ThinkGenetic, Inc.

75% of rare, genetic patients with one of 7,000+ diseases are undiagnosed. ThinkGenetic searches health records with genetic counselor curated algorithms to identify at risk patients and educate them and their physicians on the condition, how to get tested, diagnosed and treated. ThinkGenetic is currently providing solutions in the US and five countries with a contract with pharmaceutical clients and hospitals to expand to 50 countries. It is estimated that there are 350,000,000 people in the world living with one of these conditions, 25 million in the US.

ThinkGenetic has contracted with several hospitals and pharmaceutical companies to use our algorithms to identify potential patients for five conditions they are interested in treating. To add more diseases, requires funding to expand our genetic counselors and developers as well as funding to add hospitals and pharmaceutical clients. ThinkGenetic has validated its processes and will be publishing results this year on our success to date. Basic market includes 350,000,000 people worldwide with one of 7,000+ conditions and only 25% diagnosed using traditional dedicated pharma sales reps. 

Genetic counselors can identify these patients by manually looking at patient records but it is time consuming and expensive. We are developing algorithms working with genetic counselors that automate much of the process and identify at risk patients by examining medical records, lab results, physician notes and other related material. Once identified, we work with the hospital system to educate physicians and patients on what the condition is, how to test for the condition and finally how to treat it or live with it if no treatment exists.

Genetic patients, like me, who were/are diagnosed spend much time in emergency rooms, doctor visits and tests treating the obvious symptoms but never considering a genetic diagnosis. It is expensive for the hospital system and frustrating for the patient who does not improve. Many conditions lead to organ failure or dialysis and can be fatal if not correctly diagnosed and treated. 

ThinkGenetic originally started in 2015 as an IBM technology partner (Davalen) project. We started with an IBM Watson commercial AI contract and grants from Emory University to use AI to shorten the diagnosis odyssey for rare, genetic patients. As we grew, we decided to spin off ThinkGenetic, Inc from the consulting practice and we raised $2,000,000 through grants, seed money and our success in the Mass Challenge and the Louisiana Accelerate South Innovation Fund. While in these accelerators, we developed pharmaceutical and hospital clients to test our solution. 

ThinkGenetic initially raised $500,000 in grants followed by $2,000,000 in convertible notes to build and release our first product focused on five genetic conditions. We have begun a $10,000,000 Series A round to focus on scaling ThinkGenetic to more genetic conditions and expanding our business.

Currently, the pharmaceutical industry looks for patients with particular diseases in which they have developed treatments to enroll in clinical trials and treatments. Their business model employs dedicated sales reps who call on physicians hoping that they may have seen a potential patient. For this model to work, the physician has to be educated in one of the 7,000+ diseases and the timing has to be right for them to have such a patient at that time. Its not an efficient model and is equated to looking for needles in a haystack. Most potential patients are "hiding in plain sight" in the hospital system but no one is generally trained to look for a specific genetic condition. By constantly monitoring the healthcare system, we can efficiently identify potential patients and educate them and their physician with next steps for testing, diagnosis, trials and treatment.

ThinkGenetic uses proprietary algorithms (eventually moving to machine learning and AI) as we greatly increase our ability to diagnose any genetic condition by curating algorithms for the majority of genetic conditions. Additionally, we use natural language processing to pull related knowledge from free form doctor notes.

Our technology is being used by Ochsner/Lafayette General Hospital to flag potential genetic patients with Fabry, Gaucher, Pompei and HAE diseases. Here is a link  to the most recent press release - https://www.newswire.com/event...

Our work with Takeda Pharmaceutical started with five countries and the goal is to expand to the 50 countries where they do genetic business - https://www.newswire.com/news/...

We will be publishing the results of a Takeda grant for HAE in conjunction with the ThinkGenetic Foundation this summer.

The major risk for ThinkGenetic is the ability to get access to a large collection of medical records. Our initial projects focused on 300,000 patients in Louisiana soon expanding to the Ochsner medical records with a goal of accessing 30 million records this year.

We have been working hard to convince the pharmaceutical industry and hospitals that our approach is advantageous as a better way to get their genetic patients identified, diagnosed and treated. 

With our work in Louisiana this year, we will screen 300,000 patients and will find about 25 patients with the diseases on which we are focused. We don't have metrics yet on our foreign countries but expect rapid increases in the number of potential patients we will identify. We are currently in negotiations to gain access to 70 million records in the US. Depending on the incidence of the rare, disease from 1 - 10,000 to 1 - 1,000,000, we expect to greatly enhance our potential patient discovery.

ThinkGenetic has known proven that our technology works to identify at risk genetic patients for five diseases. With the formal publication of our results this summer, we will begin working with our current pharmaceutical clients to expand our relationship as well as provide a value proposition to hospital systems to gain significant additional revenue through genetic discovery, testing trials and treatment. We also describe the cost savings associated with correct diagnosis and reduced emergency room visits through a study we did with Lafayette General Hospital and Emory University.

1. Increased access to health records

2. Increased number of algorithms for rare, genetic diseases

3. Increased number of identified patients for testing, diagnosis, trials and treatment.

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The management team came from our IBM Premier partnership and has developed many large scale, complex technology solutions since 2006. We have also added a Chief Medical Officer and a genetics expert to our team to provide medical guidance in developing our solution.

Our current team is fairly small but includes an equal number of men and women, military veterans, and naturalized Americans. We continue to look for the best employee for a particular job without regard to gender, sexual orientation or ethnicity.

Yes. David Jacob (founder) has Cutis Laxa and was diagnosed by his daughter (Dawn Laney, Emory University) who is also a founder. I linked to a video earlier that describes our journey that eventually led to the formation fo ThinkGenetic. David also has a sister, Carol with Cutis Laxa.

Since our beginning with the MassChallenge, ThinkGenetic has relied on visibility and funding from the wealth of expertise, vcs and pharmaceutical companies in Boston/Cambridge. We recognize the value and expertise that MIT, MassBio Tech Counsel, and the local pharmaceutical companies can provide to our success. While the $150,000 prize would help us grow, I am more interested in the introductions and relationships that would be fostered by the recognition this prize could provide. Although the MassChallenge didn't provide direct money to the ThinkGenetic start up phase, the introductions to Takeda helped us launch our first product with them. I see similar possibilities with MIT and the Horizon Prize.

The ThinkGenetic model is proven on a small scale and needs to begin the large task of scaling our algorithms to thousands of genetic conditions. AI and machine learning will become an important part of this scaling and we will need to develop additional expertise to make this vision a reality.

We also have to scale a more robust sales and marketing organization to develop additional hospital and pharmaceutical relationships.

1. Executive pharmaceutical introductions/support

2. Executive hospital introduction

3. AI, Machine learning, natural language support to rapidly scale our solution

4. Series A support and introductions