Portfolio Modeling and Forecasting of Single-Use Rare Disease Treatments
Biopharmaceutical companies are increasingly exploring cutting-edge novel gene therapies (GTs) in an effort to cure rare diseases. This capstone develops and tests a practical forecasting framework for sharing capacity across Roche’s evolving GT portfolio and driving strategic global supply chain ne...
| Main Authors: | , |
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| Format: | Other |
| Language: | en_US |
| Published: |
2021
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| Online Access: | https://hdl.handle.net/1721.1/130983 |
| _version_ | 1826196164372856832 |
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| author | Leising, Jordan Michael Goldman, Olivia Claire |
| author_facet | Leising, Jordan Michael Goldman, Olivia Claire |
| author_sort | Leising, Jordan Michael |
| collection | MIT |
| description | Biopharmaceutical companies are increasingly exploring cutting-edge novel gene therapies (GTs) in an effort to cure rare diseases. This capstone develops and tests a practical forecasting framework for sharing capacity across Roche’s evolving GT portfolio and driving strategic global supply chain network design. Our problem is challenging, even by the highly regulated pharmaceutical industry standards, with: (1) substantial R&D and mergers and acquisitions investments, (2) some of the world’s smallest disease populations, (3) one-time patients, (4) lacking commercial infrastructure, and (5) scarce historical or long- term pipeline data. We created three forecast types based on the target disease state knowledge available to predict an asset’s prevalence and incidence patient adoption curves. The resulting asset forecasts are also aggregated into a comprehensive portfolio dashboard. Our user-friendly point model enables stakeholders to market size the prospective current pipeline and risk pool portfolio capacity by clinical phase. We then applied simulations to illustrate long-term product launch scenarios. These tools cater to various stakeholders helping address the key GT production planning and asset targeting problems. Roche has already began utilizing our capstone to methodically consider unknown future assets, with unknown orphan disease severity or populations, in their strategic make vs. buy GT network design decisions. |
| first_indexed | 2024-09-23T10:22:33Z |
| format | Other |
| id | mit-1721.1/130983 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T10:22:33Z |
| publishDate | 2021 |
| record_format | dspace |
| spelling | mit-1721.1/1309832021-06-16T19:49:24Z Portfolio Modeling and Forecasting of Single-Use Rare Disease Treatments Leising, Jordan Michael Goldman, Olivia Claire Demand Planning Healthcare Supply Chain Strategy Biopharmaceutical companies are increasingly exploring cutting-edge novel gene therapies (GTs) in an effort to cure rare diseases. This capstone develops and tests a practical forecasting framework for sharing capacity across Roche’s evolving GT portfolio and driving strategic global supply chain network design. Our problem is challenging, even by the highly regulated pharmaceutical industry standards, with: (1) substantial R&D and mergers and acquisitions investments, (2) some of the world’s smallest disease populations, (3) one-time patients, (4) lacking commercial infrastructure, and (5) scarce historical or long- term pipeline data. We created three forecast types based on the target disease state knowledge available to predict an asset’s prevalence and incidence patient adoption curves. The resulting asset forecasts are also aggregated into a comprehensive portfolio dashboard. Our user-friendly point model enables stakeholders to market size the prospective current pipeline and risk pool portfolio capacity by clinical phase. We then applied simulations to illustrate long-term product launch scenarios. These tools cater to various stakeholders helping address the key GT production planning and asset targeting problems. Roche has already began utilizing our capstone to methodically consider unknown future assets, with unknown orphan disease severity or populations, in their strategic make vs. buy GT network design decisions. 2021-06-16T19:49:23Z 2021-06-16T19:49:23Z 2021-06-16 Other https://hdl.handle.net/1721.1/130983 en_US CC0 1.0 Universal http://creativecommons.org/publicdomain/zero/1.0/ application/pdf |
| spellingShingle | Demand Planning Healthcare Supply Chain Strategy Leising, Jordan Michael Goldman, Olivia Claire Portfolio Modeling and Forecasting of Single-Use Rare Disease Treatments |
| title | Portfolio Modeling and Forecasting of Single-Use Rare Disease Treatments |
| title_full | Portfolio Modeling and Forecasting of Single-Use Rare Disease Treatments |
| title_fullStr | Portfolio Modeling and Forecasting of Single-Use Rare Disease Treatments |
| title_full_unstemmed | Portfolio Modeling and Forecasting of Single-Use Rare Disease Treatments |
| title_short | Portfolio Modeling and Forecasting of Single-Use Rare Disease Treatments |
| title_sort | portfolio modeling and forecasting of single use rare disease treatments |
| topic | Demand Planning Healthcare Supply Chain Strategy |
| url | https://hdl.handle.net/1721.1/130983 |
| work_keys_str_mv | AT leisingjordanmichael portfoliomodelingandforecastingofsingleuserarediseasetreatments AT goldmanoliviaclaire portfoliomodelingandforecastingofsingleuserarediseasetreatments |