ENDO 2026 Panel Puts Lab Failure Under a Different Microscope
At ENDO 2026 in Chicago, one of the liveliest Professional Development sessions moved the conversation away from assay problems, negative data, and underwhelming experiments. It focused on a more uncomfortable and often less discussed category of failure: the decisions that shape a laboratory, a trainee, and a career.
Kavaljit Chhabra, MPharm, PhD, an associate professor of pharmacology and nutritional sciences at the University of Kentucky and a member of the Barnstable Brown Diabetes Research Center, described a career that began in pharmacy, moved through pharmacology and neuroscience, and eventually led to independent laboratory leadership. His research focuses on how the brain and kidney help regulate blood glucose, and his group has identified ADGRL1 as a glucose-binding receptor with potential implications for type 2 diabetes and obesity. Yet the most revealing part of his session did not concern a receptor, a pathway, or a metabolic model. It concerned the human machinery around the science.
One sentence from the discussion landed hard: problems grow in the absence of conversation.
Leadership Mistakes Can Cost More Than Failed Experiments
Chhabra drew a sharp line between scientific failure and leadership failure. A hypothesis can be wrong and still represent strong science. A poorly framed experiment can still teach something useful. A weak decision, by contrast, can drain time, money, trust, and morale before anyone recognizes what has gone wrong.
His examples were concrete and familiar to anyone who has run a lab. Saying yes to an experiment with a clear design flaw may feel supportive in the moment, but it can send a trainee down a dead-end path for months. Granting too many collaborations and committee duties can quietly push the lab’s mission to the margins. Avoiding conflict may preserve temporary comfort, yet unresolved tension usually hardens into something more expensive later. Even inflated evaluations, offered in the hope of encouraging a trainee, can backfire when real concerns finally surface and no longer carry credibility.
In Chhabra’s framing, the central problem was not a lack of goodwill. It was the habit of confusing kindness with effectiveness.
Failure as a Costly Signal, Not a Moral Verdict
Chhabra defined failure in unusually practical terms. A result that does not support a hypothesis is not failure; it is science doing what science is supposed to do. Failure begins when a preventable mistake wastes time, resources, or trust. Some losses cost a week. Others cost two years.
That distinction matters in a research environment where ambiguity is normal and where most projects contain real risk. A study can fail because the data move in an unexpected direction, and that is a clean outcome if the question was worth asking. A study can also fail because the plan was underpowered from the start, because key controls were missing, or because a conflict was allowed to fester until it damaged the working relationship around the project. Those are not the same events, and treating them as equivalent blurs the lesson.
His own career reflected that difference. Early years as a principal investigator were slower than they needed to be because judgment, not biology, was being spent poorly. The issue was not that the science lacked merit. It was that leadership habits were quietly taxing the whole operation.
The Hidden Burden on Early-Career Supervisors
A particularly resonant thread in the session came after the formal panel, when attendees spoke privately about difficulty directed upward rather than downward. In most academic narratives, conflict is portrayed as something trainees experience at the hands of supervisors. Less often discussed is the reality that early-career supervisors can also face resistance, friction, and difficult behavior from the people they lead.
That asymmetry leaves many new principal investigators isolated. They assume they should solve everything internally, as though asking for help would expose incompetence. Chhabra argued the opposite. Chairs, human resources staff, and ombuds offices exist for faculty too, not only for trainees. Using those structures is part of leadership, not proof that leadership has failed.
The bedside parallel in a clinical audience is obvious. Systems that hide conflict until it becomes visible only after harm has already occurred tend to magnify error. Labs behave the same way. Silence postpones embarrassment; it also postpones repair.
Boundaries, Systems, and the Discipline of Saying No
Several of Chhabra’s practical lessons were blunt. Boundaries are part of mentorship. Focus is a resource. Systems prevent misunderstanding.
He described a change in approach that many investigators eventually learn only after losing time to overcommitment. Before saying yes to a new responsibility, he now asks whether the task advances the lab’s mission, whether he is the right person to do it, and what will have to stop if he accepts. That kind of triage protects the research agenda from being swallowed by other people’s priorities.
The same principle applies to trainees. Saying no to a flawed experiment is not a refusal of support. It is a decision to protect a trainee’s time and preserve the lab’s scientific energy for questions worth answering. Check-ins, milestones, and clearly assigned responsibilities serve the same purpose. They do not imply distrust. They reduce the chance that people will drift apart while assuming the other side understands the plan.
Risk Should Be Spread Across the Portfolio
Chhabra also advised younger researchers to manage risk at the portfolio level rather than at the level of a single project. His approach is to keep some studies high-risk and high-reward while others build more gradually on established findings. If every project can collapse at once, even a routine setback can feel catastrophic.
That strategy has a direct psychological effect. It gives a lab room to be wrong without turning every negative result into a crisis of identity. It also helps trainees see that scientific careers are not built on uninterrupted victories. They are built on a sequence of experiments, corrections, and more honest decisions.
Ask for the honest version, he said. If feedback is uniformly positive, learning is not happening.
Why Clarity Outperforms Empty Encouragement
The session ended on a point that was as managerial as it was scientific: a failed project is not the same thing as a failed person. Data are analyzed; they are not carried around as a personal burden. A wrong hypothesis is a result, not a verdict.
Chhabra’s own turning point came when he began treating leadership mistakes the way he had always been trained to treat experimental mistakes. He asked what variable had been wrong, what assumption had not been tested, and why the pattern kept repeating. Once he applied that logic to people management, the answer was plain. Protecting someone from discomfort rarely solved the underlying problem. Clarity, honesty, and accountability worked better.
That conclusion gives the session its force. In a field built on measurement, the most durable lesson was about behavior, not biology. A lab can survive a bad experiment. It has a harder time surviving years of unspoken misunderstanding.