Publications by authors named "A M Oza"
Article Synopsis
- Artificial intelligence (AI) combines machine learning and deep learning, playing a vital role in medicine by predicting drug behavior and enhancing healthcare services, leading to better clinical outcomes and cost savings.* -
- Recent advancements in biomedical research have created a demand for new technologies, workforce, and standards, paving the way for a healthcare revolution through the integration of AI and health data.* -
- The review highlights AI technologies that can expedite drug development, improve product quality, and ensure safety during manufacturing, especially by predicting the structure of target proteins.*
Background: In women with platinum sensitive recurrent ovarian cancer (PSROC) undergoing maintenance treatment, adverse events (AEs) not attributable to the current treatment are not well understood. We used data from SOLO2/ENGOT-Ov21 to evaluate AEs reported in the placebo arm and to explore their longitudinal trajectories.
Methods: SOLO2/ENGOT-Ov21 (NCT01874353) randomly assigned 295 PSROC participants with a BRCA1/2 mutation to maintenance olaparib tablets (N = 196) or matching placebo (N = 99).
Findings from clinical trials have led to advancement of care for patients with gynecologic malignancies. However, restrictive inclusion of patients into trials has been widely criticized for inadequate representation of the real-world population. Ideally, patients enrolled in clinical trials should represent a broader population to enhance external validity and facilitate translation of outcomes across all relevant groups.
View Article and Find Full Text PDFBackground: Adavosertib is a first-in-class, selective small-molecule inhibitor of Wee1. Olaparib is an inhibitor of poly(ADP-ribose) polymerase (PARP). Preclinical data suggest that adavosertib enhances the antitumor effect of PARP inhibitors.
View Article and Find Full Text PDFObjective: Mirvetuximab soravtansine-gynx (MIRV) is a novel antibody-drug conjugate targeting folate receptor alpha (FRα), which is overexpressed in epithelial ovarian cancer (EOC), with limited expression on normal tissues. This integrated safety summary sought to characterize the safety profile of MIRV monotherapy in participants with FRα-expressing recurrent EOC.
Methods: Safety data were retrospectively analyzed from 4 clinical studies (phase 1 trial [NCT01609556], phase 3 FORWARD I [NCT02631876], phase 2 SORAYA [NCT04296890], phase 3 MIRASOL [NCT04209855]) that evaluated participants with FRα-expressing recurrent EOC who received ≥1 dose of MIRV 6 mg/kg adjusted ideal body weight every 3 weeks.