On your marks get set…start up!

Fresh from the recent Mini Start Up Challenge, we take a look at some of the movers and shakers in the life science start-up space.

The Pistoia Alliance, a not-for-profit alliance of life science organisations, recently ran its inaugural Mini Start Up Challenge award in April at King’s College, London. The event was designed to recognise innovative solutions to solving life science problems. First established in 2009 in Pistoia, Italy, the alliance first consisted of members from AstraZeneca, GSK, Novartis and Pfizer but has since seen its numbers rise to more than 15 core members. The Alliance aims to create better tools for life science research and development – of which some, HELM and the CSCS Expert Community, are already in use.

The Challenge was won by AIME, a programme that can detect viral outbreaks up to three months before they happen with an accuracy rate of almost 90%. It can pinpoint Zika and dengue fever outbreaks to a 400m radius. Dr Deshi Raja, AIME co-founder, after receiving the €10,000 prize money said: “It’s time we revolutionise public health by moving from a reactive approach to a proactive one. It’s time we stop deadly outbreaks in advance and save more lives. The Pistoia Alliance has demonstrated the path towards better health, resides in preventive medicine, validating our motto of Predicting Diseases, Saving Lives.” The AIME team will also receive three months mentorship from a senior member of the Pistoia Alliance. The other Start Up finalists included Agewell Biometrics, Smartbell, MedexPrim and xRapid. All finalists received €1,000.

The Challenge was won by AIME, a programme that can detect viral outbreaks up to three months before they happen with an accuracy rate of almost 90%.

Steve Arlington, Pistoia Alliance President said: “Supporting start-up companies is a crucial component of the Pistoia Alliance’s future strategy. Start-ups are one of the best sources for innovation, and I am delighted to say our finalists this year did not disappoint. We have had an incredible range of companies enter, with new techniques in medicine, research, public health and agriculture all competing.” xRapid was voted the audience winner at the event and was awarded €5,000. Having already created a digital diagnostic test for malaria the team is now looking to develop a tuberculosis test. Currently a chest x-ray and multiple sputum cultures to test for acid-fast bacilli are needed, with a confirmation sometimes taking up to six weeks. xRapid hopes to greatly decrease the time needed for a diagnosis but with the same level of accuracy as an expert microscopist. Jean Viry-Babel, xRapid CEO, said: “Using the existing xRapid solution as a common development platform, we have been able to successfully diagnose the tuberculosis bacilli on a set of test slides (conventional microscopy of sputum, using the ZN acid-fast stain). Slides with as few as one bacillus per millilitre, and as much as a million per millilitre, were characterised with unprecedented accuracy, together with negatives. “We are pleased that the members of the Pistoia Alliance were able to see the potential of the xRapid Automated Diagnostic App. This event has been a great platform to showcase our innovative technology in front of the pharmaceutical industry."

Start Ups present at the Mini Start Up Challenge

AIME AIME predicts disease outbreaks 3 months in advance using Artificial Intelligence, Epidemiology and Public Health expertise. Currently at 88.62% accuracy, the platform provides information to public health officers, saving lives and money. Rainier Mallol, co-founder, said: “Our prediction model has a field-tested accuracy of 88.7%, tested in 2 states in Malaysia and in Rio De Janeiro, Brazil. We now plan to incorporate the model into smartphones, notifying people of zones with a high risk of infection, and acquiring data from our users to strengthen the model. The platform will not only provide the public with scientifically accurate information, but also decision makers in the public health sector, for which we plan to design specific interfaces so these authorities could use the information to take informed decisions. Armed with the information the platform provides, governments and the community will be able to manage targeted deadly outbreaks more strategically.”

xRapid After successfully developing a digital diagnostic test for malaria, xRapid wants to branch out and tackle tuberculosis by improving the process of diagnosing it. In 2014, 9.6 million people fell ill and 1.5 million died from tuberculosis turning this bacterial infection into the top infectious disease killer worldwide. Common in low-and-middle-income countries, mycobacterium tuberculosis reaches fragile population such as children, women and HIV-positive people. CEO, Jean Viry-Babel said: “xRapid is the first medical diagnostic of a major disease running on an iPhone. It detects the presence of plasmodium, the malaria parasite, in red blood cells, in a second. The app is based on a combination of digital image processing and artificial intelligence, used to analyse images captured on any microscope with a sufficient magnification and light source. Recently, we have decided to expand the capabilities of xRapid to cover the detection of new diseases.”

Smartbell Smartbell is a fully integrated health monitoring platform for the livestock industry using Internet of Things technology and offering dynamic lameness detection. It can increase average milk yield and improve fertility rates: two key indicators tracked in livestock farming. By increasing longevity of productive lifespan of cattle with better health, it lowers suffering from chronic illness and lowers veterinary costs. Smartbell Business Development Leader, Veen Adityan said: “Smartbell provides a fully integrated IoT platform for the dairy farming industry, increasing revenues by 10-12%. The technology is based around wearable devices for livestock, actionable insights and predictive analytics for resource planning.”

Agewell Biometrics Agewell Biometrics' product Equilibrium is a cloud-based analytics platform that uses wearable and mobile sensor data to measure strength, balance and function in older adults in order to identify those at risk of falls. The reporting dashboard allows for goal setting and the recommendation engine gives evidence based suggestions for preventative exercises and lifestyle modifications. Falls affect one in three people 65 and older every year, leading to significant injury, death, social isolation and a huge economic burden. Often these falls are preventable by identifying and addressing known risk factors. Joshua Wies, Agewell Biometrics CEO, said: “Using a wearable sensor or smartphone, Agewell have validated the ability to classify fallers from non-fallers. Agewell currently has a smartphone application, Equilibrium that has been tested in over 100 individuals and has the ability to integrate with a variety of wearable sensor devices, worn on the wrist or body. The system automatically analyses features of gait, strength and balance using advanced algorithms developed with Cambridge University engineers.”

MedexPrim Medexprim aims at building collaborative software solutions to enable easy secondary use of routine medical images for research. By linking this data with other existing information – medical reports, lab results, genomics, environment, behaviours, etc – it will be possible to better understand the developing mechanisms of pathologies, find new predictive models of treatment efficiency, and enable the realisation of new types of epidemiology and pharmacovigilance studies. Karine Seymour, MedexPrim President, said the tool “will enable the inclusion of medical imaging data within real world data used for predictive medicine. Secondary post-processing of medical imaging can indeed provide precious detailed information on specific patient clinical outcome and disease characteristics. We will build an intermediation imaging platform that will allow researchers within hospitals to easily extract imaging exams from their archive, extract imaging quantified data and combine them with other data.”