AI startups in Drug Development for Data Aggregation and Analysis
Only in 2020, healthcare industry produced 2314 Exabytes of data and it has been said that 5 Exabytes would be equal to all of the words ever spoken by mankind (one Exabyte is equal to 1,000 Petabytes or one billion gigabytes).
In particular, pharma companies use technology to gather data from an incredible number of sources: clinical trials, scientific publications and patient data sets, used to help scientists discover new drugs much faster than past methods.
Moreover, data can be real-time and can come from various sources like social media, sensors, wearables, activity trackers, devices, electronic surveys, telehealth, telemedicine and digital assistants, and with the help of big data analytics, hidden data patterns can be identified to make data-driven business decisions.
Thus, various investors from the healthcare and pharma domain have invested around $4.7 billion in big data analytics.
Let's see now some of these life science companies trying to make scientists' life easier by searching for hidden patterns in any kind of life science data.
BioSymetrics📉 (New York US) (Twitter) is a biomedical AI company that is offering Augusta Architect, a prediction platform for molecular mechanisms, in vivo barcoding, data set de-noising and identification of lead compounds from gene/diseases prediction. Moreover, they offer a clinical insights engine that can be used to improve value based care initiatives that combined with the above four solutions mentioned can enrich pharmaceutical research with clinical data (Overview).
On March 11 2021, BioSymetrics joined Pistoia Alliance. The Pistoia Alliance is a global, not-for-profit members’ organization conceived in 2007 and incorporated in 2009 by representatives of AstraZeneca, GSK, Novartis and Pfizer who met at a conference in Pistoia, Italy. The Pistoia Alliance’s projects help to overcome common obstacles to innovation and to transform R&D – whether identifying the root causes of inefficiencies, working with regulators to adopt new standards, or helping researchers implement AI effectively.
BioSymetrics has raised a total of $4.9M on Aug 4, 2021.
Traditionally, big data analysis has involved millions of subjects and few features, that is easy to train AI with. But, with very small patients cohorts and sample sizes (amount of information gathered through clinical trials and genetic testing), with few subjects (few patients) and millions of features (an incredibly large number of data points, like the entire genome) is impossible to train AI with. This is something biotx.ai calls “wide data”, that are incredibly hard to analyse consequently most pharmas avoid this approach entirely, opting instead to sequence entire populations.
For this reason, biotx.ai has designed its AI algorithms to make wide data manageable by separating meaningful findings from the noise and leading to the discovery of previously untedectable complex genetic patterns (Why Big Data Doesn't Work With The Human Genome).
Causaly Inc📉(London UK, 2017) (Twitter) offers an AI-platform machine which reads scientific articles and extracts causal associations through linguistic and statistical models, namely dealing with THE MOST difficult biomedical challenge: increase productivity in literature reviews by filtering out false positives (Overview).
Every month something like 100,000 biomedical articles are added to the over 30+ million already published, which makes it almost impossible (apart time-consuming and inefficient) to try to decipher key relationships and find emerging discoveries in the vast data ocean of biomedical research. For this reason Causaly’s AI is reading and understanding biomedical literature similarly to how humans do, with Causaly having read everything ever written in biomedicine visualising relevant relationships within seconds.
Datavant Inc📉 (San Francisco US, 2017) (Twitter) employs AI for the clinical trial process, as well as organises and structures healthcare data to inform actionable insights for the design and interpretation of clinical trials (aggregates and analyses biomedical datathrough ML to lower the time, cost, and risk of drug development). Datavant specialises in breaking down silos and analysing health data securely and privately (Overview).
On July 2020, Medable Inc — the leading software provider for decentralised clinical trials — and Datavant announced a partnership that will help clinical trial teams easily integrate multiple data sources to accelerate decentralised trial design, recruitment and data management.
On June 2021, Datavant and Ciox Health, the leader in clinical data exchange, announced that they have signed a definitive agreement to merge the two companies in a transaction valued at $7.0 billion. The combined entity, to be named Datavant, will be the US’s largest health data ecosystem, enabling patients, providers, payers, health data analytics companies, patient-facing applications, government agencies, and life science companies to securely exchange their patient-level data.
The Ciox platform connects healthcare decision makers with the data and hidden insights in patient medical records, helping customers securely and consistently solve last mile challenges in clinical interoperability to support a range of needs, from research to revenue cycle. Ciox Health has raised a total of $30M on Jul 25, 2019 and has acquired Medal (Extract, transport, translate, and share medical records faster) on Jul 28, 2020.
On August 2021, Real Chemistry and Datavant combined forces, for an undisclosed amount, to help biotech and pharmaceutical companies connect their proprietary de-identified first-party data with real-world data. Through its ML unit, Real Chemistry has more than 300 million identified patient journeys and 65 billion anonymised social determinants of health signals.
Genialis📉(Texas US, 2011) (Twitter) uses AI to analyse multi-omics next-generation sequencing data allowing researchers to reveal previously unseen patterns across large, heterogeneous datasets to predict targets and biomarkers (Overview). Genialis has initiated collaborations with numerous cutting edge biopharma, including Checkmate Pharmaceuticals and Oncologie, as well as renowned biotechnology leader Thermo Fisher Scientific.
With Checkmate and Oncologie, the goal has been to leverage RNA sequencing and clinical trial outcomes data to model gene signatures that stratify patients based on predicted drug response. Moreover, Genialis and Thermo Fisher Scientific struck up a collaboration to provide the scientific community with a comprehensive set of tools for RNA sequencing.
On April 12 2021, Genialis announced its contributions to a biomarker platform that demonstrated prognostic capabilities for recurrence-free and overall survival in colorectal cancer. In particular, Genialis helped support the evaluation of OncXerna Therapeutic’s Xerna™ platform and TME Panel, an RNA-based pan-tumor biomarker, in two gene expression datasets — one from 566 patients and the other from 93 patients.
On March 2021, Genialis announced together with the Bioinformatics Laboratory at the University of Ljubljana (BioLab) that the two organizations have entered into a Sponsored Research Agreement to develop new algorithms for the discovery of clinically actionable biomarkers. BioLab develops and maintains the Orange data mining software, which integrates seamlessly with Genialis’ Expressions software, bridging state-of-the-art ML and multi-omics data analysis. Expressions enables systems-level computational biology through optimised and reproducible processing of sequencing data, including RNA, whole exome/genome, and numerous flavors of chromatin and RNA binding methods like ChIP and CLIP.
Genialis has raised a total of $2.5M in funding and has a post-money valuation in the range of $1M to $10M.
Evid Science📉(California US, 2017) (Twitter) puts 70M+ evidence-based data points at your fingertips, all backed by the literature. Evid Science’s patented AI, which they claim can read up to 25 million articles in an hour, has already processed the publicly available medical literature across all endpoints, interventions and therapy areas and updates nightly, reducing weeks (or even months) of work to a few clicks (Overview).
Intelligencia’s📉 (New York US, 2017) iNsight proprietary data cube, integrates structured and unstructured data from a host of data sources, to assess the probability of technical and regulatory success of a drug, across preclinical and clinical development. They utilise ML models to assess the probability of technical and regulatory success of a drug at any stage of clinical development. Further, they interpret the reasons behind their estimates and provide insights into the drivers (positive or negative) of the probability of technical and regulatory success.
Intelligencia has raised a total of $12M on Aug 10, 2021.
Intellegens’s📉 (Cambridge UK, 2017) (Twitter) — a spin-out from the University of Cambridge — first commercial product Alchemite utilises AI to learn underlying correlations in fragmented datasets with incomplete information, allowing researchers to estimate missing knowledge of how candidate drugs act on proteins (Overview).
The Alchemite™ platform (White Paper: Alchemite™ deep learning – solving complex problems with real-world data) is based on cutting-edge deep learning algorithms that can see correlations between all available parameters, both inputs and outputs, in fragmented, unstructured, corrupt or noisy datasets that are as little as 0.05% complete. The generated models can predict missing values, find errors and optimise target properties with greater levels of accuracy than traditional approaches where complete data is needed.
On July 2021, Intellegens announced the launch of a new solution for Additive Manufacturing: the AlchemiteTM AM Package. The ML software, analytical tools and implementation services is designed to help AM teams extract value from their data, optimise build parameters and ensure more repeatable AM processes, while greatly reducing the need for testing.
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