This data article describes an original synthetic/simulated dataset designed to support materials-informatics and comparative formulation analysis of PLGA nanoparticles and liposomes for brain-cancer-relevant drug delivery. Open row-level datasets that jointly cover formulation descriptors, release kinetics, blood-brain-barrier transport proxies, and paired tumor/non-tumor cell-assay outcomes for PLGA and liposome carriers were not identified by us in a single harmonized open resource during preparation of this package, motivating a transparent synthetic benchmark for methodological and machine-learning reuse. The dataset was inspired by the scientific themes synthesized in the related review article by Makalew and Abrori 1 , but it does not reproduce bibliometric records, published tables, or experimental rows. The package contains 6,000 unique virtual formulations in a formulation master table and three linked long-format data tables describing time-resolved release profiles (360,000 rows), blood-brain-barrier-related transport proxies (54,000 rows), and paired tumor/non-tumor cell-assay proxies (432,000 rows), totaling approximately 846,000 assay-like rows. Variables include composition descriptors, preparation routes, physicochemical properties, targeting features, encapsulation efficiency, drug loading, stability, biodegradation proxy, serum stability proxy, integrated blood-brain-barrier transport score, cellular uptake score, biocompatibility score, tumor-directed cytotoxicity proxy, off-target toxicity proxy, and derived multi-criteria performance scores. The synthetic data were generated with a transparent, reproducible workflow that combines domain-informed priors, hierarchical conditional rules, latent heterogeneity, batch effects, replicate variation, bounded noise, sparse scientifically motivated missingness, and post-generation quality filters. The dataset is distributed in open tabular formats together with generation code, a codebook, validation documentation, and reproducible figure-generation scripts.
Abrori et al. (Mon,) studied this question.