In biopharma, competitive intelligence (CI) is decision insurance when stakes are high. While often reduced to competitor monitoring, true CI serves as a strategic filter for de-risking drug development and high-value dealmaking. Its power lies in distinguishing "signal" from "noise," providing the context and confidence needed to make decisive program decisions before the window of opportunity closes.
Moving from noise to insight requires a close look at the nuance and quality of different types of data.
Tracking a biomarker readout for a specific drug class is standard, but understanding its predictive limits transforms it into a strategic advantageelevates the effort to a strategic level. For example, bone mineral density (BMD) is an outcome measure often included in Osteogenesis Imperfecta trials. However, BMD correlates poorly with fracture rates1-2, which is the more meaningful phase III endpoint.a
Recognizing this discrepancy early is critical, as understanding the translatability of an earlier-stage endpoint prevents over-investment in surrogate markers that do not clearly predict success.
a Recent Phase III trials in the OI space—including Amgen’s (NCT05972551) and Ultragenyx’s (NCT05768854)—have shifted toward annualized fracture rate as the Primary Efficacy Endpoint (PEP).
While endpoint selection reflects a company’s scientific foundation, other signals are more operational and can indicate a real-time pivot in portfolio strategy. Changes to a trial's structure, protocol amendments, cohort adjustments, or eligibility refinements often signal a shift in a company’s confidence in its existing plans.
In 2019, following disappointing results in the TULIP1 study of Anifrolumab in Systemic Lupus Erythematosus (SLE), AstraZeneca changed the primary endpoint for the identical TULIP-2 study before analysis. This signaled a strategic shift toward an endpoint, specifically moving from the SRI-4 index to the BICLA, which they considered to have a higher Probability of Success (PoS) while remaining within the bounds of FDA guidance.3-5
Beyond these trial adjustments, a company’s public moves can be read as a “strategy trail”. Activity at industry conferences, more specifically, which data is highlighted often reveals where a drug is succeeding and where it is struggling.
For instance, emphasis on particular lower dose cohorts can reflect a required trade-off between efficacy and tolerability. In its recent phase III idiopathic pulmonary fibrosis (IPF) trial, Boehringer Ingelheim included a 9 mg Nerandonilstat dose to evaluate the benefit-risk at a lower dose.6-8 This was a proactive move to mitigate a tolerability issue that showed up in the earlier phase II data. In patients on background therapy, tolerability was poor at the 18 mg dose. Discontinuation due to adverse events reached 20%, while. Fforwhile for patients not on background therapy, discontinuation was down toonly 6%.8
Many strategic trialsails are further influenced by external and regulatory signals. For example, some natural history studies have the potential to establish new regulatory endpoints. A “giveaway” that such a change is on the horizon is when these studies start being referenced in company decks, as with the recent PARASOL study. This study established a reduction in proteinuria over 24 months to be a clinically meaningful endpoint for potential approval in Focal Segmental Glomerulosclerosis (FSGS).9,10
However, uUltimately, a single signal alone is rarely directional.
Interpretation in the appropriate context determines whether it is decision-grade. It is therefore important to make the distinction between always-on and scenario-based CI so that the correct context can be applied based on a sponsor’s immediate strategic needs.
While tracking competitor data is the foundation, the real value lies in how the data should informs your evolving game plan. To navigate this, companies must balance two distinct but complementary approaches: scenario-based CI and always-on CI.
The point of always-on CI is to understand what the sponsor learned, what they now believe, and what that belief implies for their future development plans and positioning. Scenario-based CI, crucially, is in place to determine the consequences of these signals on your own drug development program.
Scenario-based CI maps the “what if?” It takes a snapshot of current signals and projects them into different future states, accounting for competitor timelines and regulatory hurdles.
For instance, a scenario-based approach is vital when navigating divergent global regulations. Currently, based on prior decisions, the EMA does not accept the slowing of lesion growth as an approvable endpoint in Geographic Atrophy, instead requiring evidence of functional benefits.11,12 This creates two distinct competitive landscapes, which may influence your geography-specific strategy. It also signals that any drug that successfully halts vision loss will significantly disrupt the standard-of-care and future entry bar into the setting, and is the key competitor to watch.
This type of foresight turns uncertainty into a decision-ready view because the potential impact of new data has already been socialized. Imagine a competitor fails their Phase II proof-of-concept (PoC) trial. A scenario-based framework allows you to immediately ask: Does this invalidate our shared mechanism of action (MoA), or is our specific target/trial design sufficiently differentiated to proceed? Because these questions were asked before the data broke, the company can react with speed and resilience.
However, these scenarios cannot exist in a vacuum; they require always-on CI to remain relevant. Always-on CI provides the continuous, real-time data stream—tracking competitors, regulatory shifts, and market factors—to keep the strategic framework current. Instead of simply asking "what happened?" when a news alert hits, always-on CI allows the team to ask: "How does this specific event change our pre-mapped scenarios?"
Ultimately, the two approaches act in concert. While scenario-based CI requires episodic, deep reflection to support long-term planning, always-on CI ensures that thinking is never anchored to outdated assumptions. This combined approach turns market volatility into a clear institutional view of which assumptions still hold—and which ones must be questioned.
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We support clients throughout the preclinical and clinical development process. For example, we help shape indication strategies, design preclinical experiments and clinical trials, refine biomarker and endpoint choices, and prepare decision-ready materials for boards and regulators.
