Why Pharma Competitive Intelligence Teams Miss Programs They Already Track

Quick answer: A pharma competitive intelligence team can miss a competing program even when the drug is already on its watchlist. The gap often starts in how a drug pipeline database turns source documents into records: which sources it collects, which details it stores, and when it reviews each record.

For pharma CI teams, a record short of detail or out of date can mean the team never assesses a relevant competitor.

One drug, two programs: what the donanemab example shows

Take a team assessing treatments for Alzheimer’s disease before clear symptoms develop. Donanemab (Kisunla) is on its watchlist, and a database entry describes it as approved for Alzheimer’s disease.

That entry is accurate and incomplete. The US prescribing information says treatment should begin in patients with mild cognitive impairment or mild dementia. Separately, the Phase 3 trial TRAILBLAZER-ALZ 3 is studying donanemab in preclinical Alzheimer’s disease, the stage this team cares about.

In competitive intelligence, a drug and a program are different units. A program is a drug in development for a particular use and patient group. If a search keeps only the broad disease label and one development stage, the Phase 3 program can drop out of the assessment, and the team may underestimate future competition or prepare too late for a trial result.

Why do pipeline databases miss drug programs?

Three choices determine what a drug record can answer:

1. Which sources are collected. Source access, language coverage, and reading capacity set how much evidence reaches the database.
2. Which details are stored. A broad disease category or a single highest stage can support an overview, but a competitive assessment may require the patient group, treatment setting, or territory.
3. When the record is reviewed. Providers decide how often to revisit a record and how to classify a program that has gone quiet.

Providers use these choices to manage editorial workload. Together, they decide what a search can return and what an analyst has to recover from the documents.

The whitepaper explains each choice in detail, including why evidence from outside the US raises the stakes.

Download the whitepaper

Six ways a competitive assessment can go wrong

An incomplete record does not always look incomplete. It can return a plausible list of programs while leaving out a competitor, combining distinct uses, or keeping an old status. The whitepaper maps six failure patterns. Three of them:

• Missing programs: a relevant program never appears in the search.
• Lost patient detail: one broad status combines distinct populations, as with donanemab.
• Inactive programs shown as active: an old status remains after development has changed.

The other three are in the paper, along with why some gaps begin in the original sources.

How often does this happen in practice?

Ferma examined how differences in coverage and program status could affect one real competitive assessment: oral small molecule and peptide programs in inflammatory bowel disease (IBD), from preclinical through Phase 3. A commercial pipeline database returned 194 programs, all listed as active. Checked against public evidence, about a third showed stopped, suspended, or terminated development. Ferma conducted and scored the comparison itself, and it was not independently reviewed.

That was only one side of the result. Read [what we found in that IBD comparison], including the programs the commercial search never returned.

What’s inside the whitepaper

Why CI Teams Get Surprised by Programs They Already Track, by Sriram Subramanian and Aravindha Kumar, covers:

• All six failure patterns, what the team encounters, and why each matters
• The full IBD comparison, reconciled segment by segment, with its unresolved cases stated openly
• What adding a data source, monitoring, commissioned research, or an internal build can solve, and what each still leaves to the analyst
• A test your team can run on a competitive question it already knows well

If budget is the pressure your team feels most, read [why CI teams end up paying twice] for pipeline data, or see how Ferma builds drug and program records from original sources.

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