Sex is the most neglected factor in the pharmaceutical and longevity industry. More than half of existing drugs show clinically relevant sex differences, including many promising geroprotective candidates.

Drugs not optimized for patients’ sex can result in patients encountering a high risk of sex-related adverse drug reactions (ADRs) and/or pharmaceutical companies bearing tremendous potential revenue loss due to drug withdrawals from sex-related ADRs.

SexAGE offers a novel in silico and in vivo platform for the discovery of sex-optimized drugs. As a proof-of-concept, we combined longevity gene data from model organisms with human omics data and processed them through an in silico analysis platform. This approach identified genes/pathways that we can match with drug candidates and test using our in vivo platform.

Our platform has distinct advantages of high-throughput and low-cost screening to detect sex different/specific responses to candidate drugs. It can be provided as a service to biotechs, pharmaceutical companies and research institutions to screen compounds including geroprotective candidates to develop new sex-optimized treatments.


Team:
Yu-Xuan Lu, Ph.D. - Ageing Scientist and Postdoctoral Researcher (Team leader)
Mengjia Li, MPhil – Ageing Scientist, Bioinformatician.
Walter Luyten, MD Ph.D. - Professor and Biotechnology Consultant
Bowen Xu, MPhil - Ageing Scientist
Triyaksh Mathur - Bioengineer
Lucas Bronicki, Ph.D. - Clinical Scientist and Assistant Professor

The cost of running clinical trials is a major barrier to the approval of longevity therapeutics. The current funding opportunities from traditional scientific funding agencies are often not sufficient; however, the societal benefit of a successful longevity clinical trial would be enormous. There is an opportunity to fund many more trials by coordinating the beneficiaries of a clinical trial with the funding of the clinical trial. We have developed a token-as-a-service platform called BioCoins that allows clinical trial developers an easy way to create, tokenize, and sell incentives around their planned clinical trial in order to fund it. There are several possible incentives including trial governance, special or early trial data access, and early treatment access or IP ownership in the event of a successful trial. During the hackathon we developed a user interface for BioCoins. Our next steps will be to identify the incentives expected to have the best chance of succeeding while isolating a few trials that are great candidates for this approach. We will work with several organizations executing clinical trials to pave the way for creating a larger platform that can empower more trial designers to succeed in funding their trials and bringing more therapeutics to market. We’re working closely with CureDAO — a decentralized autonomous organization — and Ageless Partners to further our development efforts.

Website (GitHub repository)
Final Presentation

Team
Jason C. Mercurio, MFE Chief Executive Officer
Stephen Price Chief Data Officer
Sandra Ashipala Full Stack Developer Intern
Ketan Bansal Director of Machine Learning Operations
Aditiy Nagarajan Iyer Biomedical Engineer
Triyaksh Mathur Virtual Reality Developer
Chris Lovejoy Machine Learning Engineer

Optomitron is an AI assistant that analyzes your data discover which hidden factors are most likely to worsen or improve inflammatory symptom severity.

A half-billion are suffering from autoimmune diseases like Crone's disease, psoriasis, and Fibromyalgia. There's also a great deal of mounting evidence that treatment-resistant depression and other mental illnesses are strongly related to immune dysregulation. What all these diseases have in common is that they can be exacerbated or improved by hundreds of factors in daily life.

Probably the most significant is the 3 pounds of thousands of chemicals that we consume every day through our diets. Since the thousands of chemicals in our food have been GRAS or "Generally recognized as safe" by the FDA, there's no incentive to do research.

We've built a connector framework that imports data on diet, physical activity, sleep, social interaction, environmental factors, symptom severity, vital signs, and others. Then we apply pharmacokinetic predictive analysis that accounts for onset delays and durations of action. This enables her to obtain personalized effectiveness values (similar to recommended daily values) for treatments and reveal potential root causes of chronic conditions.

The accuracy of the results obtained from quasi-experimental techniques and observational data is highly dependent on the quantity and quality of the data. In order to maximize the amount of available data, we're currently in the process of creating a decentralized autonomous organization called CureDAO. Its mission is to create an open-source platform for crowd-sourced clinical research. It incentivizes collaboration and data sharing by competing entities by issuing non-fungible tokens to any contributor of intellectual property or data.

Contributors of data and intellectual property will receive ongoing royalties for their contributions linked to their non-fungible tokens. We are currently meeting with investors and recruiting members. We plan to hold a fair and public auction for CureDAO tokens in May 2022.

Project description

Team:

Divya Cohen

Nayana Varghese

Chaitrali Yeole

Anshumi Patil

Jerry John

Mike P. Sinn

Osteoporosis is a disease characterized by weak and brittle bones, so fragile that even mild stresses can lead to fracture. It is an age-associated chronic disease.

Almost 50% of women and 25% of men over the age of 50 risks of having an osteoporotic fracture in their lifetime. Early screening and early detection could help in reducing fractures, improve quality of life, reduce mortality associated with osteoporosis as well as reduce the socio-economic problem related to this disease.

We have used machine learning algorithms like CNN on CT scan datasets to know the bone density which tells us about bone health. When the patient goes for a CT scan, a bone density score will be generated which they can use to monitor their bone health without doing any other tests. This will ultimately help patients get interventions faster.

Our future step is to improve the accuracy of our machine learning model and move forward to clinical applications.

Team:
Sakshi Kodre
Pratik Wandhe

Shruti Upadhyay

Tinka Therapeutics had developed a machine learning framework that uses age-stratified transcriptomics data to generate de novo molecules for age-associated chronic diseases. We in-silico analysis the ADME and toxicology properties for all generated molecules. We prioritize molecules with a high probability of acting on molecular targets that are associated with longevity. Using our approach, it is possible to get lead compounds in a much shorter timeframe. Our future step is to synthesize these molecules and test them to be able to extend lifespan in vivo and move them forward into clinical applications.

Team:
CEO Prof. Dr. Collin Y. Ewald
CTO Noah Weber
CSO Tinka Vidovic

Every year 6 million people die of diseases that are preventable by early detection. This is more than the 4.6 million that died of COVID in 2020. The StatusQ application gives proactive people a better tool to manage their health conditions or help to achieve preventive and precision medicine. On the other hand research struggles with recruitment and engagement of participants. 86% of clinical trials do not meet recruitment goals within their proposed time frame and 30% of recruited participants drop out of a clinical trial before completion. Improving patient recruitment and engagement rates offer the biggest opportunities to reduce time to market of new Interventions.

The StatusQ health platform offers a user-centered, holistic health overview dashboard with a fine selection of high-level scores and sub scores related to biological age and health scores broken down to the body systems as a new way to incentivize participation in trials. It's a constant health monitoring tool for better early detection of diseases. It aggregates data from sources like built-in questionnaires, the EHR, wearables, and the tests from clinical trials. For example an overall health score for the cardiovascular system, easy to understand from 0-100. The magic to attract users to clinical trials is the trial recommendation page. There a patient-oriented clinical trial search is conducted automatically with the user's profile information and it shows the trials that provide the desired tests or even easier just the desired scores, or even easier the recommended tests for filling the most important gaps for a comprehensive health overview. StatusQ provides the user free health data and services like importing their own data, aggregation, interpretation, and access to participation in trials. Researchers are served with a new recruitment channel. This service is meant to be the main revenue stream. With that, researchers can bring their insights to intervention developers faster and patients ultimately get the interventions faster.

Check out the project slides or youtube video for more details.

Our team:
Vincenzo Domina
Mike P. Sinn
Hermann Hampel
Evgeny Godlevsky
Mo Mahmood

The actionable aging clock makes information about the aging process accessible. This pathway information can be used to enable groundbreaking research into new drugs, repurposing of existing drugs, and other interventions to extend the healthspan of individuals. This information can also be used by a healthcare practitioner to recommend lifestyle changes with the goal of healthspan extension. The analysis is per individual, enabling personalized recommendations and treatment. Our analysis can be done for research from samples of deceased and living individuals, or to find interventions for living individuals and extend their healthspan.
Our AI predicts the biological age of an individual and the reasons why the prediction was made. The reasons are in the form of a gene regulatory networks/pathways activity map, which is then interpreted by professionals to decide what the appropriate interventions are.
We train our AI based on a unique multi-tissue/body fluid collection of single-cell RNA-seq data, because every tissue and every cell type/state may age differently.
We assemble existing data from public sources and collect private data from the Biobanks and universities we will form a collaboration with. We will supplement the procured data with our own scRNA-seq, to make sure our product goals can be satisfied.
With a significant dataset at hand, we will refine our AI to make accurate predictions that we can validate experimentally, publish papers as proof of our effectiveness, and secure our first clients for a POC. Once our production pipeline is in place, we move forward with a focus on growth which leads to additional data collection that promotes increasing accuracy of predictions and effectiveness of interventions that increment the number of clients which in turn produce more data, increased accuracy, and so on in a circle of growth.

Our team:
Bruno dos Santos - Data Scientist
Amir Mizrahi - Team Leader, Data Scientist
Enrico Glaab - Assistant Professor and Principal Investigator
Olivia Debnath - Computational Oncology Researcher
Dr. Ana Baroni MD. PhD. - Longevity, Regenerative & Precision Medicine Consultant

SudoClock, the winner of the LongHack HEALES award, is being jointly developed by Alex Trapp and Kejun "Albert" Ying, both members of Prof. Vadim Gladyshev's lab at Harvard Medical School. Our group is generally interested in developing and validating frameworks that enable high-resolution biological age profiling from molecular data. The most common types of biological clocks, known as epigenetic clocks, leverage age-associated changes in DNA cytosine methylation to enable biological age profiling in various tissues.

While these tools are each incredibly impactful for the longevity community, particularly in the interventions profiling space, these clocks are currently fragmented and hard to use. Indeed, they require having significant bioinformatics skills, reading complex methods sections in scientific papers, and browsing through obscure supplementary tables.

Our fundamental goal with SudoClock was to build a comprehensive and integrative framework that enables individuals, particularly newcomers to the longevity space, to calculate biological age from their samples of interest with minimal computational knowledge.

Presently, SudoClock integrates 7 human and 5 mouse epigenetic clocks, and we have plans to increase the number of clocks, species, and molecular modalities. Our web server, accessible at http://gladyshevlab.org:3838/SudoClock/, currently supports upload of processed high-throughput sequencing data, as well as popular microarray ("chip") platforms. Users can easily upload data in a variety of formats, as well as metadata that provides information about samples (species, experimental groups, chronological age, etc.).

Of note, we provide interactive visualization options, where users can explore the interactions between their variables of interest and biological ages according to different clocks. We also include an intuitive tutorial for the tool, as well as an atlas of current clocks built into SudoClock for easy reference.

Fundamentally, we believe in simplifying access to essential tools in longevity science, particularly methods for assessing age and the effect of rejuvenation interventions. We introduce SudoClock as an interactive, easy-to-use, and integrative analysis platform for biological age computation.

Our team:
Alex Trapp
Kejun "Albert" Ying

Rather than design drugs to target a few well-known aging genes, we propose an innovative systems approach to screen in silico a large number of chemical compounds and existing drugs to target all hallmarks of aging.

Our team:
Zhengdong Zhang
Jhih Rong Lin
Quanwei Zhang
Reza Jabal
Joydeep Mitra
Nha H Nguyen

AccuBio team: Maryna Polyakova Mark Melnykowycz