🔬 From Microdosing to Meaningful Data: The Journey of Sample Size

CUBE chat
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CUBE ChatShaala Summary—04 November 2025

Today’s CUBE ChatShaala revolved around an essential question in biomedical research — “How do we introduce the sample size?” The discussion unfolded within the context of cancer research, tracing the process from preclinical testing to clinical trials.

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Participants began by emphasizing the importance of preclinical research, including toxicity testing, before any human trials commence. This led to a detailed exploration of the phases of clinical trials and the gradual introduction of sample size through each phase:

  • Phase 0 – Microdosing: Very small groups are used to understand the body’s initial response to a drug.

  • Phase 1 – Healthy Volunteers: The focus is on establishing a safe dosage and understanding the concentration of the medicine.

  • Phase 2 – 200–300 Patients: Individuals with the targeted condition are included to test effectiveness.

  • Phase 3 – 1000–2000 Patients: Larger groups with diverse health backgrounds participate to ensure reliability and detect side effects.

  • Phase 4 – Post-Marketing Studies: Continuous monitoring after the drug is available to the public.

Institutions such as the Agarkar Research Institute and KJ Somaiya Lab were mentioned as key collaborators in this research context.

The discussion also touched upon the Aspire Funding Program, with a deadline on 4/11/25. The group identified two major goals:

  1. To decide the timeline of the study.

  2. To finalize the focus of the research.

:seedling: What I Learned

I learned that determining sample size is not a one-time decision—it evolves with each stage of research. What starts as a handful of subjects in microdosing can grow into thousands of participants as confidence in the medicine builds. The process is both scientifically structured and ethically guided.

:bulb: TINKE Moments

  • The realization that sample size is not arbitrary—it depends on the phase, risk level, and variability of results.

  • A misconception arose around the idea that larger samples always mean better results. The ChatShaala helped clarify that statistical significance and experimental design matter just as much as size.

  • Some participants initially assumed that all trials begin directly with patients; however, the importance of preclinical toxicity testing became clear through discussion.

:mag: Gaps and Misconceptions

  • Confusion between dose-finding (Phase 1) and efficacy testing (Phase 2) stages.

  • Uncertainty about ethical approval requirements before scaling up sample size.

  • The group realized a need to learn how statistical methods (like power analysis) guide sample size determination.

:question: Queries to the CUBE Community

  1. How can citizen scientists simulate sample size determination for small-scale biological experiments in homelabs?

  2. Can we design a mini-model of clinical trial phases for our school-level cancer research projects?

  3. What tools or open-source software can help us calculate statistical power or optimal sample sizes?

  4. If microdosing works safely in a few individuals, what ensures it will scale effectively to thousands?

:books: Reference