Oncologic drug development is lengthy (~14 years) and expensive (~1.2 billion USD) with low clinical trial success rates (4.1%). Big data and artificial intelligence (AI) are widely proposed as tools to address these challenges. In this review, we examine the current performance and future potential of big data and AI applied to preclinical discovery and development, clinical trials, and the regulatory approval process. We first examine the data foundation required for effective AI, including data harmonization, data commons, and analytical tools. We then assess preclinical applications spanning target identification, compound-library curation, virtual ligand screening, generative chemical design, and high-throughput and high-content screening. In clinical development, we consider the use of big data and AI for outcome prediction, trial design, external and synthetic control arms, adaptive monitoring, and in silico trials. Finally, we discuss how post-approval electronic health records can generate real-world data and real-world evidence to support drug repurposing and improve future oncology drug discovery. Big data is conventionally characterized by a series of “Vs.” In this review, we have used seven “Vs” spanning descriptive and constraining properties of big data and a singular outcome. We have proposed an eighth, Vernacular, a constraint defined as the combined alignment of data semantics and terminology, data representation, data exchange, and data governance across heterogeneous, independently generated datasets to promote interoperability and combined analysis. Although cancer data exhibit substantial Volume, Velocity, and Variety, they remain distributed across fragmented repositories that often cannot be readily integrated. We conclude with a discussion of tabulated resources currently available for the application of big data and AI to oncologic therapeutics.
Fakhar U. Singhera, J. Overhulse, Terrence M. Lee et al.· Cancers· 0 citations
Metastatic castration-resistant prostate cancer (mCRPC) remains a leading cause of cancer-related mortality in men, with over 35,000 deaths annually in the United States and a 5-year survival rate of approximately 30%. Although AR-targeted therapies such as enzalutamide and darolutamide delay disease progression, resistance inevitably emerges, underscoring a critical unmet need for next-generation therapeutics. Here, we describe EMI-1725, a novel androgen receptor (AR) antagonist developed to overcome resistance-associated AR mutations. EMI-1725 binds the AR ligand-binding domain with affinities comparable to or higher than enzalutamide and darolutamide. In cell-based transcriptional assays, both enantiomers inhibit wild-type AR activity at potencies comparable to approved agents and retain activity against the most prevalent AR mutation T878A, where EMI-1725 is approximately 5-fold more potent than darolutamide. Critically, EMI-1725 also demonstrates antagonist activity against the enzalutamide-resistant double mutant AR F877L/T878A, a target not addressed by current therapies. In cell viability assays, EMI-1725 inhibits proliferation of two prostate cancer models (AR-amplified VCaP and LNCaP cells expressing the AR T878A mutation). In an AR-amplified xenograft model of mCRPC (VCaP), EMI-1725 reduced tumor growth as well as enzalutamide and suppressed PSA levels below those achieved with enzalutamide at standard dosing. Treatment across all groups was well tolerated over 47 days of daily oral gavage, with no significant weight loss. EMI-1725 showed improved biodistribution across organs, and importantly, displayed less accumulation in the brain compared to enzalutamide, where off-target binding leads to risk of seizure. Collectively, these in vitro and in vivo findings support the advancement of EMI-1725 as a promising therapeutic candidate for mCRPC.
Steven Kregel, Raymond J. Kostlan, John T. Phoenix, Audris Budreika, Carleen D. Deegan, Jonathan Katz, Jerry S.H. Lee, Charles McKenna, David B. Agus, Katherin Patsch. EMI-1725 Demonstrates Broad Antagonist Activity Against Drug-Resistant Androgen Receptor Mutations and Tumor Growth Suppression in Preclinical Models of Metastatic Castration-Resistant Prostate Cancer [abstract]. In: Proceedings of AACR Drug Discovery and Development (AACR D3) Conference; 2026 Jul 21-24; Boston, MA. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(14_Suppl):Abstract nr B077.
S. Kregel, R. J. Kostlan, John T. Phoenix et al.· Clinical Cancer Research· 0 citations
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