AIRA Matrix – Dilip Shanghvi’s take on AI drug discovery


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AIRA Matrix, backed by Sun Pharma, a company founded and funded by founder and CEO of Dilip Shanghvi’s family office, embarks on AI-led pharmaceutical research and development [R&D]. The company provides artificial intelligence-based solutions for life science applications and has developed deep learning-based products and services to help pharmaceutical companies accelerate the discovery and development of new drug molecules by reducing the risk, cost and time of at least some phases of drug development, drug discovery and preclinical trials.

Bringing a new drug to market typically involves several phases, including early discovery, preclinical research, clinical development, and regulatory approval. The whole process can take 10 years or more, and cost billions of dollars. But in the end, it is estimated that only one in 10,000 compounds initially envisioned eventually makes it onto the market. Therefore, there is an urgent need for solutions that accelerate time to market and reduce the cost of drug and vaccine development. A number of technology-driven tools have been developed to aid research over the past two years.

With 17 articles already published, AIRA Matrix has been selected by the accelerator GE Edison. Along with a small grant, this gives the company the opportunity to work with professionals from various disciplines at GE Healthcare and to benefit from the integration of software products into the GE ecosystem.

Established in 2011, AIRA Matrix initially worked on internal product development with a few contributors to provide feedback and additional feature requirements. The goal was to create a suite of prostate cancer products that not only automates today’s error-prone manual reporting processes, but also helps make effective treatment decisions through screening, diagnosis, and diagnosis. disease, risk stratification and prediction of disease progression and expression of biomarkers.

“After establishing the proof of concept from publicly available data and, in the process, finishing among the best in the Global MICCAI 2019 competition, we have entered into collaborations with development partners in India, UK. United and United States. Key aspects of the product suite have been developed and are currently in the clinical validation phase, ”said Chaith Kondragunta, CEO of AIRA Matrix.

The company’s deep learning-based hybrid models predict the potential toxic effects of a new drug molecule under investigation. The models analyze data from multiple modalities to aid in critical go / no-go decisions in selecting the safest drug molecule. The risk roadmap and point-of-failure predictions dramatically reduce the time and resources required in this early phase of drug development. For example, the tissue triage system improves process efficiency in preclinical toxicology by optimizing the 80% ratio of “normal” tissue images, thereby reducing the study reporting cycle from months to weeks. This gives pathologists more time to focus on “abnormal” results, which also results in faster exams.

The company’s spermatogen staging solution optimizes the staging assessment of testis images in rodents from days / weeks (depending on pathologist’s expertise) to 15 minutes per image, when assisted by AI . Finally, predictive toxicity models predict the long-term toxicity of a compound based on short-term studies, reducing, for example, a study cycle from six months to one month or less, and resulting in savings in terms of time and resources, as well as the number of animals sacrificed. AI-based solutions once used in drug research reduce the need for animal sacrifice and boost humane animal research in line with the 3R Principles of Animal Testing (Reduce, Refine and Replace).

Increasingly, organizations are investing in scanning machines because of the benefits of digital pathology. They recognize the benefits of sample storage without any deterioration, the ability to collaborate remotely, telepathology, leading AI-based solutions, and overall improvements in work process and diagnostic accuracy.

There are a number of companies in the diagnostic field that do similar work – offering digital image analysis for diagnostics, primarily telepathology based products. “A digital collaboration module that can be applied to ‘telepathology’ to provide remote diagnosis or expert consultations is one of the solutions we offer. Kondragunta said. The diagnosis is always provided by manual examination and analysis by pathologists. In contrast, AIRA Matrix is ​​primarily focused on delivering deep learning based solutions that actually automate analysis and act as decision support tools for pathologists, and help improve accuracy, reproducibility. and the timeline for reporting. These solutions perform image analysis and provide diagnostic and prognostic information to support pathologist reports. The company provides software products and vendor independent solutions for image analysis and management. He currently has ties to 10 companies.

Three pathologists and more than a dozen consultants, microbiologists and 40 people are engaged in work based on AI or analysis at AIRA Matrix.

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