How To Translate Your Nonclinical Strategy Into a Language for Investors

How To Translate Your Nonclinical Strategy Into a Language for Investors

You walked an investor through your toxicology data. Species rationale, dose-response, NOAEL, safety margins — technically airtight. They nodded. Then they turned to your CEO and asked: "So can the drug actually be given to people?" That gap isn't a science problem. It's a translation problem. In this episode, we break down the five places nonclinical scientists lose investors — and exactly how to reframe the same data so it answers the questions investors are actually asking. Key takeaways: * Investors aren't reading your nonclinical package for scientific defensibility — they're reading for what could kill the program * The same NOAEL means two different things depending on the reader: to FDA it's a dose justification floor, to an investor it's the answer to "how much room do you have before this gets dangerous?" * Species selection isn't regulatory boilerplate — it's proof that if the drug behaves badly, you'll see it in animals before it touches a patient * "Characterized and bounded" is the most powerful phrase in a pitch room — it tells investors you found the monster, measured it, and put it in a cage * The pre-IND meeting is your trump card with investors and most founders bury it on slide 14 — lead with it instead * Defensiveness in a pitch signals risk. Walk in knowing you and the investor have the same job: finding every way this program could fail Links: * The Complete Guide to Nonclinical Development: https://www.nonclinical.academy/ [https://www.nonclinical.academy/] * Work with Dessi: toxistrategy.com * Read the full newsletter issue on LinkedIn: the-nonclinical.com 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.

TK Profiles: The Good, The Bad, and The Ugly

Every animal survived. Body weights stable. No major clinical signs. You're practically popping champagne — and you're about to get an FDA hold. Because buried in the appendices is a TK table that proves your drug never actually made it into the bloodstream. In this episode, we break down toxicokinetics — what it is, why it underpins every dose justification you'll ever make, and what good, bad, and ugly TK profiles actually look like in practice. Key takeaways: * TK is just pharmacokinetics in the context of a toxicology study — Cmax, Tmax, AUC, and T½ are the bridge between the dose you give and the effects you see * Good TK tells a clean, cohesive story: consistent exposure, dose-proportional increases, well-timed sampling — it validates your NOAEL and supports your IND * Bad TK doesn't fall apart completely, but introduces enough uncertainty to make interpretation tricky — inconsistent exposure, non-linear AUC increases, and exposure overlap between dose groups * Ugly TK undermines the entire study — no systemic exposure at high dose, formulation failure, unexpected accumulation — and can force you to repeat the study entirely * TK is not a supporting actor. It's part of the main story. Review it alongside findings, not as an afterthought Links: * Early access — The Complete Guide to Nonclinical Development: https://www.nonclinical.academy/ [https://www.nonclinical.academy/] 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.

Anatomy of a Bulletproof IND — The Nonclinical Sections Explained

Your IND is rejected before a single human being ever reads it. Not because the science is wrong — because a 10-year-old legacy file was missing a digital tag. In this episode, we break down exactly what goes into the nonclinical sections of an IND, how the 5-module eCTD structure works, and the SEND dataset rules that silently kill submissions before they ever reach a reviewer. Key takeaways: * The IND is organized into 5 modules — and the nonclinical program lives primarily in Module 2 (summaries) and Module 4 (study reports and SEND datasets) * Module 2.4 (Nonclinical Overview) is written last — after the detailed 2.6 summaries are complete — because it summarizes them * Every toxicology study listed in Module 2 must have an associated study report in Module 4, and vice versa — no orphans allowed * SEND datasets are required for almost all tox studies, including nonGLP studies — and a missing SEND dataset triggers automatic rejection before a human reviewer ever sees your data * Even studies run 10 years ago still need at minimum a TS domain to pass validation — age of the study is not an exemption Links: * My course: The Complete Guide to Nonclinical Development, https://www.nonclinical.academy/ [https://www.nonclinical.academy/] * Work with Dessi: toxistrategy.com * Read the full newsletter issue on LinkedIn: https://the-nonclinical.com/ 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.

NAMs: The Promise, the Gap, and the Cold Hard Truth

Everyone in drug development is talking about New Approach Methodologies — AI, organ-on-a-chip, virtual control animals, in silico modeling. The vision is compelling: fewer animals, faster timelines, better translational data. But where are we actually? In this episode, we cut through the hype and take an honest look at where NAMs stand in nonclinical toxicology today, why the hardest part of the pipeline has been the slowest to change, and what it would actually take to get there. Key takeaways: * NAMs are advancing rapidly on either side of nonclinical safety — ADME, in vitro screening, computational modeling — but the GLP tox studies that actually get you to IND have been the slowest to change * The reason is structural: IND-enabling tox studies are the most expensive, most time-sensitive, and most risk-laden studies in the program — most companies won't experiment there * The silo problem is real: regulatory safety decisions are made by comparing data within a single study, which makes integrating external datasets architecturally difficult * Virtual control animals (Charles River/Sanofi) are one of the only dedicated computational solutions in nonclinical toxicology — and may be proof of concept for what's possible * The FDA is moving in the right direction on NAMs — but most toxicologists would not submit a full IND without GLP animal studies today, and that's not a failure of imagination, it's a responsibility to patients Links: * Check out my course: https://www.nonclinical.academy/ * Data Is Not Strategy on Amazon: https://a.co/d/02xUsV6K [https://a.co/d/02xUsV6K] * Work with Dessi: toxistrategy.com 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.

Data Hormesis: When More Data Makes Things Worse

More data should mean less risk. In nonclinical development, that's not always true. In this episode, we explore the concept of data hormesis — the inflection point where accumulating more data stops reducing uncertainty and starts creating it. If your team is running another study because the last one didn't give you the answer you needed, this episode is for you. Key takeaways: * Data hormesis is the point where more information stops helping and starts obscuring the path forward — just like a drug that's beneficial at low doses and toxic at high ones * Early in development, data reduce uncertainty. Beyond a certain point, signals compete for attention rather than converging toward resolution * When decisions aren't defined early, data accumulation quietly becomes a substitute for judgment rather than a tool to support it * The patterns are recognizable: equivocal findings trigger rework instead of interpretation, borderline results lead to more studies without clarity on what would actually change * Programs that move efficiently to IND aren't the ones with the most data — they're the ones that decided early which risks are acceptable and which questions are worth answering now * Data don't create strategy. They make strategy visible. Links: * Data Is Not Strategy on Amazon: https://a.co/d/02xUsV6K [https://a.co/d/02xUsV6K] * Work with Dessi: www.toxistrategy.com 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.