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Avishay Bransky, Ph.D., CEO and Co-founder of PixCell – Interview Series

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Avishay Bransky, Ph.D., CEO and co-founder of PixCell, is an expert in microfluidics, with extensive industrial experience in applied physics, software and systems engineering. He is one of the inventors of the Viscoelastic Focusing technique, cell analysis methods and the microfluidic based cartridge. Dr. Bransky holds a B.A. in Physics, B.Sc. in Materials Engineering, and a Ph.D. in Biomedical Engineering, all from the Technion Israel Institute of Technology.

PixCell Medical has an interesting origin story, can you explain how the company was started after accidentally discovering a unique physical phenomenon: Viscoelastic Focusing (VEF)?

While completing my PhD at the Technion – Israel Institute of Technology, a colleague and myself were researching microfluidic devices in which we flowed blood cells that were suspended in various solutions. We inspected the cells through a microscope as they were flowing, and as expected, the cells were evenly dispersed across the microchannel (part of the microfluidic device). During our research, we experimented with a unique solution that is typically used in the mining industry. When we looked through the microscope, we couldn’t see any cells. We repeated this multiple times, and still could not see any cells! Upon further investigation, we realized that all the cells were focused in a single plane, and our microscope wasn’t focused on that plane of cells. The focusing effect we discovered was extremely strong, unlike any we’d ever seen, in which all the cells were perfectly aligned in a single plane within tens of nanometers apart. This was our breakthrough in discovering viscoelastic focusing (VEF).

We approached a professor at the Technion who devised a theory that supported this discovery. Based on this research, we published a paper on VEF in one of the most prestigious physics journals – Physical Review Letters.

I realized quickly that VEF could be very useful in applications where cells are counted or analyzed. These methods all require focusing of cells because the closer the cell is to the position you expect it to be, the more accurate the analysis. The sharp focusing attained by VEF facilitates highly accurate measurements as well as image-based analysis of cells’ characteristics. An added benefit is that VEF requires much less reagents than standard cell counters do, making it cost effective and easy to use. This was the starting point when we began developing the HemoScreen hematology analyzer, which was designed around VEF as well as other patented technologies.

 

The HemoScreen™ makes blood testing simple, can you explain the skills needed to run the blood tests and what the process is?

The HemoScreen is small, sturdy and simple to use, and it doesn’t require any technical skill to set-up or operate. Moreover, it does not require maintenance or calibration. Due to this, anyone can quickly learn how to use it by just reading the quick guide, from nurses and caregivers, to the patient themselves.

The device involves a simple three-step process:

  • One drop of blood is taken from a finger and inserted into the disposable cartridge which has all reagents built in.
  • The cartridge is inserted into the analyzer; and
  • Within 6 minutes, lab-quality results for a full 5-part differential CBC test are delivered.

This process eliminates operator or user-related procedural errors that may compromise the test result quality due to the pre-analytical steps of the sample preparation. Delivering accurate readings of 20 standard blood count parameters in a safe, easy and simple point-of-care solution saves patients, clinicians and health systems significant time and costs.

In the US, the HemoScreen is cleared for point-of-care (POC) use with a CLIA moderately-complex rating.

What are some of the diseases that HemoScreen™ detects?

The HemoScreen performs the most common blood test, the complete blood count (CBC), which provides healthcare professionals with an overall picture of a person’s health. Additionally, it provides comprehensive abnormal cell flagging which can serve as biomarkers for different pathologies. Abnormal cell identification is so important yet is not available at the point-of-care or often even in large, centralized labs.

When it comes to identifying diseases such as lymphoma, certain types of leukemia and severe inflammation, premature blood cells are released from the bone marrow before reaching their mature state. The ability to flag such immature cells in a patient’s blood is a very important finding and can only be discovered with the highest quality instruments. Oftentimes, these instruments can’t detect all immature cells, resulting in the need to do a blood smear and look at the sample under a microscope. There are fewer and fewer pathologists with the expertise to do this.

This is where the HemoScreen comes in. Our AI-driven image analysis can, based on thousands of samples from different pathologies, identify the cells and give an accurate finding, representing a huge transformation in rapid blood analysis. Using this technology, patients can be told immediately whether they should go to the hospital and perform more specific tests to zero in on a pathology significantly earlier than with current testing methods.

Other disorders that can be identified through the HemoScreen’s CBC include chronic lymphomas (CLL), infection, severe anemia and internal bleeding. The addition of abnormal cells is a strong indicator that the patient has a severe issue that needs to be further analyzed and assessed. Instead of diagnosing specific diseases, the HemoScreen’s test gives a general overview of the patient’s health and helps in early detection of severe pathologies. Furthermore, it aids the physician in determining whether to prescribe antibiotics or not, which is becoming more important with the increasing issue of antibiotic microbial resistance.

 

The HemoScreen™ can also detect abnormal blood cells which may indicate potential cancerous cells. Can you elaborate on how this product can be used to detect cancer early?

Blood cells develop in the bone marrow that gradually evolve until they are released into the blood stream as one of the major 7 types of cells. These mature cells are counted as part of the standard Complete Blood Count (CBC) test as their concentration in the blood serves for diagnosing various conditions such as infection and anaemia. However, occasionally immature cells, that have not yet developed into their mature state, are prematurely released from the bone marrow into the peripheral blood stream. These cells are referred to as abnormal cells and may be indicative of a serious health disorder.

The HemoScreen analysis method is based on digital imaging of flowing cells which may be perceived as a “flowing blood smear.” It extracts hundreds of features from each cell as opposed to the 3-4 signals obtained by standard analyzers in centralized labs. This allows the technology to accurately differentiate between all types of cells, including those that are abnormal.

The FDA-cleared version of the HemoScreen specifies the 5 types of normal white cells and accurately flags for abnormal cells. However, PixCell has recently come up with a newer version that measures and outputs the counts of abnormal cells – this signifies a breakthrough in hematological diagnostics as physicians would be able to detect different types of cancers at an earlier stage at primary care and not miss these critical cases that require immediate intervention.

The HemoScreen can count immature granulocytes, nucleated RBC and blast cells, which are extremely important to detect, even at very low numbers, as they may indicate myelomas, leukemias, lymphomas and chronic blood disorders that require immediate intervention.

 

One of the things PixCell Medical is working on is Early Sepsis Diagnosis. Can you explain the current challenges behind early diagnosis of Sepsis and how PixCell Medical is tackling this challenge?

Sepsis is a global health problem. To combat sepsis, we require better diagnostic tools, more rapid testing and more specific assays. If the testing is not specific, it can’t differentiate between sepsis and other conditions that appear similar.

PixCell Medical is tackling this challenge by providing highly accessible testing for specific biomarkers that are related to bacterial infections. Some are already available with the 5-part differential CBC, such as the absolute neutrophil count (ANC) and white blood cell (WBC) count, while others are still under development.

PixCell is also adding a recently discovered biomarker: monocyte distribution width (MDW) to the CBC test. The MDW has been shown effective for sepsis detection during the initial emergency department encounter. In tandem with WBC, MDW is further predicted to enhance medical decision making during early sepsis management in the emergency department. Immature granulocytes, mentioned above, has also been shown to be a good early marker for sepsis. In addition, PixCell is developing further assays for inflammation markers such at C-reactive protein (CRP) and procalcitonin (PCT) that are extensively used in managing sepsis treatment.

The combination of these markers offers a much more specific indication of the source of the infection and the course of the disease. Furthermore, making these advanced assays accessible and providing results in real-time would lead to significantly improved clinical outcome for septic patients.

 

How is artificial intelligence used in the HemoScreen™ for both disease and cancer detection?

The HemoScreen combines viscoelastic focusing technology (VEF) with patented artificial intelligence (AI) technology and machine vision to rapidly analyze a blood sample. VEF causes the cells to focus into a single layer plane as they flow, facilitating their optical analysis. Our machine vision technology then captures thousands of images of tens of thousands of cells, in real-time, perfectly focused as they flow. Our AI algorithms then analyze these images on the fly. Using image processing, algorithms and machine vision, the technology identifies the cells and then classifies them into different types.

Using machine learning algorithms that are trained by human experts based on many different blood samples and pathologies, it can identify and classify different sub-types of cells that sometimes differ in nuances from different patients and pathologies. This is all done based on the cells’ morphologies, shapes, nucleus shape, color, and other cell properties. Altogether, our AI technology utilizes hundreds of different features of each cell to be able to determine which type of cell it is, rapidly.

 

How important is decentralized diagnostic testing when it comes to successfully diagnosing cancer and other infectious diseases early?

Decentralized diagnostic testing is vital when it comes to successfully diagnosing cancer and other infectious diseases early. Current technologies being used in centralized labs for this purpose use laser scattering or electric impedance, which allow for analysis of very few properties of the cell, usually only about three or four in comparison with the hundreds our technology can analyze in a point-of-care setting.

In addition, the process of successfully diagnosing cancer and other infectious diseases is usually done via a long and tedious testing process. Blood samples are taken from the patient after their doctor’s referral, sent to a centralized lab to be tested, and then patients can wait days, even weeks, to receive their results. Decentralized diagnostics gives physicians the opportunity to test their patients then and there, in their own office, without any technical assistance necessary. And the most important part? Results are received within six minutes, and the physician can refer their patient directly to the next necessary step in their diagnosis.

 

Is there anything else that you would like to share about PixCell Medical?

PixCell Medical are proud to have created a solution for a simple CBC test that has been sought for several decades. This is the first time that a miniature, easy-to-use instrument can provide such accurate results that are readily available and accessible to those who need it most.

Thank you for the interview and for letting us know about the amazing work that is happening at PixCell Medical.

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Antoine Tardif is a Futurist who is passionate about the future of AI and robotics. He is the CEO of BlockVentures.com, and has invested in over 50 AI & blockchain projects. He is also the Co-Founder of Securities.io a news website focusing on digital securities, and is a founding partner of unite.ai

Healthcare

Dave Ryan, General Manager, Health & Life Sciences Business at Intel – Interview Series

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Dave Ryan leads the Global Health & Life Sciences business unit at Intel that focuses on digital transformation from edge-to-cloud in order to make precision, value-based care a reality. His customers are the manufacturers who build life sciences instruments, medical equipment, clinical systems, compute appliances and devices used by research centers, hospitals, clinics, residential care settings and the home. Dave has served on the boards of Consumer Technology Association Health & Fitness Division, HIMSS’ Personal Connected Health Alliance, the Global Coalition on Aging and the Alliance for Connected Care.  

What is Intel’s Health & Life Sciences Business?

Intel’s Health & Life Sciences business helps customers create solutions in the areas of medical imaging, clinical systems, and lab and life sciences, enabling distributed, intelligent, and personalized care.

Intel’s Health business focuses on population health, medical imaging, clinical systems, and digital infrastructure.

  • Population Health examines diverse patient data to give providers insights into risks for medical issues and improved treatments across cohorts. Optimized and tuned ML and AI helps “tier” groups, so payers and providers prioritize patients at most risk.
  • Medical Imaging (e.g. MRI, CT), generate enormous data sets requiring accurate evaluation with no room for error. HPC and AI help more quickly scan image data and identify critical factors to assist radiologists in diagnosis.
  • Clinical Systems use computer vision, AI, HPC and edge computing for patient monitoring, robotic surgery, and telehealth and many others. These intelligent systems reconcile diverse source data for a complete patient view and better diagnosis, with flexibility and scalability to support changing organizational needs.
  • Digital Infrastructure integrates many technologies to enable novel approaches to patient interaction including anywhere anytime care where clinicians collaborate across space and time for condition management, surgery, and analytics.

Intel’s Lab and Life Sciences business is focused in 3 primary areas: Data Analytics, ‘Omics, and Pharma.

  • Data Analytics uses AI to drive a cascade of discoveries and insights that help enable, among other things,  precision medicine by ensuring that patients get the drugs that are most effective for them, and so reducing the risk of side effect profiles.
  • ‘omics describes and quantifies biological molecule groups, using bioinformatics and computational biology. The massive data sets involved here require high-throughput processing to receive results within reasonable timeframes. With this throughput and new databases, toolkits, libraries, and code optimizations, ‘omics institutions can reduce time to results and development costs.
  • Pharma is the study of drugs and how they interact with human biological systems, including at a molecular level where data science needs AI and ML to assist with lead generation and optimizations, target ID and preclinical research. This results in better clinical trials, smarter reaction insights and faster new drug discovery.

When did you personally initially become interested in using AI for the benefit of healthcare?

The proliferation of AI across many industries has largely been about automating those tasks routinely performed by humans. In healthcare, AI has become a tool through which we augment or assist, not replace, existing human expertise to deliver truly transformative approaches to diagnosis and treatment. And nowhere is this clearer than in medical imaging, in which data volume and complexity is both barrier and opportunity. Today, AI, and inferencing in particular, is able to perform more rapid and detailed scans of vast arrays of information than any human can and in so doing not only reveals insights previously hidden but also maximizes the valuable time of the radiologist to give reach a better diagnostic conclusion and for more patients. For example, AI solutions from customers help radiologists by analyzing data in X-rays which could indicate the presence of a collapsed lung (pneumothorax) or COVID. That is a truly remarkable achievement that is revolutionizing the efficacy of both medical imaging itself and how the human expertise is applied. Witnessing that kind of transformation in this one field naturally motivates one to seek out the next great leap in other health and life sciences pursuits where man and machine combine to produce a new whole so much greater the sum of the parts. Taking that a step further is the idea that AI can democratize knowledge across care disciplines and make scarce human expertise and experienced-based nuance go even further, raising the level of quality.

 

How important is AI to analyzing big data in a clinical setting?

The Health and Life Sciences industries generate more data with greater complexity than any other single industry in the world today. And unlike other industries, effectively managing and analyzing that data is a matter of life and death. Given these magnitudes, AI is now an indispensable enabler of a range of needs, both mundane and breakthrough, in both the clinical and lab settings to address the industry’s Triple Aim: Improve care quality and access while lowering costs.

For example, electronic health records (EHR) have enabled a digital revolution in the quality and efficiency of care delivery. Unfortunately, within these records is a messy mix of both unstructured and structured data which AI can help digitize into more unified and useful data sets. Optical character recognition (OCR) and natural language processing (NLP) are just two AI-enabled models that can convert the analogs of handwriting and voice into EHR data. And once digitized, AI can be applied across these data sets in many exciting use cases.

In other instances, data captured from medical devices and cameras is growing and, when combined with patient history data, analytics can help drive new insights to further personalize treatment. At a census level, many hospitals have already deployed algorithms that can predict sepsis onset for quicker intervention, and in ICUs, software can combine data across multiple isolated devices to create an impressively complete picture of that patient in near-real-time. Over time, all that captured and stored data can also be analyzed for better predictions in the future.

 

What are some of the more notable use cases that you are seeing for machine learning analyzing this data?

As mentioned above, NLP tools can help replace manual scribing or data entry to generate new documents, like patient visit summaries and detailed clinical notes. This enables clinicians to see more patients, and providers to improve documentation, workflow, and billing accuracy by entering orders and documentation sooner in the day.

More broadly, AI-enabled analytics help providers understand and manage a wide range of clinical applications that improve efficiency and lower costs. This allows hospitals to better manage resources and fine tune best practices, and care teams to collaborate on diagnoses and coordinate treatments and overall care they deliver to improve patient outcomes.

Clinicians can analyze for targeted abnormalities using appropriate ML approaches and filter out structured information from other raw data. This can lead to quicker and more accurate diagnosis and optimal treatments. For example, ML algorithms can convert the diagnostic system of medical images into automated decision-making by converting images to machine readable text. ML and pattern recognition techniques can also draw insights from massive volumes of clinical image data, unmanageable by human alone, to transform the diagnosis, treatment and monitoring of patients.

To assess and manage population health, ML algorithms can help predict future risk trajectories, identify risk drivers, and provide solutions for best outcomes. Deep learning modules integrated with AI technologies allow the researchers to interpret complex genomic data sets, to predict specific types of cancer (based on the gene expression profiles obtained from various large data sets) and identify multiple druggable targets.

 

Could you elaborate on how Intel is collaborating with the genomics community to transform large datasets into biomedical insights that accelerate personalized care?

Precision medicine supplies individual-level health data sources that enable better selection of disease targets and identification of patient populations that demonstrate improved clinical outcomes to novel preventative and therapeutic approaches.

Genomics is the cornerstone of this precision medicine. It provides the blueprint of who we are, and why and how we are unique which is critical for providers to understand as they combine this information with other data (images, clinical chemistry, medical history, cohort data, etc.). Clinicians use this information to develop and deliver patient-specific treatments that are lower risk and more effective.

Intel is collaborating with the genomics community by optimizing the most commonly used genetic analysis tools used in the industry to run best and across Intel architecture-based platforms and the processors that power them. For example, optimization of the Broad Institute’s industry leading genetic variant software, the Genomic Analysis Toolkit (GATK), on Intel hardware using OpenVINO to ease AI model development debug and scalable deployment, highlights our impact and commitment to this industry. The GATK toolkit provides benefits to biomedical research such as Genomics DB which efficiently stores files ~200GB in size (typical for genomic datasets) and the Genome Kernel Library running AVX512 which takes advantage of specific Intel architecture hardware instructions to accelerate genomic workloads and AI utilization.

Accelerating the speed and reducing the cost of genomic analysis while maintaining the accuracy of that analysis, continues to be compelling to biomedical and other life sciences researchers as they use Intel compute solutions to discover and harness new medical insights.

 

Could you discuss why you believe that remote healthcare is so important?

The Health industry has been working on various forms and aspects of remote care for many years. The reasons for this have been, up until recently, an intuitive and hoped for belief that remote care can be for many care delivery situations, as good as or better than traditional in-clinic models. Now spurred by the pandemic crisis and its impact, health care delivery systems around the world are forced to adopt telehealth or collapse. This sudden rush to implement is now proving those long held beliefs to be true and care at a distance to be both vital and highly viable.

Remote care has many benefits. Patient comfort and satisfaction with telehealth care delivery is rising rapidly. They are able to remain calmer and at ease in their home with less disruption and time/schedule impact. Providers like it because it allows them to see more patients, and better manage their own time and better allocate scare clinical resources. And of course, what has become the clearest and most compelling reason these past few months for everyone is the inherent ability of remote care to limit contagion and the need for in-person contact when a video chat with augmented device and compute telemetry can get most care delivery tasks done just as well.

 

Can you discuss some of the technologies that are currently being used for remote patient monitoring?

There are several critical technology elements. The most important is ease of use for the patient, quickly followed by security and privacy of the data, and the robustness of the application and the data it captures. For example, we need to prevent a user from deleting a monitoring app from her iPad by accident.

Another critical aspect for a care provider deploying across multiple patients is fleet management and the ability to send updates or tech support down the wire and tailored to each user or cohort of user. This requires:

  • standardization of the data exchange and privacy with industry standards such as FHIR and Continua;
  • secure and power-efficient compute platform to orchestrate the data and communicate it back to the clinician including appropriate software and encryption;
  • connectivity through a cellular network to make the user devices stand-alone and not dependent on Wi-Fi at home that may be unreliable or even non-existent;
  • cloud storage and analytics on the backend.

In addition, the ability to gather and aggregate the data flowing in from users is fundamental to enabling clinicians to do patient monitoring and support, and for the software and analytics to inform care teams of a nominal state or initiate an alarm notification for results that are out of tolerance.

We believe that AI will play a much larger role in patient monitoring moving forward, improving the patient experience through natural voice surveys (“How are you feeling today?”, “Your blood pressure seems a bit high”) and allowing care teams to better understand a patient’s health and identify appropriate treatments. Through the use of AI models, population health management will also progress with all patient data folding into ever larger data sets which improve accuracy of an iterative learning model. This is essential for remote monitoring at scale.

 

What are some of the problems that need to be overcome to increase the success rate of remote healthcare?

Many of the same issues that plague our current system of traditional care delivery are also factors in enhancing or inhibiting the success of remote care. These include societal sub-segment beliefs and stigmas surrounding healthcare, or socio-economic barriers stemming from lack of insurance, technology fluency, required devices, and connectivity. Data silos prevent maximizing value that larger shared data sets could produce especially now that our ability to harness learning programs is truly emerging.

But there are challenges that are unique to remote care:

  • policy and payment issues, though much improved of late, must continue their positive momentum to expand with relaxed restrictions on what is allowable and reimbursable under via remote care modality;
  • financial challenges and lack capital to invest in technology in health care requires a conversion from a CapEx model to an OpEx model.  Rather than investing in facilities and capex equipment, providers can shift to a “pay as you go” model, foregoing the need for a lot of fixed infrastructure and, like phone service, pay for the minutes (or data) used;
  • user experience, for both patient and provider, must continue to improve, ultimately to where the technology disappears into the background, and the capabilities are intuitive and seamless and the process compelling with equivalent or better outcomes and cost structures.

Ultimately, we want the technology to support the provision of care, not get in the way of it. If we are successful (and we believe we are and will continue to be), then the technology truly will allow a bridge to tomorrow’s better model of remote care delivery, making the best possible case for the normalization of remote care as standard of care delivery.

Thank you for the fantastic interview, I enjoyed learning more about Intel’s health efforts. Reader’s who wish to learn more should visit Intel’s Global Health & Life Sciences business.

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Healthcare

New Advancements in AI for Clinical Use

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Researchers from Radboudumc helped advance artificial intelligence (AI) in the clinical setting after demonstrating how AI can diagnose problems similar to a doctor, while also showing how it reaches the diagnosis. AI already plays a role in this environment, being utilized to quickly detect abnormalities that could be labeled as a disease by experts.

AI in the Clinical Setting

Artificial intelligence has been increasingly used in the diagnosis of medical imaging. What was traditionally done by a doctor studying an X-ray or biopsy to identify abnormalities can now be done with AI. Through the use of deep learning, these systems can diagnose by themselves, oftentimes being just as accurate or even better than human doctors.

The systems are not perfect, however. One of the issues is that the AI does not demonstrate how it is analyzing the images and reaching a diagnosis. Another problem is that they do not do anything extra, meaning they stop once reaching a specific diagnosis. This could lead to the system missing some abnormalities even when there is a correct diagnosis.

In this scenario, the human doctor is better at observing the patient, X-ray, or other images overall.

Advancements in the AI 

These problems for AI in the clinical setting are now being addressed by researchers. Christina González Gonzalo is a Ph.D. candidate at the A-eye Research and Diagnostic Image Analysis Group of Radboudumc. 

González Gonzalo developed a new method for the diagnostic AI by utilizing eye scans that found abnormalities of the retina. The specific abnormalities can be easily found by human doctors and AI, and they often are found in groups. 

In the case of the AI system, it would diagnose one or a few of the abnormalities and stop, demonstrating one of the downsides of using such a system. In order to address this, González Gonzalo developed a process where the AI goes over the picture multiple times. When it does this, it learns to ignore the places that it had already covered, which allows it to discover new ones. On top of that, the AI also highlights suspicious areas, making the whole diagnostic process more transparent for humans to observe. 

This new method is different from the traditional AI systems used in these settings, which base their diagnosis on one assessment of the eye scan. Now, researchers can see how the new AI system reached its diagnosis.

In order to ignore the already detected abnormalities, the AI system digitally fills them with healthy tissue from around the abnormalities. The diagnosis is then made based on all of the assessment rounds being added together. 

The study found that this new system improved the sensitivity of the detection of diabetic retinopathy and age-related macular degeneration by 11.2+/-2.0%. 

This new system could really change how AI is used when diagnosing diseases based on abnormalities, and the biggest advancement is the new transparency that it can demonstrate when undergoing this process. This transparency is what will allow even more future corrections and advancements, with the end-goal being an AI system that could diagnose problems much more accurately and faster than the best human experts within the field. All of this could also lead to a more trustworthy system, possibly resulting in the widespread adoption of it within the larger field.

 

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Naheed Kurji, Co-Founder, President and CEO of Cyclica – Interview Series

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Naheed Kurji is the President and CEO of Cyclica, a Toronto-based biotechnology company that leverages artificial intelligence and computational biophysics to reshape the drug discovery process. Cyclica provides the pharmaceutical industry with an integrated, holistic, and end-to-end enabling platform that enhances how scientists design, screen, and personalize medicines for patients, and has recently been named by Deep Knowledge Analytics as one of the top 20 AI in Pharma companies globally

Cyclica leverages artificial intelligence and computational biophysics to reshape the drug discovery process. Can you discuss in what way AI is used in this process?

Technology has played a critical role in drug discovery dating back to the ’80s. However, the drug discovery and development process  is still very inefficient, time consuming and expensive, costing more than 2 billion dollars over 12 years. The poor efficiency often results in high rates of attrition and failure to meet drug safety and efficacy milestones. Researchers are aware of this and they are actively seeking tools to holistically understand the qualities that define the best drugs in order to develop safer and more effective medicines

Recent advances in cloud computing, AI and biophysics have created an opportunity to gain deep insight from the vast amounts of biochemical, biological, healthcare and patient data that are now available in order to better understand disease. These advances have also enabled medicinal chemists to enhance the design of novel therapies and use AI to drive greater predictive insights earlier in the drug development process. At Cyclica we have developed proprietary deep-learning engines, MatchMaker and POEM to support the drug design process. MatchMaker predicts how chemical compounds and drugs interact with multiple proteins, known as polypharmacology. We found the combination of both a knowledge-based and structure-based approach yielded the greatest predictive accuracy and performance. POEM (Pareto-Optimal Embedded Modeling), is a parameter-free supervised learning approach for building drug property prediction models and addresses several limitations of other ML approaches, resulting in less overfitting and increased interpretability.

At Cyclica, we are using AI to provide scientists with a robust and validated platform to accelerate decision-making and hypothesis generation in order to increase the overall efficiency of the drug discovery process and to reduce the number of downstream failures.

 

Cyclica has designed the Ligand Design and Ligand Express platform, what is this precisely?

We are the first company to approach computational polypharmacology (an appreciation that drugs interact with multiple targets) with an integrated drug discovery platform that interrogates molecular interactions on a proteome-wide scale. Our platform is comprised of two key pieces, Ligand Express, our first generation off-target profiling and target deconvolution platform, and Ligand Design, our next generation single and multi-targeted in silico drug design technology. Ligand Express and Ligand Design are powered by two internally built, validated, and patented machine learning and deep learning engines: MatchMaker and POEM. Rooted deeply in protein biophysics, MatchMaker is a deep learning drug-target interaction engine that generalizes across both data-rich and data-poor targets (see validation notes here and here). POEM, a machine learning technology implemented for Absorption, Distribution, Metabolism, and Excretion (ADME) property prediction, is a novel, parameter-free approach to model building.

All taken together, Ligand Design and Ligand Express offer a powerful end to end AI-augmented drug discovery platform for the design of advanced, chemically novel lead-like molecules that simultaneously prioritizes compounds based on their polypharmacological profile, effectively minimizing undesirable off-target effects. Our differentiated platform opens new opportunities for drug discovery, including multi-targeted and multi-objective drug design, lead optimization, ADMET-property prediction, target deconvolution, and drug repurposing. Driven by a diverse and highly-talented team with deep expertise across machine learning, computational biophysics/chemistry/biology, biochemistry, and medicinal chemistry, we are continuing to innovate through our robust R&D pipeline.

 

How important is decentralizing the discovery of medicine to the Cyclica business model?

Our vision is to decentralize the discovery of better medicines by combining our deep roots in Artificial Intelligence (AI) and protein biophysics with an innovative business model.  And at the very core of Cyclica’s ethos is the steadfast desire to help patients by advancing the discovery and development of better medicines by taking a holistic yet personalized approach.

To this end, we believe that the future of drug discovery is in the hands of innovative research institutions and emerging biotech companies (we wrote about this in Forbes here). Supporting our philosophy, in 2019 IQIVIA reported that emerging biopharma companies account for over 70% of the total R&D pipeline (up from 50% in 2003), and that these companies patented over 2/3 of new drugs in 2018 (up from 50% in 2010). While emerging biotech companies will lead innovation in drug discovery, big pharma will continue to invest in advancing late stage clinical trials and market penetration through their sales channels.

With our Series B funding, we will accelerate commercial plans to advance a growing pipeline of pre-clinical and clinical assets through an innovative decentralized partnership model. Our goal is to create and own hundreds of drug discovery programs across multiple therapeutic areas. These programs are created via spin outs and joint ventures (JVs) with top tier research institutions, facilitated largely through the Cyclica Academic Partnership Program (“CAPP”).

Propelled by a rapidly growing portfolio of more than 30 active and advancing drug discovery programs, we will continue to spark innovation through a combination of venture creation and partnerships with early-stage and emerging biotech companies. Recent partnerships include EntheogeniX Biosciences, NineteenGale Therapeutics, Rosetta Therapeutics, the Rare Diseases Medicine Accelerator, and two stealth JVs encompassing over 50 programs across multiple therapeutic areas. By executing on our decentralized business model, creating new companies through spin-outs and joint ventures and helping them scale, we are in effect creating the biotech pipeline of the future.

 

Many of your technologies are cloud-based, why is this so important?

Access to the cloud allows us to computationally scale the workflows that we are conducting, as well as benefit from regulated security infrastructure. Also, as an early stage company, the ability to get up and running with the cloud without the overhead of investing in our own hardware was critical for the financial viability in our early days. Looking forward, while much of our R&D work is done on the cloud, over the past couple of years we have become less cloud-dependent with the ability to run projects on single machines. We are also aiming to support private cloud installations since that’s something we feel our partners may desire. Technological advancements have made it possible to do on a personal laptop what used to take many machines on the cloud, but by continuing to utilize the cloud we are able to greatly expand the scope of the problems we are solving.

 

Cyclica often takes equity positions in companies that they partner with. Can you discuss the business reasoning behind this?

Smaller biotechnology companies and academic groups are generally overlooked by the market in terms of partnership opportunities. While they may not have the resources, infrastructure or facilities in comparison to mature big pharma counterparts, small biotechs are increasingly entering the spotlight with a combination of deep subject-matter expertise in specific indications and the benefits of a lean organization conducive to rapid innovation.

This led us to think on how we can engage with these smaller companies with an avant-garde strategy. We partner scientists in research organizations who are interested in spinning out a company or early stage biotech companies, and enable them with ourAI-augmented drug discovery platform through in kind contributions. In return, take equity into the companies and/or share in the ownership of the compounds and assets that are created and pursued. By sparking a surge of innovation through a combination of venture creation and partnerships, we can capture greater value and develop long-term relationships with our partners to address a spectrum of unmet medical needs to better the lives of patients.

 

Entheogenix Biosciences is a joint venture between Cyclica and ATAI Life Science. What exactly is Entheogenix Biosciences?

There is a unique opportunity for innovation in the neuropsychiatric landscape to better serve patients suffering from complex mental ailments. Current medicines and therapies that rely on single-targeted drug interventions often fall short, requiring patients to take multiple medications that may present potential safety issues as well as reduce medication adherence. We have partnered with ATAI Life Science to leverage their deep experience in mental health and psychedelics, while empowering them with our AI-augmented drug discovery platform to create not only new medicines, but the right ones to tackle mental ailments. Entheogenix Biosciences is one of the many joint ventures we have formed and is a testament to our belief in changing the paradigm in which mental health disorders are treated by bringing our disease agonistic, robust and scientifically validated computational platform into the hands of subject-matter experts and world-class scientists.

 

Is there anything else that you would like to share about Cyclica?

While we are very excited to share the announcement of our series B round of financing. We are just as eager to share the launch of the Cyclica Academic Partnership Program (CAPP) and new partnerships over the next few months.

Thank you for the interview. I look forward to following the future progress of Cyclica.

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