Table of Contents: Introduction The abundance of venture capital, major funding rounds New AI-driven biotech startups founded in …
A few decades ago, pharmaceutical giants did most of the discovery work in-house, along with every other work …
The pharmaceutical business is one of the riskiest industries to venture into. Drug discovery is an artisanal process …
Recently, COVAXX announced it would soon enter human trials as the company is focused on rapidly developing a …
In The Spotlight
[Interview] Using Generative AI to Rapidly Identify Novel Therapies for COVID-19
R&D & Clinical Processes for the Future: Accurate, Efficient & Cost-Effective
The AI-ML: Drug Discovery Summit is the industry’s definitive guide to translating the wealth of tech available to successfully implement a working and practically effective drug discovery and development platform.
Pharmaceutical companies are increasingly outsourcing their R&D activities, including early-stage research programs, to third party organizations -- academic institutions, biotech startups, and private contract research organizations (CROs) -- as a means to stay competitive, flexible, and profitable against all odds.
Economically, there are factors such as increasing downward pressure on drug pricing by governments, an impending “patent cliff” threatening $198 billion worth of sales during 2019-2024), and downturns in income due to the increasing competition from generics and biosimilars.
From the innovation's point of view, there is a boom in life sciences, stimulating the emergence of novel biological targets, therapeutic modalities, and even whole new areas of drug discovery -- adding opportunities, but also complexity and uncertainty to research programs. In fact, according to Deloitte’s report, return on late-stage pipelines dropped for the top 12 pharma companies from 10.1% in 2010 down to 3.7% in 2016.
Technologically, there is an unfolding “digital revolution”, bringing even further complexity and investment cost to the table -- in a form of artificial intelligence (AI), data mining and big data technologies, data-driven diagnostics, and digital health.
Finally, the rise of the personalized medicine paradigm forces companies to rethink their research pipelines and “one-size-fits-all” product development programs, as well as reconsider their market strategies.
A transformative technology for gene editing - clustered regularly interspaced short palindromic repeats (CRISPR) -- has become ubiquitous, at least in research laboratories, where scientists efficiently adopted CRISPR technology to manipulate genes of interest. Still, applying it for humans is associated with greater risks and faces ethical issues. However, alongside a too reckless experiment of a Chinese scientist who edited babies’ genomes, plenty of companies have made well-thought progress toward transferring gene editing technologies into the clinic. Here we make a brief but pithy review on up-to-date gene editing approaches and start-up companies active in this area.
Computer-aided drug design (CADD) is a central part of so-called “rational drug design”, pioneered in the last century by companies like Vertex. Over the last decades, CADD had great influence on the way new therapeutics are discovered, however, it also showed limitations due to modest accuracy of computational tools.
The majority of software tools used for computational chemistry and biology rely on molecular mechanics -- a simplified representation of molecules, essentially reducing them down to “balls and sticks”: atoms and bonds between them. In this way it is easier to compute, but accuracy suffers greatly.
In order to gain adequate accuracy, one has to account for the electronic behavior of atoms and molecules, i.e. consider subatomic particles -- electrons and protons. This is what quantum mechanical (QM) methods are all about -- and the theory is not new, dating back to the early decades of the 20th century.
Interviews with experts
Recently, COVAXX announced it would soon enter human trials as the company is focused on rapidly developing a Multitope Peptide-based Vaccine (MPV) against SARS-CoV-2. While the biotech industry is now on a race to develop vaccines to curb COVID-19 pandemics, with several dozen players competing for the future market, COVAXX is a special case that can not be taken lightly.
Not only COVAXX’s new vaccine candidate is constructed off a commercially proven peptide-based vaccine platform by United Biomedical (UBI), a leader with a 30-year history in creating vaccines with over 500 million doses sold annually and 5 billion -- cumulatively in animal health indications for infectious disease that has demonstrated safety and efficacy. But it is also co-founded by two biotech “stars” of modern-day: Peter H. Diamandis, M.D., founder and executive chairman of XPRIZE, executive founder of Singularity University, and a dozen other tech companies, a popular science author; and Mei Mei Hu, co-founder of United Neuroscience, a member of Fortune “40 Under 40” and TIME “100 Next List”.
Founded by renowned database researcher, Turing Award laureate MIT Professor Michael Stonebraker, Paradigm4 is not just any data analytics company in the Life Sciences. The organization is built on the decades of pioneering research in database design and possesses unique technological know-how in scientific data management and scalable computation.
The firm has recently launched its REVEAL™: Single Cell app to offer biopharmaceutical developers the ability to break through the data wrangling and programming challenges associated with the analysis of large-scale, single-cell datasets.
The application of next-generation data analytics tools, powered by machine learning and artificial intelligence (AI) components, has become a long-term strategic priority for most companies in the pharmaceutical and biotech industries. However, such systems have to make sure the organisational data is findable, accessible, interoperable, and reusable across different sub-systems, applications, departments, teams, and even companies.
Aigenpulse, a technology company at the forefront of data management and analytics in the Life Science industry, has built a portfolio of tools for working with organisational research data at scale and accelerating the discovery and development of better targets and candidates using advanced machine learning technologies.
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The team at MIT created the most comprehensive database of metabolites, their interactions with proteins, protein-protein interactions, drug-protein interactions, and associations of metabolites with diseases. They then use the obtained interactions map to make inferences about the disease mechanisms and novel targets. With this new technology, the team launched a biotech startup ReviveMed in 2016 having raised $1.5M of funding so far.
“One-target-one-disease” paradigm has been around for decades, prompting numerous drug discovery programs focusing on identifying small molecules for targeting only one specific protein (or other targets) believed to be responsible for the disease mechanism.
Evaxion Biotech uses its machine learning-based platform to compare DNAs of tumor cells and DNAs healthy cells and identify mutations that are critical for the disease. This data is further used to design anticancer vaccines.
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