CROs In Early Drug Discovery, Part 3: Budgets, Timelines, And Avoiding Pitfalls

By Simon Cocklin, director of therapeutic discovery, Chan Soon-Shiong Institute for Molecular Medicine at Windber

In part 1 and part 2, I highlighted how to properly set up a CRO collaboration and maintain momentum once experimental work starts: choosing the right partner, defining expectations, structuring the SOW, establishing QC, and maintaining disciplined communication. These elements are essential, but they are not enough on their own.

Part 3 focuses on the operational pressures that determine whether a project stays viable: budgets, timelines, and the common pitfalls that quietly undermine data quality and progress if they are not anticipated. These issues are rarely scientific; they stem from planning errors, unrealistic assumptions, and subtle misalignments between sponsors and CROs.

This final section discusses the real-world factors that influence outsourced discovery — including how to set realistic budgets for workflows, interpret timelines accurately, handle delays effectively, and avoid operational pitfalls that often derail promising projects.

The first step is grounding budgets and timelines in how discovery actually works.

Budgeting And Timeline Reality

One of the most common reasons outsourced projects falter is not the science — it is unrealistic budgeting and timeline expectations from the start. Most of this is avoidable. In my experience, budgeting and planning work best when they reflect how discovery actually operates, not how we wish it operated.

Budget Up Front For Real Science, Not Idealized Workflows

I prefer to budget thoroughly up front. Unless a CRO explicitly states otherwise, I assume their quote includes the replicates, QC, and assay-validation steps that any competent workflow should require. Discovery is inherently iterative; a realistic budget acknowledges that. Cutting costs by reducing replicates or skipping qualification steps is a false economy — problems that would cost $2,000 to fix early become $20,000 problems downstream when the biology collapses under scrutiny.

A consistent red flag in CRO budgets is the lack of a line item for assay development. If assay development is not listed, it usually is not being done. Running a compound series on an assay that was never qualified is one of the fastest ways to sink an outsourced program. When it is missing from a quote, I ask the CRO directly how they plan to develop, test, and qualify the assay before execution.

Phase-Based Budgeting Keeps Everyone Aligned

I structure outsourced work in phases, each with a clear scientific purpose.

Phase 1: Feasibility — protein production, QC, activity confirmation, and initial assay setup

Phase 2: Assay development and pilot testing — optimization, control definition, Z′ evaluation, and early signal-to-noise checks

Phase 3: Execution — hit confirmation, compound screening, profiling, and deeper mechanistic assays

This is not a financial instrument — it is a scientific sequence. You move to the next phase only when the previous one actually works. Phase-based planning also prevents overspending early: if Phase 1 reveals that the protein is unstable or the assay cannot reach acceptable QC metrics, you adjust the plan before committing downstream funds.

Timelines: Trust Them, But Interpret Them

Timelines in CRO proposals are best viewed as helpful approximations, not guarantees. I like Gantt-style overviews because they show how the CRO sequences tasks and resources, but I take all proposed timelines with a grain of salt. Rushing purely to "stay on schedule" often produces low-value data. Therapeutic discovery requires precision: I would prefer a CRO take the time needed and get it right rather than deliver fast, unusable results.

The more revealing questions are:

• How busy is the CRO's team right now?
• Will my work queue behind larger clients?
• How many scientists will handle the project?
• How long do repeats or QC failures typically take?

Answers to those questions tell you much more than the nominal dates in a proposal.

When Timelines Slip, Lead With Collaboration

Delays happen. Proteins misbehave. Assays fall apart. Equipment fails at precisely the wrong moment. My instinct is patience, not confrontation.

Nevertheless, I always ask for a meeting — not to demand speed, but to understand what actually went wrong. Sometimes I can offer a work-around, an unpublished detail, or a troubleshooting step grounded in the biology that the CRO does not have. Collaborative escalation is almost always more productive than pressure.

There is also a practical reality many people forget: If you have not been at the bench in years, your sense of how long experiments take becomes dangerously optimistic.

Experiments done properly take time. I stay hands-on in the lab — when the lab folks allow — precisely so I can keep my expectations grounded in what real experimental work requires. If you are not actively running experiments yourself, involve someone who is. A scientist who still works at the bench can defuse timeline friction immediately by explaining what is realistic and why.

Some of the best recoveries I have seen from timeline slips came from bringing everyone onto a call, reviewing the raw data together, and solving the problem as a unified scientific team.

Common Pitfalls And How To Avoid Them

Even with a strong SOW and good communication, outsourced projects can still derail for predictable, and avoidable, reasons. These are the recurring failure modes I have seen across academic, CRO, and institute settings — and how I now prevent them.

Vague SOWs: The Root Of Most Downstream Failures

The most damaging ambiguity in any SOW is a lack of experimental detail. I have seen statements as vague as: "We will test compound X for inhibition."

That tells you nothing about the assay, controls, pH, temperature, replicates, or acceptance criteria. When an SOW lacks definition, CROs fill in the blanks with their defaults — often incorrectly.

Prevention: I now insist on SOW language that defines the exact biological context, controls, replicates, and readout. If the CRO has suggestions that improve robustness or speed, I incorporate them, but the scientific intent always comes from the sponsor.

Unvalidated Internal Protocols

Many CROs lean on "standard internal protocols" that were never designed for your biology. I routinely see:

• dogmatic adherence to legacy workflows
• missing controls
• dynamic range never evaluated
• Z′ unreported
• acceptance criteria unstated.

CROs may assume their protocol is "fine," even when it clearly is not for a specific target class.

Prevention: I ask to review the exact standard operating procedure (SOP), dynamic range data, controls, and historical QC metrics before compounds are run. Tailoring the protocol together avoids weeks of unusable output.

Speed Without Scientific Quality

Sponsors often push for faster timelines; CROs often promise them. However, "fast" only matters if the data is actually usable.

I expect efficiency — that is one of the reasons to outsource — but never at the cost of:

• replicates
• proper controls
• activity checks
• validated assay conditions.

It is always a compromise: pace plus correctness. I am in that situation right now with an active program — we will see how it resolves.

Prevention: Define early which parts of the workflow must meet complete QC before speed becomes a priority. If timelines compress, the quality bar should not.

Misalignment Of Urgency And Enthusiasm

Two things commonly drive urgency mismatch:

1. Sponsor excitement — "We need this by Monday."
2. CRO disengagement — The work does not energize the team.

I do my best work with CROs who are genuinely excited about my projects — teams that want to contribute to publications, IND-enabling packages, and real translational impact. When that enthusiasm is missing, timelines inevitably drift.

Prevention: I highlight critical path vs. background tasks at the start, and I re-highlight them when priorities shift (e.g., funding opportunities). Priorities change; communication must follow.

The Reproducibility Crisis In Outsourced Work

Two pitfalls now sit near the top of my list — both avoidable and both devastating:

1. Skipping protein activity checks

This is the fastest way to undermine an entire discovery effort.
If the protein is not active, or cannot bind its known partner, nothing downstream is meaningful.

2. Refusing to repeat failed experiments

My worst CRO experience involved:

• no controls
• all ELISA concentrations run at saturation
• no interpretable signal
• refusal to repeat the work.

It was a dead end.

And this is not isolated. Reproducibility failures are widespread in academia and biotech. I have wasted months and budget chasing published findings that collapsed immediately when tested properly. Moreover, a senior director at a biotech once described a program that failed mysteriously: “When I asked whether they had confirmed protein activity first… the answer was exactly what you would expect!”

Prevention: Define activity requirements, repeat criteria, and QC gates early so a repeat is never framed as "scope change."

Final Principle

Most CRO failures are not due to incompetence — they are due to assumptions the sponsor never corrected. A CRO will do precisely what is written down. It is your job to ensure that what is written down is unambiguous.

Conclusion: CROs Are Part Of The Scientific Team

In early discovery, CROs are not secondary — they are part of the scientific team.

Part 1 of this series outlined the foundations of effective collaboration: choosing the right CRO, clearly defining expectations, and creating an SOW that eliminates ambiguity before starting any work.

Part 2 demonstrated that these foundations only matter if they are maintained during execution — through intentional communication, defined QC, and disciplined oversight. Part 3 adds the final layer: grounding budgets and timelines in reality, recognizing where operational friction typically occurs, and preventing avoidable pitfalls that erode data quality and momentum. These issues are rarely scientific. They arise when assumptions go unspoken or when sponsors and CROs become misaligned on urgency, quality, or scope.

Across all three stages, the difference between a productive collaboration and a costly failure depends on whether both sides share an understanding of the science, the standards, and the pace. When expectations are clear, QC is established, and decisions are documented, CROs can significantly increase a small team’s capacity. When expectations are vague or open to interpretation, the results are almost always slow, inconsistent, or unusable.

The principles are straightforward:

• Write down precisely what each experiment is meant to show.
• Define QC, activity requirements, and replicates.
• Insist on raw data in usable formats.
• Keep communication structured and transparent.
• Approach delays collaboratively rather than reactively.
• Align budgets and timelines with how discovery actually works.

None of this is complex — but skipping any part of it invites the kinds of problems that waste time, budget, and scientific momentum.

A CRO is not something you “set and forget.” It is a scientific partnership that requires structure and engagement from start to finish. When the foundations established in part 1 are carried through the execution disciplines of part 2 and the operational safeguards of part 3, CROs can — and often do — deliver high-quality, decision-grade data that genuinely advances discovery.

About The Author

Simon Cocklin, Ph.D., is the founding director of therapeutic discovery at the Chan Soon-Shiong Institute of Molecular Medicine at Windber (CSSIMMW), where he leads translational drug discovery projects in immuno-oncology and fibrosis. At CSSIMMW, he is establishing a new Therapeutics Discovery Department and building scientific and infrastructural capabilities to support early-stage drug development aligned with the institute's goals.

He is the chief scientific advisor for Regenova Pharmaceuticals, an early-stage biotech that utilizes multi-omics and AI to develop antibodies against infectious diseases and cancer targets. He is also the cofounder and co-CEO of Bespoke Patient Solutions, LLC.

He previously held faculty and leadership roles at Drexel University College of Medicine, where he led NIH-funded research programs focused on drug discovery for HIV-1, infectious diseases, and oncology.