Drug-induced gastrointestinal toxicities (DI-GITs) are among the most common adverse events in clinical trials. High prevalence of DI-GIT has persisted among new drugs due in part to the lack of robust experimental tools to allow early detection or to guide optimization of safer molecules. Developing in vitro assays for the leading GI toxicities (nausea, vomiting, diarrhoea, constipation, and abdominal pain) will likely involve recapitulating complex physiological properties that require contributions from diverse cell/tissue types including epithelial, immune, microbiome, nerve, and muscle. While this stipulation may be beyond traditional 2D monocultures of intestinal cell lines, emerging 3D GI microtissues capture interactions between diverse cell and tissue types. These interactions give rise to microphysiologies fundamental to gut biology. For GI microtissues, organoid technology was the breakthrough that introduced intestinal stem cells with the capability of differentiating into each of the epithelial cell types and that self-organize into a multi-cellular tissue proxy with villus- and crypt-like domains. Recently, GI microtissues generated using miniaturized devices with microfluidic flow and cyclic peristaltic strain were shown to induce Caco2 cells to spontaneously differentiate into each of the principle intestinal epithelial cell types. Second generation models comprised of epithelial organoids or microtissues co-cultured with non-epithelial cell types can successfully reproduce cross-‘tissue’ functional interactions broadening the potential of these models to accurately study drug-induced toxicities. A new paradigm in which in vitro assays become an early part of GI safety assessment could be realized if microphysiological systems (MPS) are developed in alignment with drug-discovery needs. Herein, approaches for assessing GI toxicity of pharmaceuticals are reviewed and gaps are compared with capabilities of emerging GI microtissues (e.g., organoids, organ-on-a-chip, transwell systems) in order to provide perspective on the assay features needed for MPS models to be adopted for DI-GIT assessment.