The Preclinical Rigor Gap: Why Well-Regulated Clinical Trials Still Fail

India's clinical trial regulation is, by design, one of the clearer parts of the drug development pathway but preclinical research doesn't get the same treatment. The New Drugs and Clinical Trials Rules, 2019 were explicitly built to make clinical trial regulation unambiguous and foreseeable, clear documentation requirements, clear committee structures, clear expectations at every phase. Founders and CROs alike can point to exactly what's required and when. However, the gap between preclinical and clinical stages is responsible for a large share of the drug development failures that get blamed on "the science not working out."

Two very different regulatory postures

Ethics oversight for animal studies in India runs through the Committee for the Purpose of Control and Supervision of Experiments on Animals (CPCSEA) and Institutional Animal Ethics Committees. This system is real, mandatory, and procedurally significant, but it governs animal welfare and approval process, not experimental design or statistical rigor. There's no equivalent requirement specifying adequate sample sizes, defensible primary endpoints, or minimum standards for bias control in an exploratory efficacy study.

Good Laboratory Practice (GLP) standards do exist and are taken seriously, but they're specifically tied to formal regulatory toxicology submissions. Most of the earlier-stage, exploratory preclinical work that determines whether a program is even worth advancing isn't held to a comparable design standard at all.

The result is a structural inversion: the stage of drug development with the least regulatory specificity is the stage that determines whether everything expensive and regulated that follows is worth doing. A study can clear ethics review, generate a publishable, positive-looking result, and still rest on a sample size too small to be meaningful, a control group that doesn't isolate the right variable, or an endpoint that doesn't actually map to the clinical question it's meant to answer.

The numbers this produces downstream

This isn't a theoretical concern. Roughly 90% of drug candidates that clear preclinical testing still fail somewhere in clinical trials. In oncology specifically, the measured correlation between animal model outcomes and eventual clinical results has been found to be under 8%. Efficacy and safety issues together account for the large majority of failures at the expensive Phase II and III stages. This is precisely the point where a mismatch between what was tested and what actually mattered in humans becomes impossible to hide any longer.

Translation researchers distinguish between two kinds of validity that determine whether a preclinical study actually predicts a clinical outcome. Internal validity (e.g. sound study design, proper controls, bias mitigation) has received the most attention in the literature and in practice. External validity (i.e. whether the model itself is a genuine analog for the human condition being studied) has received comparatively little, and it's often the more consequential gap. A study can be internally airtight and still be asking the wrong question, because the model it was built on was never a strong stand-in for how the disease behaves in people.

Where the translation actually breaks

The failure point isn't a mysterious, unavoidable "biology is hard" phenomenon. It's usually a specific, identifiable design decision made too early and never revisited:

Model selection made for convenience, not translational fit. The most accessible, well-characterized model isn't always the one that best represents the human disease process. Choosing a model because it's established and easy to work with, rather than because of a clear translational rationale, is one of the most common and least discussed sources of downstream failure.

Endpoints chosen for what's measurable, not what's clinically meaningful. A biomarker that's convenient to measure in an animal model doesn't automatically predict the clinical endpoint that regulators and physicians actually care about. When preclinical and clinical endpoints aren't deliberately mapped to each other before the first experiment runs, a technically positive preclinical result is almost useless to the clinical team.

Statistical rigor treated as optional at the exploratory stage. Underpowered studies, informal or absent sample size calculations, and incomplete statistical reporting are well-documented, structural issues in preclinical research broadly, not failures specific to any one lab or company. Without a regulatory mandate forcing the issue at the preclinical stage, this discipline has to be self-imposed.

What this means in practice

None of this argues for more regulatory oversight of preclinical work, most founders already have more friction than they want from ethics committee cycles that meet only twice a year. It's an argument for treating experimental design with the same seriousness founders already bring to regulatory documentation, precisely because nothing is currently requiring it of them.

The absence of a mandate isn't the absence of a risk. It just means the risk stays invisible for longer, and it gets dramatically more expensive to discover once a program has already moved into regulated, capital-intensive clinical development.

The founders who avoid this aren't the ones with the biggest preclinical budgets. They're the ones who chose their model, their endpoints, and their study design around the eventual clinical question from the very first experiment.

Hexaura Solutions works with biotech founders and researchers on protocol development and experiment design specifically to close this gap, building preclinical studies with the clinical translation question in view from day one.