical data •  Leadership reports •  UI improvements User Interface User input with probabilistic assumptions Modeling Tools: •  MS Excel – User interface and data input •  Visual Basic for Applications – Data handling •  Crystal Ball – Monte Carlo simulation Source: NIBR Program Office Project Terminations Project Transitions from previous phase Project Transitions to next phase Pipeline Phase Dynamics Model output #1 Near-term portfolio levers: •  Current projects -> should we in-license?  •  Expansion points -> should we launch more backup programs?  Long-term portfolio levers: •  Incoming projects -> should we increase resources at specific R&D phases? •  Expansion points -> should we encourage specific types/balance of projects? “The model output suggests near-term growth in portfolio size, but the expected inflow of new projects cannot sustain the peak size achieved next year.”  *Note: Shown data is for  demonstration purposes  and not representative of Novartis’ actual research portfolio. “The model output shows a portfolio gap. The portfolio size will fall rapidly next year, but the high expected inflow of new projects will build it back up over the next 5 years.”  Model output #2 Model output #3 “The model output shows that in 3 years, we will achieve at least 6 positive proof-of-concept studies with 59% certainty.”  

Simulation Modeling to Predict Drug Pipeline Throughput

http://dspace.mit.edu/bitstream/1721.1/83883/1/SRP_100106_Heyman.pdf

Simulation Modeling to Predict Drug Pipeline Throughput

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