The Toxicogenomics Research Landscape

What people are actually working on, who they are, and where the four papers sit among them.

Chapter 2 was why the field exists — the regulatory pull. This is what the field does — the research threads, the datasets, the groups, and the competition.

The headline finding, and you need to know it before writing anything: there is a method from a different federal agency, published a year earlier in a Nature-portfolio journal, doing the exact same task as TransTissueFormer. It’s cited — in one clause, generically. See §3.5.

All 2024–2026 claims sourced.

3.1 The map

Toxicogenomics has four research arms. They’re funded separately, publish separately, and mostly don’t talk to each other. Knowing which one you’re in determines who reviews you.

Arm 1 is the smallest and the one with a regulatory product (ETAP, Ch 2 §2.5). It’s also where the data is scarcest — which is why matrix completion, augmentation, and translation are the methods that dominate. The methods in this book are a response to arm 1’s data poverty.

⭐ The interesting structural fact: GenTox is the only one of the four that touches arm 4 (chemical structure, via the compound GNN). That makes it the bridge between the two arms — and, as Ch 2 §2.9 notes, a learned read-across. That framing is worth more than “inductive matrix factorization” to this audience.

3.2 The datasets — the real infrastructure

Toxicogenomics is dataset-driven to an unusual degree. Knowing which is which tells you what a paper can and cannot claim.

Two things to notice:

DrugMatrix is unique in one respect and one only — eight organs, in vivo, same animal. TG-GATEs has two. LINCS/DRUG-Seq/sci-Plex are cells in dishes: no organs, no systemic exposure, no inter-organ communication. That is the moat (15_FRONTIER.md), and it’s real.

ArrayTrack matters because it’s what TransTox trains on — so TransTox and TransTissueFormer solve the same problem on different data, which makes them complementary evidence rather than a head-to-head. That’s a useful thing to be able to say.

3.3 Thread 1 — transcriptomic points of departure

Covered in Ch 2 §2.4–16.5. The dominant thread in arm 1, and the one with a regulatory product.

Active research questions, from Tox Sci 2024, “Bioinformatic workflows for deriving tPODs” — this paper is a ready-made list of open problems:

• which BMD method? distribution-based vs gene-set-based
• how do you filter which genes respond?
• how few animals / how short an exposure can you get away with?
• how reproducible is the whole workflow across labs?

Standard tool: BMDExpress 2/3 — built by Scott Auerbach, who is corresponding author on two of your four papers.

This is the thread your work would feed. Ch 2 §2.11’s contribution #2 — predict the tPOD, not the profile — lives here.

3.4 Thread 2 — organ toxicity prediction (DILI and friends)

DILI — drug-induced liver injury — is the field’s canonical prediction task. It’s the single biggest cause of drug withdrawal, so industry cares enormously.

The pattern is always the same:

transcriptomic profile  →  classifier  →  "is this hepatotoxic?"

Representative work:

• Open TG-GATEs + FAERS (FDA Adverse Event Reporting System) + deep nets → predict adverse drug reactions. A nice trick: link molecular profiles to real-world human outcomes.
• Att-RethinkNet — multilabel, memory + attention, correlating hepatic and renal pathology from TG-GATEs
• ToxAssay (Bioinformatics 2025) — R package over TG-GATEs and DrugMatrix
• ML for DILI to assist medicinal chemistry (J Med Chem)

Why this matters to you: TransPlatformer’s best result (F1 0.636 → 0.718, 05_TRANSPLATFORMER.md §5.8) is exactly this task. It’s the one number in the four papers that a tox audience will immediately understand and value — because it’s their benchmark, not an ML one.

3.5 Thread 3 — multi-organ, and the competitor ⭐⭐⭐

This is the most important section in this chapter.

TransTox

Li, Chen & Tong (2024), npj Digital Medicine 7(1):310 — “Bridging organ transcriptomics for advancing multiple organ toxicity assessment with a generative AI approach”. FDA National Center for Toxicological Research.

From the abstract:

“TransTox, an innovative AI model using a Generative Adversarial Network (GAN) method to facilitate bidirectional translation of transcriptomic profiles between the liver and kidney under drug treatment.”

That is TransTissueFormer’s task. Liver ↔ kidney. Drug treatment. Transcriptomic profiles.

How TransTissueFormer cites it

Reference [26]. One clause, in the Introduction:

“Beyond predictive performance, generative AI models have been proposed to produce synthetic transcriptomic profiles that preserve biological signals, facilitating downstream applications such as toxicity assessment and biomarker discovery [26].”

That describes it as generic “generative AI.” It does not say: this is a method that already does bidirectional liver–kidney translation. There’s no comparison, no discussion of how the approaches differ, no positioning.

A reviewer will find this. It is the first thing a reviewer will find.

Not because it’s hidden — it’s in npj Digital Medicine, from Weida Tong, who is one of the most visible people in computational toxicology, at the FDA.

And there’s a second tension

TransTissue §5 states:

“The very limited amount of common measurements between tissues and the vast drug space in DrugMatrix prevent the application of generative models for creating new profiles.”

TransTox is a generative model creating new profiles, for liver↔kidney, and it works.

The claim may still be defensible — on DrugMatrix specifically. DrugMatrix has 425 LI–KI pairs; ArrayTrack may have far more, and TG-GATEs-derived liver/kidney data is denser. So “generative models don’t work at our sparsity” could be exactly right.

But it has to be argued, not asserted — especially with [26] sitting in the bibliography.

What this means for you — and it’s not bad news

The strongest move: stop treating TransTox as a citation and start treating it as the baseline. “TransTox (GAN, 2 organs, ArrayTrack) vs TransTissueFormer (transformer, 8 organs, DrugMatrix) — here’s what each does better, and here’s the first head-to-head” is a better paper than either alone.

And note TransTox reports mechanism concordance, not PCC. They already evaluate the way Ch 2 §2.11 argues you should. That’s not a coincidence — it’s what an FDA audience demands.

3.6 Thread 4 — the in vitro arm (ToxCast / Tox21 / IVIVE)

Not transcriptomics, but it’s where the federal money is, and you’ll be asked about it.

Tox21 — a collaboration across four US federal agencies (EPA, NIH/NCATS, NIEHS/NTP, FDA). ToxCast is EPA’s in vitro screening programme: a large chemical library × assay panels covering signalling pathways and cellular processes.

The scale is the point: ~10,000 chemicals, run at throughput impossible for animals.

The problem is IVIVE — in vitro to in vivo extrapolation. A dish is not an animal. So:

A concentration that does something in a well → what dose in a human?

Current state: “predictive adult IVIVE models have produced fold differences less than 10 between the modelled human Administered Equivalent Dose and the measured in vivo adjusted points of departure.”

Within 10×. Same ballpark as the liver-tPOD→apical result (Ch 2 §2.7).

⭐ Notice the pattern. Every bridge in this field lands at “within 10×”:

• in vitro → in vivo: ~10×
• liver tPOD → systemic apical POD: ~10×
• tPOD → apical POD: ~3×

10× is what this field accepts as useful for a protective threshold. If your cross-tissue model can’t beat 10× on something that matters, it isn’t competitive with methods that already exist. That’s a much more honest bar than PCC, and it’s the one the field actually uses.

3.7 Thread 5 — AOPs, and why they’re stalled

Adverse Outcome Pathway: molecular initiating event → key events → adverse outcome.

The status is the interesting part:

• “the implementation of omics-based approaches in AOPs and their acceptance by the risk assessment community is still a challenge”
• “regulatory agencies show limited formal adoption of AOPs despite their potential”
• “there is a disconnect between AOP research and regulatory implementation”

Twenty years of effort, universal agreement it’s the right idea, and it’s barely used.

Why? An AOP is hand-curated. Someone reads the literature and writes down a causal chain. It doesn’t scale to 350,000 chemicals.

The gap: AOPs are the field’s lingua franca for mechanism, and there’s no automated way to go from data to AOP. A model that outputs “which key events fired” rather than “here’s a vector” would speak the field’s language. That’s adjacent to Ch 2 §2.11’s enrichment-consistency idea, and possibly the more ambitious version of it.

3.8 What the ML-in-tox literature says about itself

There’s a substantial self-critical literature. Read it before you write anything, because reviewers have.

Archives of Toxicology 2026 — “A review of machine learning in toxicology: current practices and reporting gaps” and companions list six recurring failures:

#6 is worth dwelling on. The field is explicitly complaining about unshared code and data. DrugMatrix is public; ToxCompl’s completed matrix is published; ToxiTenCompl is on arXiv. But no model code.

Releasing runnable code would be a contribution the field has explicitly asked for, costs nothing intellectually, and is the kind of thing that gets a method adopted rather than admired.

Also emerging: “Potential Role of Agentic Artificial Intelligence in Toxicologic Pathology” (arXiv 2026) — the LLM-agent wave is arriving. Note it, don’t chase it.

3.9 Who’s who

You will be reviewed by these people. It’s worth knowing the map.

Read the author lists on your four PDFs again. ORNL + NIEHS + Sciome. That’s a national-lab / regulatory-agency / contractor triangle, not a university group. It tells you the work is aimed at deployment inside a federal assessment pipeline — which is exactly what Ch 2 §2.10 says about audiences B and C.

3.10 Where the field is going in 2026

That last row is the thesis of this whole book. The FM wave has hit single-cell biology hard (10_SOTA_LANDSCAPE.md) and has not reached toxicogenomics. There is no toxicogenomics foundation model. Given a 2035 deadline and 350,000 data-poor chemicals, that’s a gap with a clock on it.

3.11 What this changes about your plan

Three concrete updates to 13_SYNTHESIS.md–15_FRONTIER.md:

1. TransTox is a baseline, not a citation. ⭐ The most valuable single experiment just became: run TransTox’s approach on DrugMatrix, or TransTissueFormer on ArrayTrack, and compare. GAN vs transformer, 2 organs vs 8, and the first head-to-head on the task. Nobody has done this, and the two agencies clearly aren’t talking.

2. “Within 10×” is the bar, not PCC. IVIVE lands at 10×. Liver-tPOD→apical lands at 10×. tPOD→apical at 3×. That’s the currency. Evaluate cross-tissue translation on whether it beats 10× on a decision someone makes — which routes straight back to Ch 2 §2.11’s predict the tPOD idea.

3. Release the code. The field’s own review literature names unshared code as a top-six failure. None of the four papers ship any. It’s free, and it’s what makes a method get used.

And one thing that got easier: TransTox de-risks the premise. You are no longer arguing that cross-organ translation is worth doing — the FDA published that argument in a Nature journal in 2024. You’re arguing about how, on more organs, with better evaluation. That’s a much easier paper to write.

3.12 Summary

THE FOUR ARMS In vivo transcriptomics (where your papers are, smallest, has the regulatory product) · high-throughput in vitro (ToxCast/Tox21, where the money is) · AOPs (the mechanism language, stalled) · chemical structure (QSAR/read-across, oldest). GenTox’s compound GNN is the only bridge between arms — and it’s a learned read-across.

THE DATASETS DrugMatrix is unique for eight organs, in vivo, same animal. Everything else is two organs, or cells in dishes. That’s the moat.

THE COMPETITOR ⭐ TransTox (Li, Chen & Tong, FDA NCTR, npj Digital Medicine, Nov 2024): a GAN for bidirectional liver↔kidney translation, validated across labs, evaluated on mechanism concordance. TransTissueFormer cites it as generic “generative AI” and never compares. A reviewer will find this immediately. It’s also the best available baseline, and it validates the premise.

THE TENSION TransTissue §5 says sparsity “prevent[s] the application of generative models.” TransTox is a generative model doing exactly this. Possibly true on DrugMatrix specifically — but it must be argued.

THE BAR Everything in this field lands at within 10× — IVIVE, liver-tPOD→apical. tPOD→apical at 3×. That’s the currency, not correlation.

THE SELF-CRITIQUE Six named failures. Three apply here: overfitting (425 samples, 4.4M params), low-quality data (92% noise), and unshared code — which none of the four papers release.

THE GAP WITH A CLOCK ON IT Foundation models are essentially absent from toxicology. EPA wants all mammalian animal testing gone by 2035. 350,000 data-poor chemicals. That is the opening.

TransTox details are from its abstract, author list, and the citation context in TransTissue (1).pdf — I have not read its full body. Do that before building on §3.5. Group/personnel mapping is from public profiles and paper author lists. §3.6’s “everything lands at 10×” observation is mine.