How Effective are Life-Cycle Management Strategies Across Top Pharmaceutical and Biotech Companies?

A client recently asked Vivpro- how effective are we compared to our peers on Life Cycle Management strategies?

We hypothesized that change to the “indications and usage” section is a reasonable indicator of robust Life-Cycle Management across pharmaceutical/biotech companies. It is reasonable to assume that these changes represent line extensions and brand growth strategies.

Vivpro finds that among the 6 Pharmaceutical/Biotech companies (selected for analysis), most of the development programs deliver ~1 change to the “indications and usage” section per Product per Year across the portfolio.

On the other hand, a few companies and therapeutic areas seem to perform better than their peers (~2–3 changes per Product per Year) owing to the therapeutic area (mostly but not necessarily driven by Oncology) and Life-Cycle Management strategies.

• In 2017, one company managed to change the “indications and usage” section for 10 products (highest within the comparison groups).
• In 2020, a different leader emerged with 9 products with changes to the “indications and usage” section.
• On average, life cycle management delivered 3 products (range 0–10) with changes to the “indications and usage” section.

Comparison of Products with at least one change in “indications and usage” section in a year

Methods-

Data Source: FDA Approvals

Date Range: Approvals after 01/01/2016

Companies: 6 Pharmaceutical/Biotech companies who are current NDA/BLA application holders (selected by a client)

Scope:

• Inclusions- Follow on submissions that led to “indications and usage” section changes.*
• Exclusions- (A) Supplements that did not lead to indications and usage section change irrespective of any section changes, (B) First approvals such as New molecular entities, New dosage forms with an existing indication.

Last 5 years of regulatory history processed through human and machine intelligence to derive insights

*Machine Learning model (medical entity detection) was trained to differentiate submissions that led to indication changes including a definition of “change” (for the entity) by training the algorithm on diagnosis, treatment, population etc.