Designing Data For Funding: Preclinical Development Strategies For Startups

By Andrew Alexander, DVM, Ph.D., MBA, DABT, senior director of preclinical toxicology, Lovelace Biomedical

For early-stage biotech companies, the instinct to build a comprehensive preclinical data package is understandable. Scientific credibility, regulatory readiness, and investor confidence are often tied to the depth of available data.

In practice, however, this approach frequently leads to over-scoped preclinical programs that consume capital, extend timelines, and complicate decision-making. In today’s funding environment, success depends less on data volume and more on deliberate data staging to support key inflection points and enable pragmatic decision-making.

The Cost Of Over-Scoping In Early Development

A common challenge for emerging biotech companies is uncertainty around what data is required. Without a clear understanding of how investor priorities, scientific questions, and regulatory expectations intersect, founders often attempt to generate efficacy, pharmacokinetics, and toxicology data in parallel — when only a subset is needed to inform the next decision.

The financial implications are significant. Estimates place the cost of bringing a new drug to market at approximately $2.6 billion when accounting for failures, with total R&D costs more than doubling once attrition is factored in.^1,2 This dynamic places outsized importance on early-stage decision-making, as inefficiencies upstream can drive substantial downstream costs. These pressures are especially acute for venture-backed startups operating with limited capital.

Investors Fund Inflection Points, Not Exhaustive Data

Investors evaluate opportunities through a portfolio lens, knowing that only a small percentage of companies will deliver outsized returns. Their initial focus is straightforward:

• Does the asset demonstrate differentiation in the market?
• Are the key risks understood or mitigated?
• Is there sufficient evidence to justify investment?

Once these questions are addressed, clarity becomes more important than completeness. Data must enable decisions, not delay them.

Defining The Minimum Effective Data Package

This investor mindset drives a critical question: what is the minimum data required to move to the next milestone?

The answer varies by program but should always be anchored to a specific question to trigger advancement. In some cases, demonstrating that a compound avoids a known class-specific toxicity may be sufficient. In others, early evidence of target engagement or bioavailability may be the key driver.

The common principle is alignment: every study should directly inform whether a program advances, pivots, or stops.

Strategic Staging And Early Pivots

Designing toward these inflection points requires a staged rather than linear development model. Early preclinical work should prioritize lower cost, high-information studies that identify risks and establish feasibility. Scientific milestones — confirming bioavailability, resolving safety concerns, or demonstrating early efficacy — drive practical decisions that further advance the candidate or point to a need to pivot.

This approach is particularly valuable for companies managing multiple candidates. Smaller gated evaluation points increase the likelihood of identifying viable assets without overcommitting resources.

Equally important is the willingness to act on results. Timely go/no-go decisions are an essential part of early development. While discontinuing a candidate can be difficult, it is viewed positively by investors when supported by clear data. Early pivots preserve capital and allow resources to be redirected more effectively.

Strategic Tips For Startups Preparing For Funding

Startups approaching funding should apply the same discipline to data strategy that investors apply to capital allocation.

• Start with the decision, not the data set: Define the specific question that unlocks the next milestone.
• Design for inflection points: Align studies to clear the go/no-go criteria that investors prioritize.
• Define the minimum effective data package: Focus only on data required to answer the next critical question.
• Prioritize high-information, low-cost studies early: Establish feasibility and de-risk before scaling investment.
• Avoid parallel overreach: Limit simultaneous complex studies to those that are decision critical.
• Build in decision gates: Create structured checkpoints to reassess strategy as data emerges.
• Be prepared to pivot or stop: Clear negative data is valuable if it preserves capital and redirects focus.

A More Deliberate Path Forward

Early-stage drug development is inherently uncertain, but it does not need to be inefficient. Companies that structure preclinical strategy as a sequence of targeted, decision-oriented steps are better positioned to conserve capital, maintain momentum, and engage investors effectively. Scientific inflection points become business inflection points, guiding both program trajectory and organizational sustainability.

References

1. DiMasi, Joseph A., Henry G. Grabowski, and Ronald W. Hansen.
   “Innovation in the Pharmaceutical Industry: New Estimates of R&D Costs.” Journal of Health Economics, vol. 47, 2016, pp. 20–33.
2. Wouters, Olivier J., et al. “Estimated Research and Development Investment Needed to Bring a New Medicine to Market, 2009–2018.” JAMA, vol. 323, no. 9, 2020, pp. 844–853

About The Author

Andy Alexander, Ph.D., is the senior director of toxicology at Lovelace Biomedical, a not-for-profit preclinical contract research organization. He has more than 30 years of experience in academic research and CROs, with a career centered on building efficient, scalable preclinical operations that deliver high-quality science on predictable timelines. His background spans exploratory, non-GLP, and GLP studies across a wide range of therapeutic modalities, species, and delivery routes.