One company in the artificial intelligence (AI) space, Menlo Park, Calif.-based NuMedii, has been pioneering the use of big data, AI and systems biology since 2010 to accelerate the discovery of precision therapies to address high unmet medical needs. As a next generation biopharma company, NuMedii has built a powerful technology – AIDD Technology – that harnesses big data and AI to rapidly discover connections between drugs and diseases at a systems level. The company has efficiently extracted information from a vast array of disparate data stores to create a structured, proprietary data resource spanning hundreds of diseases and thousands of compounds. NuMedii’s proprietary AI and machine learning algorithms enable the company to extend well beyond conventional ‘target-centric’ drug discovery approaches by facilitating the exploration of favorable ‘poly-pharmacology’ profiles that can potentially improve therapeutic efficacy by modulating effects on multiple disease pathways.

The founder and Chief Executive Officer of NuMedii is Dr. Gini Deshpande, a molecular biologist by training who has more than 16 years of experience turning cutting-edge scientific concepts into products that benefit patients.

As CEO of NuMedii, Deshpande structured the company’s critical partnerships with some of the world’s largest pharmaceutical companies including Allergan, Astellas and Aptalis, and raised the initial rounds of financing. Before NuMedii, she helped companies identify optimal markets and whole-product solutions for their groundbreaking technologies, including Affymetrix and iPierian, and led innovation at Children’s Hospital Boston, where she focused on the creation of new devices for the tiniest of patients. Deshpande has helped commercialize early-stage technologies in research tools, diagnostics and therapeutics and has closed licensing deals worth several million. She received her MS from the University of Pune (India), her Ph.D. in biological sciences from Purdue University, and did post-doctoral work at the Massachusetts General Hospital.

WuXi AppTec Communications, as part of a new industry series, recently interviewed Gini about the clinical direction and goals of NuMedii, as well as what the future looks like for AI in drug development.

WuXi: How would you describe the landscape of companies involved in this new field of AI? Are they mostly start-ups, or are the big data companies, such as Google and Intel, the dominant players?

Gini Deshpande: The potential for AI to significantly alter the landscape in medicine is huge, and there is a lot of excitement and interest in this space. We are seeing sizeable, established companies jumping in with large-scale investments, as well as the launch of hundreds of small startups. Part of the excitement comes from the fact that healthcare comprises a large portion of the US economy, and thus is of interest to many companies, especially tech companies. Today there are AI companies that now touch each of the four key stakeholder pillars: patients (e.g., Apple), providers (e.g., Sense.ly), payers (e.g., Optum, GNS) and pharma (e.g., NuMedii, Benevolent AI, Berg).

WuXi: How would you describe AI, or machine learning, to the general public and to patients?

Gini Deshpande: I think it’s important to demystify what AI is. Many people interchangeably use many different terms to refer to AI such as machine learning, cognitive computing, big data or data sciences. Artificial Intelligence or machine learning is a set of software tools that enable us to find patterns in data – either patterns one might be trying to look for or patterns one didn’t know or wasn’t expecting to find. For instance, one could train software to look at measurements from patients who went on to do poorly with a treatment and then use the trained system to look for new patients who might be predicted to do poorly. Identifying patient types early on could be useful as it could help doctors as they consider optimal treatment options, and in turn, could save precious time and possibly extend patients’ lives. We need to think of AI as an important tool in a toolkit – it is only useful if we clearly define the problem we are trying to solve and the end users are trained to understand both its benefits and limitations.

WuXi: How does your company differ from others using AI in drug discovery and development? How are you applying AI?

Gini Deshpande: NuMedii has been pioneering the combined use of big data, AI and systems biology since 2010 to accelerate the discovery of precision therapies to address high unmet medical needs. Artificial Intelligence approaches are a natural fit to harness big data as they provide a framework to ‘train’ computers to recognize patterns and sift through vast amounts of new and existing genomic and other biomedical data to unravel diverse complex biological networks involved in disease processes. We use multiple AI methods, ranging from classical machine learning techniques to newer deep learning systems, to rapidly discover connections between medicines and diseases at a systems level. Our AI approaches are also being used to identify subsets of patients and therapies that are likely to modulate these complex networks for each patient subgroup. As we have worked extensively with big data and AI, we have developed a deep appreciation of the limitations of AI. At NuMedii, we prefer to think of AI more as “Augmented Intelligence” than “Artificial Intelligence.” We couple AI, big data and systems biology with human intelligence, enabling our scientists to have access to more and better synthesized information than otherwise feasible. Our goal is to use this combined system of human and machine intelligence to help speed up drug discovery, cut R&D costs and decrease failure rates in clinical trials, all of which can eventually lead to better, more precise medicines.

WuXi: What are the major benefits of using AI, or deep learning, in drug discovery and development?

Gini Deshpande: Drug discovery and development are highly data-intensive processes, with disparate types of data being generated (from molecules to clinical trial encounters) and a lot of information being tracked. These processes have historically been trial-and-error ridden processes, with high failure rates and costs that are in the billions. Several factors have contributed to these problems: biology is inherently complex and disease manifestation in patients varies across the patient population. Genetic, environmental and other factors also determine how a disease will progress and how patients respond to a given therapy. Thus, these R&D processes and the variabilities involved become a big data problem. Artificial Intelligence, coupled with correct data, has the potential to make drug discovery and development less error prone and increase the likelihood of success both in trials and the real world setting. The hope is that ideally, with AI, some predictability could be regained in these processes.

WuXi:  How will AI change drug discovery and development, and clinical research? 

Gini Deshpande: Patient stratification, the creation of precision therapeutics and streamlining of clinical trials are the main areas in drug discovery and development that will significantly benefit from the use of AI and big data in the near term. Clinical research is well suited for applications of AI to improve how we diagnose and treat patients. As more physician practices shift from paper to electronic systems to capture patient information, there will be more ways for AI to help derive more information and assist physicians with decisions. Areas like radiology and ophthalmology are particularly well suited for immediate applications of AI; in fact we are seeing many such uses at leading academic centers as evidenced by the recent partnership between the University of California, San Francisco and Google Brain. Detection of rare diseases via these imaging approaches is an area where AI may end up being superior in its abilities compared to humans. In drug discovery and development, AI will be particularly useful when evaluating large-scale data to look for trends that are indicative of treatments providing benefit. Companies like NuMedii are using AI and biomedical data to streamline the discovery of relevant disease biology, thereby reducing the time for discovery, but more importantly significantly improving the probability of success.

Complementary to these disease biology searches, applications of AI in drug design have also started to take shape, enabling us to streamline the screening and library development process. Recent reports from companies like Exscientia and Numerate, which use AI to explore the chemical universe, suggest that AI has the ability to significantly reduce the time it takes to discover and optimize drug candidates for preclinical testing from four-and-a-half years to one year.

WuXi: What are the limitations in applying deep learning to AI drug discovery and development? And what are the challenges/barriers in employing AI in pharmaceutical and biotech drug development?

Gini Deshpande: Compute power is certainly no longer a rate-limiting step for the use of AI in drug discovery. Effective use of AI requires large amounts of relevant, high quality and consistent data to train algorithms for accurate pattern recognition. These data can be particularly challenging to access. Often data are kept in silos and spread across organizations. In addition, biomedical data are very diverse, spanning multiple “omics” information, such as genetic, genomic, proteomic and metabolomics, as well as environmental exposures, ranging from chemical structures to clinical information. Mapping relationships between these diverse data are challenges that need to be solved in order to make effective use of these data for applications of AI. One of the biggest challenges in this space is a well-quantified cohort dataset that can help train algorithms with a “true” pattern. For instance if we had high resolution datasets from patients where we collected a multitude of “omics” information and had corresponding longitudinal clinical data, we could then use these datasets to train our AI systems and generate novel insights and accelerate drug discovery. Companies such as Human Longevity are trying to do this with their Health Nucleus efforts, which would be useful to drug discovery.

As of today, there are no successes of AI-driven drugs approved by the FDA so companies are reluctant to broadly adopt AI-based approaches in their R&D programs. Once there are a couple of success stories, we should see expansion of such approaches into regular workflows. The good news is that there is strong interest from pharma and biotech to see whether AI can help. We are seeing adoption of AI approaches in discrete parts of the R&D pipeline – from early stages of discovery, to clinical development at smaller scale vis à vis pilot projects. However, we are far from the point where AI can fully automate the drug discovery process and there still is a great need for people skilled in understanding the positive attributes and limitations of AI to make best use of the output of these technologies. And, the lack of people cross-trained in both AI and traditional drug discovery and development is another barrier to broad adoption of AI.

WuXi: How will this field evolve over the next five-to-ten years? Will AI applications eventually become the norm for biotech and pharmaceutical R&D? Will AI start-ups evolve to become drug developers themselves, or will they focus on providing their technology in partnership with drug developers?

Gini Deshpande: Artificial Intelligence will continue to explode in every aspect of technology. Quantum computing is projected to become more readily available, enabling new applications in both the front end of the drug discovery process and also downstream in clinical development. Once we have a few successful examples of how AI has streamlined and enabled us to expedite drug discovery, we will see broader adoption and expect that AI will be routinely used in R&D in the next five-to-ten years.

Most AI start-ups have ended up working with pharma and biotech partners in various ways to help them with their drug discovery efforts. Going forward, there will continue to be several providers of AI services. But we will also see more startups become drug developers themselves – a full integration of both AI-driven drug discovery coupled with serious drug development capabilities – to speed up the process all along the way. At NuMedii, we’re certainly going down this road ourselves by developing our own pipeline – we refer to it as “eating our own caviar.” Several others like BenevolentAI and Berg Pharma are also developing their own pipelines, and we expect to see more companies follow in our footsteps.

At the end of the day, the true value of AI to the end user – the patient – is not how we come up with an effective drug, but how soon we do so. To that end, AI is a great tool that, if used correctly with the right data sets, can yield revolutionary therapies for the people who need them the most.