Mohan Uttarwar, Founder & CEO
A cure-for-cancer. This elusive goal has been a reason for decades of extensive research. However, the disease is yet to be conquered. Still undeterred and optimistic, Mohan Uttarwar, with more than 25 years of experience as a high-tech executive and entrepreneur, emphasizes immuno cancer therapy (ICT) or immune-oncology (IO) as the key to treat cancer. “IO is driven by precision medicine because one shoe doesn’t fit all,” remarks Uttarwar. Genomics drove the first wave of precision medicine, but genomic data alone is insufficient to tackle the higher level of complexity in oncology today, particularly in combination trials. “The next wave of precision medicine that I call precision medicine 2.0 will be driven by multi-omics data analytics,” adds the entrepreneur. The applications of different multi-omics approaches in the field of cancer research are set to reshape the field of personalized oncomedicine. However, analyzing multi-omics data is easier said than done. Its sheer volume and diversity make the process of establishing a relationship between the data sets complicated. Uttarwar founded iNDX.Ai to simplify multi-omics data analysis by leveraging data sciences with AI, ML, NLP, and other technologies, to help pharma researchers make the early-stage clinical trials and translation research—faster and cheaper. “We are combining the innovations in life sciences with the innovations in data sciences and computational logic that will help us find a cure for cancer,” remarks Mohan Uttarwar, Founder and CEO, iNDX.Ai.
iNDX.Ai’s solutions such as iCore, iDiscovery, iQuery, integrated correlation engine (iCE), and others help aggregate, analyze, visualize, interrogate biomedical data and gather strategic and predictive insights in a secure cloud-based environment. The biomedical data consists of data sets from multiple institutions, multiple sites, and multiple parties such as CROs, CRCs, and laboratories. The different types of data include genomic data, proteomic data, flow cytometry, metabolomics data, and clinical data from the electronic data capture (EDC) systems.
We are combining the innovations in life sciences with the innovations in data sciences and computational logic that will help us find a cure for cancer
Besides, there is imaging data such as CT, MRI, PET scans, and immunofluorescence.
The iCore platform performs the function of a data lake that connects disjointed silos of data. Besides, it aggregates, organizes, and analyzes the data, which can be visualized using iDiscovery. This solution enables researchers to use advanced ML algorithms, interactive data analysis tools, and intuitive visualization windows with rich APIs that allow the superimposition of omics data on clinical data. Thus, iNDX.Ai offers a succinct way of creating a cloud-based mobile-enabled, extensible, secure, scalable platform for pharma analytics. “iCore is now 21 CFR Part 11, GDPR and HIPAA compliant,” says Uttarwar.
Another iNDX.Ai solution—iQuery, enables clinical researchers to define queries in simple text language across clinical, biomarker, and imaging datasets. The queries can be executed instantly or saved for later. This way, the company facilitates the investigation of relationships between multi-omics and clinical datasets to accelerate the identification of clinically relevant biomarkers with the help of iCE.
A source of experience and learning for iNDX.Ai was the Parker Institute of Immuno Cancer Therapy. The institute is a consortium of six academic centers comprising Stanford, UCLA, UCSF, Sloan Kettering, MD Anderson, and UPEN. They were conducting two trials—a CD40 for the metastasized pancreatic cancer trial and the second was a trial for melanoma. The institute had to develop an integrated console to facilitate joint research amongst the six centers, and iNDX.Ai deployed its iCore platform to help the centers effectively collaborate and accelerate IO trials.
iNDX.Ai is one of the first companies to offer a blockchain-based streamlined sample collection and tracking workflow to ensure authenticity and integrity. The company also plans to create a proprietary database of cancer patient data and curated longitudinal data that they can use for training purposes, as well as a multi-omics profile of patients that can be used by biopharma biotech companies.