Drug candidates fail – most of them, in fact. And a significant proportion fail because of poor pharmacokinetics, or the safety risks were unacceptable. Accordingly, integrated ADME-Tox studies are designed to characterize these parameters early, enabling the identification and de-risking of compounds with inadequate bioavailability, metabolic instability, or toxicity signals before progression into costly late-stage failures.
Here are the questions we hear most often when it comes drug discovery, answered directly.
Why should ADME-Tox be integrated early in drug discovery?
The answer is partly economic, partly scientific – and the scientific reason is the stronger one.
Screening for ADME liabilities at lead identification stage is inexpensive relative to the cost of discovering those liabilities later. A compound found to be metabolically unstable early enough can be redesigned, while a compound found to have the same liability after advancing through development studies represents sunk cost and delayed programmes.
But the more consequential benefit is structural. When small molecule ADME data feeds into medicinal chemistry in real time – during the design-make-test cycle – chemists can modify a compound to address pharmacokinetic weaknesses without sacrificing the potency and selectivity they’ve worked to build. Once a lead is locked, those options narrow considerably.
ADME-Tox is often framed as a downstream checkpoint that compounds must pass; however, it is more accurately treated as a multidimensional optimization parameter, to be balanced alongside pharmacological activity from the earliest stages of structure evaluation.
What does an ADME-Tox profile actually tell you about a compound?
An ADME-Tox profile maps how a compound behaves in a biological system across five dimensions: how well it’s absorbed, where it distributes, how it’s metabolised, how it is excreted and whether it – or its metabolites – causes harm in the process.
Together, those parameters determine whether a compound with the right pharmacological activity can function as a drug. A metabolically unstable compound may not reach its target at therapeutic concentrations, another with poor membrane permeability may not reach it at all, while one that inhibits key cytochrome P450 (CYP) enzymes creates drug-drug interaction (DDI) liability that could make it clinically unworkable in a polypharmacy setting.
The data describe what happens, sure, but they also inform every subsequent decision about candidate selection, dose, formulation, and development path – and they do so most usefully when they arrive early enough to act on.
How does the Admescope platform approach drug-drug interaction assessment?
DDI risk arises when a compound alters the activity of enzymes or transporters involved in the disposition of co-administered drugs. This can lead to changes in exposure, either through inhibition or induction of metabolic pathways. The most clinically significant DDIs are mediated by CYP enzymes, which are involved in the metabolism of ~70–80% of small molecule drugs in clinical use. Given that patients are frequently on polypharmacy regimens, potential drug-drug interactions should be assessed early, with consideration of the co-medications most likely to be used within the intended patient population.
Early DDI screening focuses on CYP inhibition and induction assays. These can be run cost-efficiently to flag compounds with meaningful CYP liability before significant resources are committed to further development. However, it’s also good to bear in mind the risk of the compound being a DDI object, and hence, its metabolic routes should also be assessed.
No single assay provides a definitive answer, but a combination of inhibition, induction, and understanding the metabolic fate, generate the profile required to make informed go/no-go decisions at the point in development where they’re most actionable.
Drug-induced liver injury (DILI) remains a leading cause of drug attrition – how do you screen for it early?
DILI is one of the more difficult safety liabilities to predict, partly because it encompasses multiple distinct mechanistic pathways rather than a single failure mode. These include oxidative stress, mitochondrial dysfunction, hepatocellular fat accumulation (steatosis), phospholipid accumulation (phospholipidosis), bile flow disruption via efflux transporter inhibition and the formation of reactive metabolites capable of covalently binding cellular proteins.
Our DILIscope panel was developed specifically to address this mechanistic breadth at the early screening stage. It uses multiparametric imaging endpoints to assess multiple DILI-relevant cellular responses simultaneously – which provides more mechanistic resolution than single-endpoint assays, and more efficiently than running each parameter in isolation.
Efflux transporter inhibition assays targeting hepatocyte bile acid transporters add further specificity. Impaired bile flow is a key DILI mechanism that standard cytotoxicity assays would miss. Reactive metabolite trapping assays identify compounds that form chemically reactive species – a mechanism linked to both direct hepatotoxicity and immune-mediated adverse reactions.
The combination doesn’t eliminate DILI risk; that ultimately requires clinical data. What it does is substantially narrow the range of mechanistic uncertainty before those studies begin.
The field has shifted towards new modalities. How does ADME-Tox adapt for peptides, oligonucleotides, PROTACs, and ADCs?
Small molecules and biomolecules behave differently, and those differences are significant for how ADME data is generated and interpreted.
- Peptides typically show poor oral bioavailability and rapid proteolytic degradation – challenges that standard permeability and metabolic stability assays aren’t designed to directly capture.
- Oligonucleotides distribute heavily to liver and kidney and are cleared via nuclease-mediated pathways rather than CYP enzymes, making standard DDI screens largely uninformative and cell-based distribution assays considerably more relevant.
- PROTACs introduce bifunctional complexity: their larger molecular weight challenges conventional permeability models, and their mechanism of action – targeted protein degradation via the ubiquitin-proteasome system – creates pharmacodynamic/ pharmacokinetic relationships that standard exposure-response frameworks don’t fully describe.
- For ADCs, the ADME challenge sits at the intersection of the antibody, the linker and the small-molecule payload – payload release kinetics and metabolite profiling require assays adapted to the conjugate, not to the component parts in isolation.
These modalities still need rigorous ADME-Tox characterisation and they’ve expanded what that characterisation needs to cover. Applying small-molecule frameworks to biomolecule programmes generates incomplete data, and the evidence for interpreting novel modality ADME results remains less established than for traditional small molecules. That gap is closing, but it needs ADME scientists to stay on top of how this field is changing.
What does the future of ADME-Tox look like?
Two developments are likely to reshape routine practice over the next five to ten years.
The first is AI and machine learning for ADME prediction. Computational models for metabolic stability, permeability and toxicity have existed for years, but with limited or varying predictive accuracy. Training on larger and more chemically diverse datasets, including proprietary assay data from laboratory platforms, is beginning to produce models with meaningful predictive value for well-characterised parameters. There’s no question around whether these models are going to become routine, it’s just a matter of how quickly they generate the training data required to extend reliably to novel modalities.
The second is the progressive displacement of animal studies by complex in vitro systems. Spheroid and organoid cultures, organ-on-a-chip microfluidic platforms (microphysiological systems, or MPS) and advanced co-culture hepatocyte models are improving reproducibility and mechanistic depth. Regulatory agencies in both the EU and US (such as the FDA’s iterations on the Modernization Act) have begun signalling openness to these platforms as partial replacements for some in vivo studies. Progress is incremental, but the direction is consistent.
Both developments require ADME scientists to build proficiency in data science and computational methods alongside wet-lab expertise – something more and more ADME-Tox programmes require.
What’s the most important thing a drug discovery team can do when designing their ADME-Tox strategy?
Bringing ADME expertise into the programme before candidate selection and connect it directly to the medicinal chemistry team.
In our experience, the compounds that reach IND tend to be the ones where ADME and chemistry were optimised together from the start – not sequentially. Detecting an ADME liability in a compound that still carries optimal potency and selectivity creates a specific, addressable problem. Detecting it after those properties have been locked creates a much harder one.
About the author
Dr. Sanna-Mari Aatsinki is Head of Drug-Drug Interactions and In Vitro Toxicology at Admescope, a Symeres company. With deep expertise in in vitro toxicology, drug-drug interaction assessment, and mechanistic safety evaluation, she supports drug discovery teams in identifying and mitigating risk early through integrated ADME-Tox strategies.
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