Your Study Is Done — So Why Are You Still Waiting for the Report?

The science is finished. The data is locked. So why is your IND still weeks away? In this episode, we pull back the curtain on one of the most consistent — and avoidable — sources of delay in nonclinical programs: the gap between data lock and final report delivery. We break down the legacy dual-track CRO workflow that's been quietly adding 8-10 weeks to timelines, and explore the single-track solution that's finally fixing it.

Key takeaways:

• Data lock is not the finish line — sponsors who treat it that way get blindsided every time
• The legacy dual-track system has two separate teams manually handling the same data, sequentially — and it was built for an era that no longer exists
• Manual transcription doesn't just slow things down, it introduces reconciliation risk that can cascade into regulatory rejection
• PointCross's single-track process runs report generation and SEND dataset preparation in parallel from a single source of truth — eliminating duplication entirely
• For CROs, slow turnaround is no longer just an operational issue — it's a competitive liability

The Nonclinical is hosted by Dessi McEntee, MS, DABT — board-certified toxicologist and Fractional Head of Toxicology. Subscribe to the newsletter on LinkedIn, take the course at nonclinical.academy, or work with Dessi at toxistrategy.com.

Transcript

SPEAKER_00 0:00

Imagine spending, you know, 10 years and millions of dollars engineering a groundbreaking new drug.

SPEAKER_01 0:06

Yeah, an absolutely massive investment of time and capital. Trevor Burrus, Jr.

SPEAKER_00 0:09

Right. And the molecular science is just flawless. The trials are done. The results are incredible. Exactly. You can't submit it for approval and you can't get it to a single patient. And why? Because you're waiting 10 weeks for someone in a back office to manually retype all that brilliant data into a Microsoft Word document.

SPEAKER_01 0:28

Trevor Burrus, I mean it sounds like a complete exaggeration, right?

SPEAKER_00 0:31

It really does.

SPEAKER_01 0:32

But that is the precise reality for countless companies in the biotech space today. We have this uh massive disconnect between the 21st century science happening in the lab and the 20th century administrative systems that are processing it.

SPEAKER_00 0:45

Aaron Powell Welcome to this deep dive. Today, our mission is to uncover a hidden, surprisingly manual bottleneck in scientific research, specifically in the world of non-clinical toxicology. Trevor Burrus, Jr.

SPEAKER_01 0:57

Right, the stuff that happens before the human trials.

SPEAKER_00 0:59

Exactly. And we're going to be pulling from some incredible insights shared by Desi McInty. She is a biotech board member, the chief development officer of Imogen, and the author of Data is Not Strategy.

SPEAKER_01 1:11

And crucially for our discussion, she operates as a fractional head of toxicology.

SPEAKER_00 1:16

Yeah, so through her work, we're going to explore why biotech companies face these agonizing delays, you know, after the actual science is finished, and look at the specific operational approach that is finally fixing it.

SPEAKER_01 1:29

Because when Desi comes into a program, she isn't just looking at the molecular structures or um the study designs. She acts as an operational architect.

SPEAKER_00 1:38

Which is a cool way to think about it.

SPEAKER_01 1:40

It really is. She evaluates the entire infrastructure surrounding the science. And her core premise is that operational structure is just as vital as the study design itself.

SPEAKER_00 1:49

Right, because brilliance in the lab is neutralized if the data just gets stuck in a structural traffic jam on its way to the regulators.

SPEAKER_01 1:56

Exactly.

SPEAKER_00 1:56

So to ground this for anyone listening who might not, you know, live and breathe drug development every day, let's briefly define what we mean by non-clinical toxicology. Sure.

SPEAKER_01 2:06

This is the critical, highly regulated phase of testing, usually involving animal models that absolutely must happen before a drug is ever allowed near a human being in clinical trials.

SPEAKER_00 2:18

Yeah. If you want to submit an investigational new drug application or IND to the FDA, this non-clinical data is literally your ticket to entry.

SPEAKER_01 2:28

And the stakes there are astronomical, right?

SPEAKER_00 2:30

Oh, massive. Startups plan their entire corporate strategy, their VC funding rounds, their cash burn runway, all around getting that IND submitted. Wow. And the milestone they usually fixate on is something called data lock.

SPEAKER_01 2:42

Data lock. Okay, what exactly is that?

SPEAKER_00 2:44

So data lock is the exact moment when the in-life phase of an animal study ends. The physical experiments are over, the biological observations are complete, and the laboratory data is secured.

SPEAKER_01 2:55

So for a startup CEO or um a sponsor paying for this research, data lock feels like the finish line.

SPEAKER_00 3:02

Yes, exactly.

SPEAKER_01 3:02

They assume they have the data, the science is locked, so they could just, you know, pop the champagne and send it off to the FDA by Friday. Right, and that is the trap. Sponsors pop the champagne because they think data lock equals delivery.

SPEAKER_00 3:14

Uh but it doesn't.

SPEAKER_01 3:15

No, not at all. DESI warns that sponsors often just don't know what to ask about the timeline after data lock. The biological reality of the study is finished, but the operational reality of packaging that data for the FDA is just beginning.

SPEAKER_00 3:30

Wow.

SPEAKER_01 3:30

And by the time this massive delay becomes visible to the sponsor, their critical milestones have already slipped.

SPEAKER_00 3:36

Which raises the most obvious question. I mean, what is actually happening during those weeks of waiting? Right. If the data is secured, why does it take so long to just hand it over?

SPEAKER_01 3:46

Well, the delay comes down to how contract research organizations or CROs architect their data processing.

SPEAKER_00 3:53

These are the big external labs running the studies for the biotechs.

SPEAKER_01 3:57

Exactly. And at most CROs, the standard workflow results in an eight to ten week lag from data lock to final sponsor delivery.

SPEAKER_00 4:03

Aaron Powell Eight to 10 week we of just moving data around. Yes. For a startup burning through venture capital, I mean that is an absolute eternity.

SPEAKER_01 4:09

It's brutal. It creates what DESI refers to as operational debt.

SPEAKER_00 4:14

Operational debt. I like that term.

SPEAKER_01 4:16

Yeah, because it isn't just a time delay, right? It's compounding risk. Every single time human hands touch, move, or reformat scientific data, the interest rate on that debt goes up.

SPEAKER_00 4:27

In the form of potential errors, I assume?

SPEAKER_01 4:30

Exactly. Errors and ultimately regulatory rejection. And the current system at most CROs is this dual track sequential workflow that basically maximizes human touch points.

SPEAKER_00 4:41

Okay, let's break down that dual track system because I think this is where the gap between the science and the administration gets really pronounced.

SPEAKER_01 4:48

Yeah, let's look at track one.

SPEAKER_00 4:50

Right. So track one begins right after data lock. You have a highly trained study director who has to pull data from all these multiple isolated digital systems. Aaron Powell Right.

SPEAKER_01 4:59

So there are LIMS laboratory information management systems tracking the basic metrics. There are histopathology systems tracking tissue samples. Yeah. And then there are bioanalytical platforms.

SPEAKER_00 5:09

And I'm guessing these systems don't exactly play nicely together.

SPEAKER_01 5:12

They do not naturally speak to each other at all. So the study director begins this incredibly tedious process of manually transcribing that disparate data into Word document templates.

SPEAKER_00 5:23

Wait, into Word, like Microsoft Word.

SPEAKER_01 5:26

Yes. To draft a massive narrative final report. They are literally copying and pasting or retyping numbers from laboratory software into a Word document.

SPEAKER_00 5:37

Wow. Wait, we are talking about cutting-edge biotech research, and their data management strategy relies on manually transcribing data into Word.

SPEAKER_01 5:45

I know it sounds archaic.

SPEAKER_00 5:46

Isn't that a massive invitation for typos and errors?

SPEAKER_01 5:49

It absolutely is. And as painful as that sounds, that is only track one.

SPEAKER_00 5:53

Oh, right. The dual track.

SPEAKER_01 5:54

Yeah, because once that Word document is finally finished, weeks later, track two begins. A completely separate team takes over to build the Send data set.

SPEAKER_00 6:03

Okay, Send. Let's clarify that for anyone wondering why the FDA demands such a rigid format. Send stands for Standard for Exchange of Nonclinical Data, right?

SPEAKER_01 6:11

Correct. The FDA implemented this because reviewers used to receive literally thousands of pages of PDF reports.

SPEAKER_00 6:17

No, that sounds awful for the reviewers.

SPEAKER_01 6:19

It was. They couldn't easily search or cross-reference the data. So Send forces all non-clinical data into a highly rigid machine readable format.

SPEAKER_00 6:28

But getting it into that format sounds like a nightmare. You're dealing with millions of data points across all these varying biological domains that must perfectly align with standardized controlled terminology.

SPEAKER_01 6:40

That is the exact crux of the issue. The FDA software will instantly reject a submission if the data set isn't perfect. Wow. So the severed send team takes the exact same source data that the study director just used for the word report, and they start their own completely separate manual process from scratch.

SPEAKER_00 6:59

Wait, so two teams, the exact same data, handled twice.

SPEAKER_01 7:02

In sequence, yes.

SPEAKER_00 7:03

That is wild. It's like um imagine a film crew shooting a massive, beautiful 3D animated movie. Okay. But instead of just rendering the final digital cut to show the studio, they hire a second, entirely separate crew to sit in a screening room, watch the movie, and trace every single frame by hand to make a flipbook.

SPEAKER_01 7:21

That is a brilliant metaphor. It captures the sheer redundancy perfectly.

SPEAKER_00 7:24

It's just so inefficient.

SPEAKER_01 7:26

And the flipbook analogy highlights the darkest part of this dual-track system, which is reconciliation.

SPEAKER_00 7:32

Ah.

SPEAKER_01 7:32

When you have two separate teams manually entering millions of data points into two different formats, discrepancies aren't just possible. They are a mathematical certainty.

SPEAKER_00 7:42

Right. Because if the study director, you know, corrects a decimal point in the word report during week seven of this process, the flipbook team has to stop what they're doing.

SPEAKER_01 7:52

Exactly. They have to erase their pages and redraw them from scratch to ensure it perfectly matches.

SPEAKER_00 7:57

That sounds miserable.

SPEAKER_01 7:58

The reconciliation process is brutal. The send data set and the narrative word report must mirror each other flawlessly. Right. If team A types a five-foot and team B types a six-foot, the entire process grinds to a halt. They literally have to trace that single data point back to the raw lab equipment to find out who made the typo.

SPEAKER_00 8:18

Oh man. So that reconciliation lag is what pushes a what four-week drafting process into a 10-week administrative marathon?

SPEAKER_01 8:26

You nailed it. That's exactly where the time goes.

SPEAKER_00 8:28

Okay, wait. I need to push back here for a second.

SPEAKER_01 8:30

Yeah.

SPEAKER_00 8:31

We're talking about CROs, right? Contract organizations running these studies as a business. Right. If I am running a CRO and my business model involves billing biotech sponsors for the hours my teams work. Aren't I financially incentivized to stick with a 10-week labor-intensive manual process? I mean, why would any CRO rush to adopt a faster model if it means they bill for fewer hours?

SPEAKER_01 8:57

Aaron Ross Powell That is a highly pragmatic question. And honestly, 10 years ago, you might have been absolutely right.

SPEAKER_00 9:02

Really?

SPEAKER_01 9:02

Yeah. But the business dynamics of biotech have fundamentally shifted. CROs are rarely billing purely by the hour for these standardized regulatory packages anymore.

SPEAKER_00 9:11

Oh, interesting.

SPEAKER_01 9:12

They're increasingly operating on fixed price contracts or milestone-based payments.

SPEAKER_00 9:16

Aaron Powell Oh, I see. So the CRO is actually absorbing the cost of that 10-week delay.

SPEAKER_01 9:20

Aaron Powell Exactly. The CRO absorbs the internal labor cost, but the sponsor absorbs the timeline penalty.

SPEAKER_00 9:25

Aaron Powell Right, which the sponsors must hate.

SPEAKER_01 9:27

Oh, they do. Biotech companies today are incredibly educated about these delays. They don't have endless cash reserves. Their internal review windows get heavily compressed because they're always trying to make up for lost time.

SPEAKER_00 9:39

So they're shopping around.

SPEAKER_01 9:40

Constantly. Before a sponsor even signs a multimillion dollar contract with the CRO, they are interrogating the turnaround times.

SPEAKER_00 9:48

So we've really reached a tipping point then. I mean, if an archaic workflow is costing you contracts because the CRO next door can deliver the exact same scientific rigor three weeks faster, it's no longer just an administrative headache.

SPEAKER_01 10:02

No, it is a severe business liability. Wow. You see, the legacy systems were built in an era before modern cloud infrastructure and APIs. You historically had to finish one step to generate the static files needed for the next step.

SPEAKER_00 10:14

Well, right, but technology evolved.

SPEAKER_01 10:16

It did. But the operational workflow at many CROs simply didn't keep pace. So this sequential model has become an existential threat to CRO competitiveness.

SPEAKER_00 10:26

Okay, so if the core bottleneck is having two isolated teams doing sequential data entry, the obvious fix is getting them on the same track. Exactly.

SPEAKER_01 10:35

But how do you actually do that when limbs and histopathology systems speak completely different digital languages?

SPEAKER_00 10:42

Aaron Powell And this brings us to the technological solution DESI points to, which is single track processing, specifically spearheaded by point cross life sciences.

SPEAKER_01 10:50

Single track processing.

SPEAKER_00 10:51

Okay.

SPEAKER_01 10:51

Let's get into the mechanism of how point cross actually achieves this, because I imagine it's not just a matter of putting both teams in the same Slack channel, right?

SPEAKER_00 10:59

No, definitely not. The premise of the single track process is that study report generation and send dataset preparation should pull from the exact same digital source of truth. And they must run in parallel, not in sequence.

SPEAKER_01 11:12

So how do they bridge the gap between unstructured lab data and the rigid send format without humans retyping it?

SPEAKER_00 11:19

Deep API level integration. Okay. The platform connects directly to the digital laboratory sources, the as collected raw data from the limbs, the histopath systems, the bioanalytical platforms, it all flows directly into the point-cross ecosystem.

SPEAKER_01 11:32

Aaron Powell So there's zero manual transcription from screen to screen.

SPEAKER_00 11:36

That's huge. But let's focus on histopathology for a second, because I imagine that's the hardest part to automate.

SPEAKER_01 11:41

It is notoriously difficult, yeah. Trevor Burrus, Jr.

SPEAKER_00 11:43

Right. Because you have a pathologist looking through a microscope at a tissue sample and they're writing paragraphs of highly nuanced medical notes, but the FDA SEN format doesn't want paragraphs, right? It wants specific, standardized codes from a controlled dictionary.

SPEAKER_01 11:58

Exactly.

SPEAKER_00 11:59

How does an automated platform handle that translation?

SPEAKER_01 12:02

Aaron Powell That is where the AI augmented architecture of the platform becomes crucial. It utilizes specialized natural language processing, or NLP, that is trained specifically on toxicological and pathological lexicons.

SPEAKER_00 12:16

Oh wow.

SPEAKER_01 12:16

Yeah. So when the pathologist enters their narrative findings, the NLP engine scans that unstructured text and automatically maps it to the highly rigid sin-controlled terminology.

SPEAKER_00 12:27

Automatically. Okay. But I imagine that triggers a massive red flag for a lot of scientists.

SPEAKER_01 12:31

How so?

SPEAKER_00 12:31

Well, if an AI platform is mapping pathological data and drafting reports, does the human study director lose control over the science? I mean, if I'm a biotech sponsor, I am paying millions of dollars for the expert judgment of that human toxicologist, not an AI summary.

SPEAKER_01 12:48

That is a very valid concern, and it's why the design of this single track process specifically protects the scientists' authority.

SPEAKER_00 12:56

Okay, how?

SPEAKER_01 12:57

The AI is not interpreting the biology, it is interpreting the formatting.

SPEAKER_00 13:01

Ah, okay.

SPEAKER_01 13:02

It is purely an administrative engine. The drafting environment is shaped entirely around the individual study director's personal templates, their writing style, and their preferred narrative structure.

SPEAKER_00 13:13

So the platform is essentially acting as the world's fastest, most accurate administrative assistant.

SPEAKER_01 13:18

Precisely. It handles the mapping, the terminology coding, and the formatting, which frees the human toxicologist to actually just do the science.

SPEAKER_00 13:26

Which is what they want to be doing anyway.

SPEAKER_01 13:27

Exactly. The study director retains absolute control. They review the AI's terminology mapping and approve it. They write the final scientific conclusions. But here is the magic. While the study director is utilizing this augmented environment to finalize their narrative report, the send data set is being generated in the background from that exact same approved source data simultaneously.

SPEAKER_00 13:52

That is the parallel processing. So to go back to our analogy, the flip book is being generated automatically, frame by frame, as the movie is being edited.

SPEAKER_01 13:59

Yes. Meaning you never have to wait for the report to be finalized before starting the send data set. That's incredible. At the exact moment the study director signs off on the narrative report, the send data set is already built, it's computationally validated, and it is perfectly aligned with the written report.

SPEAKER_00 14:16

Which completely vaporizes that reconciliation nightmare we talked about earlier.

SPEAKER_01 14:20

Completely.

SPEAKER_00 14:20

Because if the study director catches a subtle anomaly in week four and updates their narrative, well, the send data set is anchored to the exact same data lake. So the data set inherently updates. The cascading error never happens.

SPEAKER_01 14:35

And this translates into incredibly straightforward, game-changing ROI for everyone involved.

SPEAKER_00 14:41

Let's talk about that ROI.

SPEAKER_01 14:43

Sure. With a single track process, study reports are consistently delivered two or more weeks faster from the moment of data lock.

SPEAKER_00 14:50

Two weeks. Wow. Two weeks of recovered runway for a startup biotech can literally be the difference between making it to their next funding round or closing their doors.

SPEAKER_01 15:00

It's massive. And because the data set is generated in parallel, there are literally zero weeks to send.

SPEAKER_00 15:06

Zero weeks to send.

SPEAKER_01 15:08

Yep. The then package is a natural byproduct of the workflow. It's ready the minute the report is finalized.

SPEAKER_00 15:13

Aaron Powell Beyond just the speed, though, I have to imagine the sheer quality of the data improves. If you remove the manual transcription, you're inherently removing the typos.

SPEAKER_01 15:22

Absolutely. The quality improvement manifests as absolute traceability. For the biotech sponsors, this is a massive reduction in operational debt.

SPEAKER_00 15:31

Right, because they can trust the data.

SPEAKER_01 15:32

Exactly. When they receive this faster deliverable, they can click on any data point in the final submission and trace it directly back to the raw as collected laboratory data.

SPEAKER_00 15:41

Oh, that's brilliant.

SPEAKER_01 15:42

There is an unbroken digital thread, which is exactly what regulatory agencies like the FDA want to see.

SPEAKER_00 15:48

Let's circle back to the CROs for a second. The contract labs running the studies. We established that the old sequential model was becoming a business liability for them. Yes. What does adopting the single track platform do for their market position?

SPEAKER_01 16:04

It provides what business strategists define as a structural advantage.

SPEAKER_00 16:09

A structural advantage. Meaning it's not just a tiny tweak.

SPEAKER_01 16:12

Right. This isn't a marginal efficiency gain of a few percentage points. It fundamentally alters their service offer.

SPEAKER_00 16:18

Because they can offer speed.

SPEAKER_01 16:20

Yes, they can aggressively compete on turnaround time. They offer sponsors significantly faster data delivery without sacrificing a single ounce of scientific rigor, regulatory compliance, or study director autonomy.

SPEAKER_00 16:32

And in a market where sponsors are, as we said, incredibly sensitive to timelines and budgets, offering a structurally faster, higher quality submission package, I mean that's an overwhelming competitive edge.

SPEAKER_01 16:45

It truly is. The constraint that necessitated the dual track system, the lack of integrated data architecture, it just no longer exists. Wow. As DESI evaluates the landscape, her takeaway is that the industry is moving well past the proof-of-concept phase.

SPEAKER_00 17:00

People are actually doing this.

SPEAKER_01 17:01

The technology is here and it works. The defining question for CROs now is simply how long they can afford to bleed contracts to competitors who have already adopted single-track processing.

SPEAKER_00 17:13

Yeah, it really sounds like an adapter-dye moment for data management in the life sciences.

SPEAKER_01 17:17

Without a doubt.

SPEAKER_00 17:17

So for anyone listening who wants to dive deeper into the mechanics of this or, you know, hear directly from the source, Desi McIntyre is actually speaking on the point cross panel at SOT, the Society of Toxicology conference.

SPEAKER_01 17:28

Yes, highly recommend checking that out.

SPEAKER_00 17:30

That is happening on Monday, March 23rd at 3.15 p.m. in San Diego. So if you are heading to SOT, definitely put that on your schedule.

SPEAKER_01 17:38

She bridges this incredibly rare gap between deep scientific expertise and pragmatic operational architecture.

SPEAKER_00 17:45

And you can also subscribe to her excellent newsletter, The Nonclinical, at its new home, toxistrategy.com forward slash the dash nonclinical. It is highly recommended for anyone navigating this space. Absolutely. Well, let's take a step back and recap the journey of this deep dive today. We started by looking at a hidden compounding operational debt in the biotech world, that agonizing delay between finishing the science and actually delivering the data.

SPEAKER_01 18:11

Right, the data lock illusion.

SPEAKER_00 18:12

Exactly. We unpacked the mechanics of the dual-track legacy system, exploring how manually transcribing unstructured histopathology and LIMS data into rigid send terminology created a massive reconciliation. Nightmare?

SPEAKER_01 18:26

The flipbook.

SPEAKER_00 18:27

The hand-drawn flipbook, exactly. And finally, we explored the solution: an AI-augmented single-track process from point-cross life sciences that uses API integrations and NLP mapping to run report generation and send packaging in perfect traceable parallel.

SPEAKER_01 18:41

And the ultimate takeaway here for you, the listener, extends far beyond the highly specialized world of non-clinical toxicology.

SPEAKER_00 18:48

Oh, for sure.

SPEAKER_01 18:48

Whether you are in finance, software, architecture, manufacturing, or any industry that relies on complex information, the operational pipes carrying your data dictate your velocity just as much as the data itself. That is such a crucial point. Brilliance in your core product simply doesn't matter if your delivery mechanism relies on redundant human transcription.

SPEAKER_00 19:09

Right. It's the multimillion dollar sports car waiting 10 weeks for a hand-typed manual. Exactly. And that leaves us with an aha question for you to ponder as you go about your week. If a fiercely regulated high-stakes field like biotech can utilize AI to eliminate redundant manual data entry mapping, unstructured data into rigid formats while keeping human experts firmly in control of the final judgment? Where else in your own industry are you paying the hidden, invisible tax of sequential workflows?

SPEAKER_01 19:40

It's a great question.

SPEAKER_00 19:41

Look closely at your own operations. What is the custom built sports car in your office that is currently gathering dust, just waiting for someone to finish typing up the manual?