Drug discovery is broken.
Katamaran is fixing it.
95% of oncology drugs fail in clinical development
Traditional preclinical models often fail to predict how real human tumors respond. Cell lines and animal systems miss critical features of tumor biology, including microenvironment, architecture, and context.
Conventional models often do not translate to human patients (“The Mouse Trap”)
Al trained on this noise hallucinates better failures. Better biological data is essential for better prediction
Upto 70%
cost
reduction
Upto 30x
katamaran cost efficiency for human relevant NAMs over US competitors
faster design-to-validation
timeline
Upto 10x
$200K vs. $60K
per year advanced drug costs:
(Developed nations vs India)
Failures in clinical trials
95%
10-12
years to bring drug to the market
50,000,000+
By the numbers
50,000,000+
people worldwide are living with cancer today.
*Based on internal benchmarking assumptions comparing katamran Ai guided human relevant workflow with conventional discovery and validation flows
Katamaran Approach
Our Solution
From legacy models to human-relevant NAMs-driven drug discovery
Gen 1
(2013-2018)
Data: 2D cell lines, mice, and public genomic datasets
Method: Historical literature mining and disconnected data layers
Outcome: Weak clinical translation
Gen 2 (2019-2023)
Data: Generative AI applied to largely the same public datasets
Method: Structural prediction without sufficient biological context
Outcome: More computational novelty, but limited translational grounding. AI hallucinates better failures
Gen 3 (Katamaran)
Data: Fresh human tumor tissue, tumoroids, ex vivo tissue models, NAMs and multimodal datasets
Method: Human lab-in-the-loop AI built around real biological context, designed to evolve toward fine-tuned biological foundation models grounded in ex vivo spatial biology
Outcome: High-fidelity target, drug, and response prediction
*Modeled against conventional workflows; based on internal data and published benchmarks.
Our Solution
From legacy models to human-relevant NAMs-driven drug discovery
Gen 1
(2013-2018)
Data
2D cell lines, mice data, and noisy
public genomic datasets
Method
Historical literature mining and
disconnected data layers
Outcome
Weak clinical translation
Gen 2
(2019-2023)
Data
Generative AI applied to largely the same public datasets
Method
Structural prediction without
sufficient biological context
Outcome
More computational novelty, but
limited translational grounding. AI
hallucinates better failures
Gen 3
Katamaran
Data
Fresh human tumor tissue, tumoroids, ex vivo tissue models, NAMs and multimodal datasets
Method
Human lab-in-the-loop AI built around real biological context, designed to evolve toward fine-tuned biological foundation models grounded in ex vivo spatial biology
Outcome
High-fidelity target, drug, and response prediction
Our R&D and Pipeline
We use AI to support target selection, indication prioritization, biomarker discovery, responder hypothesis generation, and therapeutic design across small molecules and biologics.
Predict
As our platform generates more proprietary human tumor data, we use those insights to improve models, standardize assays, expand throughput through automation and microfluidics, and build toward fine-tuned biological foundation models grounded in real human tumor biology.
Validate
We generate patient-derived tumoroids and ex vivo tissue models from fresh human tumor samples to evaluate drug response in a more biologically relevant setting, preserving key aspects of tumor architecture and microenvironment.
Scale
(Lab-in-the-Loop)
(The Ex-Vivo Edge)
(Al-Driven)
Katamaran is building a proprietary Gen 3 human-data engine for oncology drug discovery.
Our platform moves beyond public-data-only bio-AI by integrating proprietary multi-modal spatial human tumor datasets to build fine-tuned biological foundation models grounded in ex vivo human tumor data
Our Business
Our Company
Arun Asaithambi has been working at the intersection of AI and oncology drug discovery since 2013. As Founder and former CEO of Lantern Pharma [NASDAQ: LTRN], he helped pioneer early AI-driven drug development and is now building Katamaran’s next-generation platform.
Arun Asaithambi, Ph.D.
Founder and CEO
Anand Asaithambi,
Co-Founder
Research labs
- Arun Asaithambi, Ph.D.
Founder and CEO
October 10, 2017
October 10, 2017
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Featured News
Sep 26, 2023
Aug 01, 2025
October 10, 2017
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What Experts Are Saying
“Head and neck cancers are extremely common but low awareness and non-specific symptoms delay diagnosis. When caught early they have excellent cure rates. Precision therapies are becoming more crucial as a one size fits all approach is ineffective.”
-Dr. Narayana Subramaniam
Lead Consultant, Head & Neck Oncology, Aster Hospitals, Bangalore
Our Collaborators
We’re looking for collaborators, investors, and visionary scientists. Reach out and be part of the mission.
Quick Links
Our Address
India :
Unit, #603, 6th floor, Sigmasoft tech park, Gamma block, Whitefeild, Bengaluru, Karnataka, 560066
US :
Unit 419,180 talmadge rd, Edison nj 08817
UK :
41 Clooney Terrace, Londonderry, Northern Ireland, BT47 6AP
Mail :
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