Drug-response measurements across pre-clinical pharmacogenomic studies remain poorly correlated, limiting biomarker discovery, precision oncology, and predictive modelling. The drivers of this inconsistency have been debated but not yet resolved. By integrating 15 pharmacogenomic studies encompassing 760 small-molecule compounds, 1,111 cell models, and 9.8 million dose-response measurements, we demonstrate that dose-response metric is the strongest driver of inconsistency, followed by experimental factors, such as treatment duration, plate format, and viability readout, while cell line molecular features contribute only minimally. Among drug classes, hormone therapies and PARP inhibitors show the highest concordance, whereas antimetabolites, topoisomerase inhibitors, and mitotic inhibitors exhibit substantial variability in response across studies. To improve consistency, we developed a novel Drug Response Score (DRS), a proximity-weighted measure that emphasize pharmacologically informative concentrations near IC₅₀, and show in systematic benchmarking how DRS markedly improved cross-dataset concordance. Applications to patient-derived neuroblastoma organoids and leukemia patient cells demonstrate that DRS improves replicate-level consistency in patients’ drug-response profiles. To improve reproducible pharmacogenomic analysis, we make openly available an integrated Drug Response Resource (iDRR, https://aittokallio.group/iDRR/), a standardized 15-dataset portal that supports robust biomarker discovery and cross-study benchmarking.