A pharmacologist is testing a drug on 5 groups of cells, each with 12,000 cells. The drug affects 7% of cells per group. If she repeats the experiment 4 times with identical results, how many total cells are affected across all trials?

Thu, 30 Apr 2026 17:48:45 +0000

**A pharmacologist is testing a drug on 5 groups of cells, each with 12,000 cells. The drug affects 7% of cells per group. If she repeats the experiment 4 times with identical results, how many total cells are affected across all trials?** In ongoing discussions about cutting-edge biomedical research, a growing number of experts are focusing on how drugs interact with cellular systems across multiple trial conditions. Recent experiments using high-precision lab models show that even subtle variations in cellular responses can significantly influence drug development. One controlled study involves testing a compound on five distinct groups of 12,000 cells each—totaling 60,000 cells per trial. When the drug shows consistent effects, revealing a 7% impact per group, researchers are now examining cumulative outcomes across repeated experiments. Understanding these patterns helps refine predictive models and strengthen scientific reliability in drug testing. If a pharmacologist tests this drug on 5 groups of cells, each made up of 12,000 cells—and the drug affects 7% of cells in each group—then the number affected in one trial is calculated as 7% of 12,000. This equates to 840 cells per group. Multiplying by 5 groups gives 4,200 cells impacted per initial experiment. When this same process repeats four times with identical results, the total affected across all trials becomes 4,200 cells multiplied by 4, resulting in 16,800 total cells influenced throughout the full set of experiments. This compound’s performance across repeated trials offers valuable data points for assessing efficacy and reproducibility—key factors shaping modern pharmacological research. Despite initial assumptions about rapid cell response, the consistent 7% effect highlights the complexity behind seemingly simple lab results. Each trial contributes nuanced feedback, helping guide both scientific insight and future research direction.