ADDED
As of March 13, 2026, the registrant had 11,254,697 shares of common stock, $ 0.0001 par value per share outstanding.
On average, our newly developed drug programs have been advanced from initial A.I.
insights to first-in-human clinical trials in 2-3 years and at approximately $1.0-$2.5 million per program.
In January 2026, we introduced withZeta.ai a generative AI platform purpose-built to empower researchers and clinicians to accelerate rare cancer research and drug development, dramatically improve research quality, and reduce R D costs.
withZeta s multi-agentic architecture combines intelligent orchestration using a combination of proprietary knowledge bases and publicly available data with autonomous task completion to deliver a true co-scientist experience one that brings the collective insight of thousands of domain experts, millions of publications, and billions of data points to address some of oncology s most difficult challenges and disease subtypes.
In 2025, the FDA cleared two new Phase 1b/2 investigational new drug (IND) applications for LP-184, further expanding our clinical pipeline opportunities.
The first planned LP-184 Phase 1b/2 trial is positioned to evaluate LP-184 in recurrent triple negative breast cancer (TNBC) patients as both a monotherapy and in combination with the PARP inhibitor olaparib.
The second planned LP-184 Phase 1b/2 trial is positioned to evaluate LP-184 in a biomarker-defined population of non-small cell lung cancer (NSCLC) patients harboring KEAP1 and/or STK11 mutations with low PD-L1 expression, in combination with the immune checkpoint inhibitors nivolumab and ipilimumab a population with high unmet clinical need and a market opportunity estimated to exceed $2 billion annually.
Additionally, LP-184 has received FDA Fast Track Designations for GBM and TNBC, as well as multiple Orphan Drug and Rare Pediatric Disease Designations across various solid tumor indications.
Our strategy is to both develop new drug candidates using our RADR platform and other machine learning driven methodologies, and to pursue the development of drug candidates that have undergone previous clinical trial testing or that may have been halted in development or deprioritized because of insufficient clinical trial efficacy or for strategic reasons by the owner or development team responsible for the compound.
REMOVED
As of March 17, 2025, the registrant had 10,784,725 shares of common stock, $ 0.0001 par value per share outstanding.
Our strategy is to both develop new drug candidates using our RADR platform and other machine learning driven methodologies, and to pursue the development of drug candidates that have undergone previous clinical trial testing or that may have been halted in development or deprioritized because of insufficient clinical trial efficacy (i.e., a meaningful treatment benefit relevant for the disease or condition under study as measured against the comparator treatment used in the relevant clinical testing) or for strategic reasons by the owner or development team responsible for the compound.
Additionally, these drug candidates may also have a body of existing data supporting the potential mechanism(s) by which they achieve their intended biologic effect, but often require more targeted trials in a stratified group of patients to demonstrate statistically meaningful results.
In this context, we intend to create a diverse portfolio of oncology drug candidates for further development towards regulatory and marketing approval with the objective of establishing a leading A.I.-driven, methodology for treating the right patient with the right oncology therapy.
We believe the combination of our therapeutic area expertise, our A.I.
expertise, and our ability to identify and develop promising drug candidates through our collaborative relationships with research institutions in selected areas of oncology gives us a significant competitive advantage.
Our RADR platform has been developed and refined over the last several years and integrates billions of data points immediately relevant for oncology drug development and patient response prediction using artificial intelligence and proprietary machine learning algorithms.
By identifying clinical candidates, together with relevant genomic and phenotypic data, we believe our approach will help us design more efficient preclinical studies, and more targeted clinical trials, thereby accelerating our drug candidates time to approval and eventually to market.
Although we have not yet applied for or received regulatory or marketing approval for any of our drug candidates, we believe our RADR platform has the ability to reduce the cost and time to bring drug candidates to specifically targeted patient groups.
We believe we have developed a sustainable and scalable biopharma business model by combining a unique, oncology-focused big-data platform that leverages artificial intelligence along with active clinical and preclinical programs that are being advanced in targeted cancer therapeutic areas to address today s treatment needs.